diff --git a/bin/unjson-dashboards/go.sum b/bin/unjson-dashboards/go.sum
index 4a1211711..c78a50cc1 100644
--- a/bin/unjson-dashboards/go.sum
+++ b/bin/unjson-dashboards/go.sum
@@ -20,12 +20,14 @@ github.com/k0kubun/colorstring v0.0.0-20150214042306-9440f1994b88 h1:uC1QfSlInpQ
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diff --git a/ci/it/go.sum b/ci/it/go.sum
index 58d04871a..87cae7fab 100644
--- a/ci/it/go.sum
+++ b/ci/it/go.sum
@@ -2,26 +2,56 @@ dario.cat/mergo v1.0.0 h1:AGCNq9Evsj31mOgNPcLyXc+4PNABt905YmuqPYYpBWk=
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@@ -29,18 +59,25 @@ github.com/davecgh/go-spew v1.1.1 h1:vj9j/u1bqnvCEfJOwUhtlOARqs3+rkHYY13jYWTU97c
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@@ -48,9 +85,12 @@ github.com/go-logr/stdr v1.2.2 h1:hSWxHoqTgW2S2qGc0LTAI563KZ5YKYRhT3MFKZMbjag=
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@@ -58,15 +98,25 @@ github.com/google/go-cmp v0.5.6/go.mod h1:v8dTdLbMG2kIc/vJvl+f65V22dbkXbowE6jgT/
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@@ -77,18 +127,30 @@ github.com/lufia/plan9stats v0.0.0-20211012122336-39d0f177ccd0 h1:6E+4a0GO5zZEnZ
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@@ -96,9 +158,14 @@ github.com/opencontainers/go-digest v1.0.0 h1:apOUWs51W5PlhuyGyz9FCeeBIOUDA/6nW8
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@@ -107,10 +174,17 @@ github.com/pmezard/go-difflib v1.0.0 h1:4DBwDE0NGyQoBHbLQYPwSUPoCMWR5BEzIk/f1lZb
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@@ -121,9 +195,12 @@ github.com/shopspring/decimal v1.3.1 h1:2Usl1nmF/WZucqkFZhnfFYxxxu8LG21F6nPQBE5g
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@@ -131,6 +208,8 @@ github.com/stretchr/testify v1.8.0/go.mod h1:yNjHg4UonilssWZ8iaSj1OCr/vHnekPRkoO
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@@ -138,21 +217,32 @@ github.com/tklauser/go-sysconf v0.3.12 h1:0QaGUFOdQaIVdPgfITYzaTegZvdCjmYO52cSFA
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+go.opencensus.io v0.24.0/go.mod h1:vNK8G9p7aAivkbmorf4v+7Hgx+Zs0yY+0fOtgBfjQKo=
+go.opentelemetry.io/contrib/instrumentation/google.golang.org/grpc/otelgrpc v0.46.1/go.mod h1:4UoMYEZOC0yN/sPGH76KPkkU7zgiEWYWL9vwmbnTJPE=
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 go.opentelemetry.io/otel/exporters/otlp/otlptrace v1.19.0/go.mod h1:IPtUMKL4O3tH5y+iXVyAXqpAwMuzC1IrxVS81rummfE=
+go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracegrpc v1.19.0/go.mod h1:0+KuTDyKL4gjKCF75pHOX4wuzYDUZYfAQdSu43o+Z2I=
 go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracehttp v1.19.0 h1:IeMeyr1aBvBiPVYihXIaeIZba6b8E1bYp7lbdxK8CQg=
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@@ -163,14 +253,19 @@ go.opentelemetry.io/otel/trace v1.24.0 h1:CsKnnL4dUAr/0llH9FKuc698G04IrpWV0MQA/Y
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+go.uber.org/atomic v1.11.0/go.mod h1:LUxbIzbOniOlMKjJjyPfpl4v+PKK2cNJn91OQbhoJI0=
+go.uber.org/multierr v1.11.0/go.mod h1:20+QtiLqy0Nd6FdQB9TLXag12DsQkrbs3htMFfDN80Y=
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 golang.org/x/crypto v0.0.0-20200622213623-75b288015ac9/go.mod h1:LzIPMQfyMNhhGPhUkYOs5KpL4U8rLKemX1yGLhDgUto=
 golang.org/x/crypto v0.0.0-20220622213112-05595931fe9d/go.mod h1:IxCIyHEi3zRg3s0A5j5BB6A9Jmi73HwBIUl50j+osU4=
 golang.org/x/crypto v0.36.0 h1:AnAEvhDddvBdpY+uR+MyHmuZzzNqXSe/GvuDeob5L34=
 golang.org/x/crypto v0.36.0/go.mod h1:Y4J0ReaxCR1IMaabaSMugxJES1EpwhBHhv2bDHklZvc=
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 golang.org/x/net v0.0.0-20190404232315-eb5bcb51f2a3/go.mod h1:t9HGtf8HONx5eT2rtn7q6eTqICYqUVnKs3thJo3Qplg=
 golang.org/x/net v0.0.0-20190620200207-3b0461eec859/go.mod h1:z5CRVTTTmAJ677TzLLGU+0bjPO0LkuOLi4/5GtJWs/s=
 golang.org/x/net v0.0.0-20200226121028-0de0cce0169b/go.mod h1:z5CRVTTTmAJ677TzLLGU+0bjPO0LkuOLi4/5GtJWs/s=
@@ -178,10 +273,12 @@ golang.org/x/net v0.0.0-20201021035429-f5854403a974/go.mod h1:sp8m0HH+o8qH0wwXwY
 golang.org/x/net v0.0.0-20211112202133-69e39bad7dc2/go.mod h1:9nx3DQGgdP8bBQD5qxJ1jj9UTztislL4KSBs9R2vV5Y=
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@@ -212,11 +309,14 @@ golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGm
 golang.org/x/tools v0.0.0-20191119224855-298f0cb1881e/go.mod h1:b+2E5dAYhXwXZwtnZ6UAqBI28+e2cm9otk0dWdXHAEo=
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@@ -231,8 +331,23 @@ gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405/go.mod h1:Co6ibVJAznAaIkqp8
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diff --git a/cmd/experimental/go.sum b/cmd/experimental/go.sum
index 93feef1a6..9cb44e940 100644
--- a/cmd/experimental/go.sum
+++ b/cmd/experimental/go.sum
@@ -1,38 +1,113 @@
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@@ -55,21 +134,45 @@ github.com/google/go-cmp v0.7.0 h1:wk8382ETsv4JYUZwIsn6YpYiWiBsYLSJiTsyBybVuN8=
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@@ -88,6 +195,7 @@ github.com/kisielk/sqlstruct v0.0.0-20201105191214-5f3e10d3ab46/go.mod h1:yyMNCy
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@@ -216,6 +408,7 @@ golang.org/x/exp v0.0.0-20250106191152-7588d65b2ba8 h1:yqrTHse8TCMW1M1ZCP+VAR/l0
 golang.org/x/exp v0.0.0-20250106191152-7588d65b2ba8/go.mod h1:tujkw807nyEEAamNbDrEGzRav+ilXA7PCRAd6xsmwiU=
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 golang.org/x/net v0.0.0-20190404232315-eb5bcb51f2a3/go.mod h1:t9HGtf8HONx5eT2rtn7q6eTqICYqUVnKs3thJo3Qplg=
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 golang.org/x/net v0.0.0-20200226121028-0de0cce0169b/go.mod h1:z5CRVTTTmAJ677TzLLGU+0bjPO0LkuOLi4/5GtJWs/s=
@@ -246,6 +439,7 @@ golang.org/x/sys v0.12.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
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@@ -258,24 +452,47 @@ golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGm
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diff --git a/cmd/go.sum b/cmd/go.sum
index 93feef1a6..f2d2f05df 100644
--- a/cmd/go.sum
+++ b/cmd/go.sum
@@ -1,38 +1,113 @@
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@@ -216,6 +409,7 @@ golang.org/x/exp v0.0.0-20250106191152-7588d65b2ba8 h1:yqrTHse8TCMW1M1ZCP+VAR/l0
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@@ -246,6 +440,7 @@ golang.org/x/sys v0.12.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
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@@ -258,24 +453,47 @@ golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGm
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 google.golang.org/grpc v1.66.2 h1:3QdXkuq3Bkh7w+ywLdLvM56cmGvQHUMZpiCzt6Rqaoo=
 google.golang.org/grpc v1.66.2/go.mod h1:s3/l6xSSCURdVfAnL+TqCNMyTDAGN6+lZeVxnZR128Y=
+google.golang.org/grpc/cmd/protoc-gen-go-grpc v1.3.0/go.mod h1:Dk1tviKTvMCz5tvh7t+fh94dhmQVHuCt2OzJB3CTW9Y=
+google.golang.org/grpc/examples v0.0.0-20210430044426-28078834f35b/go.mod h1:Ly7ZA/ARzg8fnPU9TyZIxoz33sEUuWX7txiqs8lPTgE=
 google.golang.org/protobuf v1.26.0-rc.1/go.mod h1:jlhhOSvTdKEhbULTjvd4ARK9grFBp09yW+WbY/TyQbw=
 google.golang.org/protobuf v1.27.1/go.mod h1:9q0QmTI4eRPtz6boOQmLYwt+qCgq0jsYwAQnmE0givc=
 google.golang.org/protobuf v1.36.1 h1:yBPeRvTftaleIgM3PZ/WBIZ7XM/eEYAaEyCwvyjq/gk=
 google.golang.org/protobuf v1.36.1/go.mod h1:9fA7Ob0pmnwhb644+1+CVWFRbNajQ6iRojtC/QF5bRE=
+gopkg.in/DataDog/dd-trace-go.v1 v1.67.1/go.mod h1:6DdiJPKOeJfZyd/IUGCAd5elY8qPGkztK6wbYYsMjag=
+gopkg.in/asn1-ber.v1 v1.0.0-20181015200546-f715ec2f112d/go.mod h1:cuepJuh7vyXfUyUwEgHQXw849cJrilpS5NeIjOWESAw=
 gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
 gopkg.in/check.v1 v1.0.0-20180628173108-788fd7840127/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
 gopkg.in/check.v1 v1.0.0-20201130134442-10cb98267c6c h1:Hei/4ADfdWqJk1ZMxUNpqntNwaWcugrBjAiHlqqRiVk=
 gopkg.in/check.v1 v1.0.0-20201130134442-10cb98267c6c/go.mod h1:JHkPIbrfpd72SG/EVd6muEfDQjcINNoR0C8j2r3qZ4Q=
+gopkg.in/ini.v1 v1.67.0/go.mod h1:pNLf8WUiyNEtQjuu5G5vTm06TEv9tsIgeAvK8hOrP4k=
+gopkg.in/ldap.v2 v2.5.1/go.mod h1:oI0cpe/D7HRtBQl8aTg+ZmzFUAvu4lsv3eLXMLGFxWk=
+gopkg.in/yaml.v2 v2.4.0/go.mod h1:RDklbk79AGWmwhnvt/jBztapEOGDOx6ZbXqjP6csGnQ=
 gopkg.in/yaml.v3 v3.0.0-20200313102051-9f266ea9e77c/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
 gopkg.in/yaml.v3 v3.0.1 h1:fxVm/GzAzEWqLHuvctI91KS9hhNmmWOoWu0XTYJS7CA=
 gopkg.in/yaml.v3 v3.0.1/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
+lukechampine.com/uint128 v1.2.0/go.mod h1:c4eWIwlEGaxC/+H1VguhU4PHXNWDCDMUlWdIWl2j1gk=
+modernc.org/gc/v3 v3.0.0-20240801135723-a856999a2e4a/go.mod h1:Qz0X07sNOR1jWYCrJMEnbW/X55x206Q7Vt4mz6/wHp4=
+modernc.org/libc v1.60.1/go.mod h1:xJuobKuNxKH3RUatS7GjR+suWj+5c2K7bi4m/S5arOY=
+modernc.org/mathutil v1.6.0/go.mod h1:Ui5Q9q1TR2gFm0AQRqQUaBWFLAhQpCwNcuhBOSedWPo=
+modernc.org/memory v1.8.0/go.mod h1:XPZ936zp5OMKGWPqbD3JShgd/ZoQ7899TUuQqxY+peU=
+modernc.org/sqlite v1.33.1/go.mod h1:pXV2xHxhzXZsgT/RtTFAPY6JJDEvOTcTdwADQCCWD4k=
+modernc.org/strutil v1.2.0/go.mod h1:/mdcBmfOibveCTBxUl5B5l6W+TTH1FXPLHZE6bTosX0=
+modernc.org/token v1.1.0/go.mod h1:UGzOrNV1mAFSEB63lOFHIpNRUVMvYTc6yu1SMY/XTDM=
+sigs.k8s.io/yaml v1.4.0/go.mod h1:Ejl7/uTz7PSA4eKMyQCUTnhZYNmLIl+5c2lQPGR2BPY=
 vitess.io/vitess v0.21.2 h1:SbMdGngyhYurvh2KTZ92VkR5DH5taib+HH4xTkftUWU=
 vitess.io/vitess v0.21.2/go.mod h1:n37n5rmIBHYWnoPZod9umrtExlUR/9SbR3VGmanYNMU=
diff --git a/platform/frontend_connectors/search_common_table_test.go b/platform/frontend_connectors/search_common_table_test.go
index 96e7f3108..8326e71bd 100644
--- a/platform/frontend_connectors/search_common_table_test.go
+++ b/platform/frontend_connectors/search_common_table_test.go
@@ -29,7 +29,6 @@ func TestSearchCommonTable(t *testing.T) {
 		Rows         []*sqlmock.Rows
 		IndexPattern string
 	}{
-
 		{ // [0]
 			Name:         "query non virtual table",
 			IndexPattern: "logs-3",
@@ -43,7 +42,7 @@ func TestSearchCommonTable(t *testing.T) {
         }
 }`,
 			WantedSql: []string{
-				`SELECT "@timestamp", "message" FROM "logs-3" LIMIT 10`,
+				"SELECT `@timestamp`, `message` FROM `logs-3` LIMIT 10",
 			},
 		},
 
@@ -60,7 +59,7 @@ func TestSearchCommonTable(t *testing.T) {
         }
 }`,
 			WantedSql: []string{
-				`SELECT "@timestamp", "message" FROM quesma_common_table WHERE "__quesma_index_name"='logs-1' LIMIT 10`,
+				"SELECT `@timestamp`, `message` FROM `quesma_common_table` WHERE `__quesma_index_name`='logs-1' LIMIT 10",
 			},
 		},
 
@@ -77,7 +76,7 @@ func TestSearchCommonTable(t *testing.T) {
         }
 }`,
 			WantedSql: []string{
-				`SELECT "@timestamp", "message", "__quesma_index_name" FROM quesma_common_table WHERE ("__quesma_index_name"='logs-1' OR "__quesma_index_name"='logs-2') LIMIT 10`,
+				"SELECT `@timestamp`, `message`, `__quesma_index_name` FROM `quesma_common_table` WHERE (`__quesma_index_name`='logs-1' OR `__quesma_index_name`='logs-2') LIMIT 10",
 			},
 		},
 
@@ -94,7 +93,7 @@ func TestSearchCommonTable(t *testing.T) {
         }
 }`,
 			WantedSql: []string{
-				`SELECT "@timestamp", "message", "__quesma_index_name" FROM quesma_common_table WHERE ("__quesma_index_name"='logs-1' OR "__quesma_index_name"='logs-2') LIMIT 10`,
+				"SELECT `@timestamp`, `message`, `__quesma_index_name` FROM `quesma_common_table` WHERE (`__quesma_index_name`='logs-1' OR `__quesma_index_name`='logs-2') LIMIT 10",
 			},
 		},
 
@@ -111,7 +110,7 @@ func TestSearchCommonTable(t *testing.T) {
         }
 }`,
 			WantedSql: []string{
-				`SELECT "@timestamp", "message", "__quesma_index_name" FROM quesma_common_table WHERE startsWith("__quesma_index_name",'daily-') LIMIT 10`,
+				"SELECT `@timestamp`, `message`, `__quesma_index_name` FROM `quesma_common_table` WHERE startsWith(`__quesma_index_name`,'daily-') LIMIT 10",
 			},
 		},
 
@@ -128,7 +127,7 @@ func TestSearchCommonTable(t *testing.T) {
         }
 }`,
 			WantedSql: []string{
-				`SELECT "@timestamp", "message", "__quesma_index_name" FROM quesma_common_table WHERE ("__quesma_index_name"='logs-1' OR "__quesma_index_name"='logs-2') LIMIT 10`,
+				"SELECT `@timestamp`, `message`, `__quesma_index_name` FROM `quesma_common_table` WHERE (`__quesma_index_name`='logs-1' OR `__quesma_index_name`='logs-2') LIMIT 10",
 			},
 		},
 
@@ -145,7 +144,7 @@ func TestSearchCommonTable(t *testing.T) {
         }
 }`,
 			WantedSql: []string{
-				`SELECT "@timestamp", "message", "__quesma_index_name" FROM quesma_common_table WHERE ("__quesma_index_name"='logs-1' OR "__quesma_index_name"='logs-2') LIMIT 10`,
+				"SELECT `@timestamp`, `message`, `__quesma_index_name` FROM `quesma_common_table` WHERE (`__quesma_index_name`='logs-1' OR `__quesma_index_name`='logs-2') LIMIT 10",
 			},
 		},
 
@@ -185,8 +184,8 @@ func TestSearchCommonTable(t *testing.T) {
         }
 }`,
 			WantedSql: []string{
-				`SELECT countIf("@timestamp"<toInt64(toUnixTimestamp(now()))) AS "range_0__aggr__2__count", countIf(("@timestamp">=toInt64(toUnixTimestamp(toStartOfDay(subDate(now(), INTERVAL 3 week)))) AND "@timestamp"<toInt64(toUnixTimestamp(now())))) AS "range_1__aggr__2__count", countIf("@timestamp">=toInt64(toUnixTimestamp('2024-04-14'))) AS "range_2__aggr__2__count" FROM quesma_common_table WHERE ("__quesma_index_name"='logs-1' OR "__quesma_index_name"='logs-2') -- optimizations: pancake(half)`,
-				`SELECT "@timestamp", "message", "__quesma_index_name" FROM quesma_common_table WHERE ("__quesma_index_name"='logs-1' OR "__quesma_index_name"='logs-2') LIMIT 10`,
+				"SELECT countIf(`@timestamp`<toInt64(toUnixTimestamp(now()))) AS `range_0__aggr__2__count`, countIf((`@timestamp`>=toInt64(toUnixTimestamp(toStartOfDay(subDate(now(), INTERVAL 3 week)))) AND `@timestamp`<toInt64(toUnixTimestamp(now())))) AS `range_1__aggr__2__count`, countIf(`@timestamp`>=toInt64(toUnixTimestamp('2024-04-14'))) AS `range_2__aggr__2__count` FROM `quesma_common_table` WHERE (`__quesma_index_name`='logs-1' OR `__quesma_index_name`='logs-2') -- optimizations: pancake(half)",
+				"SELECT `@timestamp`, `message`, `__quesma_index_name` FROM `quesma_common_table` WHERE (`__quesma_index_name`='logs-1' OR `__quesma_index_name`='logs-2') LIMIT 10",
 			},
 			// we need to return some rows, otherwise pancakes will fail
 			Rows: []*sqlmock.Rows{
diff --git a/platform/frontend_connectors/search_test.go b/platform/frontend_connectors/search_test.go
index b7cfad782..038e62956 100644
--- a/platform/frontend_connectors/search_test.go
+++ b/platform/frontend_connectors/search_test.go
@@ -289,11 +289,15 @@ func TestSearchHandler(t *testing.T) {
 
 	var selectColumns []string
 	for k := range tab.Cols {
-		selectColumns = append(selectColumns, strconv.Quote(k))
+		selectColumns = append(selectColumns, k)
 	}
 	sort.Strings(selectColumns)
 
-	testSuiteSelectPlaceHolder := "SELECT \"message\""
+	for i := range selectColumns {
+		selectColumns[i] = util.BackquoteIdentifier(selectColumns[i])
+	}
+
+	testSuiteSelectPlaceHolder := "SELECT `message`"
 	selectCMD := fmt.Sprintf("SELECT %s ", strings.Join(selectColumns, ", "))
 
 	s := &schema.StaticRegistry{
@@ -322,7 +326,7 @@ func TestSearchHandler(t *testing.T) {
 					// So we should have a different expectation here.
 
 					// HACK. we change expectations here
-					wantedRegex = strings.ReplaceAll(wantedRegex, model.FullTextFieldNamePlaceHolder, "message")
+					wantedRegex = strings.ReplaceAll(wantedRegex, model.FullTextFieldNamePlaceHolder, "`message`")
 
 					if tt.WantedQueryType == model.ListAllFields && strings.HasPrefix(wantedRegex, testSuiteSelectPlaceHolder) {
 
@@ -330,8 +334,9 @@ func TestSearchHandler(t *testing.T) {
 
 					}
 
-					mock.ExpectQuery(testdata.EscapeWildcard(testdata.EscapeBrackets(wantedRegex))).
-						WillReturnRows(sqlmock.NewRows([]string{"@timestamp", "host.name"}))
+					ss := testdata.EscapeWildcard(testdata.EscapeBrackets(wantedRegex))
+					mock.ExpectQuery(ss).
+						WillReturnRows(sqlmock.NewRows([]string{"@timestamp", "host_name"}))
 				}
 			} else {
 				for _, query := range tt.WantedQueries {
@@ -584,38 +589,38 @@ func TestHandlingDateTimeFields(t *testing.T) {
 		}`
 	}
 	expectedSelectStatement := map[string]string{
-		dateTimeTimestampField: `SELECT toInt64(toUnixTimestamp("timestamp") / 60) AS "aggr__0__key_0",
-									  count(*) AS "aggr__0__count"
-									FROM __quesma_table_name
-									WHERE ((("timestamp64">=fromUnixTimestamp64Milli(1706542596491) AND
-									  "timestamp64"<=fromUnixTimestamp64Milli(1706551896491)) AND ("timestamp">=
-									  fromUnixTimestamp(1706542596) AND "timestamp"<=fromUnixTimestamp(1706551896)))
-									  AND NOT (("@timestamp">=fromUnixTimestamp64Milli(1706542596491) AND
-									  "@timestamp"<=fromUnixTimestamp64Milli(1706551896491)))) 
-									GROUP BY toInt64(toUnixTimestamp("timestamp") / 60) AS "aggr__0__key_0"
-									ORDER BY "aggr__0__key_0" ASC`,
-		dateTime64TimestampField: `SELECT toInt64(toUnixTimestamp64Milli("timestamp64") / 60000) AS
-									  "aggr__0__key_0", count(*) AS "aggr__0__count"
-									FROM __quesma_table_name
-									WHERE ((("timestamp64">=fromUnixTimestamp64Milli(1706542596491) AND
-									  "timestamp64"<=fromUnixTimestamp64Milli(1706551896491)) AND ("timestamp">=
-									  fromUnixTimestamp(1706542596) AND "timestamp"<=fromUnixTimestamp(1706551896)))
-									  AND NOT (("@timestamp">=fromUnixTimestamp64Milli(1706542596491) AND
-									  "@timestamp"<=fromUnixTimestamp64Milli(1706551896491))))
-									GROUP BY toInt64(toUnixTimestamp64Milli("timestamp64") / 60000) AS
-									  "aggr__0__key_0"
-									ORDER BY "aggr__0__key_0" ASC`,
-		dateTime64OurTimestampField: `SELECT toInt64(toUnixTimestamp64Milli("@timestamp") / 60000) AS "aggr__0__key_0"
-									  , count(*) AS "aggr__0__count"
-									FROM __quesma_table_name
-									WHERE ((("timestamp64">=fromUnixTimestamp64Milli(1706542596491) AND
-									  "timestamp64"<=fromUnixTimestamp64Milli(1706551896491)) AND ("timestamp">=
-									  fromUnixTimestamp(1706542596) AND "timestamp"<=fromUnixTimestamp(1706551896)))
-									  AND NOT (("@timestamp">=fromUnixTimestamp64Milli(1706542596491) AND
-									  "@timestamp"<=fromUnixTimestamp64Milli(1706551896491))))
-									GROUP BY toInt64(toUnixTimestamp64Milli("@timestamp") / 60000) AS
-									  "aggr__0__key_0"
-									ORDER BY "aggr__0__key_0" ASC`,
+		dateTimeTimestampField: "SELECT toInt64(toUnixTimestamp(`timestamp`) / 60) AS `aggr__0__key_0`,\n" +
+			"  count(*) AS `aggr__0__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (((`timestamp64`>=fromUnixTimestamp64Milli(1706542596491) AND\n" +
+			"  `timestamp64`<=fromUnixTimestamp64Milli(1706551896491)) AND (`timestamp`>=\n" +
+			"  fromUnixTimestamp(1706542596) AND `timestamp`<=fromUnixTimestamp(1706551896)))\n" +
+			"  AND NOT ((`@timestamp`>=fromUnixTimestamp64Milli(1706542596491) AND\n" +
+			"  `@timestamp`<=fromUnixTimestamp64Milli(1706551896491)))) \n" +
+			"GROUP BY toInt64(toUnixTimestamp(`timestamp`) / 60) AS `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__key_0` ASC",
+		dateTime64TimestampField: "SELECT toInt64(toUnixTimestamp64Milli(`timestamp64`) / 60000) AS\n" +
+			"  `aggr__0__key_0`, count(*) AS `aggr__0__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (((`timestamp64`>=fromUnixTimestamp64Milli(1706542596491) AND\n" +
+			"  `timestamp64`<=fromUnixTimestamp64Milli(1706551896491)) AND (`timestamp`>=\n" +
+			"  fromUnixTimestamp(1706542596) AND `timestamp`<=fromUnixTimestamp(1706551896)))\n" +
+			"  AND NOT ((`@timestamp`>=fromUnixTimestamp64Milli(1706542596491) AND\n" +
+			"  `@timestamp`<=fromUnixTimestamp64Milli(1706551896491))))\n" +
+			"GROUP BY toInt64(toUnixTimestamp64Milli(`timestamp64`) / 60000) AS\n" +
+			"  `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__key_0` ASC",
+		dateTime64OurTimestampField: "SELECT toInt64(toUnixTimestamp64Milli(`@timestamp`) / 60000) AS `aggr__0__key_0`\n" +
+			"  , count(*) AS `aggr__0__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (((`timestamp64`>=fromUnixTimestamp64Milli(1706542596491) AND\n" +
+			"  `timestamp64`<=fromUnixTimestamp64Milli(1706551896491)) AND (`timestamp`>=\n" +
+			"  fromUnixTimestamp(1706542596) AND `timestamp`<=fromUnixTimestamp(1706551896)))\n" +
+			"  AND NOT ((`@timestamp`>=fromUnixTimestamp64Milli(1706542596491) AND\n" +
+			"  `@timestamp`<=fromUnixTimestamp64Milli(1706551896491))))\n" +
+			"GROUP BY toInt64(toUnixTimestamp64Milli(`@timestamp`) / 60000) AS\n" +
+			"  `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__key_0` ASC",
 	}
 
 	conn, mock := util.InitSqlMockWithPrettySqlAndPrint(t, false)
@@ -1001,7 +1006,7 @@ func TestSearchAfterParameter_sortByJustTimestamp(t *testing.T) {
 	}{
 		{
 			request:                     `{"size": 3, "track_total_hits": false, "sort": [{"@timestamp": {"order": "desc"}}]}`,
-			expectedSQL:                 `SELECT "@timestamp", "message" FROM __quesma_table_name ORDER BY "@timestamp" DESC LIMIT 3`,
+			expectedSQL:                 "SELECT `@timestamp`, `message` FROM `__quesma_table_name` ORDER BY `@timestamp` DESC LIMIT 3",
 			resultRowsFromDB:            [][]any{{someTime, "m1"}, {someTime, "m2"}, {someTime, "m3"}},
 			basicAndFastSortFieldPerHit: []int64{someTime.UnixMilli(), someTime.UnixMilli(), someTime.UnixMilli()},
 			expectedSortValuesCount:     1,
@@ -1017,21 +1022,21 @@ func TestSearchAfterParameter_sortByJustTimestamp(t *testing.T) {
 						{"_doc": {"unmapped_type": "boolean", "order": "desc"}}
 					]
 				}`,
-			expectedSQL:                 `SELECT "@timestamp", "message" FROM __quesma_table_name WHERE fromUnixTimestamp64Milli(1706551896491)>"@timestamp" ORDER BY "@timestamp" DESC LIMIT 3`,
+			expectedSQL:                 "SELECT `@timestamp`, `message` FROM `__quesma_table_name` WHERE fromUnixTimestamp64Milli(1706551896491)>`@timestamp` ORDER BY `@timestamp` DESC LIMIT 3",
 			resultRowsFromDB:            [][]any{{sub(1), "m8"}, {sub(2), "m9"}, {sub(3), "m10"}},
 			basicAndFastSortFieldPerHit: []int64{sub(1).UnixMilli(), sub(2).UnixMilli(), sub(3).UnixMilli()},
 			expectedSortValuesCount:     2,
 		},
 		{
 			request:                     `{"search_after": [1706551896488], "size": 3, "track_total_hits": false, "sort": [{"@timestamp": {"order": "desc"}}]}`,
-			expectedSQL:                 `SELECT "@timestamp", "message" FROM __quesma_table_name WHERE fromUnixTimestamp64Milli(1706551896488)>"@timestamp" ORDER BY "@timestamp" DESC LIMIT 3`,
+			expectedSQL:                 "SELECT `@timestamp`, `message` FROM `__quesma_table_name` WHERE fromUnixTimestamp64Milli(1706551896488)>`@timestamp` ORDER BY `@timestamp` DESC LIMIT 3",
 			resultRowsFromDB:            [][]any{{sub(4), "m11"}, {sub(5), "m12"}, {sub(6), "m13"}},
 			basicAndFastSortFieldPerHit: []int64{sub(4).UnixMilli(), sub(5).UnixMilli(), sub(6).UnixMilli()},
 			expectedSortValuesCount:     1,
 		},
 		{
 			request:                     `{"search_after": [1706551896485], "size": 3, "track_total_hits": false, "sort": [{"@timestamp": {"order": "desc"}}]}`,
-			expectedSQL:                 `SELECT "@timestamp", "message" FROM __quesma_table_name WHERE fromUnixTimestamp64Milli(1706551896485)>"@timestamp" ORDER BY "@timestamp" DESC LIMIT 3`,
+			expectedSQL:                 "SELECT `@timestamp`, `message` FROM `__quesma_table_name` WHERE fromUnixTimestamp64Milli(1706551896485)>`@timestamp` ORDER BY `@timestamp` DESC LIMIT 3",
 			resultRowsFromDB:            [][]any{{sub(7), "m14"}, {sub(8), "m15"}, {sub(9), "m16"}},
 			basicAndFastSortFieldPerHit: []int64{sub(7).UnixMilli(), sub(8).UnixMilli(), sub(9).UnixMilli()},
 			expectedSortValuesCount:     1,
@@ -1131,22 +1136,22 @@ func TestSearchAfterParameter_sortByJustOneStringField(t *testing.T) {
 	}{
 		{
 			request:          `{"size": 3, "track_total_hits": false, "sort": [{"message": {"order": "asc"}}]}`,
-			expectedSQL:      `SELECT "message" FROM __quesma_table_name ORDER BY "message" ASC LIMIT 3`,
+			expectedSQL:      "SELECT `message` FROM `__quesma_table_name` ORDER BY `message` ASC LIMIT 3",
 			resultRowsFromDB: []any{"m1", "m1", "m1"},
 		},
 		{
 			request:          `{"search_after": ["m1"], "size": 3, "track_total_hits": false, "sort": [{"message": {"order": "asc"}}]}`,
-			expectedSQL:      `SELECT "message" FROM __quesma_table_name WHERE "message">'m1' ORDER BY "message" ASC LIMIT 3`,
+			expectedSQL:      "SELECT `message` FROM `__quesma_table_name` WHERE `message`>'m1' ORDER BY `message` ASC LIMIT 3",
 			resultRowsFromDB: []any{"m2", "m2", "m3"},
 		},
 		{
 			request:          `{"search_after": ["m3"], "size": 3, "track_total_hits": false, "sort": [{"message": {"order": "asc"}}]}`,
-			expectedSQL:      `SELECT "message" FROM __quesma_table_name WHERE "message">'m3' ORDER BY "message" ASC LIMIT 3`,
+			expectedSQL:      "SELECT `message` FROM `__quesma_table_name` WHERE `message`>'m3' ORDER BY `message` ASC LIMIT 3",
 			resultRowsFromDB: []any{"m4", "m5", "m6"},
 		},
 		{
 			request:          `{"search_after": ["m6"], "size": 3, "track_total_hits": false, "sort": [{"message": {"order": "asc"}}]}`,
-			expectedSQL:      `SELECT "message" FROM __quesma_table_name WHERE "message">'m6' ORDER BY "message" ASC LIMIT 3`,
+			expectedSQL:      "SELECT `message` FROM `__quesma_table_name` WHERE `message`>'m6' ORDER BY `message` ASC LIMIT 3",
 			resultRowsFromDB: []any{"m7", "m8", "m9"},
 		},
 	}
@@ -1254,7 +1259,7 @@ func TestSearchAfterParameter_sortByMultipleFields(t *testing.T) {
 	}{
 		{
 			request:     `{"size": 3, "track_total_hits": false, "sort": [{"@timestamp": {"order": "desc"}}, {"message": {"order": "asc"}}, {"bicep_size": {"order": "desc"}}]}`,
-			expectedSQL: `SELECT "@timestamp", "bicep_size", "message" FROM __quesma_table_name ORDER BY "@timestamp" DESC, "message" ASC, "bicep_size" DESC LIMIT 3`,
+			expectedSQL: "SELECT `@timestamp`, `bicep_size`, `message` FROM `__quesma_table_name` ORDER BY `@timestamp` DESC, `message` ASC, `bicep_size` DESC LIMIT 3",
 			resultRowsFromDB: [][]any{
 				{someTime, int64(1), "m1"},
 				{someTime, int64(2), "m2"},
@@ -1268,7 +1273,7 @@ func TestSearchAfterParameter_sortByMultipleFields(t *testing.T) {
 		},
 		{
 			request:     `{"search_after": [1706551896491, "m3", 3], "size": 3, "track_total_hits": false,  "sort": [{"@timestamp": {"order": "desc"}}, {"message": {"order": "asc"}}, {"bicep_size": {"order": "desc"}}]}`,
-			expectedSQL: `SELECT "@timestamp", "bicep_size", "message" FROM __quesma_table_name WHERE tuple(fromUnixTimestamp64Milli(1706551896491), "message", 3)>tuple("@timestamp", 'm3', "bicep_size") ORDER BY "@timestamp" DESC, "message" ASC, "bicep_size" DESC LIMIT 3`,
+			expectedSQL: "SELECT `@timestamp`, `bicep_size`, `message` FROM `__quesma_table_name` WHERE tuple(fromUnixTimestamp64Milli(1706551896491), `message`, 3)>tuple(`@timestamp`, 'm3', `bicep_size`) ORDER BY `@timestamp` DESC, `message` ASC, `bicep_size` DESC LIMIT 3",
 			resultRowsFromDB: [][]any{
 				{someTime, int64(4), "m4"},
 				{someTime, int64(5), "m5"},
@@ -1282,7 +1287,7 @@ func TestSearchAfterParameter_sortByMultipleFields(t *testing.T) {
 		},
 		{
 			request:     `{"search_after": [1706551896491, "m5", 0], "size": 3, "track_total_hits": false,  "sort": [{"@timestamp": {"order": "desc"}}, {"message": {"order": "asc"}}, {"bicep_size": {"order": "desc"}}]}`,
-			expectedSQL: `SELECT "@timestamp", "bicep_size", "message" FROM __quesma_table_name WHERE tuple(fromUnixTimestamp64Milli(1706551896491), "message", 0)>tuple("@timestamp", 'm5', "bicep_size") ORDER BY "@timestamp" DESC, "message" ASC, "bicep_size" DESC LIMIT 3`,
+			expectedSQL: "SELECT `@timestamp`, `bicep_size`, `message` FROM `__quesma_table_name` WHERE tuple(fromUnixTimestamp64Milli(1706551896491), `message`, 0)>tuple(`@timestamp`, 'm5', `bicep_size`) ORDER BY `@timestamp` DESC, `message` ASC, `bicep_size` DESC LIMIT 3",
 			resultRowsFromDB: [][]any{
 				{sub(1), int64(0), "m6"},
 				{sub(1), int64(0), "m7"},
@@ -1296,7 +1301,7 @@ func TestSearchAfterParameter_sortByMultipleFields(t *testing.T) {
 		},
 		{
 			request:     `{"search_after": [1706551896491, "m8", 0], "size": 3, "track_total_hits": false,  "sort": [{"@timestamp": {"order": "desc"}}, {"message": {"order": "asc"}}, {"bicep_size": {"order": "desc"}}]}`,
-			expectedSQL: `SELECT "@timestamp", "bicep_size", "message" FROM __quesma_table_name WHERE tuple(fromUnixTimestamp64Milli(1706551896491), "message", 0)>tuple("@timestamp", 'm8', "bicep_size") ORDER BY "@timestamp" DESC, "message" ASC, "bicep_size" DESC LIMIT 3`,
+			expectedSQL: "SELECT `@timestamp`, `bicep_size`, `message` FROM `__quesma_table_name` WHERE tuple(fromUnixTimestamp64Milli(1706551896491), `message`, 0)>tuple(`@timestamp`, 'm8', `bicep_size`) ORDER BY `@timestamp` DESC, `message` ASC, `bicep_size` DESC LIMIT 3",
 			resultRowsFromDB: [][]any{
 				{sub(1), int64(0), "m9"},
 				{sub(2), int64(0), "m10"},
@@ -1407,7 +1412,7 @@ func TestSearchAfterParameter_sortByNoField(t *testing.T) {
 	}{
 		{
 			request:     `{"size": 3, "track_total_hits": false, "sort": [{"_score": {"order": "desc"}}]}`,
-			expectedSQL: `SELECT "@timestamp", "bicep_size", "message" FROM __quesma_table_name LIMIT 3`,
+			expectedSQL: "SELECT `@timestamp`, `bicep_size`, `message` FROM `__quesma_table_name` LIMIT 3",
 			resultRowsFromDB: [][]any{
 				{someTime, int64(1), "m1"},
 				{someTime, int64(2), "m2"},
diff --git a/platform/frontend_connectors/terms_enum_test.go b/platform/frontend_connectors/terms_enum_test.go
index 08ee6e267..55fec3886 100644
--- a/platform/frontend_connectors/terms_enum_test.go
+++ b/platform/frontend_connectors/terms_enum_test.go
@@ -68,7 +68,7 @@ var rawRequestBody = []byte(`{
   }
 }`)
 
-func testHandleTermsEnumRequest(t *testing.T, requestBody []byte, fieldName string) {
+func testHandleTermsEnumRequest(t *testing.T, requestBody []byte, fieldName string, isContainsFunctionName bool) {
 	table := &database_common.Table{
 		Name:   testTableName,
 		Config: database_common.NewDefaultCHConfig(),
@@ -127,10 +127,16 @@ func testHandleTermsEnumRequest(t *testing.T, requestBody []byte, fieldName stri
 	}
 	ctx = context.WithValue(context.Background(), tracing.RequestIdCtxKey, "test")
 	qt := &elastic_query_dsl.ClickhouseQueryTranslator{Table: table, Ctx: ctx, Schema: s.Tables[schema.IndexName(testTableName)]}
-	// Here we additionally verify that terms for `_tier` are **NOT** included in the SQL query
-	expectedQuery1 := fmt.Sprintf(`SELECT DISTINCT %s FROM %s WHERE (("epoch_time">=fromUnixTimestamp(1709036700) AND "epoch_time"<=fromUnixTimestamp(1709037659)) AND ("epoch_time_datetime64">=fromUnixTimestamp64Milli(1709036700000) AND "epoch_time_datetime64"<=fromUnixTimestamp64Milli(1709037659999))) LIMIT 13`, fieldName, testTableName)
-	expectedQuery2 := fmt.Sprintf(`SELECT DISTINCT %s FROM %s WHERE (("epoch_time">=fromUnixTimestamp(1709036700) AND "epoch_time"<=fromUnixTimestamp(1709037659)) AND ("epoch_time_datetime64">=fromUnixTimestamp64Milli(1709036700000) AND "epoch_time_datetime64"<=fromUnixTimestamp64Milli(1709037659999))) LIMIT 13`, fieldName, testTableName)
-
+	var expectedQuery1 string
+	var expectedQuery2 string
+	if isContainsFunctionName {
+		expectedQuery1 = fmt.Sprintf("SELECT DISTINCT %s FROM `%s` WHERE ((`epoch_time`>=fromUnixTimestamp(1709036700) AND `epoch_time`<=fromUnixTimestamp(1709037659)) AND (`epoch_time_datetime64`>=fromUnixTimestamp64Milli(1709036700000) AND `epoch_time_datetime64`<=fromUnixTimestamp64Milli(1709037659999))) LIMIT 13", fieldName, testTableName)
+		expectedQuery2 = fmt.Sprintf("SELECT DISTINCT %s FROM `%s` WHERE ((`epoch_time`>=fromUnixTimestamp(1709036700) AND `epoch_time`<=fromUnixTimestamp(1709037659)) AND (`epoch_time_datetime64`>=fromUnixTimestamp64Milli(1709036700000) AND `epoch_time_datetime64`<=fromUnixTimestamp64Milli(1709037659999))) LIMIT 13", fieldName, testTableName)
+	} else {
+		// Here we additionally verify that terms for `_tier` are **NOT** included in the SQL query
+		expectedQuery1 = fmt.Sprintf("SELECT DISTINCT `%s` FROM `%s` WHERE ((`epoch_time`>=fromUnixTimestamp(1709036700) AND `epoch_time`<=fromUnixTimestamp(1709037659)) AND (`epoch_time_datetime64`>=fromUnixTimestamp64Milli(1709036700000) AND `epoch_time_datetime64`<=fromUnixTimestamp64Milli(1709037659999))) LIMIT 13", fieldName, testTableName)
+		expectedQuery2 = fmt.Sprintf("SELECT DISTINCT `%s` FROM `%s` WHERE ((`epoch_time`>=fromUnixTimestamp(1709036700) AND `epoch_time`<=fromUnixTimestamp(1709037659)) AND (`epoch_time_datetime64`>=fromUnixTimestamp64Milli(1709036700000) AND `epoch_time_datetime64`<=fromUnixTimestamp64Milli(1709037659999))) LIMIT 13", fieldName, testTableName)
+	}
 	// Once in a while `AND` conditions could be swapped, so we match both cases
 	mock.ExpectQuery(fmt.Sprintf("%s|%s", regexp.QuoteMeta(expectedQuery1), regexp.QuoteMeta(expectedQuery2))).
 		WillReturnRows(sqlmock.NewRows([]string{"client_name"}).AddRow("client_a").AddRow("client_b"))
@@ -153,17 +159,17 @@ func testHandleTermsEnumRequest(t *testing.T, requestBody []byte, fieldName stri
 }
 
 func TestHandleTermsEnumRequest(t *testing.T) {
-	testHandleTermsEnumRequest(t, rawRequestBody, `"client_name"`)
+	testHandleTermsEnumRequest(t, rawRequestBody, "client_name", false)
 }
 
 // Basic test.
 // "client.name" should be replaced by "client_name", and results should stay the same
 func TestIfHandleTermsEnumUsesSchema(t *testing.T) {
 	requestBodyWithAliasedField := bytes.ReplaceAll(rawRequestBody, []byte(`"field": "client_name"`), []byte(`"field": "client.name"`))
-	testHandleTermsEnumRequest(t, requestBodyWithAliasedField, `"client_name"`)
+	testHandleTermsEnumRequest(t, requestBodyWithAliasedField, "client_name", false)
 }
 
 func TestIfHandleTermsEnumUsesSchemaForMapColumn(t *testing.T) {
 	requestBodyWithAliasedField := bytes.ReplaceAll(rawRequestBody, []byte(`"field": "client_name"`), []byte(`"field": "map_name.key_name"`))
-	testHandleTermsEnumRequest(t, requestBodyWithAliasedField, "arrayElement(\"map_name\",'key_name')")
+	testHandleTermsEnumRequest(t, requestBodyWithAliasedField, "arrayElement(`map_name`,'key_name')", true)
 }
diff --git a/platform/go.sum b/platform/go.sum
index a5cf61188..8df2e4395 100644
--- a/platform/go.sum
+++ b/platform/go.sum
@@ -1,38 +1,113 @@
+cel.dev/expr v0.15.0/go.mod h1:TRSuuV7DlVCE/uwv5QbAiW/v8l5O8C4eEPHeu7gf7Sg=
+cloud.google.com/go v0.115.1/go.mod h1:DuujITeaufu3gL68/lOFIirVNJwQeyf5UXyi+Wbgknc=
+cloud.google.com/go/auth v0.9.4/go.mod h1:SHia8n6//Ya940F1rLimhJCjjx7KE17t0ctFEci3HkA=
+cloud.google.com/go/auth/oauth2adapt v0.2.4/go.mod h1:jC/jOpwFP6JBxhB3P5Rr0a9HLMC/Pe3eaL4NmdvqPtc=
+cloud.google.com/go/compute v1.20.1/go.mod h1:4tCnrn48xsqlwSAiLf1HXMQk8CONslYbdiEZc9FEIbM=
+cloud.google.com/go/compute/metadata v0.5.1/go.mod h1:C66sj2AluDcIqakBq/M8lw8/ybHgOZqin2obFxa/E5k=
+cloud.google.com/go/iam v1.2.1/go.mod h1:3VUIJDPpwT6p/amXRC5GY8fCCh70lxPygguVtI0Z4/g=
+cloud.google.com/go/storage v1.43.0/go.mod h1:ajvxEa7WmZS1PxvKRq4bq0tFT3vMd502JwstCcYv0Q0=
+dario.cat/mergo v1.0.0/go.mod h1:uNxQE+84aUszobStD9th8a29P2fMDhsBdgRYvZOxGmk=
 filippo.io/edwards25519 v1.1.0 h1:FNf4tywRC1HmFuKW5xopWpigGjJKiJSV0Cqo0cJWDaA=
 filippo.io/edwards25519 v1.1.0/go.mod h1:BxyFTGdWcka3PhytdK4V28tE5sGfRvvvRV7EaN4VDT4=
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@@ -55,25 +134,47 @@ github.com/google/go-cmp v0.7.0 h1:wk8382ETsv4JYUZwIsn6YpYiWiBsYLSJiTsyBybVuN8=
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@@ -94,6 +199,7 @@ github.com/kisielk/sqlstruct v0.0.0-20201105191214-5f3e10d3ab46/go.mod h1:yyMNCy
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@@ -111,33 +217,77 @@ github.com/kr/pty v1.1.1/go.mod h1:pFQYn66WHrOpPYNljwOMqo10TkYh1fy3cYio2l3bCsQ=
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 github.com/xdg-go/pbkdf2 v1.0.0/go.mod h1:jrpuAogTd400dnrH08LKmI/xc1MbPOebTwRqcT5RDeI=
 github.com/xdg-go/scram v1.1.1/go.mod h1:RaEWvsqvNKKvBPvcKeFjrG2cJqOkHTiyTpzz23ni57g=
 github.com/xdg-go/stringprep v1.0.3/go.mod h1:W3f5j4i+9rC0kuIEJL0ky1VpHXQU3ocBgklLGvcBnW8=
+github.com/xhit/go-str2duration/v2 v2.1.0/go.mod h1:ohY8p+0f07DiV6Em5LKB0s2YpLtXVyJfNt1+BlmyAsU=
+github.com/xlab/treeprint v1.2.0/go.mod h1:gj5Gd3gPdKtR1ikdDK6fnFLdmIS0X30kTTuNd/WEJu0=
 github.com/xyproto/randomstring v1.0.5 h1:YtlWPoRdgMu3NZtP45drfy1GKoojuR7hmRcnhZqKjWU=
 github.com/xyproto/randomstring v1.0.5/go.mod h1:rgmS5DeNXLivK7YprL0pY+lTuhNQW3iGxZ18UQApw/E=
 github.com/youmark/pkcs8 v0.0.0-20181117223130-1be2e3e5546d/go.mod h1:rHwXgn7JulP+udvsHwJoVG1YGAP6VLg4y9I5dyZdqmA=
+github.com/yudai/golcs v0.0.0-20170316035057-ecda9a501e82/go.mod h1:lgjkn3NuSvDfVJdfcVVdX+jpBxNmX4rDAzaS45IcYoM=
 github.com/yuin/goldmark v1.1.27/go.mod h1:3hX8gzYuyVAZsxl0MRgGTJEmQBFcNTphYh9decYSb74=
 github.com/yuin/goldmark v1.2.1/go.mod h1:3hX8gzYuyVAZsxl0MRgGTJEmQBFcNTphYh9decYSb74=
 github.com/yusufpapurcu/wmi v1.2.4 h1:zFUKzehAFReQwLys1b/iSMl+JQGSCSjtVqQn9bBrPo0=
 github.com/yusufpapurcu/wmi v1.2.4/go.mod h1:SBZ9tNy3G9/m5Oi98Zks0QjeHVDvuK0qfxQmPyzfmi0=
+github.com/z-division/go-zookeeper v1.0.0/go.mod h1:6X4UioQXpvyezJJl4J9NHAJKsoffCwy5wCaaTktXjOA=
+go.etcd.io/etcd/api/v3 v3.5.16/go.mod h1:1P4SlIP/VwkDmGo3OlOD7faPeP8KDIFhqvciH5EfN28=
+go.etcd.io/etcd/client/pkg/v3 v3.5.16/go.mod h1:V8acl8pcEK0Y2g19YlOV9m9ssUe6MgiDSobSoaBAM0E=
+go.etcd.io/etcd/client/v3 v3.5.16/go.mod h1:X+rExSGkyqxvu276cr2OwPLBaeqFu1cIl4vmRjAD/50=
 go.mongodb.org/mongo-driver v1.11.4/go.mod h1:PTSz5yu21bkT/wXpkS7WR5f0ddqw5quethTUn9WM+2g=
+go.opencensus.io v0.24.0/go.mod h1:vNK8G9p7aAivkbmorf4v+7Hgx+Zs0yY+0fOtgBfjQKo=
+go.opentelemetry.io/auto/sdk v1.1.0/go.mod h1:3wSPjt5PWp2RhlCcmmOial7AvC4DQqZb7a7wCow3W8A=
+go.opentelemetry.io/contrib/instrumentation/google.golang.org/grpc/otelgrpc v0.55.0/go.mod h1:LqaApwGx/oUmzsbqxkzuBvyoPpkxk3JQWnqfVrJ3wCA=
+go.opentelemetry.io/contrib/instrumentation/net/http/otelhttp v0.55.0/go.mod h1:DQAwmETtZV00skUwgD6+0U89g80NKsJE3DCKeLLPQMI=
 go.opentelemetry.io/otel v1.34.0 h1:zRLXxLCgL1WyKsPVrgbSdMN4c0FMkDAskSTQP+0hdUY=
 go.opentelemetry.io/otel v1.34.0/go.mod h1:OWFPOQ+h4G8xpyjgqo4SxJYdDQ/qmRH+wivy7zzx9oI=
+go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracehttp v1.19.0/go.mod h1:oVdCUtjq9MK9BlS7TtucsQwUcXcymNiEDjgDD2jMtZU=
+go.opentelemetry.io/otel/metric v1.34.0/go.mod h1:CEDrp0fy2D0MvkXE+dPV7cMi8tWZwX3dmaIhwPOaqHE=
+go.opentelemetry.io/otel/sdk v1.34.0/go.mod h1:0e/pNiaMAqaykJGKbi+tSjWfNNHMTxoC9qANsCzbyxU=
 go.opentelemetry.io/otel/trace v1.34.0 h1:+ouXS2V8Rd4hp4580a8q23bg0azF2nI8cqLYnC8mh/k=
 go.opentelemetry.io/otel/trace v1.34.0/go.mod h1:Svm7lSjQD7kG7KJ/MUHPVXSDGz2OX4h0M2jHBhmSfRE=
+go.uber.org/atomic v1.11.0/go.mod h1:LUxbIzbOniOlMKjJjyPfpl4v+PKK2cNJn91OQbhoJI0=
 go.uber.org/goleak v1.3.0 h1:2K3zAYmnTNqV73imy9J1T3WC+gmCePx2hEGkimedGto=
 go.uber.org/goleak v1.3.0/go.mod h1:CoHD4mav9JJNrW/WLlf7HGZPjdw8EucARQHekz1X6bE=
+go.uber.org/mock v0.2.0/go.mod h1:J0y0rp9L3xiff1+ZBfKxlC1fz2+aO16tw0tsDOixfuM=
+go.uber.org/multierr v1.11.0/go.mod h1:20+QtiLqy0Nd6FdQB9TLXag12DsQkrbs3htMFfDN80Y=
+go.uber.org/zap v1.27.0/go.mod h1:GB2qFLM7cTU87MWRP2mPIjqfIDnGu+VIO4V/SdhGo2E=
 golang.org/x/crypto v0.0.0-20190308221718-c2843e01d9a2/go.mod h1:djNgcEr1/C05ACkg1iLfiJU5Ep61QUkGW8qpdssI0+w=
 golang.org/x/crypto v0.0.0-20191011191535-87dc89f01550/go.mod h1:yigFU9vqHzYiE8UmvKecakEJjdnWj3jj499lnFckfCI=
 golang.org/x/crypto v0.0.0-20200622213623-75b288015ac9/go.mod h1:LzIPMQfyMNhhGPhUkYOs5KpL4U8rLKemX1yGLhDgUto=
@@ -223,6 +414,7 @@ golang.org/x/exp v0.0.0-20250106191152-7588d65b2ba8 h1:yqrTHse8TCMW1M1ZCP+VAR/l0
 golang.org/x/exp v0.0.0-20250106191152-7588d65b2ba8/go.mod h1:tujkw807nyEEAamNbDrEGzRav+ilXA7PCRAd6xsmwiU=
 golang.org/x/mod v0.2.0/go.mod h1:s0Qsj1ACt9ePp/hMypM3fl4fZqREWJwdYDEqhRiZZUA=
 golang.org/x/mod v0.3.0/go.mod h1:s0Qsj1ACt9ePp/hMypM3fl4fZqREWJwdYDEqhRiZZUA=
+golang.org/x/mod v0.23.0/go.mod h1:6SkKJ3Xj0I0BrPOZoBy3bdMptDDU9oJrpohJ3eWZ1fY=
 golang.org/x/net v0.0.0-20190404232315-eb5bcb51f2a3/go.mod h1:t9HGtf8HONx5eT2rtn7q6eTqICYqUVnKs3thJo3Qplg=
 golang.org/x/net v0.0.0-20190620200207-3b0461eec859/go.mod h1:z5CRVTTTmAJ677TzLLGU+0bjPO0LkuOLi4/5GtJWs/s=
 golang.org/x/net v0.0.0-20200226121028-0de0cce0169b/go.mod h1:z5CRVTTTmAJ677TzLLGU+0bjPO0LkuOLi4/5GtJWs/s=
@@ -253,6 +445,7 @@ golang.org/x/sys v0.12.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.30.0 h1:QjkSwP/36a20jFYWkSue1YwXzLmsV5Gfq7Eiy72C1uc=
 golang.org/x/sys v0.30.0/go.mod h1:/VUhepiaJMQUp4+oa/7Zr1D23ma6VTLIYjOOTFZPUcA=
 golang.org/x/term v0.0.0-20201126162022-7de9c90e9dd1/go.mod h1:bj7SfCRtBDWHUb9snDiAeCFNEtKQo2Wmx5Cou7ajbmo=
+golang.org/x/term v0.29.0/go.mod h1:6bl4lRlvVuDgSf3179VpIxBF0o10JUpXWOnI7nErv7s=
 golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
 golang.org/x/text v0.3.3/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
 golang.org/x/text v0.3.6/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
@@ -265,27 +458,49 @@ golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGm
 golang.org/x/tools v0.0.0-20191119224855-298f0cb1881e/go.mod h1:b+2E5dAYhXwXZwtnZ6UAqBI28+e2cm9otk0dWdXHAEo=
 golang.org/x/tools v0.0.0-20200619180055-7c47624df98f/go.mod h1:EkVYQZoAsY45+roYkvgYkIh4xh/qjgUK9TdY2XT94GE=
 golang.org/x/tools v0.0.0-20210106214847-113979e3529a/go.mod h1:emZCQorbCU4vsT4fOWvOPXz4eW1wZW4PmDk9uLelYpA=
+golang.org/x/tools v0.30.0/go.mod h1:c347cR/OJfw5TI+GfX7RUPNMdDRRbjvYTS0jPyvsVtY=
 golang.org/x/xerrors v0.0.0-20190717185122-a985d3407aa7/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
 golang.org/x/xerrors v0.0.0-20191011141410-1b5146add898/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
 golang.org/x/xerrors v0.0.0-20191204190536-9bdfabe68543/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
 golang.org/x/xerrors v0.0.0-20200804184101-5ec99f83aff1/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
+golang.org/x/xerrors v0.0.0-20240903120638-7835f813f4da/go.mod h1:NDW/Ps6MPRej6fsCIbMTohpP40sJ/P/vI1MoTEGwX90=
+gonum.org/v1/gonum v0.14.0/go.mod h1:AoWeoz0becf9QMWtE8iWXNXc27fK4fNeHNf/oMejGfU=
+google.golang.org/api v0.197.0/go.mod h1:AuOuo20GoQ331nq7DquGHlU6d+2wN2fZ8O0ta60nRNw=
+google.golang.org/appengine v1.6.8/go.mod h1:1jJ3jBArFh5pcgW8gCtRJnepW8FzD1V44FJffLiz/Ds=
+google.golang.org/genproto v0.0.0-20240903143218-8af14fe29dc1/go.mod h1:hL97c3SYopEHblzpxRL4lSs523++l8DYxGM1FQiYmb4=
+google.golang.org/genproto/googleapis/api v0.0.0-20240903143218-8af14fe29dc1/go.mod h1:qpvKtACPCQhAdu3PyQgV4l3LMXZEtft7y8QcarRsp9I=
 google.golang.org/genproto/googleapis/rpc v0.0.0-20240903143218-8af14fe29dc1 h1:pPJltXNxVzT4pK9yD8vR9X75DaWYYmLGMsEvBfFQZzQ=
 google.golang.org/genproto/googleapis/rpc v0.0.0-20240903143218-8af14fe29dc1/go.mod h1:UqMtugtsSgubUsoxbuAoiCXvqvErP7Gf0so0mK9tHxU=
 google.golang.org/grpc v1.66.2 h1:3QdXkuq3Bkh7w+ywLdLvM56cmGvQHUMZpiCzt6Rqaoo=
 google.golang.org/grpc v1.66.2/go.mod h1:s3/l6xSSCURdVfAnL+TqCNMyTDAGN6+lZeVxnZR128Y=
+google.golang.org/grpc/cmd/protoc-gen-go-grpc v1.3.0/go.mod h1:Dk1tviKTvMCz5tvh7t+fh94dhmQVHuCt2OzJB3CTW9Y=
+google.golang.org/grpc/examples v0.0.0-20210430044426-28078834f35b/go.mod h1:Ly7ZA/ARzg8fnPU9TyZIxoz33sEUuWX7txiqs8lPTgE=
 google.golang.org/protobuf v1.26.0-rc.1/go.mod h1:jlhhOSvTdKEhbULTjvd4ARK9grFBp09yW+WbY/TyQbw=
 google.golang.org/protobuf v1.27.1/go.mod h1:9q0QmTI4eRPtz6boOQmLYwt+qCgq0jsYwAQnmE0givc=
 google.golang.org/protobuf v1.36.1 h1:yBPeRvTftaleIgM3PZ/WBIZ7XM/eEYAaEyCwvyjq/gk=
 google.golang.org/protobuf v1.36.1/go.mod h1:9fA7Ob0pmnwhb644+1+CVWFRbNajQ6iRojtC/QF5bRE=
+gopkg.in/DataDog/dd-trace-go.v1 v1.67.1/go.mod h1:6DdiJPKOeJfZyd/IUGCAd5elY8qPGkztK6wbYYsMjag=
+gopkg.in/asn1-ber.v1 v1.0.0-20181015200546-f715ec2f112d/go.mod h1:cuepJuh7vyXfUyUwEgHQXw849cJrilpS5NeIjOWESAw=
 gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
 gopkg.in/check.v1 v1.0.0-20180628173108-788fd7840127/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
 gopkg.in/check.v1 v1.0.0-20201130134442-10cb98267c6c h1:Hei/4ADfdWqJk1ZMxUNpqntNwaWcugrBjAiHlqqRiVk=
 gopkg.in/check.v1 v1.0.0-20201130134442-10cb98267c6c/go.mod h1:JHkPIbrfpd72SG/EVd6muEfDQjcINNoR0C8j2r3qZ4Q=
+gopkg.in/ini.v1 v1.67.0/go.mod h1:pNLf8WUiyNEtQjuu5G5vTm06TEv9tsIgeAvK8hOrP4k=
+gopkg.in/ldap.v2 v2.5.1/go.mod h1:oI0cpe/D7HRtBQl8aTg+ZmzFUAvu4lsv3eLXMLGFxWk=
 gopkg.in/yaml.v2 v2.2.2/go.mod h1:hI93XBmqTisBFMUTm0b8Fm+jr3Dg1NNxqwp+5A1VGuI=
+gopkg.in/yaml.v2 v2.4.0/go.mod h1:RDklbk79AGWmwhnvt/jBztapEOGDOx6ZbXqjP6csGnQ=
 gopkg.in/yaml.v3 v3.0.0-20200313102051-9f266ea9e77c/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
 gopkg.in/yaml.v3 v3.0.1 h1:fxVm/GzAzEWqLHuvctI91KS9hhNmmWOoWu0XTYJS7CA=
 gopkg.in/yaml.v3 v3.0.1/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
 lukechampine.com/uint128 v1.2.0 h1:mBi/5l91vocEN8otkC5bDLhi2KdCticRiwbdB0O+rjI=
 lukechampine.com/uint128 v1.2.0/go.mod h1:c4eWIwlEGaxC/+H1VguhU4PHXNWDCDMUlWdIWl2j1gk=
+modernc.org/gc/v3 v3.0.0-20240801135723-a856999a2e4a/go.mod h1:Qz0X07sNOR1jWYCrJMEnbW/X55x206Q7Vt4mz6/wHp4=
+modernc.org/libc v1.60.1/go.mod h1:xJuobKuNxKH3RUatS7GjR+suWj+5c2K7bi4m/S5arOY=
+modernc.org/mathutil v1.6.0/go.mod h1:Ui5Q9q1TR2gFm0AQRqQUaBWFLAhQpCwNcuhBOSedWPo=
+modernc.org/memory v1.8.0/go.mod h1:XPZ936zp5OMKGWPqbD3JShgd/ZoQ7899TUuQqxY+peU=
+modernc.org/sqlite v1.33.1/go.mod h1:pXV2xHxhzXZsgT/RtTFAPY6JJDEvOTcTdwADQCCWD4k=
+modernc.org/strutil v1.2.0/go.mod h1:/mdcBmfOibveCTBxUl5B5l6W+TTH1FXPLHZE6bTosX0=
+modernc.org/token v1.1.0/go.mod h1:UGzOrNV1mAFSEB63lOFHIpNRUVMvYTc6yu1SMY/XTDM=
+sigs.k8s.io/yaml v1.4.0/go.mod h1:Ejl7/uTz7PSA4eKMyQCUTnhZYNmLIl+5c2lQPGR2BPY=
 vitess.io/vitess v0.21.2 h1:SbMdGngyhYurvh2KTZ92VkR5DH5taib+HH4xTkftUWU=
 vitess.io/vitess v0.21.2/go.mod h1:n37n5rmIBHYWnoPZod9umrtExlUR/9SbR3VGmanYNMU=
diff --git a/platform/model/expr.go b/platform/model/expr.go
index 0a1ab86a2..a682b9dff 100644
--- a/platform/model/expr.go
+++ b/platform/model/expr.go
@@ -4,8 +4,8 @@ package model
 
 import (
 	"fmt"
+	"github.com/QuesmaOrg/quesma/platform/util"
 	"github.com/k0kubun/pp"
-	"strconv"
 )
 
 // Expr is a generic representation of an expression which is a part of the SQL query.
@@ -301,7 +301,7 @@ func NewAliasedExpr(expr Expr, alias string) AliasedExpr {
 func (a AliasedExpr) Accept(v ExprVisitor) interface{} { return v.VisitAliasedExpr(a) }
 
 func (a AliasedExpr) AliasRef() LiteralExpr {
-	return LiteralExpr{Value: strconv.Quote(a.Alias)}
+	return LiteralExpr{Value: util.BackquoteIdentifier(a.Alias)}
 }
 
 // WindowFunction representation e.g. `SUM(x) OVER (PARTITION BY y ORDER BY z)`
diff --git a/platform/model/expr_string_renderer.go b/platform/model/expr_string_renderer.go
index 99100a1a6..9968ea36c 100644
--- a/platform/model/expr_string_renderer.go
+++ b/platform/model/expr_string_renderer.go
@@ -7,14 +7,10 @@ import (
 	"github.com/QuesmaOrg/quesma/platform/logger"
 	"github.com/QuesmaOrg/quesma/platform/types"
 	"github.com/QuesmaOrg/quesma/platform/util"
-	"regexp"
 	"sort"
-	"strconv"
 	"strings"
 )
 
-var identifierRegexp = regexp.MustCompile(`^([a-zA-Z_][a-zA-Z0-9_]*|".*")$`)
-
 type renderer struct{}
 
 // AsString renders the given expression to string which can be used to build SQL query
@@ -32,9 +28,9 @@ func (v *renderer) VisitColumnRef(e ColumnRef) interface{} {
 	name = strings.TrimSuffix(name, types.MultifieldMapKeysSuffix)
 	name = strings.TrimSuffix(name, types.MultifieldMapValuesSuffix)
 	if len(e.TableAlias) > 0 {
-		return fmt.Sprintf("%s.%s", strconv.Quote(e.TableAlias), strconv.Quote(name))
+		return fmt.Sprintf("%s.%s", util.BackquoteIdentifier(e.TableAlias), util.BackquoteIdentifier(name))
 	} else {
-		return strconv.Quote(name)
+		return util.BackquoteIdentifier(name)
 	}
 }
 
@@ -153,24 +149,17 @@ func (v *renderer) VisitTableRef(e TableRef) interface{} {
 	var result []string
 
 	if e.DatabaseName != "" {
-		if identifierRegexp.MatchString(e.DatabaseName) {
-			result = append(result, e.DatabaseName)
-		} else {
-			result = append(result, strconv.Quote(e.DatabaseName))
-		}
+		result = append(result, util.BackquoteIdentifier(e.DatabaseName))
 	}
 
-	if identifierRegexp.MatchString(e.Name) {
-		result = append(result, e.Name)
-	} else {
-		result = append(result, strconv.Quote(e.Name))
-	}
+	// append table name
+	result = append(result, util.BackquoteIdentifier(e.Name))
 
 	return strings.Join(result, ".")
 }
 
 func (v *renderer) VisitAliasedExpr(e AliasedExpr) interface{} {
-	return fmt.Sprintf("%s AS %s", e.Expr.Accept(v).(string), strconv.Quote(e.Alias))
+	return fmt.Sprintf("%s AS %s", e.Expr.Accept(v).(string), util.BackquoteIdentifier(e.Alias))
 }
 
 func (v *renderer) VisitSelectCommand(c SelectCommand) interface{} {
diff --git a/platform/optimize/pipeline_test.go b/platform/optimize/pipeline_test.go
index 07db87394..f8eb60f8c 100644
--- a/platform/optimize/pipeline_test.go
+++ b/platform/optimize/pipeline_test.go
@@ -432,7 +432,7 @@ func Test_materialized_view_replace(t *testing.T) {
 			"materialized_view_replace": {
 				Properties: map[string]string{
 					"table":     "foo",
-					"condition": `"a">10`,
+					"condition": "`a`>10",
 					"view":      "foo_view",
 				},
 			},
diff --git a/platform/parsers/elastic_query_dsl/lucene/lucene_parser_test.go b/platform/parsers/elastic_query_dsl/lucene/lucene_parser_test.go
index 9e7cdea6a..74805d9bb 100644
--- a/platform/parsers/elastic_query_dsl/lucene/lucene_parser_test.go
+++ b/platform/parsers/elastic_query_dsl/lucene/lucene_parser_test.go
@@ -17,72 +17,71 @@ func TestTranslatingLuceneQueriesToSQL(t *testing.T) {
 		query string
 		want  string
 	}{
-		{`title:"The Right Way" AND text:go!!`, `("title" __quesma_match 'The Right Way' AND "text" __quesma_match 'go!!')`},
-		{`title:Do it right AND right`, `((("title" __quesma_match 'Do' OR ("title" __quesma_match 'it' OR "text" __quesma_match 'it')) OR ("title" __quesma_match 'right' OR "text" __quesma_match 'right')) AND ("title" __quesma_match 'right' OR "text" __quesma_match 'right'))`},
-		{`roam~`, `("title" __quesma_match 'roam' OR "text" __quesma_match 'roam')`},
-		{`roam~0.8`, `("title" __quesma_match 'roam' OR "text" __quesma_match 'roam')`},
-		{`jakarta^4 apache`, `(("title" __quesma_match 'jakarta' OR "text" __quesma_match 'jakarta') OR ("title" __quesma_match 'apache' OR "text" __quesma_match 'apache'))`},
-		{`"jakarta apache"^10`, `("title" __quesma_match 'jakarta apache' OR "text" __quesma_match 'jakarta apache')`},
-		{`"jakarta apache"~10`, `("title" __quesma_match 'jakarta apache' OR "text" __quesma_match 'jakarta apache')`},
-		{`mod_date:[2002-01-01 TO 2003-02-15]`, `("mod_date" >= '2002-01-01' AND "mod_date" <= '2003-02-15')`}, // 7
-		{`mod_date:[2002-01-01 TO 2003-02-15}`, `("mod_date" >= '2002-01-01' AND "mod_date" < '2003-02-15')`},
-		{`age:>10`, `"age" > '10'`},
-		{`age:>=10`, `"age" >= '10'`},
-		{`age:<10`, `"age" < '10'`},
-		{`age:<=10.2`, `"age" <= '10.2'`},
-		{`age:10.2`, `"age" = 10.2`},
-		{`age:-10.2`, `"age" = -10.2`},
-		{`age:<-10.2`, `"age" < '-10.2'`},
-		{`age:        10.2`, `"age" = 10.2`},
-		{`age:  <-10.2`, `"age" < '-10.2'`},
-		{`age:  <   -10.2`, `"age" < '-10.2'`},
-		{`age:10.2 age2:[12 TO 15] age3:{11 TO *}`, `(("age" = 10.2 OR ("age2" >= '12' AND "age2" <= '15')) OR "age3" > '11')`},
-		{`date:{* TO 2012-01-01} another`, `("date" < '2012-01-01' OR ("title" __quesma_match 'another' OR "text" __quesma_match 'another'))`},
-		{`date:{2012-01-15 TO *} another`, `("date" > '2012-01-15' OR ("title" __quesma_match 'another' OR "text" __quesma_match 'another'))`},
-		{`date:{* TO *}`, `"date" IS NOT NULL`},
-		{`title:{Aida TO Carmen]`, `("title" > 'Aida' AND "title" <= 'Carmen')`},
-		{`count:[1 TO 5]`, `("count" >= '1' AND "count" <= '5')`}, // 17
-		{`"jakarta apache" AND "Apache Lucene"`, `(("title" __quesma_match 'jakarta apache' OR "text" __quesma_match 'jakarta apache') AND ("title" __quesma_match 'Apache Lucene' OR "text" __quesma_match 'Apache Lucene'))`},
-		{`NOT status:"jakarta apache"`, `NOT ("status" __quesma_match 'jakarta apache')`},
-		{`"jakarta apache" NOT "Apache Lucene"`, `(("title" __quesma_match 'jakarta apache' OR "text" __quesma_match 'jakarta apache') AND NOT (("title" __quesma_match 'Apache Lucene' OR "text" __quesma_match 'Apache Lucene')))`},
-		{`(jakarta OR apache) AND website`, `(((("title" __quesma_match 'jakarta' OR "text" __quesma_match 'jakarta')) OR ("title" __quesma_match 'apache' OR "text" __quesma_match 'apache')) AND ("title" __quesma_match 'website' OR "text" __quesma_match 'website'))`},
-		{`title:(return "pink panther")`, `("title" __quesma_match 'return' OR "title" __quesma_match 'pink panther')`},
-		{`status:(active OR pending) title:(full text search)^2`, `(("status" __quesma_match 'active' OR "status" __quesma_match 'pending') OR (("title" __quesma_match 'full' OR "title" __quesma_match 'text') OR "title" __quesma_match 'search'))`},
-		{`status:(active OR NOT (pending AND in-progress)) title:(full text search)^2`, `(("status" __quesma_match 'active' OR NOT (("status" __quesma_match 'pending' AND "status" __quesma_match 'in-progress'))) OR (("title" __quesma_match 'full' OR "title" __quesma_match 'text') OR "title" __quesma_match 'search'))`},
-		{`status:(NOT active OR NOT (pending AND in-progress)) title:(full text search)^2`, `((NOT ("status" __quesma_match 'active') OR NOT (("status" __quesma_match 'pending' AND "status" __quesma_match 'in-progress'))) OR (("title" __quesma_match 'full' OR "title" __quesma_match 'text') OR "title" __quesma_match 'search'))`},
-		{`status:(active OR (pending AND in-progress)) title:(full text search)^2`, `(("status" __quesma_match 'active' OR ("status" __quesma_match 'pending' AND "status" __quesma_match 'in-progress')) OR (("title" __quesma_match 'full' OR "title" __quesma_match 'text') OR "title" __quesma_match 'search'))`},
-		{`status:((a OR (b AND c)) AND d)`, `(("status" __quesma_match 'a' OR ("status" __quesma_match 'b' AND "status" __quesma_match 'c')) AND "status" __quesma_match 'd')`},
-		{`title:(return [Aida TO Carmen])`, `("title" __quesma_match 'return' OR ("title" >= 'Aida' AND "title" <= 'Carmen'))`},
-		{`host.name:(NOT active OR NOT (pending OR in-progress)) (full text search)^2`, `((((NOT ("host.name" __quesma_match 'active') OR NOT (("host.name" __quesma_match 'pending' OR "host.name" __quesma_match 'in-progress'))) OR (("title" __quesma_match 'full' OR "text" __quesma_match 'full'))) OR ("title" __quesma_match 'text' OR "text" __quesma_match 'text')) OR ("title" __quesma_match 'search' OR "text" __quesma_match 'search'))`},
-		{`host.name:(active AND NOT (pending OR in-progress)) hermes nemesis^2`, `((("host.name" __quesma_match 'active' AND NOT (("host.name" __quesma_match 'pending' OR "host.name" __quesma_match 'in-progress'))) OR ("title" __quesma_match 'hermes' OR "text" __quesma_match 'hermes')) OR ("title" __quesma_match 'nemesis' OR "text" __quesma_match 'nemesis'))`},
-
+		{`title:"The Right Way" AND text:go!!`, "(`title` __quesma_match 'The Right Way' AND `text` __quesma_match 'go!!')"},
+		{`title:Do it right AND right`, "(((`title` __quesma_match 'Do' OR (`title` __quesma_match 'it' OR `text` __quesma_match 'it')) OR (`title` __quesma_match 'right' OR `text` __quesma_match 'right')) AND (`title` __quesma_match 'right' OR `text` __quesma_match 'right'))"},
+		{`roam~`, "(`title` __quesma_match 'roam' OR `text` __quesma_match 'roam')"},
+		{`roam~0.8`, "(`title` __quesma_match 'roam' OR `text` __quesma_match 'roam')"},
+		{`jakarta^4 apache`, "((`title` __quesma_match 'jakarta' OR `text` __quesma_match 'jakarta') OR (`title` __quesma_match 'apache' OR `text` __quesma_match 'apache'))"},
+		{`"jakarta apache"^10`, "(`title` __quesma_match 'jakarta apache' OR `text` __quesma_match 'jakarta apache')"},
+		{`"jakarta apache"~10`, "(`title` __quesma_match 'jakarta apache' OR `text` __quesma_match 'jakarta apache')"},
+		{`mod_date:[2002-01-01 TO 2003-02-15]`, "(`mod_date` >= '2002-01-01' AND `mod_date` <= '2003-02-15')"}, // 7
+		{`mod_date:[2002-01-01 TO 2003-02-15}`, "(`mod_date` >= '2002-01-01' AND `mod_date` < '2003-02-15')"},
+		{`age:>10`, "`age` > '10'"},
+		{`age:>=10`, "`age` >= '10'"},
+		{`age:<10`, "`age` < '10'"},
+		{`age:<=10.2`, "`age` <= '10.2'"},
+		{`age:10.2`, "`age` = 10.2"},
+		{`age:-10.2`, "`age` = -10.2"},
+		{`age:<-10.2`, "`age` < '-10.2'"},
+		{`age:<10.2`, "`age` < '10.2'"},
+		{`age:<-10.2`, "`age` < '-10.2'"},
+		{`age:<-10.2`, "`age` < '-10.2'"},
+		{`age:10.2 age2:[12 TO 15] age3:{11 TO *}`, "((`age` = 10.2 OR (`age2` >= '12' AND `age2` <= '15')) OR `age3` > '11')"},
+		{`date:{* TO 2012-01-01} another`, "(`date` < '2012-01-01' OR (`title` __quesma_match 'another' OR `text` __quesma_match 'another'))"},
+		{`date:{2012-01-15 TO *} another`, "(`date` > '2012-01-15' OR (`title` __quesma_match 'another' OR `text` __quesma_match 'another'))"},
+		{`date:{* TO *}`, "`date` IS NOT NULL"},
+		{`title:{Aida TO Carmen]`, "(`title` > 'Aida' AND `title` <= 'Carmen')"},
+		{`count:[1 TO 5]`, "(`count` >= '1' AND `count` <= '5')"}, // 17
+		{`"jakarta apache" AND "Apache Lucene"`, "((`title` __quesma_match 'jakarta apache' OR `text` __quesma_match 'jakarta apache') AND (`title` __quesma_match 'Apache Lucene' OR `text` __quesma_match 'Apache Lucene'))"},
+		{`NOT status:"jakarta apache"`, "NOT (`status` __quesma_match 'jakarta apache')"},
+		{`"jakarta apache" NOT "Apache Lucene"`, "((`title` __quesma_match 'jakarta apache' OR `text` __quesma_match 'jakarta apache') AND NOT ((`title` __quesma_match 'Apache Lucene' OR `text` __quesma_match 'Apache Lucene')))"},
+		{`(jakarta OR apache) AND website`, "((((`title` __quesma_match 'jakarta' OR `text` __quesma_match 'jakarta')) OR (`title` __quesma_match 'apache' OR `text` __quesma_match 'apache')) AND (`title` __quesma_match 'website' OR `text` __quesma_match 'website'))"},
+		{`title:(return "pink panther")`, "(`title` __quesma_match 'return' OR `title` __quesma_match 'pink panther')"},
+		{`status:(active OR pending) title:(full text search)^2`, "((`status` __quesma_match 'active' OR `status` __quesma_match 'pending') OR ((`title` __quesma_match 'full' OR `title` __quesma_match 'text') OR `title` __quesma_match 'search'))"},
+		{`status:(active OR NOT (pending AND in-progress)) title:(full text search)^2`, "((`status` __quesma_match 'active' OR NOT ((`status` __quesma_match 'pending' AND `status` __quesma_match 'in-progress'))) OR ((`title` __quesma_match 'full' OR `title` __quesma_match 'text') OR `title` __quesma_match 'search'))"},
+		{`status:(NOT active OR NOT (pending AND in-progress)) title:(full text search)^2`, "((NOT (`status` __quesma_match 'active') OR NOT ((`status` __quesma_match 'pending' AND `status` __quesma_match 'in-progress'))) OR ((`title` __quesma_match 'full' OR `title` __quesma_match 'text') OR `title` __quesma_match 'search'))"},
+		{`status:(active OR (pending AND in-progress)) title:(full text search)^2`, "((`status` __quesma_match 'active' OR (`status` __quesma_match 'pending' AND `status` __quesma_match 'in-progress')) OR ((`title` __quesma_match 'full' OR `title` __quesma_match 'text') OR `title` __quesma_match 'search'))"},
+		{`status:((a OR (b AND c)) AND d)`, "((`status` __quesma_match 'a' OR (`status` __quesma_match 'b' AND `status` __quesma_match 'c')) AND `status` __quesma_match 'd')"},
+		{`title:(return [Aida TO Carmen])`, "(`title` __quesma_match 'return' OR (`title` >= 'Aida' AND `title` <= 'Carmen'))"},
+		{`host.name:(NOT active OR NOT (pending OR in-progress)) (full text search)^2`, "((((NOT (`host.name` __quesma_match 'active') OR NOT ((`host.name` __quesma_match 'pending' OR `host.name` __quesma_match 'in-progress'))) OR ((`title` __quesma_match 'full' OR `text` __quesma_match 'full'))) OR (`title` __quesma_match 'text' OR `text` __quesma_match 'text')) OR (`title` __quesma_match 'search' OR `text` __quesma_match 'search'))"},
+		{`host.name:(active AND NOT (pending OR in-progress)) hermes nemesis^2`, "(((`host.name` __quesma_match 'active' AND NOT ((`host.name` __quesma_match 'pending' OR `host.name` __quesma_match 'in-progress'))) OR (`title` __quesma_match 'hermes' OR `text` __quesma_match 'hermes')) OR (`title` __quesma_match 'nemesis' OR `text` __quesma_match 'nemesis'))"},
 		// special characters
-		{`dajhd \(%&RY#WFDG`, `(("title" __quesma_match 'dajhd' OR "text" __quesma_match 'dajhd') OR ("title" __quesma_match '(\%&RY#WFDG' OR "text" __quesma_match '(\%&RY#WFDG'))`},
-		{`x:aaa'bbb`, `"x" __quesma_match 'aaa\'bbb'`},
-		{`x:aaa\bbb`, `"x" __quesma_match 'aaa\\bbb'`},
-		{`x:aaa*bbb`, `"x" __quesma_match 'aaa%bbb'`},
-		{`x:aaa_bbb`, `"x" __quesma_match 'aaa\_bbb'`},
-		{`x:aaa%bbb`, `"x" __quesma_match 'aaa\%bbb'`},
-		{`x:aaa%\*_bbb`, `"x" __quesma_match 'aaa\%*\_bbb'`},
+		{`dajhd \(%&RY#WFDG`, "((`title` __quesma_match 'dajhd' OR `text` __quesma_match 'dajhd') OR (`title` __quesma_match '(\\%&RY#WFDG' OR `text` __quesma_match '(\\%&RY#WFDG'))"},
+		{`x:aaa'bbb`, "`x` __quesma_match 'aaa\\'bbb'"},
+		{`x:aaa\bbb`, "`x` __quesma_match 'aaa\\\\bbb'"},
+		{`x:aaa*bbb`, "`x` __quesma_match 'aaa%bbb'"},
+		{`x:aaa_bbb`, "`x` __quesma_match 'aaa\\_bbb'"},
+		{`x:aaa%bbb`, "`x` __quesma_match 'aaa\\%bbb'"},
+		{`x:aaa%\*_bbb`, "`x` __quesma_match 'aaa\\%*\\_bbb'"},
 
 		// tests for wildcards
-		{"%", `("title" __quesma_match '\%' OR "text" __quesma_match '\%')`},
-		{`*`, `("title" __quesma_match '%' OR "text" __quesma_match '%')`},
-		{`*neme*`, `("title" __quesma_match '%neme%' OR "text" __quesma_match '%neme%')`},
-		{`*nem?* abc:ne*`, `(("title" __quesma_match '%nem_%' OR "text" __quesma_match '%nem_%') OR "abc" __quesma_match 'ne%')`},
-		{`title:(NOT a* AND NOT (b* OR *))`, `(NOT ("title" __quesma_match 'a%') AND NOT (("title" __quesma_match 'b%' OR "title" __quesma_match '%')))`},
-		{`title:abc\*`, `"title" __quesma_match 'abc*'`},
-		{`title:abc*\*`, `"title" __quesma_match 'abc%*'`},
-		{`ab\+c`, `("title" __quesma_match 'ab+c' OR "text" __quesma_match 'ab+c')`},
-		{`!db.str:FAIL`, `NOT ("db.str" __quesma_match 'FAIL')`},
-		{`_exists_:title`, `"title" IS NOT NULL`},
-		{`!_exists_:title`, `NOT ("title" IS NOT NULL)`},
-		{"db.str:*weaver%12*", `"db.str" __quesma_match '%weaver\%12%'`},
-		{"(db.str:*weaver*)", `("db.str" __quesma_match '%weaver%')`},
-		{"(a.type:*ab* OR a.type:*Ab*)", `(("a.type" __quesma_match '%ab%') OR "a.type" __quesma_match '%Ab%')`},
-		{"log:  \"lalala lala la\" AND log: \"troll\"", `("log" __quesma_match 'lalala lala la' AND "log" __quesma_match 'troll')`},
-		{"int: 20", `"int" = 20`},
-		{`int: "20"`, `"int" __quesma_match '20'`},
+		{"%", "(`title` __quesma_match '\\%' OR `text` __quesma_match '\\%')"},
+		{`*`, "(`title` __quesma_match '%' OR `text` __quesma_match '%')"},
+		{`*neme*`, "(`title` __quesma_match '%neme%' OR `text` __quesma_match '%neme%')"},
+		{`*nem?* abc:ne*`, "((`title` __quesma_match '%nem_%' OR `text` __quesma_match '%nem_%') OR `abc` __quesma_match 'ne%')"},
+		{`title:(NOT a* AND NOT (b* OR *))`, "(NOT (`title` __quesma_match 'a%') AND NOT ((`title` __quesma_match 'b%' OR `title` __quesma_match '%')))"},
+		{`title:abc\*`, "`title` __quesma_match 'abc*'"},
+		{`title:abc*\*`, "`title` __quesma_match 'abc%*'"},
+		{`ab\+c`, "(`title` __quesma_match 'ab+c' OR `text` __quesma_match 'ab+c')"},
+		{`!db.str:FAIL`, "NOT (`db.str` __quesma_match 'FAIL')"},
+		{`_exists_:title`, "`title` IS NOT NULL"},
+		{`!_exists_:title`, "NOT (`title` IS NOT NULL)"},
+		{"db.str:*weaver%12*", "`db.str` __quesma_match '%weaver\\%12%'"},
+		{"(db.str:*weaver*)", "(`db.str` __quesma_match '%weaver%')"},
+		{"(a.type:*ab* OR a.type:*Ab*)", "((`a.type` __quesma_match '%ab%') OR `a.type` __quesma_match '%Ab%')"},
+		{"log:  \"lalala lala la\" AND log: \"troll\"", "(`log` __quesma_match 'lalala lala la' AND `log` __quesma_match 'troll')"},
+		{"int: 20", "`int` = 20"},
+		{`int: "20"`, "`int` __quesma_match '20'"},
 	}
 	var randomQueriesWithPossiblyIncorrectInput = []struct {
 		query string
@@ -90,18 +89,18 @@ func TestTranslatingLuceneQueriesToSQL(t *testing.T) {
 	}{
 		{``, `true`},
 		{`          `, `true`},
-		{`  2 `, `("title" = 2 OR "text" = 2)`},
-		{`  2df$ ! `, `(("title" __quesma_match '2df$' OR "text" __quesma_match '2df$') AND NOT (false))`}, // TODO: this should probably just be "false"
+		{`  2 `, "(`title` = 2 OR `text` = 2)"},
+		{`  2df$ ! `, "((`title` __quesma_match '2df$' OR `text` __quesma_match '2df$') AND NOT (false))"}, // TODO: this should probably just be "false"
 		{`title:`, `false`},
-		{`title: abc`, `"title" __quesma_match 'abc'`},
-		{`title[`, `("title" __quesma_match 'title[' OR "text" __quesma_match 'title[')`},
-		{`title[]`, `("title" __quesma_match 'title[]' OR "text" __quesma_match 'title[]')`},
-		{`title[ TO ]`, `((("title" __quesma_match 'title[' OR "text" __quesma_match 'title[') OR ("title" __quesma_match 'TO' OR "text" __quesma_match 'TO')) OR ("title" __quesma_match ']' OR "text" __quesma_match ']'))`},
-		{`title:[ TO 2]`, `("title" >= '' AND "title" <= '2')`},
-		{`  title       `, `("title" __quesma_match 'title' OR "text" __quesma_match 'title')`},
-		{`  title : (+a -b c)`, `(("title" __quesma_match '+a' OR "title" __quesma_match '-b') OR "title" __quesma_match 'c')`}, // we don't support '+', '-' operators, but in that case the answer seems good enough + nothing crashes
+		{`title: abc`, "`title` __quesma_match 'abc'"},
+		{`title[`, "(`title` __quesma_match 'title[' OR `text` __quesma_match 'title[')"},
+		{`title[]`, "(`title` __quesma_match 'title[]' OR `text` __quesma_match 'title[]')"},
+		{`title[ TO ]`, "(((`title` __quesma_match 'title[' OR `text` __quesma_match 'title[') OR (`title` __quesma_match 'TO' OR `text` __quesma_match 'TO')) OR (`title` __quesma_match ']' OR `text` __quesma_match ']'))"},
+		{`title:[ TO 2]`, "(`title` >= '' AND `title` <= '2')"},
+		{`  title       `, "(`title` __quesma_match 'title' OR `text` __quesma_match 'title')"},
+		{`  title : (+a -b c)`, "((`title` __quesma_match '+a' OR `title` __quesma_match '-b') OR `title` __quesma_match 'c')"}, // we don't support '+', '-' operators, but in that case the answer seems good enough + nothing crashes
 		{`title:()`, `false`},
-		{`() a`, `((false) OR ("title" __quesma_match 'a' OR "text" __quesma_match 'a'))`}, // a bit weird, but '(false)' is OK as I think nothing should match '()'
+		{`() a`, "((false) OR (`title` __quesma_match 'a' OR `text` __quesma_match 'a'))"}, // a bit weird, but '(false)' is OK as I think nothing should match '()'
 	}
 
 	currentSchema := schema.Schema{
@@ -127,8 +126,8 @@ func TestResolvePropertyNamesWhenTranslatingToSQL(t *testing.T) {
 		mapping map[string]string
 		want    string
 	}{
-		{query: `title:"The Right Way" AND text:go!!`, mapping: map[string]string{}, want: `("title" __quesma_match 'The Right Way' AND "text" __quesma_match 'go!!')`},
-		{query: `age:>10`, mapping: map[string]string{"age": "foo"}, want: `"foo" > '10'`},
+		{query: `title:"The Right Way" AND text:go!!`, mapping: map[string]string{}, want: "(`title` __quesma_match 'The Right Way' AND `text` __quesma_match 'go!!')"},
+		{query: `age:>10`, mapping: map[string]string{"age": "foo"}, want: "`foo` > '10'"},
 	}
 	for i, tt := range properQueries {
 		t.Run(util.PrettyTestName(tt.query, i), func(t *testing.T) {
diff --git a/platform/parsers/elastic_query_dsl/pancake_sql_query_generation_test.go b/platform/parsers/elastic_query_dsl/pancake_sql_query_generation_test.go
index c4de202d0..93c596462 100644
--- a/platform/parsers/elastic_query_dsl/pancake_sql_query_generation_test.go
+++ b/platform/parsers/elastic_query_dsl/pancake_sql_query_generation_test.go
@@ -246,13 +246,7 @@ func TestPancakeQueryGeneration_halfpancake(t *testing.T) {
 }
 
 `,
-			sql: `
-SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-  "host.name" AS "aggr__0__key_0", count(*) AS "aggr__0__count"
-FROM ` + TableName + `
-GROUP BY "host.name" AS "aggr__0__key_0"
-ORDER BY "aggr__0__count" DESC, "aggr__0__key_0" ASC
-LIMIT 4`, // -- we added one more as filtering nulls happens during rendering
+			sql: "SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n  `host.name` AS `aggr__0__key_0`, count(*) AS `aggr__0__count`\nFROM `__quesma_table_name`\nGROUP BY `host.name` AS `aggr__0__key_0`\nORDER BY `aggr__0__count` DESC, `aggr__0__key_0` ASC\nLIMIT 4", // -- we added one more as filtering nulls happens during rendering
 		},
 
 		{"test2",
@@ -275,14 +269,7 @@ LIMIT 4`, // -- we added one more as filtering nulls happens during rendering
   }
 }
 `,
-			`
-SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-  "host.name" AS "aggr__0__key_0", count(*) AS "aggr__0__count",
-  avgOrNull("bytes_gauge") AS "metric__0__2_col_0"
-FROM ` + TableName + `
-GROUP BY "host.name" AS "aggr__0__key_0"
-ORDER BY "aggr__0__count" DESC, "aggr__0__key_0" ASC
-LIMIT 4`, // we increased limit by 1 to allow filtering of nulls druing json rendering
+			"SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n  `host.name` AS `aggr__0__key_0`, count(*) AS `aggr__0__count`,\n  avgOrNull(`bytes_gauge`) AS `metric__0__2_col_0`\nFROM `__quesma_table_name`\nGROUP BY `host.name` AS `aggr__0__key_0`\nORDER BY `aggr__0__count` DESC, `aggr__0__key_0` ASC\nLIMIT 4", // we increased limit by 1 to allow filtering of nulls druing json rendering
 		},
 	}
 
diff --git a/platform/parsers/elastic_query_dsl/pancake_top_hits.go b/platform/parsers/elastic_query_dsl/pancake_top_hits.go
index 4414daa41..d4f0e49d8 100644
--- a/platform/parsers/elastic_query_dsl/pancake_top_hits.go
+++ b/platform/parsers/elastic_query_dsl/pancake_top_hits.go
@@ -6,11 +6,12 @@ import (
 	"fmt"
 	"github.com/QuesmaOrg/quesma/platform/model"
 	"github.com/QuesmaOrg/quesma/platform/model/metrics_aggregations"
+	"github.com/QuesmaOrg/quesma/platform/util"
 	"strconv"
 )
 
 func (p *pancakeSqlQueryGenerator) quotedLiteral(name string) model.LiteralExpr {
-	return model.NewLiteral(strconv.Quote(name))
+	return model.NewLiteral(util.BackquoteIdentifier(name))
 }
 
 // generateSimpleTopHitsQuery generates an SQL for top_hits/top_metrics
@@ -83,7 +84,7 @@ func (p *pancakeSqlQueryGenerator) generateTopHitsQuery(aggregation *pancakeMode
 	hitTableName := "hit_table"
 
 	groupTableLiteral := func(reference string) model.Expr {
-		return model.NewLiteral(strconv.Quote(groupTableName) + "." + strconv.Quote(reference))
+		return model.NewLiteral(util.BackquoteIdentifier(groupTableName) + "." + util.BackquoteIdentifier(reference))
 	}
 
 	convertColumnRefToHitTable := func(expr model.Expr) model.Expr {
diff --git a/platform/parsers/elastic_query_dsl/query_parser_range_test.go b/platform/parsers/elastic_query_dsl/query_parser_range_test.go
index 95262f592..d2e06bdd1 100644
--- a/platform/parsers/elastic_query_dsl/query_parser_range_test.go
+++ b/platform/parsers/elastic_query_dsl/query_parser_range_test.go
@@ -36,7 +36,7 @@ var parseRangeTests = []parseRangeTest{
 			},
 			Config: database_common.NewNoTimestampOnlyStringAttrCHConfig(),
 		},
-		`("timestamp">=fromUnixTimestamp64Milli(1706881636029) AND "timestamp"<=fromUnixTimestamp64Milli(1707486436029))`,
+		"(`timestamp`>=fromUnixTimestamp64Milli(1706881636029) AND `timestamp`<=fromUnixTimestamp64Milli(1707486436029))",
 	},
 	{
 		"parseDateTimeBestEffort",
@@ -55,7 +55,7 @@ var parseRangeTests = []parseRangeTest{
 			},
 			Config: database_common.NewNoTimestampOnlyStringAttrCHConfig(),
 		},
-		`("timestamp">=fromUnixTimestamp(1706881636) AND "timestamp"<=fromUnixTimestamp(1707486436))`,
+		"(`timestamp`>=fromUnixTimestamp(1706881636) AND `timestamp`<=fromUnixTimestamp(1707486436))",
 	},
 	{
 		"numeric range",
@@ -72,7 +72,7 @@ var parseRangeTests = []parseRangeTest{
 			},
 			Config: database_common.NewNoTimestampOnlyStringAttrCHConfig(),
 		},
-		`"time_taken">100`,
+		"`time_taken`>100",
 	},
 	{
 		"DateTime64",
@@ -91,7 +91,7 @@ var parseRangeTests = []parseRangeTest{
 			},
 			Config: database_common.NewNoTimestampOnlyStringAttrCHConfig(),
 		},
-		`("timestamp">=fromUnixTimestamp64Milli(1706881636000) AND "timestamp"<=fromUnixTimestamp64Milli(1707486436000))`,
+		"(`timestamp`>=fromUnixTimestamp64Milli(1706881636000) AND `timestamp`<=fromUnixTimestamp64Milli(1707486436000))",
 	},
 }
 
diff --git a/platform/testdata/aggregation_requests.go b/platform/testdata/aggregation_requests.go
index 572036db6..17c365a72 100644
--- a/platform/testdata/aggregation_requests.go
+++ b/platform/testdata/aggregation_requests.go
@@ -16,7 +16,7 @@ func groupBySQL(fieldName string, typ database_common.DateTimeType, groupByInter
 	return model.AsString(database_common.TimestampGroupBy(model.NewColumnRef(fieldName), typ, groupByInterval))
 }
 
-const fullTextFieldName = `"` + model.FullTextFieldNamePlaceHolder + `"`
+const fullTextFieldName = "`" + model.FullTextFieldNamePlaceHolder + "`"
 
 // TODO change some tests to size > 0, and track_total_hits different values
 var AggregationTests = []AggregationTestCase{
@@ -118,10 +118,7 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("metric__minAgg_col_0", 100.14596557617188),
 			}},
 		},
-		ExpectedPancakeSQL: `SELECT maxOrNull("AvgTicketPrice") AS "metric__maxAgg_col_0", ` +
-			`minOrNull("AvgTicketPrice") AS "metric__minAgg_col_0" ` +
-			`FROM ` + TableName + ` ` +
-			`WHERE ("timestamp">=fromUnixTimestamp64Milli(1706881636029) AND "timestamp"<=fromUnixTimestamp64Milli(1707486436029))`,
+		ExpectedPancakeSQL: "SELECT maxOrNull(`AvgTicketPrice`) AS `metric__maxAgg_col_0`,\n  minOrNull(`AvgTicketPrice`) AS `metric__minAgg_col_0`\nFROM `__quesma_table_name`\nWHERE (`timestamp`>=fromUnixTimestamp64Milli(1706881636029) AND `timestamp`<=\n  fromUnixTimestamp64Milli(1707486436029))",
 	},
 	{ // [1]
 		TestName: "2 sibling count aggregations",
@@ -306,16 +303,7 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0__3-bucket_col_0", 2),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-			  "OriginCityName" AS "aggr__0__key_0", count(*) AS "aggr__0__count",
-			  countIf("FlightDelay" __quesma_match true) AS "metric__0__1-bucket_col_0",
-			  countIf("Cancelled" __quesma_match true) AS "metric__0__3-bucket_col_0"
-			FROM ` + TableName + `
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1706881636029) AND "timestamp"<=fromUnixTimestamp64Milli(1707486436029))
-			GROUP BY "OriginCityName" AS "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC
-			LIMIT 1001`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n          `OriginCityName` AS `aggr__0__key_0`, count(*) AS `aggr__0__count`,\n          countIf(`FlightDelay` __quesma_match true) AS `metric__0__1-bucket_col_0`,\n          countIf(`Cancelled` __quesma_match true) AS `metric__0__3-bucket_col_0`\n        FROM `__quesma_table_name`\n        WHERE (`timestamp`>=fromUnixTimestamp64Milli(1706881636029) AND `timestamp`<=\n          fromUnixTimestamp64Milli(1707486436029))\n        GROUP BY `OriginCityName` AS `aggr__0__key_0`\n        ORDER BY `aggr__0__key_0` ASC\n        LIMIT 1001",
 	},
 	{ // [2]
 		TestName: "date_histogram + size as string",
@@ -497,30 +485,29 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__0__1__count", uint64(2)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-			  "aggr__0__1__key_0", "aggr__0__1__count"
-			FROM (
-			  SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-				"aggr__0__1__key_0", "aggr__0__1__count",
-				dense_rank() OVER (ORDER BY "aggr__0__count" DESC, "aggr__0__key_0" ASC)
-				AS "aggr__0__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0" ORDER BY
-				"aggr__0__1__key_0" ASC) AS "aggr__0__1__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-				  "FlightDelayType" AS "aggr__0__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS "aggr__0__count",
-				  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-				  "timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__0__1__key_0",
-				  count(*) AS "aggr__0__1__count"
-				FROM ` + TableName + `
-				WHERE ("timestamp">=fromUnixTimestamp64Milli(1706881636029) AND "timestamp"<=fromUnixTimestamp64Milli(1707486436029))
-				GROUP BY "FlightDelayType" AS "aggr__0__key_0",
-				  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-				  "timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__0__1__key_0"))
-			WHERE "aggr__0__order_1_rank"<=11
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"  `aggr__0__1__key_0`, `aggr__0__1__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"    `aggr__0__1__key_0`, `aggr__0__1__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__0__count` DESC, `aggr__0__key_0` ASC)\n" +
+			"    AS `aggr__0__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0` ORDER BY\n" +
+			"    `aggr__0__1__key_0` ASC) AS `aggr__0__1__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"      `FlightDelayType` AS `aggr__0__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS `aggr__0__count`,\n" +
+			"      toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"      `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__0__1__key_0`,\n" +
+			"      count(*) AS `aggr__0__1__count`\n" +
+			"    FROM `" + TableName + "`\n" +
+			"    WHERE (`timestamp`>=fromUnixTimestamp64Milli(1706881636029) AND `timestamp`<=fromUnixTimestamp64Milli(1707486436029))\n" +
+			"    GROUP BY `FlightDelayType` AS `aggr__0__key_0`,\n" +
+			"      toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"      `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__0__1__key_0`))\n" +
+			"WHERE `aggr__0__order_1_rank`<=11\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC",
 	},
 	{ // [3]
 		TestName: "Sum",
@@ -613,9 +600,7 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0_col_0", 76631.67578125),
 			}},
 		},
-		ExpectedPancakeSQL: `SELECT sumOrNull("taxful_total_price") AS "metric__0_col_0" ` +
-			`FROM ` + TableName + ` ` +
-			`WHERE ("order_date">=fromUnixTimestamp64Milli(1707213597034) AND "order_date"<=fromUnixTimestamp64Milli(1707818397034))`,
+		ExpectedPancakeSQL: "SELECT sumOrNull(`taxful_total_price`) AS `metric__0_col_0`\n        FROM `__quesma_table_name`\n        WHERE (`order_date`>=fromUnixTimestamp64Milli(1707213597034) AND `order_date`<=\n          fromUnixTimestamp64Milli(1707818397034))",
 	},
 	{ // [4]
 		TestName: "cardinality",
@@ -740,17 +725,7 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__suggestions__count", uint64(32)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT uniqMerge(uniqState("OriginCityName")) OVER () AS
-			  "metric__unique_terms_col_0",
-			  sum(count(*)) OVER () AS "aggr__suggestions__parent_count",
-			  "OriginCityName" AS "aggr__suggestions__key_0",
-			  count(*) AS "aggr__suggestions__count"
-			FROM ` + TableName + `
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1706881636029) AND "timestamp"<=fromUnixTimestamp64Milli(1707486436029))
-			GROUP BY "OriginCityName" AS "aggr__suggestions__key_0"
-			ORDER BY "aggr__suggestions__count" DESC, "aggr__suggestions__key_0" ASC
-			LIMIT 11`,
+		ExpectedPancakeSQL: "SELECT uniqMerge(uniqState(`OriginCityName`)) OVER () AS `\n          metric__unique_terms_col_0`,\n          sum(count(*)) OVER () AS `aggr__suggestions__parent_count`,\n          `OriginCityName` AS `aggr__suggestions__key_0`,\n          count(*) AS `aggr__suggestions__count`\n        FROM `__quesma_table_name`\n        WHERE (`timestamp`>=fromUnixTimestamp64Milli(1706881636029) AND `timestamp`<=\n          fromUnixTimestamp64Milli(1707486436029))\n        GROUP BY `OriginCityName` AS `aggr__suggestions__key_0`\n        ORDER BY `aggr__suggestions__count` DESC, `aggr__suggestions__key_0` ASC\n        LIMIT 11",
 	},
 	{ // [5]
 		TestName: "simple filter/count",
@@ -860,10 +835,7 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0-bucket_col_0", uint64(553)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT countIf("FlightDelay" __quesma_match true) AS "metric__0-bucket_col_0"
-			FROM ` + TableName + `
-			 WHERE ("timestamp">=fromUnixTimestamp64Milli(1706881636029) AND "timestamp"<=fromUnixTimestamp64Milli(1707486436029))`,
+		ExpectedPancakeSQL: "SELECT countIf(`FlightDelay` __quesma_match true) AS `metric__0-bucket_col_0`\n        FROM `__quesma_table_name`\n        WHERE (`timestamp`>=fromUnixTimestamp64Milli(1706881636029) AND `timestamp`<=\n          fromUnixTimestamp64Milli(1707486436029))",
 	},
 	{ // [6]
 		TestName: "filters",
@@ -1016,18 +988,16 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("filter_1__aggr__time_offset_split__count", uint64(351)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT countIf(("timestamp">=fromUnixTimestamp64Milli(1706881636029) AND
-			  "timestamp"<=fromUnixTimestamp64Milli(1707486436029))) AS
-			  "filter_0__aggr__time_offset_split__count",
-			  countIf(("timestamp">=fromUnixTimestamp64Milli(1706276836029) AND "timestamp"
-			  <=fromUnixTimestamp64Milli(1706881636029))) AS
-			  "filter_1__aggr__time_offset_split__count"
-			FROM __quesma_table_name
-			WHERE ("FlightDelay" __quesma_match true AND (("timestamp">=fromUnixTimestamp64Milli(
-			  1706881636029) AND "timestamp"<=fromUnixTimestamp64Milli(1707486436029)) OR (
-			  "timestamp">=fromUnixTimestamp64Milli(1706276836029) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1706881636029))))`,
+		ExpectedPancakeSQL: "SELECT countIf((`timestamp`>=fromUnixTimestamp64Milli(1706881636029) AND\n" +
+			"  `timestamp`<=fromUnixTimestamp64Milli(1707486436029))) AS\n" +
+			"  `filter_0__aggr__time_offset_split__count`,\n" +
+			"  countIf((`timestamp`>=fromUnixTimestamp64Milli(1706276836029) AND `timestamp`<=fromUnixTimestamp64Milli(1706881636029))) AS\n" +
+			"  `filter_1__aggr__time_offset_split__count`\n" +
+			"FROM `" + TableName + "`\n" +
+			"WHERE (`FlightDelay` __quesma_match true AND ((`timestamp`>=fromUnixTimestamp64Milli(\n" +
+			"  1706881636029) AND `timestamp`<=fromUnixTimestamp64Milli(1707486436029)) OR (\n" +
+			"  `timestamp`>=fromUnixTimestamp64Milli(1706276836029) AND `timestamp`<=\n" +
+			"  fromUnixTimestamp64Milli(1706881636029))))",
 	},
 	{ // [7]
 		TestName: "top hits, quite complex",
@@ -1389,100 +1359,97 @@ var AggregationTests = []AggregationTestCase{
 				},
 			},
 		},
-		ExpectedPancakeSQL: `
-			WITH quesma_top_hits_group_table AS (
-			  SELECT "aggr__origins__parent_count", "aggr__origins__key_0",
-				"aggr__origins__count", "aggr__origins__distinations__parent_count",
-				"aggr__origins__distinations__key_0", "aggr__origins__distinations__count",
-				"aggr__origins__order_1_rank", "aggr__origins__distinations__order_1_rank"
-			  FROM (
-				SELECT "aggr__origins__parent_count", "aggr__origins__key_0",
-				  "aggr__origins__count", "aggr__origins__distinations__parent_count",
-				  "aggr__origins__distinations__key_0",
-				  "aggr__origins__distinations__count",
-				  dense_rank() OVER (ORDER BY "aggr__origins__count" DESC,
-				  "aggr__origins__key_0" ASC) AS "aggr__origins__order_1_rank",
-				  dense_rank() OVER (PARTITION BY "aggr__origins__key_0" ORDER BY
-				  "aggr__origins__distinations__count" DESC,
-				  "aggr__origins__distinations__key_0" ASC) AS
-				  "aggr__origins__distinations__order_1_rank"
-				FROM (
-				  SELECT sum(count(*)) OVER () AS "aggr__origins__parent_count",
-					"OriginAirportID" AS "aggr__origins__key_0",
-					sum(count(*)) OVER (PARTITION BY "aggr__origins__key_0") AS
-					"aggr__origins__count",
-					sum(count(*)) OVER (PARTITION BY "aggr__origins__key_0") AS
-					"aggr__origins__distinations__parent_count",
-					"DestAirportID" AS "aggr__origins__distinations__key_0",
-					count(*) AS "aggr__origins__distinations__count"
-				  FROM __quesma_table_name
-				  GROUP BY "OriginAirportID" AS "aggr__origins__key_0",
-					"DestAirportID" AS "aggr__origins__distinations__key_0"))
-			  WHERE ("aggr__origins__order_1_rank"<=10001 AND
-				"aggr__origins__distinations__order_1_rank"<=10001)
-			  ORDER BY "aggr__origins__order_1_rank" ASC,
-				"aggr__origins__distinations__order_1_rank" ASC) ,
-			quesma_top_hits_join AS (
-			  SELECT "group_table"."aggr__origins__parent_count" AS
-				"aggr__origins__parent_count",
-				"group_table"."aggr__origins__key_0" AS "aggr__origins__key_0",
-				"group_table"."aggr__origins__count" AS "aggr__origins__count",
-				"group_table"."aggr__origins__distinations__parent_count" AS
-				"aggr__origins__distinations__parent_count",
-				"group_table"."aggr__origins__distinations__key_0" AS
-				"aggr__origins__distinations__key_0",
-				"group_table"."aggr__origins__distinations__count" AS
-				"aggr__origins__distinations__count",
-				"hit_table"."DestLocation" AS 
-                "top_hits__origins__distinations__destLocation_col_0",
-				ROW_NUMBER() OVER (PARTITION BY "group_table"."aggr__origins__key_0",
-				"group_table"."aggr__origins__distinations__key_0") AS "top_hits_rank",
-				"group_table"."aggr__origins__order_1_rank" AS "aggr__origins__order_1_rank"
-				,
-				"group_table"."aggr__origins__distinations__order_1_rank" AS
-				"aggr__origins__distinations__order_1_rank"
-			  FROM quesma_top_hits_group_table AS "group_table" LEFT OUTER JOIN
-				__quesma_table_name AS "hit_table" ON (("group_table"."aggr__origins__key_0"
-				="hit_table"."OriginAirportID" AND
-				"group_table"."aggr__origins__distinations__key_0"=
-				"hit_table"."DestAirportID")))
-			SELECT "aggr__origins__parent_count", "aggr__origins__key_0",
-			  "aggr__origins__count", "aggr__origins__distinations__parent_count",
-			  "aggr__origins__distinations__key_0", "aggr__origins__distinations__count",
-			  "top_hits__origins__distinations__destLocation_col_0", "top_hits_rank"
-			FROM "quesma_top_hits_join"
-			WHERE "top_hits_rank"<=1
-			ORDER BY "aggr__origins__order_1_rank" ASC,
-			  "aggr__origins__distinations__order_1_rank" ASC, "top_hits_rank" ASC`,
-		ExpectedAdditionalPancakeSQLs: []string{`
-			WITH quesma_top_hits_group_table AS (
-			  SELECT sum(count(*)) OVER () AS "aggr__origins__parent_count",
-				"OriginAirportID" AS "aggr__origins__key_0",
-				count(*) AS "aggr__origins__count"
-			  FROM __quesma_table_name
-			  GROUP BY "OriginAirportID" AS "aggr__origins__key_0"
-			  ORDER BY "aggr__origins__count" DESC, "aggr__origins__key_0" ASC
-			  LIMIT 10001) ,
-			quesma_top_hits_join AS (
-			  SELECT "group_table"."aggr__origins__parent_count" AS
-				"aggr__origins__parent_count",
-				"group_table"."aggr__origins__key_0" AS "aggr__origins__key_0",
-				"group_table"."aggr__origins__count" AS "aggr__origins__count",
-				"hit_table"."OriginLocation" AS
-				"top_hits__origins__originLocation_col_0",
-				"hit_table"."Origin" AS "top_hits__origins__originLocation_col_1",
-				ROW_NUMBER() OVER (PARTITION BY "group_table"."aggr__origins__key_0") AS
-				"top_hits_rank"
-			  FROM quesma_top_hits_group_table AS "group_table" LEFT OUTER JOIN
-				__quesma_table_name AS "hit_table" ON ("group_table"."aggr__origins__key_0"=
-				"hit_table"."OriginAirportID"))
-			SELECT "aggr__origins__parent_count", "aggr__origins__key_0",
-			  "aggr__origins__count", "top_hits__origins__originLocation_col_0",
-			  "top_hits__origins__originLocation_col_1", "top_hits_rank"
-			FROM "quesma_top_hits_join"
-			WHERE "top_hits_rank"<=1
-			ORDER BY "aggr__origins__count" DESC, "aggr__origins__key_0" ASC,
-			  "top_hits_rank" ASC`},
+		ExpectedPancakeSQL: "WITH quesma_top_hits_group_table AS (\n" +
+			"  SELECT `aggr__origins__parent_count`, `aggr__origins__key_0`,\n" +
+			"    `aggr__origins__count`, `aggr__origins__distinations__parent_count`,\n" +
+			"    `aggr__origins__distinations__key_0`, `aggr__origins__distinations__count`,\n" +
+			"    `aggr__origins__order_1_rank`, `aggr__origins__distinations__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT `aggr__origins__parent_count`, `aggr__origins__key_0`,\n" +
+			"      `aggr__origins__count`, `aggr__origins__distinations__parent_count`,\n" +
+			"      `aggr__origins__distinations__key_0`,\n" +
+			"      `aggr__origins__distinations__count`,\n" +
+			"      dense_rank() OVER (ORDER BY `aggr__origins__count` DESC,\n" +
+			"      `aggr__origins__key_0` ASC) AS `aggr__origins__order_1_rank`,\n" +
+			"      dense_rank() OVER (PARTITION BY `aggr__origins__key_0` ORDER BY\n" +
+			"      `aggr__origins__distinations__count` DESC,\n" +
+			"      `aggr__origins__distinations__key_0` ASC) AS\n" +
+			"      `aggr__origins__distinations__order_1_rank`\n" +
+			"    FROM (\n" +
+			"      SELECT sum(count(*)) OVER () AS `aggr__origins__parent_count`,\n" +
+			"        `OriginAirportID` AS `aggr__origins__key_0`,\n" +
+			"        sum(count(*)) OVER (PARTITION BY `aggr__origins__key_0`) AS\n" +
+			"        `aggr__origins__count`,\n" +
+			"        sum(count(*)) OVER (PARTITION BY `aggr__origins__key_0`) AS\n" +
+			"        `aggr__origins__distinations__parent_count`,\n" +
+			"        `DestAirportID` AS `aggr__origins__distinations__key_0`,\n" +
+			"        count(*) AS `aggr__origins__distinations__count`\n" +
+			"      FROM `" + TableName + "`\n" +
+			"      GROUP BY `OriginAirportID` AS `aggr__origins__key_0`,\n" +
+			"        `DestAirportID` AS `aggr__origins__distinations__key_0`))\n" +
+			"  WHERE (`aggr__origins__order_1_rank`<=10001 AND\n" +
+			"    `aggr__origins__distinations__order_1_rank`<=10001)\n" +
+			"  ORDER BY `aggr__origins__order_1_rank` ASC,\n" +
+			"    `aggr__origins__distinations__order_1_rank` ASC) ,\n" +
+			"quesma_top_hits_join AS (\n" +
+			"  SELECT `group_table`.`aggr__origins__parent_count` AS\n" +
+			"    `aggr__origins__parent_count`,\n" +
+			"    `group_table`.`aggr__origins__key_0` AS `aggr__origins__key_0`,\n" +
+			"    `group_table`.`aggr__origins__count` AS `aggr__origins__count`,\n" +
+			"    `group_table`.`aggr__origins__distinations__parent_count` AS\n" +
+			"    `aggr__origins__distinations__parent_count`,\n" +
+			"    `group_table`.`aggr__origins__distinations__key_0` AS\n" +
+			"    `aggr__origins__distinations__key_0`,\n" +
+			"    `group_table`.`aggr__origins__distinations__count` AS\n" +
+			"    `aggr__origins__distinations__count`,\n" +
+			"    `hit_table`.`DestLocation` AS \n" +
+			"    `top_hits__origins__distinations__destLocation_col_0`,\n" +
+			"    ROW_NUMBER() OVER (PARTITION BY `group_table`.`aggr__origins__key_0`,\n" +
+			"    `group_table`.`aggr__origins__distinations__key_0`) AS `top_hits_rank`,\n" +
+			"    `group_table`.`aggr__origins__order_1_rank` AS `aggr__origins__order_1_rank`\n" +
+			"    ,\n" +
+			"    `group_table`.`aggr__origins__distinations__order_1_rank` AS\n" +
+			"    `aggr__origins__distinations__order_1_rank`\n" +
+			"  FROM quesma_top_hits_group_table AS `group_table` LEFT OUTER JOIN\n" +
+			"    `" + TableName + "` AS `hit_table` ON ((`group_table`.`aggr__origins__key_0`=`hit_table`.`OriginAirportID` AND\n" +
+			"    `group_table`.`aggr__origins__distinations__key_0`=`hit_table`.`DestAirportID`)))\n" +
+			"SELECT `aggr__origins__parent_count`, `aggr__origins__key_0`,\n" +
+			"  `aggr__origins__count`, `aggr__origins__distinations__parent_count`,\n" +
+			"  `aggr__origins__distinations__key_0`, `aggr__origins__distinations__count`,\n" +
+			"  `top_hits__origins__distinations__destLocation_col_0`, `top_hits_rank`\n" +
+			"FROM `quesma_top_hits_join`\n" +
+			"WHERE `top_hits_rank`<=1\n" +
+			"ORDER BY `aggr__origins__order_1_rank` ASC,\n" +
+			"  `aggr__origins__distinations__order_1_rank` ASC, `top_hits_rank` ASC",
+		ExpectedAdditionalPancakeSQLs: []string{
+			"WITH quesma_top_hits_group_table AS (\n" +
+				"  SELECT sum(count(*)) OVER () AS `aggr__origins__parent_count`,\n" +
+				"    `OriginAirportID` AS `aggr__origins__key_0`,\n" +
+				"    count(*) AS `aggr__origins__count`\n" +
+				"  FROM `" + TableName + "`\n" +
+				"  GROUP BY `OriginAirportID` AS `aggr__origins__key_0`\n" +
+				"  ORDER BY `aggr__origins__count` DESC, `aggr__origins__key_0` ASC\n" +
+				"  LIMIT 10001) ,\n" +
+				"quesma_top_hits_join AS (\n" +
+				"  SELECT `group_table`.`aggr__origins__parent_count` AS\n" +
+				"    `aggr__origins__parent_count`,\n" +
+				"    `group_table`.`aggr__origins__key_0` AS `aggr__origins__key_0`,\n" +
+				"    `group_table`.`aggr__origins__count` AS `aggr__origins__count`,\n" +
+				"    `hit_table`.`OriginLocation` AS\n" +
+				"    `top_hits__origins__originLocation_col_0`,\n" +
+				"    `hit_table`.`Origin` AS `top_hits__origins__originLocation_col_1`,\n" +
+				"    ROW_NUMBER() OVER (PARTITION BY `group_table`.`aggr__origins__key_0`) AS\n" +
+				"    `top_hits_rank`\n" +
+				"  FROM quesma_top_hits_group_table AS `group_table` LEFT OUTER JOIN\n" +
+				"    `" + TableName + "` AS `hit_table` ON (`group_table`.`aggr__origins__key_0`=`hit_table`.`OriginAirportID`))\n" +
+				"SELECT `aggr__origins__parent_count`, `aggr__origins__key_0`,\n" +
+				"  `aggr__origins__count`, `top_hits__origins__originLocation_col_0`,\n" +
+				"  `top_hits__origins__originLocation_col_1`, `top_hits_rank`\n" +
+				"FROM `quesma_top_hits_join`\n" +
+				"WHERE `top_hits_rank`<=1\n" +
+				"ORDER BY `aggr__origins__count` DESC, `aggr__origins__key_0` ASC,\n" +
+				"  `top_hits_rank` ASC",
+		},
 	},
 	{ // [8]
 		TestName: "histogram, different field than timestamp",
@@ -1614,13 +1581,7 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__0__count", 22),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "FlightDelayMin" AS "aggr__0__key_0", count(*) AS "aggr__0__count"
-			FROM ` + TableName + `
-			WHERE (("timestamp">=fromUnixTimestamp64Milli(1706881636029) AND "timestamp"<=
-              fromUnixTimestamp64Milli(1707486436029)) AND NOT ("FlightDelayMin" __quesma_match 0))
-			GROUP BY "FlightDelayMin" AS "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT `FlightDelayMin` AS `aggr__0__key_0`, count(*) AS `aggr__0__count`\n        FROM `__quesma_table_name`\n        WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1706881636029) AND `timestamp`<=\n          fromUnixTimestamp64Milli(1707486436029)) AND NOT (`FlightDelayMin`\n          __quesma_match 0))\n        GROUP BY `FlightDelayMin` AS `aggr__0__key_0`\n        ORDER BY `aggr__0__key_0` ASC",
 	},
 	{ // [9]
 		TestName: "double aggregation with histogram + harder query",
@@ -1822,32 +1783,7 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__0__1__count", 11),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-			  "aggr__0__1__key_0", "aggr__0__1__count"
-			FROM (
-			  SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-				"aggr__0__1__key_0", "aggr__0__1__count",
-				dense_rank() OVER (ORDER BY "aggr__0__count" DESC, "aggr__0__key_0" ASC) AS
-				"aggr__0__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0" ORDER BY
-				"aggr__0__1__key_0" ASC) AS "aggr__0__1__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-				  "severity" AS "aggr__0__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS "aggr__0__count",
-				  toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone(
-				  "@timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__0__1__key_0",
-				  count(*) AS "aggr__0__1__count"
-				FROM __quesma_table_name
-				WHERE ("host.name" __quesma_match '%prometheus%' AND ("@timestamp">=
-				  fromUnixTimestamp64Milli(1706891809940) AND "@timestamp"<=
-				  fromUnixTimestamp64Milli(1707496609940)))
-				GROUP BY "severity" AS "aggr__0__key_0",
-				  toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone(
-				  "@timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__0__1__key_0"))
-			WHERE "aggr__0__order_1_rank"<=4
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n          `aggr__0__1__key_0`, `aggr__0__1__count`\n        FROM (\n          SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n            `aggr__0__1__key_0`, `aggr__0__1__count`,\n            dense_rank() OVER (ORDER BY `aggr__0__count` DESC, `aggr__0__key_0` ASC) AS\n            `aggr__0__order_1_rank`,\n            dense_rank() OVER (PARTITION BY `aggr__0__key_0` ORDER BY `aggr__0__1__key_0\n            ` ASC) AS `aggr__0__1__order_1_rank`\n          FROM (\n            SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n              `severity` AS `aggr__0__key_0`,\n              sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS `aggr__0__count`,\n              toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone(`@\n              timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__0__1__key_0`,\n              count(*) AS `aggr__0__1__count`\n            FROM `__quesma_table_name`\n            WHERE (`host.name` __quesma_match '%prometheus%' AND (`@timestamp`>=\n              fromUnixTimestamp64Milli(1706891809940) AND `@timestamp`<=\n              fromUnixTimestamp64Milli(1707496609940)))\n            GROUP BY `severity` AS `aggr__0__key_0`,\n              toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone(`@\n              timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__0__1__key_0`))\n        WHERE `aggr__0__order_1_rank`<=4\n        ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC",
 	},
 	{ // [10]
 		TestName: "very long: multiple top_metrics + histogram",
@@ -2184,80 +2120,75 @@ var AggregationTests = []AggregationTestCase{
 				}},
 			},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__1__count",
-			  toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone(
-			  "order_date", 'Europe/Warsaw'))*1000) / 43200000) AS "aggr__1__2__key_0",
-			  count(*) AS "aggr__1__2__count"
-			FROM __quesma_table_name
-			WHERE (("order_date">=fromUnixTimestamp64Milli(1707213597034) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1707818397034)) AND "taxful_total_price" > '250')
-			GROUP BY toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone
-			  ("order_date", 'Europe/Warsaw'))*1000) / 43200000) AS "aggr__1__2__key_0"
-			ORDER BY "aggr__1__2__key_0" ASC`,
-		ExpectedAdditionalPancakeSQLs: []string{`
-			WITH quesma_top_hits_group_table AS (
-			  SELECT sum(count(*)) OVER () AS "aggr__1__count",
-				toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone(
-				"order_date", 'Europe/Warsaw'))*1000) / 43200000) AS "aggr__1__2__key_0",
-				count(*) AS "aggr__1__2__count"
-			  FROM __quesma_table_name
-			  WHERE (("order_date">=fromUnixTimestamp64Milli(1707213597034) AND "order_date"
-				<=fromUnixTimestamp64Milli(1707818397034)) AND "taxful_total_price" > '250')
-			  GROUP BY toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(
-				toTimezone("order_date", 'Europe/Warsaw'))*1000) / 43200000) AS
-				"aggr__1__2__key_0"
-			  ORDER BY "aggr__1__2__key_0" ASC) ,
-			quesma_top_hits_join AS (
-			  SELECT "group_table"."aggr__1__count" AS "aggr__1__count",
-				"group_table"."aggr__1__2__key_0" AS "aggr__1__2__key_0",
-				"group_table"."aggr__1__2__count" AS "aggr__1__2__count",
-				"hit_table"."order_date" AS "top_metrics__1__2__4_col_0",
-				"hit_table"."order_date" AS "top_metrics__1__2__4_col_1",
-				ROW_NUMBER() OVER (PARTITION BY "group_table"."aggr__1__2__key_0" ORDER BY
-				"order_date" ASC) AS "top_hits_rank"
-			  FROM quesma_top_hits_group_table AS "group_table" LEFT OUTER JOIN
-				__quesma_table_name AS "hit_table" ON ("group_table"."aggr__1__2__key_0"=
-				toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone(
-				"order_date", 'Europe/Warsaw'))*1000) / 43200000))
-			  WHERE (("order_date">=fromUnixTimestamp64Milli(1707213597034) AND "order_date"
-				<=fromUnixTimestamp64Milli(1707818397034)) AND "taxful_total_price" > '250'))
-			SELECT "aggr__1__count", "aggr__1__2__key_0", "aggr__1__2__count",
-			  "top_metrics__1__2__4_col_0", "top_metrics__1__2__4_col_1", "top_hits_rank"
-			FROM "quesma_top_hits_join"
-			WHERE "top_hits_rank"<=10
-			ORDER BY "aggr__1__2__key_0" ASC, "top_hits_rank" ASC`,
-			`WITH quesma_top_hits_group_table AS (
-			  SELECT sum(count(*)) OVER () AS "aggr__1__count",
-				toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone(
-				"order_date", 'Europe/Warsaw'))*1000) / 43200000) AS "aggr__1__2__key_0",
-				count(*) AS "aggr__1__2__count"
-			  FROM __quesma_table_name
-			  WHERE (("order_date">=fromUnixTimestamp64Milli(1707213597034) AND "order_date"
-				<=fromUnixTimestamp64Milli(1707818397034)) AND "taxful_total_price" > '250')
-			  GROUP BY toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(
-				toTimezone("order_date", 'Europe/Warsaw'))*1000) / 43200000) AS
-				"aggr__1__2__key_0"
-			  ORDER BY "aggr__1__2__key_0" ASC) ,
-			quesma_top_hits_join AS (
-			  SELECT "group_table"."aggr__1__count" AS "aggr__1__count",
-				"group_table"."aggr__1__2__key_0" AS "aggr__1__2__key_0",
-				"group_table"."aggr__1__2__count" AS "aggr__1__2__count",
-				"hit_table"."taxful_total_price" AS "top_metrics__1__2__5_col_0",
-				"hit_table"."order_date" AS "top_metrics__1__2__5_col_1",
-				ROW_NUMBER() OVER (PARTITION BY "group_table"."aggr__1__2__key_0" ORDER BY
-				"order_date" ASC) AS "top_hits_rank"
-			  FROM quesma_top_hits_group_table AS "group_table" LEFT OUTER JOIN
-				__quesma_table_name AS "hit_table" ON ("group_table"."aggr__1__2__key_0"=
-				toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone(
-				"order_date", 'Europe/Warsaw'))*1000) / 43200000))
-			  WHERE (("order_date">=fromUnixTimestamp64Milli(1707213597034) AND "order_date"
-				<=fromUnixTimestamp64Milli(1707818397034)) AND "taxful_total_price" > '250'))
-			SELECT "aggr__1__count", "aggr__1__2__key_0", "aggr__1__2__count",
-			  "top_metrics__1__2__5_col_0", "top_metrics__1__2__5_col_1", "top_hits_rank"
-			FROM "quesma_top_hits_join"
-			WHERE "top_hits_rank"<=10
-			ORDER BY "aggr__1__2__key_0" ASC, "top_hits_rank" ASC`},
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__1__count`,\n" +
+			"  toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone(\n" +
+			"  `order_date`, 'Europe/Warsaw'))*1000) / 43200000) AS `aggr__1__2__key_0`,\n" +
+			"  count(*) AS `aggr__1__2__count`\n" +
+			"FROM `" + TableName + "`\n" +
+			"WHERE ((`order_date`>=fromUnixTimestamp64Milli(1707213597034) AND `order_date`<=fromUnixTimestamp64Milli(1707818397034)) AND `taxful_total_price` > '250')\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(\n" +
+			"  toTimezone(`order_date`, 'Europe/Warsaw'))*1000) / 43200000) AS `aggr__1__2__key_0`\n" +
+			"ORDER BY `aggr__1__2__key_0` ASC",
+		ExpectedAdditionalPancakeSQLs: []string{
+			"WITH quesma_top_hits_group_table AS (\n" +
+				"  SELECT sum(count(*)) OVER () AS `aggr__1__count`,\n" +
+				"    toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone(\n" +
+				"    `order_date`, 'Europe/Warsaw'))*1000) / 43200000) AS `aggr__1__2__key_0`,\n" +
+				"    count(*) AS `aggr__1__2__count`\n" +
+				"  FROM `" + TableName + "`\n" +
+				"  WHERE ((`order_date`>=fromUnixTimestamp64Milli(1707213597034) AND `order_date`<=fromUnixTimestamp64Milli(1707818397034)) AND `taxful_total_price` > '250')\n" +
+				"  GROUP BY toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(\n" +
+				"    toTimezone(`order_date`, 'Europe/Warsaw'))*1000) / 43200000) AS\n" +
+				"    `aggr__1__2__key_0`\n" +
+				"  ORDER BY `aggr__1__2__key_0` ASC) ,\n" +
+				"quesma_top_hits_join AS (\n" +
+				"  SELECT `group_table`.`aggr__1__count` AS `aggr__1__count`,\n" +
+				"    `group_table`.`aggr__1__2__key_0` AS `aggr__1__2__key_0`,\n" +
+				"    `group_table`.`aggr__1__2__count` AS `aggr__1__2__count`,\n" +
+				"    `hit_table`.`order_date` AS `top_metrics__1__2__4_col_0`,\n" +
+				"    `hit_table`.`order_date` AS `top_metrics__1__2__4_col_1`,\n" +
+				"    ROW_NUMBER() OVER (PARTITION BY `group_table`.`aggr__1__2__key_0` ORDER BY\n" +
+				"    `order_date` ASC) AS `top_hits_rank`\n" +
+				"  FROM quesma_top_hits_group_table AS `group_table` LEFT OUTER JOIN\n" +
+				"    `" + TableName + "` AS `hit_table` ON (`group_table`.`aggr__1__2__key_0`=\n" +
+				"    toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone(\n" +
+				"    `order_date`, 'Europe/Warsaw'))*1000) / 43200000))\n" +
+				"  WHERE ((`order_date`>=fromUnixTimestamp64Milli(1707213597034) AND `order_date`<=fromUnixTimestamp64Milli(1707818397034)) AND `taxful_total_price` > '250'))\n" +
+				"SELECT `aggr__1__count`, `aggr__1__2__key_0`, `aggr__1__2__count`,\n" +
+				"  `top_metrics__1__2__4_col_0`, `top_metrics__1__2__4_col_1`, `top_hits_rank`\n" +
+				"FROM `quesma_top_hits_join`\n" +
+				"WHERE `top_hits_rank`<=10\n" +
+				"ORDER BY `aggr__1__2__key_0` ASC, `top_hits_rank` ASC",
+			"WITH quesma_top_hits_group_table AS (\n" +
+				"  SELECT sum(count(*)) OVER () AS `aggr__1__count`,\n" +
+				"    toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone(\n" +
+				"    `order_date`, 'Europe/Warsaw'))*1000) / 43200000) AS `aggr__1__2__key_0`,\n" +
+				"    count(*) AS `aggr__1__2__count`\n" +
+				"  FROM `" + TableName + "`\n" +
+				"  WHERE ((`order_date`>=fromUnixTimestamp64Milli(1707213597034) AND `order_date`<=fromUnixTimestamp64Milli(1707818397034)) AND `taxful_total_price` > '250')\n" +
+				"  GROUP BY toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(\n" +
+				"    toTimezone(`order_date`, 'Europe/Warsaw'))*1000) / 43200000) AS\n" +
+				"    `aggr__1__2__key_0`\n" +
+				"  ORDER BY `aggr__1__2__key_0` ASC) ,\n" +
+				"quesma_top_hits_join AS (\n" +
+				"  SELECT `group_table`.`aggr__1__count` AS `aggr__1__count`,\n" +
+				"    `group_table`.`aggr__1__2__key_0` AS `aggr__1__2__key_0`,\n" +
+				"    `group_table`.`aggr__1__2__count` AS `aggr__1__2__count`,\n" +
+				"    `hit_table`.`taxful_total_price` AS `top_metrics__1__2__5_col_0`,\n" +
+				"    `hit_table`.`order_date` AS `top_metrics__1__2__5_col_1`,\n" +
+				"    ROW_NUMBER() OVER (PARTITION BY `group_table`.`aggr__1__2__key_0` ORDER BY\n" +
+				"    `order_date` ASC) AS `top_hits_rank`\n" +
+				"  FROM quesma_top_hits_group_table AS `group_table` LEFT OUTER JOIN\n" +
+				"    `" + TableName + "` AS `hit_table` ON (`group_table`.`aggr__1__2__key_0`=\n" +
+				"    toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone(\n" +
+				"    `order_date`, 'Europe/Warsaw'))*1000) / 43200000))\n" +
+				"  WHERE ((`order_date`>=fromUnixTimestamp64Milli(1707213597034) AND `order_date`<=fromUnixTimestamp64Milli(1707818397034)) AND `taxful_total_price` > '250'))\n" +
+				"SELECT `aggr__1__count`, `aggr__1__2__key_0`, `aggr__1__2__count`,\n" +
+				"  `top_metrics__1__2__5_col_0`, `top_metrics__1__2__5_col_1`, `top_hits_rank`\n" +
+				"FROM `quesma_top_hits_join`\n" +
+				"WHERE `top_hits_rank`<=10\n" +
+				"ORDER BY `aggr__1__2__key_0` ASC, `top_hits_rank` ASC",
+		},
 	},
 	{ // [11], "old" test, also can be found in testdata/requests.go TestAsyncSearch[0]
 		// Copied it also here to be more sure we do not create some regression
@@ -2442,23 +2373,21 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__sample__top_values__count", int64(21)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__sample__count",
-			  sum(count("host.name")) OVER () AS "metric__sample__sample_count_col_0",
-			  sum(count(*)) OVER () AS "aggr__sample__top_values__parent_count",
-			  "host.name" AS "aggr__sample__top_values__key_0",
-			  count(*) AS "aggr__sample__top_values__count"
-			FROM (
-			  SELECT "host.name"
-			  FROM __quesma_table_name
-			  WHERE (("@timestamp">=fromUnixTimestamp64Milli(1706009236820) AND "@timestamp"
-				<=fromUnixTimestamp64Milli(1706010136820)) AND
-				"__quesma_fulltext_field_name" iLIKE '%user%')
-			  LIMIT 8000)
-			GROUP BY "host.name" AS "aggr__sample__top_values__key_0"
-			ORDER BY "aggr__sample__top_values__count" DESC,
-			  "aggr__sample__top_values__key_0" ASC
-			LIMIT 11`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__sample__count`,\n" +
+			"  sum(count(`host.name`)) OVER () AS `metric__sample__sample_count_col_0`,\n" +
+			"  sum(count(*)) OVER () AS `aggr__sample__top_values__parent_count`,\n" +
+			"  `host.name` AS `aggr__sample__top_values__key_0`,\n" +
+			"  count(*) AS `aggr__sample__top_values__count`\n" +
+			"FROM (\n" +
+			"  SELECT `host.name`\n" +
+			"  FROM `" + TableName + "`" +
+			"  WHERE ((`@timestamp`>=fromUnixTimestamp64Milli(1706009236820) AND `@timestamp`<=fromUnixTimestamp64Milli(1706010136820)) AND" +
+			"    `__quesma_fulltext_field_name` iLIKE '%user%')\n" +
+			"  LIMIT 8000)\n" +
+			"GROUP BY `host.name` AS `aggr__sample__top_values__key_0`\n" +
+			"ORDER BY `aggr__sample__top_values__count` DESC,\n" +
+			"  `aggr__sample__top_values__key_0` ASC\n" +
+			"LIMIT 11",
 	},
 	{ // [12], "old" test, also can be found in testdata/requests.go TestAsyncSearch[3]
 		// Copied it also here to be more sure we do not create some regression
@@ -2591,16 +2520,7 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__0__count", 11),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp64Milli("@timestamp") / 30000) AS "aggr__0__key_0"
-			  , count(*) AS "aggr__0__count"
-			FROM ` + TableName + `
-
-			WHERE (` + fullTextFieldName + ` iLIKE '%user%' AND
-              ("@timestamp">=fromUnixTimestamp64Milli(1706020999481) AND "@timestamp"<=fromUnixTimestamp64Milli(1706021899481)))
-			GROUP BY toInt64(toUnixTimestamp64Milli("@timestamp") / 30000) AS
-			  "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp64Milli(`@timestamp`) / 30000) AS `aggr__0__key_0`\n          , count(*) AS `aggr__0__count`\n        FROM `__quesma_table_name`\n        WHERE (`__quesma_fulltext_field_name` iLIKE '%user%' AND (`@timestamp`>=\n          fromUnixTimestamp64Milli(1706020999481) AND `@timestamp`<=\n          fromUnixTimestamp64Milli(1706021899481)))\n        GROUP BY toInt64(toUnixTimestamp64Milli(`@timestamp`) / 30000) AS `\n          aggr__0__key_0`\n        ORDER BY `aggr__0__key_0` ASC",
 	},
 	{ // [13], "old" test, also can be found in testdata/requests.go TestAsyncSearch[4]
 		// Copied it also here to be more sure we do not create some regression
@@ -2718,32 +2638,7 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__stats__series__count", 35),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__stats__parent_count", "aggr__stats__key_0", "aggr__stats__count",
-			  "aggr__stats__series__key_0", "aggr__stats__series__count"
-			FROM (
-			  SELECT "aggr__stats__parent_count", "aggr__stats__key_0",
-				"aggr__stats__count", "aggr__stats__series__key_0",
-				"aggr__stats__series__count",
-				dense_rank() OVER (ORDER BY "aggr__stats__count" DESC,
-				"aggr__stats__key_0" ASC) AS "aggr__stats__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__stats__key_0" ORDER BY
-				"aggr__stats__series__key_0" ASC) AS "aggr__stats__series__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__stats__parent_count",
-				  COALESCE("event.dataset", 'unknown') AS "aggr__stats__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__stats__key_0") AS
-				  "aggr__stats__count",
-				  toInt64(toUnixTimestamp64Milli("@timestamp") / 60000) AS
-				  "aggr__stats__series__key_0", count(*) AS "aggr__stats__series__count"
-				FROM ` + TableName + `
-				WHERE ("@timestamp">fromUnixTimestamp64Milli(1706194439033) AND "@timestamp"<=fromUnixTimestamp64Milli(1706195339033))
-				GROUP BY COALESCE("event.dataset", 'unknown') AS "aggr__stats__key_0",
-				  toInt64(toUnixTimestamp64Milli("@timestamp") / 60000) AS
-				  "aggr__stats__series__key_0"))
-			WHERE "aggr__stats__order_1_rank"<=4
-			ORDER BY "aggr__stats__order_1_rank" ASC,
-			  "aggr__stats__series__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__stats__parent_count`, `aggr__stats__key_0`, `aggr__stats__count`,\n          `aggr__stats__series__key_0`, `aggr__stats__series__count`\n        FROM (\n          SELECT `aggr__stats__parent_count`, `aggr__stats__key_0`,\n            `aggr__stats__count`, `aggr__stats__series__key_0`,\n            `aggr__stats__series__count`,\n            dense_rank() OVER (ORDER BY `aggr__stats__count` DESC, `aggr__stats__key_0`\n            ASC) AS `aggr__stats__order_1_rank`,\n            dense_rank() OVER (PARTITION BY `aggr__stats__key_0` ORDER BY `\n            aggr__stats__series__key_0` ASC) AS `aggr__stats__series__order_1_rank`\n          FROM (\n            SELECT sum(count(*)) OVER () AS `aggr__stats__parent_count`,\n              COALESCE(`event.dataset`, 'unknown') AS `aggr__stats__key_0`,\n              sum(count(*)) OVER (PARTITION BY `aggr__stats__key_0`) AS `\n              aggr__stats__count`,\n              toInt64(toUnixTimestamp64Milli(`@timestamp`) / 60000) AS `\n              aggr__stats__series__key_0`, count(*) AS `aggr__stats__series__count`\n            FROM `__quesma_table_name`\n            WHERE (`@timestamp`>fromUnixTimestamp64Milli(1706194439033) AND `@timestamp\n              `<=fromUnixTimestamp64Milli(1706195339033))\n            GROUP BY COALESCE(`event.dataset`, 'unknown') AS `aggr__stats__key_0`,\n              toInt64(toUnixTimestamp64Milli(`@timestamp`) / 60000) AS `\n              aggr__stats__series__key_0`))\n        WHERE `aggr__stats__order_1_rank`<=4\n        ORDER BY `aggr__stats__order_1_rank` ASC,\n          `aggr__stats__series__order_1_rank` ASC",
 	},
 	{ // [14], "old" test, also can be found in testdata/requests.go TestAsyncSearch[5]
 		// Copied it also here to be more sure we do not create some regression
@@ -2840,13 +2735,7 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("metric__latest_timestamp_col_0", nil),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT avgOrNull("@timestamp") AS "metric__average_timestamp_col_0", minOrNull(
-			  "@timestamp") AS "metric__earliest_timestamp_col_0", maxOrNull("@timestamp")
-			  AS "metric__latest_timestamp_col_0"
-			FROM ` + TableName + `
-			WHERE ((` + fullTextFieldName + ` iLIKE '%posei%' AND "message" __quesma_match '%User logged out%') AND
-			  "host.name" __quesma_match '%poseidon%')`,
+		ExpectedPancakeSQL: "SELECT avgOrNull(`@timestamp`) AS `metric__average_timestamp_col_0`,\n          minOrNull(`@timestamp`) AS `metric__earliest_timestamp_col_0`,\n          maxOrNull(`@timestamp`) AS `metric__latest_timestamp_col_0`\n        FROM `__quesma_table_name`\n        WHERE ((`__quesma_fulltext_field_name` iLIKE '%posei%' AND `message`\n          __quesma_match '%User logged out%') AND `host.name` __quesma_match\n          '%poseidon%')",
 	},
 	{ // [15]
 		TestName: "date_histogram: regression test",
@@ -2967,15 +2856,7 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0__1_col_0", 11116.45703125),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp64Milli("order_date") / 86400000) AS
-			  "aggr__0__key_0", count(*) AS "aggr__0__count",
-			  sumOrNull("taxful_total_price") AS "metric__0__1_col_0"
-			FROM ` + TableName + `
-			WHERE ("order_date">=fromUnixTimestamp64Milli(1708364456351) AND "order_date"<=fromUnixTimestamp64Milli(1708969256351))
-			GROUP BY toInt64(toUnixTimestamp64Milli("order_date") / 86400000) AS
-			  "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp64Milli(`order_date`) / 86400000) AS `\n          aggr__0__key_0`, count(*) AS `aggr__0__count`,\n          sumOrNull(`taxful_total_price`) AS `metric__0__1_col_0`\n        FROM `__quesma_table_name`\n        WHERE (`order_date`>=fromUnixTimestamp64Milli(1708364456351) AND `order_date`<=\n          fromUnixTimestamp64Milli(1708969256351))\n        GROUP BY toInt64(toUnixTimestamp64Milli(`order_date`) / 86400000) AS `\n          aggr__0__key_0`\n        ORDER BY `aggr__0__key_0` ASC",
 	},
 	{ // [16]
 		TestName: "simple terms, seen at client's",
@@ -3078,15 +2959,7 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__0__count", int64(1757)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-			  "message" AS "aggr__0__key_0", count(*) AS "aggr__0__count"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1708456413795) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1708488074920))
-			GROUP BY "message" AS "aggr__0__key_0"
-			ORDER BY "aggr__0__count" DESC, "aggr__0__key_0" ASC
-			LIMIT 4`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n          `message` AS `aggr__0__key_0`, count(*) AS `aggr__0__count`\n        FROM `__quesma_table_name`\n        WHERE (`timestamp`>=fromUnixTimestamp64Milli(1708456413795) AND `timestamp`<=\n          fromUnixTimestamp64Milli(1708488074920))\n        GROUP BY `message` AS `aggr__0__key_0`\n        ORDER BY `aggr__0__count` DESC, `aggr__0__key_0` ASC\n        LIMIT 4",
 	},
 	{ // [17]
 		TestName: "triple nested aggs",
@@ -3267,18 +3140,7 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0__1-bucket__1-metric_col_0", 931.96875),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp64Milli("order_date") / 43200000) AS
-			  "aggr__0__key_0", count(*) AS "aggr__0__count",
-			  countIf("products.product_name" __quesma_match '%watch%') AS
-			  "aggr__0__1-bucket__count",
-			  sumOrNullIf("taxful_total_price", "products.product_name" __quesma_match '%watch%') AS
-			  "metric__0__1-bucket__1-metric_col_0"
-			FROM ` + TableName + `
-			WHERE ("order_date">=fromUnixTimestamp64Milli(1708627654149) AND "order_date"<=fromUnixTimestamp64Milli(1709232454149))
-			GROUP BY toInt64(toUnixTimestamp64Milli("order_date") / 43200000) AS
-			  "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp64Milli(`order_date`) / 43200000) AS `\n          aggr__0__key_0`, count(*) AS `aggr__0__count`,\n          countIf(`products.product_name` __quesma_match '%watch%') AS `aggr__0__1-\n          bucket__count`,\n          sumOrNullIf(`taxful_total_price`, `products.product_name` __quesma_match\n          '%watch%') AS `metric__0__1-bucket__1-metric_col_0`\n        FROM `__quesma_table_name`\n        WHERE (`order_date`>=fromUnixTimestamp64Milli(1708627654149) AND `order_date`<=\n          fromUnixTimestamp64Milli(1709232454149))\n        GROUP BY toInt64(toUnixTimestamp64Milli(`order_date`) / 43200000) AS `\n          aggr__0__key_0`\n        ORDER BY `aggr__0__key_0` ASC",
 	},
 	{ // [18]
 		TestName: "complex filters",
@@ -3494,40 +3356,38 @@ var AggregationTests = []AggregationTestCase{
 				//model.NewQueryResultCol("filter_1__metric__time_offset_split__0__2_col_0", nil),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__time_offset_split__count",
-			  toInt64(toUnixTimestamp64Milli("order_date") / 86400000) AS
-			  "aggr__time_offset_split__0__key_0",
-			  count(*) AS "aggr__time_offset_split__0__count",
-			  sumOrNull("taxful_total_price") AS "metric__time_offset_split__0__1_col_0",
-			  sumOrNull("taxful_total_price") AS "metric__time_offset_split__0__2_col_0"
-			FROM __quesma_table_name
-			WHERE ((("order_date">=fromUnixTimestamp64Milli(1708639056376) AND "order_date"
-			  <=fromUnixTimestamp64Milli(1709243856376)) OR ("order_date">=
-			  fromUnixTimestamp64Milli(1708034256376) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1708639056376))) AND ("order_date">=
-			  fromUnixTimestamp64Milli(1708639056376) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1709243856376)))
-			GROUP BY toInt64(toUnixTimestamp64Milli("order_date") / 86400000) AS
-			  "aggr__time_offset_split__0__key_0"
-			ORDER BY "aggr__time_offset_split__0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__time_offset_split__count`,\n" +
+			"  toInt64(toUnixTimestamp64Milli(`order_date`) / 86400000) AS\n" +
+			"  `aggr__time_offset_split__0__key_0`,\n" +
+			"  count(*) AS `aggr__time_offset_split__0__count`,\n" +
+			"  sumOrNull(`taxful_total_price`) AS `metric__time_offset_split__0__1_col_0`,\n" +
+			"  sumOrNull(`taxful_total_price`) AS `metric__time_offset_split__0__2_col_0`\n" +
+			"FROM `" + TableName + "`\n" +
+			"WHERE (((`order_date`>=fromUnixTimestamp64Milli(1708639056376) AND `order_date`<=fromUnixTimestamp64Milli(1709243856376)) OR (`order_date`>=\n" +
+			"  fromUnixTimestamp64Milli(1708034256376) AND `order_date`<=\n" +
+			"  fromUnixTimestamp64Milli(1708639056376))) AND (`order_date`>=\n" +
+			"  fromUnixTimestamp64Milli(1708639056376) AND `order_date`<=\n" +
+			"  fromUnixTimestamp64Milli(1709243856376)))\n" +
+			"GROUP BY toInt64(toUnixTimestamp64Milli(`order_date`) / 86400000) AS\n" +
+			"  `aggr__time_offset_split__0__key_0`\n" +
+			"ORDER BY `aggr__time_offset_split__0__key_0` ASC",
 		ExpectedAdditionalPancakeSQLs: []string{
-			`SELECT sum(count(*)) OVER () AS "aggr__time_offset_split__count",
-			  toInt64(toUnixTimestamp64Milli("order_date") / 86400000) AS
-			  "aggr__time_offset_split__0__key_0",
-			  count(*) AS "aggr__time_offset_split__0__count",
-			  sumOrNull("taxful_total_price") AS "metric__time_offset_split__0__1_col_0",
-			  sumOrNull("taxful_total_price") AS "metric__time_offset_split__0__2_col_0"
-			FROM __quesma_table_name
-			WHERE ((("order_date">=fromUnixTimestamp64Milli(1708639056376) AND
-			  "order_date"<=fromUnixTimestamp64Milli(1709243856376)) OR
-			  ("order_date">=fromUnixTimestamp64Milli(1708034256376) AND
-			  "order_date"<=fromUnixTimestamp64Milli(1708639056376))) AND
-			  ("order_date">=fromUnixTimestamp64Milli(1708034256376) AND
-			  "order_date"<=fromUnixTimestamp64Milli(1708639056376)))
-			GROUP BY toInt64(toUnixTimestamp64Milli("order_date") / 86400000) AS
-			  "aggr__time_offset_split__0__key_0"
-			ORDER BY "aggr__time_offset_split__0__key_0" ASC`,
+			"SELECT sum(count(*)) OVER () AS `aggr__time_offset_split__count`,\n" +
+				"  toInt64(toUnixTimestamp64Milli(`order_date`) / 86400000) AS\n" +
+				"  `aggr__time_offset_split__0__key_0`,\n" +
+				"  count(*) AS `aggr__time_offset_split__0__count`,\n" +
+				"  sumOrNull(`taxful_total_price`) AS `metric__time_offset_split__0__1_col_0`,\n" +
+				"  sumOrNull(`taxful_total_price`) AS `metric__time_offset_split__0__2_col_0`\n" +
+				"FROM `" + TableName + "`\n" +
+				"WHERE (((`order_date`>=fromUnixTimestamp64Milli(1708639056376) AND\n" +
+				"  `order_date`<=fromUnixTimestamp64Milli(1709243856376)) OR\n" +
+				"  (`order_date`>=fromUnixTimestamp64Milli(1708034256376) AND\n" +
+				"  `order_date`<=fromUnixTimestamp64Milli(1708639056376))) AND\n" +
+				"  (`order_date`>=fromUnixTimestamp64Milli(1708034256376) AND\n" +
+				"  `order_date`<=fromUnixTimestamp64Milli(1708639056376)))\n" +
+				"GROUP BY toInt64(toUnixTimestamp64Milli(`order_date`) / 86400000) AS\n" +
+				"  `aggr__time_offset_split__0__key_0`\n" +
+				"ORDER BY `aggr__time_offset_split__0__key_0` ASC",
 		},
 		ExpectedAdditionalPancakeResults: [][]model.QueryResultRow{{
 			{Cols: []model.QueryResultCol{
@@ -3653,19 +3513,7 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__sampler__eventRate__count", 1),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__sampler__count",
-			  toInt64(toUnixTimestamp64Milli("@timestamp") / 15000) AS
-			  "aggr__sampler__eventRate__key_0",
-			  count(*) AS "aggr__sampler__eventRate__count"
-			FROM (
-			  SELECT "@timestamp"
-			  FROM ` + TableName + `
-			  WHERE ("@timestamp">=fromUnixTimestamp64Milli(1709815794995) AND "@timestamp"<=fromUnixTimestamp64Milli(1709816694995))
-			  LIMIT 20000)
-			GROUP BY toInt64(toUnixTimestamp64Milli("@timestamp") / 15000) AS
-			  "aggr__sampler__eventRate__key_0"
-			ORDER BY "aggr__sampler__eventRate__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__sampler__count`,\n          toInt64(toUnixTimestamp64Milli(`@timestamp`) / 15000) AS `\n          aggr__sampler__eventRate__key_0`,\n          count(*) AS `aggr__sampler__eventRate__count`\n        FROM (\n          SELECT `@timestamp`\n          FROM `__quesma_table_name`\n          WHERE (`@timestamp`>=fromUnixTimestamp64Milli(1709815794995) AND `@timestamp\n            `<=fromUnixTimestamp64Milli(1709816694995))\n          LIMIT 20000)\n        GROUP BY toInt64(toUnixTimestamp64Milli(`@timestamp`) / 15000) AS `\n          aggr__sampler__eventRate__key_0`\n        ORDER BY `aggr__sampler__eventRate__key_0` ASC",
 	},
 	{ // [20]
 		TestName: "Field statistics > summary for numeric fields",
@@ -3939,201 +3787,200 @@ var AggregationTests = []AggregationTestCase{
 				}},
 			},
 		},
-		ExpectedPancakeSQL: `
-			SELECT count(*) AS "aggr__sample__count",
-			  quantiles(0.050000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_0",
-			  quantiles(0.100000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_1",
-			  quantiles(0.150000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_2",
-			  quantiles(0.200000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_3",
-			  quantiles(0.250000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_4",
-			  quantiles(0.300000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_5",
-			  quantiles(0.350000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_6",
-			  quantiles(0.400000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_7",
-			  quantiles(0.450000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_8",
-			  quantiles(0.500000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_9",
-			  quantiles(0.550000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_10",
-			  quantiles(0.600000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_11",
-			  quantiles(0.650000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_12",
-			  quantiles(0.700000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_13",
-			  quantiles(0.750000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_14",
-			  quantiles(0.800000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_15",
-			  quantiles(0.850000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_16",
-			  quantiles(0.900000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_17",
-			  quantiles(0.950000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_18",
-			  quantiles(0.999999)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_col_19",
-			  quantiles(0.050000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_0",
-			  quantiles(0.100000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_1",
-			  quantiles(0.150000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_2",
-			  quantiles(0.200000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_3",
-			  quantiles(0.250000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_4",
-			  quantiles(0.300000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_5",
-			  quantiles(0.350000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_6",
-			  quantiles(0.400000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_7",
-			  quantiles(0.450000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_8",
-			  quantiles(0.500000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_9",
-			  quantiles(0.550000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_10",
-			  quantiles(0.600000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_11",
-			  quantiles(0.650000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_12",
-			  quantiles(0.700000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_13",
-			  quantiles(0.750000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_14",
-			  quantiles(0.800000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_15",
-			  quantiles(0.850000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_16",
-			  quantiles(0.900000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_17",
-			  quantiles(0.950000)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_18",
-			  quantiles(0.999999)("bytes_gauge") AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_19",
-			  countIf("bytes_gauge" IS NOT NULL) AS
-			  "aggr__sample__bytes_gauge_field_stats__count",
-			  countIf("bytes_gauge" IS NOT NULL) AS
-			  "metric__sample__bytes_gauge_field_stats__actual_stats_col_0",
-			  minOrNullIf("bytes_gauge", "bytes_gauge" IS NOT NULL) AS
-			  "metric__sample__bytes_gauge_field_stats__actual_stats_col_1",
-			  maxOrNullIf("bytes_gauge", "bytes_gauge" IS NOT NULL) AS
-			  "metric__sample__bytes_gauge_field_stats__actual_stats_col_2",
-			  avgOrNullIf("bytes_gauge", "bytes_gauge" IS NOT NULL) AS
-			  "metric__sample__bytes_gauge_field_stats__actual_stats_col_3",
-			  sumOrNullIf("bytes_gauge", "bytes_gauge" IS NOT NULL) AS
-			  "metric__sample__bytes_gauge_field_stats__actual_stats_col_4"
-			FROM (
-			  SELECT "bytes_gauge"
-			  FROM __quesma_table_name
-			  WHERE ("timestamp">=fromUnixTimestamp64Milli(1709932426749) AND "timestamp"<=
-				fromUnixTimestamp64Milli(1711228426749))
-			  LIMIT 20000)`,
-		ExpectedAdditionalPancakeSQLs: []string{`
-			SELECT sum(count(*)) OVER () AS "aggr__sample__count",
-			  quantilesMerge(0.050000)(quantilesState(0.050000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_0",
-			  quantilesMerge(0.100000)(quantilesState(0.100000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_1",
-			  quantilesMerge(0.150000)(quantilesState(0.150000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_2",
-			  quantilesMerge(0.200000)(quantilesState(0.200000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_3",
-			  quantilesMerge(0.250000)(quantilesState(0.250000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_4",
-			  quantilesMerge(0.300000)(quantilesState(0.300000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_5",
-			  quantilesMerge(0.350000)(quantilesState(0.350000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_6",
-			  quantilesMerge(0.400000)(quantilesState(0.400000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_7",
-			  quantilesMerge(0.450000)(quantilesState(0.450000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_8",
-			  quantilesMerge(0.500000)(quantilesState(0.500000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_9",
-			  quantilesMerge(0.550000)(quantilesState(0.550000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_10",
-			  quantilesMerge(0.600000)(quantilesState(0.600000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_11",
-			  quantilesMerge(0.650000)(quantilesState(0.650000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_12",
-			  quantilesMerge(0.700000)(quantilesState(0.700000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_13",
-			  quantilesMerge(0.750000)(quantilesState(0.750000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_14",
-			  quantilesMerge(0.800000)(quantilesState(0.800000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_15",
-			  quantilesMerge(0.850000)(quantilesState(0.850000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_16",
-			  quantilesMerge(0.900000)(quantilesState(0.900000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_17",
-			  quantilesMerge(0.950000)(quantilesState(0.950000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_18",
-			  quantilesMerge(0.999999)(quantilesState(0.999999)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_col_19",
-			  quantilesMerge(0.050000)(quantilesState(0.050000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_0",
-			  quantilesMerge(0.100000)(quantilesState(0.100000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_1",
-			  quantilesMerge(0.150000)(quantilesState(0.150000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_2",
-			  quantilesMerge(0.200000)(quantilesState(0.200000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_3",
-			  quantilesMerge(0.250000)(quantilesState(0.250000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_4",
-			  quantilesMerge(0.300000)(quantilesState(0.300000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_5",
-			  quantilesMerge(0.350000)(quantilesState(0.350000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_6",
-			  quantilesMerge(0.400000)(quantilesState(0.400000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_7",
-			  quantilesMerge(0.450000)(quantilesState(0.450000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_8",
-			  quantilesMerge(0.500000)(quantilesState(0.500000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_9",
-			  quantilesMerge(0.550000)(quantilesState(0.550000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_10",
-			  quantilesMerge(0.600000)(quantilesState(0.600000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_11",
-			  quantilesMerge(0.650000)(quantilesState(0.650000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_12",
-			  quantilesMerge(0.700000)(quantilesState(0.700000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_13",
-			  quantilesMerge(0.750000)(quantilesState(0.750000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_14",
-			  quantilesMerge(0.800000)(quantilesState(0.800000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_15",
-			  quantilesMerge(0.850000)(quantilesState(0.850000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_16",
-			  quantilesMerge(0.900000)(quantilesState(0.900000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_17",
-			  quantilesMerge(0.950000)(quantilesState(0.950000)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_18",
-			  quantilesMerge(0.999999)(quantilesState(0.999999)("bytes_gauge")) OVER () AS
-			  "metric__sample__bytes_gauge_percentiles_keyed_true_col_19",
-			  sum(count(*)) OVER () AS "aggr__sample__bytes_gauge_top__parent_count",
-			  "bytes_gauge" AS "aggr__sample__bytes_gauge_top__key_0",
-			  count(*) AS "aggr__sample__bytes_gauge_top__count"
-			FROM (
-			  SELECT "bytes_gauge"
-			  FROM __quesma_table_name
-			  WHERE ("timestamp">=fromUnixTimestamp64Milli(1709932426749) AND "timestamp"<=
-				fromUnixTimestamp64Milli(1711228426749))
-			  LIMIT 20000)
-			GROUP BY "bytes_gauge" AS "aggr__sample__bytes_gauge_top__key_0"
-			ORDER BY "aggr__sample__bytes_gauge_top__count" DESC,
-				  "aggr__sample__bytes_gauge_top__key_0" ASC
-				LIMIT 11`,
+		ExpectedPancakeSQL: "SELECT count(*) AS `aggr__sample__count`,\n" +
+			"  quantiles(0.050000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_0`,\n" +
+			"  quantiles(0.100000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_1`,\n" +
+			"  quantiles(0.150000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_2`,\n" +
+			"  quantiles(0.200000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_3`,\n" +
+			"  quantiles(0.250000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_4`,\n" +
+			"  quantiles(0.300000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_5`,\n" +
+			"  quantiles(0.350000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_6`,\n" +
+			"  quantiles(0.400000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_7`,\n" +
+			"  quantiles(0.450000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_8`,\n" +
+			"  quantiles(0.500000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_9`,\n" +
+			"  quantiles(0.550000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_10`,\n" +
+			"  quantiles(0.600000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_11`,\n" +
+			"  quantiles(0.650000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_12`,\n" +
+			"  quantiles(0.700000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_13`,\n" +
+			"  quantiles(0.750000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_14`,\n" +
+			"  quantiles(0.800000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_15`,\n" +
+			"  quantiles(0.850000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_16`,\n" +
+			"  quantiles(0.900000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_17`,\n" +
+			"  quantiles(0.950000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_18`,\n" +
+			"  quantiles(0.999999)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_col_19`,\n" +
+			"  quantiles(0.050000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_0`,\n" +
+			"  quantiles(0.100000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_1`,\n" +
+			"  quantiles(0.150000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_2`,\n" +
+			"  quantiles(0.200000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_3`,\n" +
+			"  quantiles(0.250000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_4`,\n" +
+			"  quantiles(0.300000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_5`,\n" +
+			"  quantiles(0.350000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_6`,\n" +
+			"  quantiles(0.400000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_7`,\n" +
+			"  quantiles(0.450000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_8`,\n" +
+			"  quantiles(0.500000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_9`,\n" +
+			"  quantiles(0.550000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_10`,\n" +
+			"  quantiles(0.600000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_11`,\n" +
+			"  quantiles(0.650000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_12`,\n" +
+			"  quantiles(0.700000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_13`,\n" +
+			"  quantiles(0.750000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_14`,\n" +
+			"  quantiles(0.800000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_15`,\n" +
+			"  quantiles(0.850000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_16`,\n" +
+			"  quantiles(0.900000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_17`,\n" +
+			"  quantiles(0.950000)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_18`,\n" +
+			"  quantiles(0.999999)(`bytes_gauge`) AS\n" +
+			"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_19`,\n" +
+			"  countIf(`bytes_gauge` IS NOT NULL) AS\n" +
+			"  `aggr__sample__bytes_gauge_field_stats__count`,\n" +
+			"  countIf(`bytes_gauge` IS NOT NULL) AS\n" +
+			"  `metric__sample__bytes_gauge_field_stats__actual_stats_col_0`,\n" +
+			"  minOrNullIf(`bytes_gauge`, `bytes_gauge` IS NOT NULL) AS\n" +
+			"  `metric__sample__bytes_gauge_field_stats__actual_stats_col_1`,\n" +
+			"  maxOrNullIf(`bytes_gauge`, `bytes_gauge` IS NOT NULL) AS\n" +
+			"  `metric__sample__bytes_gauge_field_stats__actual_stats_col_2`,\n" +
+			"  avgOrNullIf(`bytes_gauge`, `bytes_gauge` IS NOT NULL) AS\n" +
+			"  `metric__sample__bytes_gauge_field_stats__actual_stats_col_3`,\n" +
+			"  sumOrNullIf(`bytes_gauge`, `bytes_gauge` IS NOT NULL) AS\n" +
+			"  `metric__sample__bytes_gauge_field_stats__actual_stats_col_4`\n" +
+			"FROM (\n" +
+			"  SELECT `bytes_gauge`\n" +
+			"  FROM `" + TableName + "`\n" +
+			"  WHERE (`timestamp`>=fromUnixTimestamp64Milli(1709932426749) AND `timestamp`<=\n" +
+			"    fromUnixTimestamp64Milli(1711228426749))\n" +
+			"  LIMIT 20000)",
+		ExpectedAdditionalPancakeSQLs: []string{
+			"SELECT sum(count(*)) OVER () AS `aggr__sample__count`,\n" +
+				"  quantilesMerge(0.050000)(quantilesState(0.050000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_0`,\n" +
+				"  quantilesMerge(0.100000)(quantilesState(0.100000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_1`,\n" +
+				"  quantilesMerge(0.150000)(quantilesState(0.150000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_2`,\n" +
+				"  quantilesMerge(0.200000)(quantilesState(0.200000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_3`,\n" +
+				"  quantilesMerge(0.250000)(quantilesState(0.250000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_4`,\n" +
+				"  quantilesMerge(0.300000)(quantilesState(0.300000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_5`,\n" +
+				"  quantilesMerge(0.350000)(quantilesState(0.350000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_6`,\n" +
+				"  quantilesMerge(0.400000)(quantilesState(0.400000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_7`,\n" +
+				"  quantilesMerge(0.450000)(quantilesState(0.450000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_8`,\n" +
+				"  quantilesMerge(0.500000)(quantilesState(0.500000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_9`,\n" +
+				"  quantilesMerge(0.550000)(quantilesState(0.550000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_10`,\n" +
+				"  quantilesMerge(0.600000)(quantilesState(0.600000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_11`,\n" +
+				"  quantilesMerge(0.650000)(quantilesState(0.650000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_12`,\n" +
+				"  quantilesMerge(0.700000)(quantilesState(0.700000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_13`,\n" +
+				"  quantilesMerge(0.750000)(quantilesState(0.750000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_14`,\n" +
+				"  quantilesMerge(0.800000)(quantilesState(0.800000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_15`,\n" +
+				"  quantilesMerge(0.850000)(quantilesState(0.850000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_16`,\n" +
+				"  quantilesMerge(0.900000)(quantilesState(0.900000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_17`,\n" +
+				"  quantilesMerge(0.950000)(quantilesState(0.950000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_18`,\n" +
+				"  quantilesMerge(0.999999)(quantilesState(0.999999)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_col_19`,\n" +
+				"  quantilesMerge(0.050000)(quantilesState(0.050000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_0`,\n" +
+				"  quantilesMerge(0.100000)(quantilesState(0.100000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_1`,\n" +
+				"  quantilesMerge(0.150000)(quantilesState(0.150000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_2`,\n" +
+				"  quantilesMerge(0.200000)(quantilesState(0.200000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_3`,\n" +
+				"  quantilesMerge(0.250000)(quantilesState(0.250000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_4`,\n" +
+				"  quantilesMerge(0.300000)(quantilesState(0.300000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_5`,\n" +
+				"  quantilesMerge(0.350000)(quantilesState(0.350000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_6`,\n" +
+				"  quantilesMerge(0.400000)(quantilesState(0.400000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_7`,\n" +
+				"  quantilesMerge(0.450000)(quantilesState(0.450000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_8`,\n" +
+				"  quantilesMerge(0.500000)(quantilesState(0.500000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_9`,\n" +
+				"  quantilesMerge(0.550000)(quantilesState(0.550000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_10`,\n" +
+				"  quantilesMerge(0.600000)(quantilesState(0.600000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_11`,\n" +
+				"  quantilesMerge(0.650000)(quantilesState(0.650000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_12`,\n" +
+				"  quantilesMerge(0.700000)(quantilesState(0.700000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_13`,\n" +
+				"  quantilesMerge(0.750000)(quantilesState(0.750000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_14`,\n" +
+				"  quantilesMerge(0.800000)(quantilesState(0.800000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_15`,\n" +
+				"  quantilesMerge(0.850000)(quantilesState(0.850000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_16`,\n" +
+				"  quantilesMerge(0.900000)(quantilesState(0.900000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_17`,\n" +
+				"  quantilesMerge(0.950000)(quantilesState(0.950000)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_18`,\n" +
+				"  quantilesMerge(0.999999)(quantilesState(0.999999)(`bytes_gauge`)) OVER () AS\n" +
+				"  `metric__sample__bytes_gauge_percentiles_keyed_true_col_19`,\n" +
+				"  sum(count(*)) OVER () AS `aggr__sample__bytes_gauge_top__parent_count`,\n" +
+				"  `bytes_gauge` AS `aggr__sample__bytes_gauge_top__key_0`,\n" +
+				"  count(*) AS `aggr__sample__bytes_gauge_top__count`\n" +
+				"FROM (\n" +
+				"  SELECT `bytes_gauge`\n" +
+				"  FROM `" + TableName + "`\n" +
+				"  WHERE (`timestamp`>=fromUnixTimestamp64Milli(1709932426749) AND `timestamp`<=\n" +
+				"    fromUnixTimestamp64Milli(1711228426749))\n" +
+				"  LIMIT 20000)\n" +
+				"GROUP BY `bytes_gauge` AS `aggr__sample__bytes_gauge_top__key_0`\n" +
+				"ORDER BY `aggr__sample__bytes_gauge_top__count` DESC,\n" +
+				"      `aggr__sample__bytes_gauge_top__key_0` ASC\n" +
+				"    LIMIT 11",
 		},
 	},
 	{ // [21]
@@ -4331,28 +4178,27 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("range_4__aggr__3__count", uint64(10)),
 			}}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT countIf(("bytes_gauge">=0 AND "bytes_gauge"<1000)) AS
-			  "range_0__aggr__2__count",
-			  countIf(("bytes_gauge">=1000 AND "bytes_gauge"<2000)) AS
-			  "range_1__aggr__2__count",
-			  countIf("bytes_gauge">=-5.5) AS "range_2__aggr__2__count",
-			  countIf("bytes_gauge"<6.555) AS "range_3__aggr__2__count",
-			  countIf("bytes_gauge" IS NOT NULL) AS "range_4__aggr__2__count"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1713269711790) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1713270611790))`,
-		ExpectedAdditionalPancakeSQLs: []string{`
-			SELECT countIf(("bytes_gauge">=0 AND "bytes_gauge"<1000)) AS
-			  "range_0__aggr__3__count",
-			  countIf(("bytes_gauge">=1000 AND "bytes_gauge"<2000)) AS
-			  "range_1__aggr__3__count",
-			  countIf("bytes_gauge">=-5.5) AS "range_2__aggr__3__count",
-			  countIf("bytes_gauge"<6.555) AS "range_3__aggr__3__count",
-			  countIf("bytes_gauge" IS NOT NULL) AS "range_4__aggr__3__count"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1713269711790) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1713270611790))`,
+		ExpectedPancakeSQL: "SELECT countIf((`bytes_gauge`>=0 AND `bytes_gauge`<1000)) AS\n" +
+			"  `range_0__aggr__2__count`,\n" +
+			"  countIf((`bytes_gauge`>=1000 AND `bytes_gauge`<2000)) AS\n" +
+			"  `range_1__aggr__2__count`,\n" +
+			"  countIf(`bytes_gauge`>=-5.5) AS `range_2__aggr__2__count`,\n" +
+			"  countIf(`bytes_gauge`<6.555) AS `range_3__aggr__2__count`,\n" +
+			"  countIf(`bytes_gauge` IS NOT NULL) AS `range_4__aggr__2__count`\n" +
+			"FROM `" + TableName + "`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1713269711790) AND `timestamp`<=\n" +
+			"  fromUnixTimestamp64Milli(1713270611790))",
+		ExpectedAdditionalPancakeSQLs: []string{
+			"SELECT countIf((`bytes_gauge`>=0 AND `bytes_gauge`<1000)) AS\n" +
+				"  `range_0__aggr__3__count`,\n" +
+				"  countIf((`bytes_gauge`>=1000 AND `bytes_gauge`<2000)) AS\n" +
+				"  `range_1__aggr__3__count`,\n" +
+				"  countIf(`bytes_gauge`>=-5.5) AS `range_2__aggr__3__count`,\n" +
+				"  countIf(`bytes_gauge`<6.555) AS `range_3__aggr__3__count`,\n" +
+				"  countIf(`bytes_gauge` IS NOT NULL) AS `range_4__aggr__3__count`\n" +
+				"FROM `" + TableName + "`\n" +
+				"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1713269711790) AND `timestamp`<=\n" +
+				"  fromUnixTimestamp64Milli(1713270611790))",
 		},
 	},
 	{ // [22]
@@ -4483,16 +4329,15 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("range_2__aggr__2__count", int(414)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT countIf("timestamp"<toInt64(toUnixTimestamp(now()))) AS
-			  "range_0__aggr__2__count",
-			  countIf(("timestamp">=toInt64(toUnixTimestamp(toStartOfDay(subDate(now(),
-			  INTERVAL 3 week)))) AND "timestamp"<toInt64(toUnixTimestamp(now())))) AS
-			  "range_1__aggr__2__count",
-			  countIf("timestamp">=toInt64(toUnixTimestamp('2024-04-14'))) AS
-			  "range_2__aggr__2__count"
-			FROM ` + TableName + ` 
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1712388530059) AND "timestamp"<=fromUnixTimestamp64Milli(1713288530059))`,
+		ExpectedPancakeSQL: "SELECT countIf(`timestamp`<toInt64(toUnixTimestamp(now()))) AS\n" +
+			"  `range_0__aggr__2__count`,\n" +
+			"  countIf((`timestamp`>=toInt64(toUnixTimestamp(toStartOfDay(subDate(now(),\n" +
+			"  INTERVAL 3 week)))) AND `timestamp`<toInt64(toUnixTimestamp(now())))) AS\n" +
+			"  `range_1__aggr__2__count`,\n" +
+			"  countIf(`timestamp`>=toInt64(toUnixTimestamp('2024-04-14'))) AS\n" +
+			"  `range_2__aggr__2__count`\n" +
+			"FROM `" + TableName + "` \n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1712388530059) AND `timestamp`<=fromUnixTimestamp64Milli(1713288530059))",
 	},
 	{ // [23]
 		TestName: "significant terms aggregation: same as terms for now",
@@ -4596,14 +4441,7 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", uint64(206)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-			  "message" AS "aggr__2__key_0", 
-			  count(*) AS "aggr__2__count"
-			FROM ` + TableName + `
-			GROUP BY "message" AS "aggr__2__key_0"
-			ORDER BY "aggr__2__count" DESC, "aggr__2__key_0" ASC
-			LIMIT 5`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n          `message` AS `aggr__2__key_0`, count(*) AS `aggr__2__count`\n        FROM `__quesma_table_name`\n        GROUP BY `message` AS `aggr__2__key_0`\n        ORDER BY `aggr__2__count` DESC, `aggr__2__key_0` ASC\n        LIMIT 5",
 	},
 	{ // [24]
 		TestName: "meta field in aggregation",
@@ -4685,15 +4523,7 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("metric__timeseries__61ca57f2-469d-11e7-af02-69e470af7417_col_0", 21),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp64Milli("@timestamp") / 79200000) AS
-			  "aggr__timeseries__key_0", count(*) AS "aggr__timeseries__count",
-			  uniq("host.name") AS
-			  "metric__timeseries__61ca57f2-469d-11e7-af02-69e470af7417_col_0"
-			FROM ` + TableName + `
-			GROUP BY toInt64(toUnixTimestamp64Milli("@timestamp") / 79200000) AS
-			  "aggr__timeseries__key_0"
-			ORDER BY "aggr__timeseries__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp64Milli(`@timestamp`) / 79200000) AS `\n          aggr__timeseries__key_0`, count(*) AS `aggr__timeseries__count`,\n          uniq(`host.name`) AS `metric__timeseries__61ca57f2-469d-11e7-af02-69e470\n          af7417_col_0`\n        FROM `__quesma_table_name`\n        GROUP BY toInt64(toUnixTimestamp64Milli(`@timestamp`) / 79200000) AS `\n          aggr__timeseries__key_0`\n        ORDER BY `aggr__timeseries__key_0` ASC",
 	},
 	{ // [25]
 		TestName: "simple histogram, but min_doc_count: 0",
@@ -4824,13 +4654,12 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", 2),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT floor("bytes"/100)*100 AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count"
-			FROM ` + TableName + `
-			 WHERE ("timestamp">=fromUnixTimestamp64Milli(1715348876077) AND "timestamp"<=fromUnixTimestamp64Milli(1715349776077))
-			GROUP BY floor("bytes"/100)*100 AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT floor(`bytes`/100)*100 AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`\n" +
+			"FROM `" + TableName + "`\n" +
+			" WHERE (`timestamp`>=fromUnixTimestamp64Milli(1715348876077) AND `timestamp`<=fromUnixTimestamp64Milli(1715349776077))\n" +
+			"GROUP BY floor(`bytes`/100)*100 AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [26]
 		TestName: "simple date_histogram, but min_doc_count: 0",
@@ -4962,15 +4791,14 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", 1),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp64Milli("timestamp") / 30000) AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1715351342900) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1715352242900))
-			GROUP BY toInt64(toUnixTimestamp64Milli("timestamp") / 30000) AS
-			  "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp64Milli(`timestamp`) / 30000) AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`\n" +
+			"FROM `" + TableName + "`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1715351342900) AND `timestamp`<=\n" +
+			"  fromUnixTimestamp64Milli(1715352242900))\n" +
+			"GROUP BY toInt64(toUnixTimestamp64Milli(`timestamp`) / 30000) AS\n" +
+			"  `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [27]
 		TestName: "simple date_histogram, but min_doc_count: 0",
@@ -5124,28 +4952,27 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__0__2__count", int64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__key_0", "aggr__0__count", "aggr__0__2__parent_count",
-			  "aggr__0__2__key_0", "aggr__0__2__count"
-			FROM (
-			  SELECT "aggr__0__key_0", "aggr__0__count", "aggr__0__2__parent_count",
-				"aggr__0__2__key_0", "aggr__0__2__count",
-				dense_rank() OVER (ORDER BY "aggr__0__key_0" ASC) AS "aggr__0__order_1_rank"
-				,
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0" ORDER BY
-				"aggr__0__2__count" DESC, "aggr__0__2__key_0" ASC) AS
-				"aggr__0__2__order_1_rank"
-			  FROM (
-				SELECT floor("rspContentLen"/2000)*2000 AS "aggr__0__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS "aggr__0__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS
-				  "aggr__0__2__parent_count", "message" AS "aggr__0__2__key_0",
-				  count(*) AS "aggr__0__2__count"
-				FROM ` + TableName + `
-				GROUP BY floor("rspContentLen"/2000)*2000 AS "aggr__0__key_0",
-				  "message" AS "aggr__0__2__key_0"))
-			WHERE "aggr__0__2__order_1_rank"<=5
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__2__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__key_0`, `aggr__0__count`, `aggr__0__2__parent_count`,\n" +
+			"  `aggr__0__2__key_0`, `aggr__0__2__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__key_0`, `aggr__0__count`, `aggr__0__2__parent_count`,\n" +
+			"    `aggr__0__2__key_0`, `aggr__0__2__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__0__key_0` ASC) AS `aggr__0__order_1_rank`\n" +
+			"    ,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0` ORDER BY\n" +
+			"    `aggr__0__2__count` DESC, `aggr__0__2__key_0` ASC) AS\n" +
+			"    `aggr__0__2__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT floor(`rspContentLen`/2000)*2000 AS `aggr__0__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS `aggr__0__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS\n" +
+			"      `aggr__0__2__parent_count`, `message` AS `aggr__0__2__key_0`,\n" +
+			"      count(*) AS `aggr__0__2__count`\n" +
+			"    FROM `" + TableName + "`\n" +
+			"    GROUP BY floor(`rspContentLen`/2000)*2000 AS `aggr__0__key_0`,\n" +
+			"      `message` AS `aggr__0__2__key_0`))\n" +
+			"WHERE `aggr__0__2__order_1_rank`<=5\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__2__order_1_rank` ASC",
 	},
 	{ // [28]
 		TestName: "Terms, completely different tree results from 2 queries - merging them didn't work before",
@@ -5333,15 +5160,14 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0__3-bucket_col_0", uint64(0)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-			  "OriginCityName" AS "aggr__0__key_0", count(*) AS "aggr__0__count",
-			    countIf("FlightDelay" __quesma_match true) AS "metric__0__1-bucket_col_0",
-  				countIf("Cancelled" __quesma_match true) AS "metric__0__3-bucket_col_0"
-			FROM ` + TableName + `
-			GROUP BY "OriginCityName" AS "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC
-			LIMIT 1001`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"  `OriginCityName` AS `aggr__0__key_0`, count(*) AS `aggr__0__count`,\n" +
+			"    countIf(`FlightDelay` __quesma_match true) AS `metric__0__1-bucket_col_0`,\n" +
+			"    countIf(`Cancelled` __quesma_match true) AS `metric__0__3-bucket_col_0`\n" +
+			"FROM `" + TableName + "`\n" +
+			"GROUP BY `OriginCityName` AS `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__key_0` ASC\n" +
+			"LIMIT 1001",
 	},
 	{ // [29]
 		TestName: "Terms, completely different tree results from 2 queries - merging them didn't work before (logs) TODO add results",
@@ -5509,33 +5335,32 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("metric__3__2__1_col_0", 2),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__3__parent_count", "aggr__3__key_0", "aggr__3__count",
-			  "metric__3__1_col_0", "aggr__3__2__parent_count", "aggr__3__2__key_0",
-			  "aggr__3__2__count", "metric__3__2__1_col_0"
-			FROM (
-			  SELECT "aggr__3__parent_count", "aggr__3__key_0", "aggr__3__count",
-				"metric__3__1_col_0", "aggr__3__2__parent_count", "aggr__3__2__key_0",
-				"aggr__3__2__count", "metric__3__2__1_col_0",
-				dense_rank() OVER (ORDER BY "metric__3__1_col_0" DESC, "aggr__3__key_0" ASC)
-				AS "aggr__3__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__3__key_0" ORDER BY
-				"metric__3__2__1_col_0" DESC, "aggr__3__2__key_0" ASC) AS
-				"aggr__3__2__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__3__parent_count",
-				  "geo.src" AS "aggr__3__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__3__key_0") AS "aggr__3__count",
-				  sumOrNull(sumOrNull("memory")) OVER (PARTITION BY "aggr__3__key_0") AS
-				  "metric__3__1_col_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__3__key_0") AS
-				  "aggr__3__2__parent_count", "machine.os" AS "aggr__3__2__key_0",
-				  count(*) AS "aggr__3__2__count",
-				  sumOrNull("memory") AS "metric__3__2__1_col_0"
-				FROM __quesma_table_name
-				GROUP BY "geo.src" AS "aggr__3__key_0", "machine.os" AS "aggr__3__2__key_0"))
-			WHERE ("aggr__3__order_1_rank"<=6 AND "aggr__3__2__order_1_rank"<=6)
-			ORDER BY "aggr__3__order_1_rank" ASC, "aggr__3__2__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__3__parent_count`, `aggr__3__key_0`, `aggr__3__count`,\n" +
+			"  `metric__3__1_col_0`, `aggr__3__2__parent_count`, `aggr__3__2__key_0`,\n" +
+			"  `aggr__3__2__count`, `metric__3__2__1_col_0`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__3__parent_count`, `aggr__3__key_0`, `aggr__3__count`,\n" +
+			"    `metric__3__1_col_0`, `aggr__3__2__parent_count`, `aggr__3__2__key_0`,\n" +
+			"    `aggr__3__2__count`, `metric__3__2__1_col_0`,\n" +
+			"    dense_rank() OVER (ORDER BY `metric__3__1_col_0` DESC, `aggr__3__key_0` ASC)\n" +
+			"    AS `aggr__3__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__3__key_0` ORDER BY\n" +
+			"    `metric__3__2__1_col_0` DESC, `aggr__3__2__key_0` ASC) AS\n" +
+			"    `aggr__3__2__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__3__parent_count`,\n" +
+			"      `geo.src` AS `aggr__3__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__3__key_0`) AS `aggr__3__count`,\n" +
+			"      sumOrNull(sumOrNull(`memory`)) OVER (PARTITION BY `aggr__3__key_0`) AS\n" +
+			"      `metric__3__1_col_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__3__key_0`) AS\n" +
+			"      `aggr__3__2__parent_count`, `machine.os` AS `aggr__3__2__key_0`,\n" +
+			"      count(*) AS `aggr__3__2__count`,\n" +
+			"      sumOrNull(`memory`) AS `metric__3__2__1_col_0`\n" +
+			"    FROM `" + TableName + "`\n" +
+			"    GROUP BY `geo.src` AS `aggr__3__key_0`, `machine.os` AS `aggr__3__2__key_0`))\n" +
+			"WHERE (`aggr__3__order_1_rank`<=6 AND `aggr__3__2__order_1_rank`<=6)\n" +
+			"ORDER BY `aggr__3__order_1_rank` ASC, `aggr__3__2__order_1_rank` ASC",
 	},
 	{ // [30]
 		TestName: "Terms, completely different tree results from 2 queries - merging them didn't work before (logs). what when cardinality = 0?",
@@ -5685,15 +5510,14 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("metric__2__1_col_0", int64(0)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-			  "machine.os" AS "aggr__2__key_0", count(*) AS "aggr__2__count",
-			  uniq("clientip") AS "metric__2__1_col_0"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1715322159037) AND "timestamp"<=fromUnixTimestamp64Milli(1715376159037))
-			GROUP BY "machine.os" AS "aggr__2__key_0"
-			ORDER BY "metric__2__1_col_0" DESC, "aggr__2__key_0" ASC
-			LIMIT 6`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n" +
+			"  `machine.os` AS `aggr__2__key_0`, count(*) AS `aggr__2__count`,\n" +
+			"  uniq(`clientip`) AS `metric__2__1_col_0`\n" +
+			"FROM `" + TableName + "`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1715322159037) AND `timestamp`<=fromUnixTimestamp64Milli(1715376159037))\n" +
+			"GROUP BY `machine.os` AS `aggr__2__key_0`\n" +
+			"ORDER BY `metric__2__1_col_0` DESC, `aggr__2__key_0` ASC\n" +
+			"LIMIT 6",
 	},
 	// terms + histogram
 	// histogram + terms
@@ -5884,41 +5708,41 @@ var AggregationTests = []AggregationTestCase{
 				}},
 			},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp64Milli("@timestamp") / 86400000) AS
-			  "aggr__0__key_0", count(*) AS "aggr__0__count",
-			  countIf("message" IS NOT NULL) AS "aggr__0__1-bucket__count"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp64Milli("@timestamp") / 86400000) AS
-			  "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
-		ExpectedAdditionalPancakeSQLs: []string{`
-			WITH quesma_top_hits_group_table AS (
-			  SELECT toInt64(toUnixTimestamp64Milli("@timestamp") / 86400000) AS
-				"aggr__0__key_0", count(*) AS "aggr__0__count",
-				countIf("message" IS NOT NULL) AS "aggr__0__1-bucket__count"
-			  FROM __quesma_table_name
-			  GROUP BY toInt64(toUnixTimestamp64Milli("@timestamp") / 86400000) AS
-				"aggr__0__key_0"
-			  ORDER BY "aggr__0__key_0" ASC) ,
-			quesma_top_hits_join AS (
-			  SELECT "group_table"."aggr__0__key_0" AS "aggr__0__key_0",
-				"group_table"."aggr__0__count" AS "aggr__0__count",
-				"group_table"."aggr__0__1-bucket__count" AS "aggr__0__1-bucket__count",
-				"hit_table"."message" AS "top_metrics__0__1-bucket__1-metric_col_0",
-				"hit_table"."order_date" AS "top_metrics__0__1-bucket__1-metric_col_1",
-				ROW_NUMBER() OVER (PARTITION BY "group_table"."aggr__0__key_0" ORDER BY
-				"order_date" DESC) AS "top_hits_rank"
-			  FROM quesma_top_hits_group_table AS "group_table" LEFT OUTER JOIN
-				__quesma_table_name AS "hit_table" ON ("group_table"."aggr__0__key_0"=
-				toInt64(toUnixTimestamp64Milli("@timestamp") / 86400000))
-			  WHERE "message" IS NOT NULL)
-			SELECT "aggr__0__key_0", "aggr__0__count", "aggr__0__1-bucket__count",
-			  "top_metrics__0__1-bucket__1-metric_col_0",
-			  "top_metrics__0__1-bucket__1-metric_col_1", "top_hits_rank"
-			FROM "quesma_top_hits_join"
-			WHERE "top_hits_rank"<=1
-			ORDER BY "aggr__0__key_0" ASC, "top_hits_rank" ASC`},
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp64Milli(`@timestamp`) / 86400000) AS\n" +
+			"  `aggr__0__key_0`, count(*) AS `aggr__0__count`,\n" +
+			"  countIf(`message` IS NOT NULL) AS `aggr__0__1-bucket__count`\n" +
+			"FROM `" + TableName + "`\n" +
+			"GROUP BY toInt64(toUnixTimestamp64Milli(`@timestamp`) / 86400000) AS\n" +
+			"  `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__key_0` ASC",
+		ExpectedAdditionalPancakeSQLs: []string{
+			"WITH quesma_top_hits_group_table AS (\n" +
+				"  SELECT toInt64(toUnixTimestamp64Milli(`@timestamp`) / 86400000) AS\n" +
+				"    `aggr__0__key_0`, count(*) AS `aggr__0__count`,\n" +
+				"    countIf(`message` IS NOT NULL) AS `aggr__0__1-bucket__count`\n" +
+				"  FROM `" + TableName + "`\n" +
+				"  GROUP BY toInt64(toUnixTimestamp64Milli(`@timestamp`) / 86400000) AS\n" +
+				"    `aggr__0__key_0`\n" +
+				"  ORDER BY `aggr__0__key_0` ASC) ,\n" +
+				"quesma_top_hits_join AS (\n" +
+				"  SELECT `group_table`.`aggr__0__key_0` AS `aggr__0__key_0`,\n" +
+				"    `group_table`.`aggr__0__count` AS `aggr__0__count`,\n" +
+				"    `group_table`.`aggr__0__1-bucket__count` AS `aggr__0__1-bucket__count`,\n" +
+				"    `hit_table`.`message` AS `top_metrics__0__1-bucket__1-metric_col_0`,\n" +
+				"    `hit_table`.`order_date` AS `top_metrics__0__1-bucket__1-metric_col_1`,\n" +
+				"    ROW_NUMBER() OVER (PARTITION BY `group_table`.`aggr__0__key_0` ORDER BY\n" +
+				"    `order_date` DESC) AS `top_hits_rank`\n" +
+				"  FROM quesma_top_hits_group_table AS `group_table` LEFT OUTER JOIN\n" +
+				"    `" + TableName + "` AS `hit_table` ON (`group_table`.`aggr__0__key_0`=\n" +
+				"    toInt64(toUnixTimestamp64Milli(`@timestamp`) / 86400000))\n" +
+				"  WHERE `message` IS NOT NULL)\n" +
+				"SELECT `aggr__0__key_0`, `aggr__0__count`, `aggr__0__1-bucket__count`,\n" +
+				"  `top_metrics__0__1-bucket__1-metric_col_0`,\n" +
+				"  `top_metrics__0__1-bucket__1-metric_col_1`, `top_hits_rank`\n" +
+				"FROM `quesma_top_hits_join`\n" +
+				"WHERE `top_hits_rank`<=1\n" +
+				"ORDER BY `aggr__0__key_0` ASC, `top_hits_rank` ASC",
+		},
 	},
 	{ // [32]
 		TestName: "Standard deviation",
@@ -6178,35 +6002,34 @@ var AggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0__2_col_9", 1689.9999101859655),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-			  "timestamp", 'Europe/Warsaw'))*1000) / 600000) AS "aggr__0__key_0",
-			  count(*) AS "aggr__0__count", count("bytes") AS "metric__0__1_col_0",
-			  minOrNull("bytes") AS "metric__0__1_col_1",
-			  maxOrNull("bytes") AS "metric__0__1_col_2",
-			  avgOrNull("bytes") AS "metric__0__1_col_3",
-			  sumOrNull("bytes") AS "metric__0__1_col_4",
-			  sumOrNull("bytes"*"bytes") AS "metric__0__1_col_5",
-			  varPop("bytes") AS "metric__0__1_col_6",
-			  varSamp("bytes") AS "metric__0__1_col_7",
-			  stddevPop("bytes") AS "metric__0__1_col_8",
-			  stddevSamp("bytes") AS "metric__0__1_col_9",
-			  count("bytes") AS "metric__0__2_col_0",
-			  minOrNull("bytes") AS "metric__0__2_col_1",
-			  maxOrNull("bytes") AS "metric__0__2_col_2",
-			  avgOrNull("bytes") AS "metric__0__2_col_3",
-			  sumOrNull("bytes") AS "metric__0__2_col_4",
-			  sumOrNull("bytes"*"bytes") AS "metric__0__2_col_5",
-			  varPop("bytes") AS "metric__0__2_col_6",
-			  varSamp("bytes") AS "metric__0__2_col_7",
-			  stddevPop("bytes") AS "metric__0__2_col_8",
-			  stddevSamp("bytes") AS "metric__0__2_col_9"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1716327334210) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1716381334210))
-			GROUP BY toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-			  "timestamp", 'Europe/Warsaw'))*1000) / 600000) AS "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 600000) AS `aggr__0__key_0`,\n" +
+			"  count(*) AS `aggr__0__count`, count(`bytes`) AS `metric__0__1_col_0`,\n" +
+			"  minOrNull(`bytes`) AS `metric__0__1_col_1`,\n" +
+			"  maxOrNull(`bytes`) AS `metric__0__1_col_2`,\n" +
+			"  avgOrNull(`bytes`) AS `metric__0__1_col_3`,\n" +
+			"  sumOrNull(`bytes`) AS `metric__0__1_col_4`,\n" +
+			"  sumOrNull(`bytes`*`bytes`) AS `metric__0__1_col_5`,\n" +
+			"  varPop(`bytes`) AS `metric__0__1_col_6`,\n" +
+			"  varSamp(`bytes`) AS `metric__0__1_col_7`,\n" +
+			"  stddevPop(`bytes`) AS `metric__0__1_col_8`,\n" +
+			"  stddevSamp(`bytes`) AS `metric__0__1_col_9`,\n" +
+			"  count(`bytes`) AS `metric__0__2_col_0`,\n" +
+			"  minOrNull(`bytes`) AS `metric__0__2_col_1`,\n" +
+			"  maxOrNull(`bytes`) AS `metric__0__2_col_2`,\n" +
+			"  avgOrNull(`bytes`) AS `metric__0__2_col_3`,\n" +
+			"  sumOrNull(`bytes`) AS `metric__0__2_col_4`,\n" +
+			"  sumOrNull(`bytes`*`bytes`) AS `metric__0__2_col_5`,\n" +
+			"  varPop(`bytes`) AS `metric__0__2_col_6`,\n" +
+			"  varSamp(`bytes`) AS `metric__0__2_col_7`,\n" +
+			"  stddevPop(`bytes`) AS `metric__0__2_col_8`,\n" +
+			"  stddevSamp(`bytes`) AS `metric__0__2_col_9`\n" +
+			"FROM `" + TableName + "`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1716327334210) AND `timestamp`<=\n" +
+			"  fromUnixTimestamp64Milli(1716381334210))\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 600000) AS `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__key_0` ASC",
 	},
 	{ // [33]
 		TestName: "0 result rows in 2x terms",
@@ -6279,29 +6102,28 @@ var AggregationTests = []AggregationTestCase{
 		}`,
 		ExpectedResponse:       `{"response": {"aggregations":{"0": {"buckets": []}}}}`,
 		ExpectedPancakeResults: make([]model.QueryResultRow, 0),
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-			  "aggr__0__1__parent_count", "aggr__0__1__key_0", "aggr__0__1__count"
-			FROM (
-			  SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-				"aggr__0__1__parent_count", "aggr__0__1__key_0", "aggr__0__1__count",
-				dense_rank() OVER (ORDER BY "aggr__0__count" DESC, "aggr__0__key_0" ASC) AS
-				"aggr__0__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0" ORDER BY
-				"aggr__0__1__count" DESC, "aggr__0__1__key_0" ASC) AS
-				"aggr__0__1__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-				  "host.name" AS "aggr__0__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS "aggr__0__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS
-				  "aggr__0__1__parent_count", "message" AS "aggr__0__1__key_0",
-				  count(*) AS "aggr__0__1__count"
-				FROM __quesma_table_name
-				WHERE ("message" IS NOT NULL AND NOT ("message" __quesma_match '%US%'))
-				GROUP BY "host.name" AS "aggr__0__key_0", "message" AS "aggr__0__1__key_0"))
-			WHERE ("aggr__0__order_1_rank"<=11 AND "aggr__0__1__order_1_rank"<=4)
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"  `aggr__0__1__parent_count`, `aggr__0__1__key_0`, `aggr__0__1__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"    `aggr__0__1__parent_count`, `aggr__0__1__key_0`, `aggr__0__1__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__0__count` DESC, `aggr__0__key_0` ASC) AS\n" +
+			"    `aggr__0__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0` ORDER BY\n" +
+			"    `aggr__0__1__count` DESC, `aggr__0__1__key_0` ASC) AS\n" +
+			"    `aggr__0__1__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"      `host.name` AS `aggr__0__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS `aggr__0__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS\n" +
+			"      `aggr__0__1__parent_count`, `message` AS `aggr__0__1__key_0`,\n" +
+			"      count(*) AS `aggr__0__1__count`\n" +
+			"    FROM `" + TableName + "`\n" +
+			"    WHERE (`message` IS NOT NULL AND NOT (`message` __quesma_match '%US%'))\n" +
+			"    GROUP BY `host.name` AS `aggr__0__key_0`, `message` AS `aggr__0__1__key_0`))\n" +
+			"WHERE (`aggr__0__order_1_rank`<=11 AND `aggr__0__1__order_1_rank`<=4)\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC",
 	},
 	{ // [34]
 		TestName: "0 result rows in 3x terms",
@@ -6386,41 +6208,40 @@ var AggregationTests = []AggregationTestCase{
 		}`,
 		ExpectedResponse:       `{"response": {"aggregations":{"0": {"buckets": []}}}}`,
 		ExpectedPancakeResults: make([]model.QueryResultRow, 0),
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-			  "aggr__0__1__parent_count", "aggr__0__1__key_0", "aggr__0__1__count",
-			  "aggr__0__1__2__parent_count", "aggr__0__1__2__key_0", "aggr__0__1__2__count"
-			FROM (
-			  SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-				"aggr__0__1__parent_count", "aggr__0__1__key_0", "aggr__0__1__count",
-				"aggr__0__1__2__parent_count", "aggr__0__1__2__key_0",
-				"aggr__0__1__2__count",
-				dense_rank() OVER (ORDER BY "aggr__0__count" DESC, "aggr__0__key_0" ASC) AS
-				"aggr__0__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0" ORDER BY
-				"aggr__0__1__count" DESC, "aggr__0__1__key_0" ASC) AS
-				"aggr__0__1__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0", "aggr__0__1__key_0" ORDER
-				BY "aggr__0__1__2__count" DESC, "aggr__0__1__2__key_0" ASC) AS "aggr__0__1__2__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-				  "host.name" AS "aggr__0__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS "aggr__0__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS
-				  "aggr__0__1__parent_count", "message" AS "aggr__0__1__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0", "aggr__0__1__key_0") AS
-				  "aggr__0__1__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0", "aggr__0__1__key_0") AS
-				  "aggr__0__1__2__parent_count", "message" AS "aggr__0__1__2__key_0",
-				  count(*) AS "aggr__0__1__2__count"
-				FROM __quesma_table_name
-				WHERE ("message" IS NOT NULL AND NOT ("message" __quesma_match '%US%'))
-				GROUP BY "host.name" AS "aggr__0__key_0", "message" AS "aggr__0__1__key_0",
-				  "message" AS "aggr__0__1__2__key_0"))
-			WHERE (("aggr__0__order_1_rank"<=11 AND "aggr__0__1__order_1_rank"<=4) AND
-			  "aggr__0__1__2__order_1_rank"<=4)
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC,
-			  "aggr__0__1__2__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"  `aggr__0__1__parent_count`, `aggr__0__1__key_0`, `aggr__0__1__count`,\n" +
+			"  `aggr__0__1__2__parent_count`, `aggr__0__1__2__key_0`, `aggr__0__1__2__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"    `aggr__0__1__parent_count`, `aggr__0__1__key_0`, `aggr__0__1__count`,\n" +
+			"    `aggr__0__1__2__parent_count`, `aggr__0__1__2__key_0`,\n" +
+			"    `aggr__0__1__2__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__0__count` DESC, `aggr__0__key_0` ASC) AS\n" +
+			"    `aggr__0__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0` ORDER BY\n" +
+			"    `aggr__0__1__count` DESC, `aggr__0__1__key_0` ASC) AS\n" +
+			"    `aggr__0__1__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0`, `aggr__0__1__key_0` ORDER\n" +
+			"    BY `aggr__0__1__2__count` DESC, `aggr__0__1__2__key_0` ASC) AS `aggr__0__1__2__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"      `host.name` AS `aggr__0__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS `aggr__0__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS\n" +
+			"      `aggr__0__1__parent_count`, `message` AS `aggr__0__1__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`, `aggr__0__1__key_0`) AS\n" +
+			"      `aggr__0__1__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`, `aggr__0__1__key_0`) AS\n" +
+			"      `aggr__0__1__2__parent_count`, `message` AS `aggr__0__1__2__key_0`,\n" +
+			"      count(*) AS `aggr__0__1__2__count`\n" +
+			"    FROM `" + TableName + "`\n" +
+			"    WHERE (`message` IS NOT NULL AND NOT (`message` __quesma_match '%US%'))\n" +
+			"    GROUP BY `host.name` AS `aggr__0__key_0`, `message` AS `aggr__0__1__key_0`,\n" +
+			"      `message` AS `aggr__0__1__2__key_0`))\n" +
+			"WHERE ((`aggr__0__order_1_rank`<=11 AND `aggr__0__1__order_1_rank`<=4) AND\n" +
+			"  `aggr__0__1__2__order_1_rank`<=4)\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC,\n" +
+			"  `aggr__0__1__2__order_1_rank` ASC",
 	},
 	{ // [35]
 		TestName: "0 result rows in terms+histogram",
@@ -6490,27 +6311,26 @@ var AggregationTests = []AggregationTestCase{
 		}`,
 		ExpectedResponse:       `{"response": {"aggregations":{"0": {"buckets": []}}}}`,
 		ExpectedPancakeResults: make([]model.QueryResultRow, 0),
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-			  "aggr__0__1__key_0", "aggr__0__1__count"
-			FROM (
-			  SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-				"aggr__0__1__key_0", "aggr__0__1__count",
-				dense_rank() OVER (ORDER BY "aggr__0__count" DESC, "aggr__0__key_0" ASC)
-				AS "aggr__0__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0" ORDER BY
-				"aggr__0__1__key_0" ASC) AS "aggr__0__1__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-				  "host.name" AS "aggr__0__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS "aggr__0__count",
-				  "FlightDelayMin" AS "aggr__0__1__key_0", count(*) AS "aggr__0__1__count"
-				FROM ` + TableName + `
-				WHERE ("message" IS NOT NULL AND NOT ("message" __quesma_match '%US%'))
-				GROUP BY "host.name" AS "aggr__0__key_0",
-				  "FlightDelayMin" AS "aggr__0__1__key_0"))
-			WHERE "aggr__0__order_1_rank"<=9
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"  `aggr__0__1__key_0`, `aggr__0__1__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"    `aggr__0__1__key_0`, `aggr__0__1__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__0__count` DESC, `aggr__0__key_0` ASC)\n" +
+			"    AS `aggr__0__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0` ORDER BY\n" +
+			"    `aggr__0__1__key_0` ASC) AS `aggr__0__1__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"      `host.name` AS `aggr__0__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS `aggr__0__count`,\n" +
+			"      `FlightDelayMin` AS `aggr__0__1__key_0`, count(*) AS `aggr__0__1__count`\n" +
+			"    FROM `" + TableName + "`\n" +
+			"    WHERE (`message` IS NOT NULL AND NOT (`message` __quesma_match '%US%'))\n" +
+			"    GROUP BY `host.name` AS `aggr__0__key_0`,\n" +
+			"      `FlightDelayMin` AS `aggr__0__1__key_0`))\n" +
+			"WHERE `aggr__0__order_1_rank`<=9\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC",
 	},
 	{ // [36]
 		TestName: "0 result rows in terms+histogram + meta field",
@@ -6601,27 +6421,26 @@ var AggregationTests = []AggregationTestCase{
 			}
 		}`,
 		ExpectedPancakeResults: make([]model.QueryResultRow, 0),
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-			  "aggr__0__1__key_0", "aggr__0__1__count"
-			FROM (
-			  SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-				"aggr__0__1__key_0", "aggr__0__1__count",
-				dense_rank() OVER (ORDER BY "aggr__0__count" DESC, "aggr__0__key_0" ASC)
-				AS "aggr__0__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0" ORDER BY
-				"aggr__0__1__key_0" ASC) AS "aggr__0__1__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-				  "host.name" AS "aggr__0__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS "aggr__0__count",
-				  "FlightDelayMin" AS "aggr__0__1__key_0", count(*) AS "aggr__0__1__count"
-				FROM ` + TableName + `
-				WHERE ("message" IS NOT NULL AND NOT ("message" __quesma_match '%US%'))
-				GROUP BY "host.name" AS "aggr__0__key_0",
-				  "FlightDelayMin" AS "aggr__0__1__key_0"))
-			WHERE "aggr__0__order_1_rank"<=11
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"  `aggr__0__1__key_0`, `aggr__0__1__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"    `aggr__0__1__key_0`, `aggr__0__1__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__0__count` DESC, `aggr__0__key_0` ASC)\n" +
+			"    AS `aggr__0__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0` ORDER BY\n" +
+			"    `aggr__0__1__key_0` ASC) AS `aggr__0__1__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"      `host.name` AS `aggr__0__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS `aggr__0__count`,\n" +
+			"      `FlightDelayMin` AS `aggr__0__1__key_0`, count(*) AS `aggr__0__1__count`\n" +
+			"    FROM `" + TableName + "`\n" +
+			"    WHERE (`message` IS NOT NULL AND NOT (`message` __quesma_match '%US%'))\n" +
+			"    GROUP BY `host.name` AS `aggr__0__key_0`,\n" +
+			"      `FlightDelayMin` AS `aggr__0__1__key_0`))\n" +
+			"WHERE `aggr__0__order_1_rank`<=11\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC",
 	},
 	{ // [37]
 		// Now we don't copy, as it's nested. Tested with Elasticsearch.
@@ -6698,27 +6517,26 @@ var AggregationTests = []AggregationTestCase{
 		}`,
 		ExpectedResponse:       `{"response": {"aggregations":{"0": {"buckets": []}}}}`,
 		ExpectedPancakeResults: make([]model.QueryResultRow, 0),
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-			  "aggr__0__1__key_0", "aggr__0__1__count"
-			FROM (
-			  SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-				"aggr__0__1__key_0", "aggr__0__1__count",
-				dense_rank() OVER (ORDER BY "aggr__0__count" DESC, "aggr__0__key_0" ASC) AS
-				"aggr__0__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0" ORDER BY
-				"aggr__0__1__key_0" ASC) AS "aggr__0__1__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-				  "host.name" AS "aggr__0__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS "aggr__0__count",
-				  "FlightDelayMin" AS "aggr__0__1__key_0", count(*) AS "aggr__0__1__count"
-				FROM __quesma_table_name
-				WHERE ("message" IS NOT NULL AND NOT ("message" __quesma_match '%US%'))
-				GROUP BY "host.name" AS "aggr__0__key_0",
-				  "FlightDelayMin" AS "aggr__0__1__key_0"))
-			WHERE "aggr__0__order_1_rank"<=11
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"  `aggr__0__1__key_0`, `aggr__0__1__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"    `aggr__0__1__key_0`, `aggr__0__1__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__0__count` DESC, `aggr__0__key_0` ASC) AS\n" +
+			"    `aggr__0__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0` ORDER BY\n" +
+			"    `aggr__0__1__key_0` ASC) AS `aggr__0__1__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"      `host.name` AS `aggr__0__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS `aggr__0__count`,\n" +
+			"      `FlightDelayMin` AS `aggr__0__1__key_0`, count(*) AS `aggr__0__1__count`\n" +
+			"    FROM `" + TableName + "`\n" +
+			"    WHERE (`message` IS NOT NULL AND NOT (`message` __quesma_match '%US%'))\n" +
+			"    GROUP BY `host.name` AS `aggr__0__key_0`,\n" +
+			"      `FlightDelayMin` AS `aggr__0__1__key_0`))\n" +
+			"WHERE `aggr__0__order_1_rank`<=11\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC",
 	},
 	{ // [38]
 		TestName: "simplest top_metrics, no sort",
@@ -6778,14 +6596,14 @@ var AggregationTests = []AggregationTestCase{
 			{},
 		},
 		AdditionalAcceptableDifference: []string{"tm_empty_result"}, // TODO: check, but we should return empty result
-		ExpectedPancakeSQL: `
-			SELECT "message" AS "top_metrics__tm_with_result_col_0"
-			FROM __quesma_table_name
-			LIMIT 2`,
-		ExpectedAdditionalPancakeSQLs: []string{`
-			SELECT "message" AS "top_metrics__tm_empty_result_col_0"
-			FROM __quesma_table_name
-			LIMIT 1`},
+		ExpectedPancakeSQL: "SELECT `message` AS `top_metrics__tm_with_result_col_0`\n" +
+			"FROM `" + TableName + "`\n" +
+			"LIMIT 2",
+		ExpectedAdditionalPancakeSQLs: []string{
+			"SELECT `message` AS `top_metrics__tm_empty_result_col_0`\n" +
+				"FROM `" + TableName + "`\n" +
+				"LIMIT 1",
+		},
 	},
 	{ // [39]
 		TestName: "simplest top_metrics, with sort",
@@ -6851,18 +6669,18 @@ var AggregationTests = []AggregationTestCase{
 			{},
 		},
 		AdditionalAcceptableDifference: []string{"tm_empty_result"}, // TODO: check, but we should return empty result
-		ExpectedPancakeSQL: `
-			SELECT "message" AS "top_metrics__tm_with_result_col_0",
-			  "timestamp" AS "top_metrics__tm_with_result_col_1"
-			FROM __quesma_table_name
-			ORDER BY "timestamp" DESC
-			LIMIT 1`,
-		ExpectedAdditionalPancakeSQLs: []string{`
-			SELECT "message" AS "top_metrics__tm_empty_result_col_0",
-			  "timestamp" AS "top_metrics__tm_empty_result_col_1"
-			FROM __quesma_table_name
-			ORDER BY "timestamp" DESC
-			LIMIT 1`},
+		ExpectedPancakeSQL: "SELECT `message` AS `top_metrics__tm_with_result_col_0`,\n" +
+			"  `timestamp` AS `top_metrics__tm_with_result_col_1`\n" +
+			"FROM `" + TableName + "`\n" +
+			"ORDER BY `timestamp` DESC\n" +
+			"LIMIT 1",
+		ExpectedAdditionalPancakeSQLs: []string{
+			"SELECT `message` AS `top_metrics__tm_empty_result_col_0`,\n" +
+				"  `timestamp` AS `top_metrics__tm_empty_result_col_1`\n" +
+				"FROM `" + TableName + "`\n" +
+				"ORDER BY `timestamp` DESC\n" +
+				"LIMIT 1",
+		},
 	},
 	{ // [40]
 		TestName: "terms ordered by subaggregation",
@@ -6924,16 +6742,15 @@ var AggregationTests = []AggregationTestCase{
 		}`,
 		ExpectedResponse:       `{"aggregations": {"2": {"buckets": []}}}`,
 		ExpectedPancakeResults: make([]model.QueryResultRow, 0),
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-			  "name" AS "aggr__2__key_0", 
-			  count(*) AS "aggr__2__count",
-  			  sumOrNull("total") AS "metric__2__1_col_0"
-			FROM ` + TableName + `
-			WHERE NOT ((("abc">=0 AND "abc"<600) OR "type" __quesma_match '%def%'))
-			GROUP BY "name" AS "aggr__2__key_0"
-			ORDER BY "metric__2__1_col_0" DESC, "aggr__2__key_0" ASC
-			LIMIT 11`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n" +
+			"  `name` AS `aggr__2__key_0`, \n" +
+			"  count(*) AS `aggr__2__count`,\n" +
+			"  sumOrNull(`total`) AS `metric__2__1_col_0`\n" +
+			"FROM `" + TableName + "`\n" +
+			"WHERE NOT (((`abc`>=0 AND `abc`<600) OR `type` __quesma_match '%def%'))\n" +
+			"GROUP BY `name` AS `aggr__2__key_0`\n" +
+			"ORDER BY `metric__2__1_col_0` DESC, `aggr__2__key_0` ASC\n" +
+			"LIMIT 11",
 	},
 	{ // [41]
 		TestName: "0 result rows in 2x terms",
@@ -6986,28 +6803,27 @@ var AggregationTests = []AggregationTestCase{
 		}`,
 		ExpectedResponse:       `{"response": {"aggregations":{"0": {"buckets": []}}}}`,
 		ExpectedPancakeResults: make([]model.QueryResultRow, 0),
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-			  "aggr__0__1__parent_count", "aggr__0__1__key_0", "aggr__0__1__count"
-			FROM (
-			  SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-				"aggr__0__1__parent_count", "aggr__0__1__key_0", "aggr__0__1__count",
-				dense_rank() OVER (ORDER BY "aggr__0__count" DESC, "aggr__0__key_0" ASC) AS
-				"aggr__0__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0" ORDER BY
-				"aggr__0__1__count" DESC, "aggr__0__1__key_0" ASC) AS
-				"aggr__0__1__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-				  "OriginAirportID" AS "aggr__0__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS "aggr__0__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS
-				  "aggr__0__1__parent_count", "DestAirportID" AS "aggr__0__1__key_0",
-				  count(*) AS "aggr__0__1__count"
-				FROM __quesma_table_name
-				GROUP BY "OriginAirportID" AS "aggr__0__key_0",
-				  "DestAirportID" AS "aggr__0__1__key_0"))
-			WHERE ("aggr__0__order_1_rank"<=11 AND "aggr__0__1__order_1_rank"<=4)
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"  `aggr__0__1__parent_count`, `aggr__0__1__key_0`, `aggr__0__1__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"    `aggr__0__1__parent_count`, `aggr__0__1__key_0`, `aggr__0__1__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__0__count` DESC, `aggr__0__key_0` ASC) AS\n" +
+			"    `aggr__0__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0` ORDER BY\n" +
+			"    `aggr__0__1__count` DESC, `aggr__0__1__key_0` ASC) AS\n" +
+			"    `aggr__0__1__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"      `OriginAirportID` AS `aggr__0__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS `aggr__0__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS\n" +
+			"      `aggr__0__1__parent_count`, `DestAirportID` AS `aggr__0__1__key_0`,\n" +
+			"      count(*) AS `aggr__0__1__count`\n" +
+			"    FROM `" + TableName + "`\n" +
+			"    GROUP BY `OriginAirportID` AS `aggr__0__key_0`,\n" +
+			"      `DestAirportID` AS `aggr__0__1__key_0`))\n" +
+			"WHERE (`aggr__0__order_1_rank`<=11 AND `aggr__0__1__order_1_rank`<=4)\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC",
 	},
 }
diff --git a/platform/testdata/aggregation_requests_2.go b/platform/testdata/aggregation_requests_2.go
index 2dd2e106f..11db0a0fe 100644
--- a/platform/testdata/aggregation_requests_2.go
+++ b/platform/testdata/aggregation_requests_2.go
@@ -370,95 +370,21 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__year2__count", uint64(33)),
 			}}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone(
-              "@timestamp",'Europe/Warsaw'))*1000) / 86400000) AS "aggr__day1__key_0",
-			  count(*) AS "aggr__day1__count"
-			FROM ` + TableName + `
-			GROUP BY toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone(
-              "@timestamp",'Europe/Warsaw'))*1000) / 86400000) AS "aggr__day1__key_0"
-			ORDER BY "aggr__day1__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone(`@timestamp`,'Europe/Warsaw'))*1000) / 86400000) AS `aggr__day1__key_0`, count(*) AS `aggr__day1__count` FROM `__quesma_table_name` GROUP BY toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone(`@timestamp`,'Europe/Warsaw'))*1000) / 86400000) AS `aggr__day1__key_0` ORDER BY `aggr__day1__key_0` ASC",
 		ExpectedAdditionalPancakeSQLs: []string{
-			`SELECT toInt64(toUnixTimestamp64Milli("@timestamp") / 86400000) AS
-			  "aggr__day2__key_0", count(*) AS "aggr__day2__count"
-			FROM ` + TableName + `
-			GROUP BY toInt64(toUnixTimestamp64Milli("@timestamp") / 86400000) AS
-			  "aggr__day2__key_0"
-			ORDER BY "aggr__day2__key_0" ASC`,
-			`SELECT toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone(
-			"@timestamp",'Europe/Warsaw'))*1000) / 3600000) AS "aggr__hour1__key_0",
-			count(*) AS "aggr__hour1__count"
-			FROM ` + TableName + `
-			GROUP BY toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone(
-			"@timestamp",'Europe/Warsaw'))*1000) / 3600000) AS "aggr__hour1__key_0"
-			ORDER BY "aggr__hour1__key_0" ASC`,
-			`SELECT toInt64(toUnixTimestamp64Milli("@timestamp") / 3600000) AS
-			  "aggr__hour2__key_0", count(*) AS "aggr__hour2__count"
-			FROM ` + TableName + `
-			GROUP BY toInt64(toUnixTimestamp64Milli("@timestamp") / 3600000) AS
-			  "aggr__hour2__key_0"
-			ORDER BY "aggr__hour2__key_0" ASC`,
-			`SELECT toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone(
-			"@timestamp",'Europe/Warsaw'))*1000) / 60000) AS "aggr__minute1__key_0",
-			count(*) AS "aggr__minute1__count"
-			FROM ` + TableName + `
-			GROUP BY toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone(
-			"@timestamp",'Europe/Warsaw'))*1000) / 60000) AS "aggr__minute1__key_0"
-			ORDER BY "aggr__minute1__key_0" ASC`,
-			`SELECT toInt64(toUnixTimestamp64Milli("@timestamp") / 60000) AS
-			  "aggr__minute2__key_0", count(*) AS "aggr__minute2__count"
-			FROM ` + TableName + `
-			GROUP BY toInt64(toUnixTimestamp64Milli("@timestamp") / 60000) AS
-			  "aggr__minute2__key_0"
-			ORDER BY "aggr__minute2__key_0" ASC`,
-			`SELECT toInt64(toUnixTimestamp(toStartOfMonth(toTimezone("@timestamp",'Europe/Warsaw'))))*1000
-    		  AS "aggr__month1__key_0", count(*) AS "aggr__month1__count"
-			FROM ` + TableName + `
-			GROUP BY toInt64(toUnixTimestamp(toStartOfMonth(toTimezone("@timestamp",'Europe/Warsaw'))))*1000
-			  AS "aggr__month1__key_0"
-			ORDER BY "aggr__month1__key_0" ASC`,
-			`SELECT toInt64(toUnixTimestamp(toStartOfMonth(toTimezone("@timestamp",'UTC'))))*1000 AS
-			  "aggr__month2__key_0", count(*) AS "aggr__month2__count"
-			FROM ` + TableName + `
-			GROUP BY toInt64(toUnixTimestamp(toStartOfMonth(toTimezone("@timestamp",'UTC'))))*1000 AS
-			  "aggr__month2__key_0"
-			ORDER BY "aggr__month2__key_0" ASC`,
-			`SELECT toInt64(toUnixTimestamp(toStartOfQuarter(toTimezone("@timestamp",'Europe/Warsaw'))))*1000
-    		  AS "aggr__quarter1__key_0", count(*) AS "aggr__quarter1__count"
-			FROM ` + TableName + `
-			GROUP BY toInt64(toUnixTimestamp(toStartOfQuarter(toTimezone("@timestamp",'Europe/Warsaw'))))*1000 AS
-			  "aggr__quarter1__key_0"
-			ORDER BY "aggr__quarter1__key_0" ASC`,
-			`SELECT toInt64(toUnixTimestamp(toStartOfQuarter(toTimezone("@timestamp",'UTC'))))*1000 AS
-			  "aggr__quarter2__key_0", count(*) AS "aggr__quarter2__count"
-			FROM ` + TableName + `
-			GROUP BY toInt64(toUnixTimestamp(toStartOfQuarter(toTimezone("@timestamp",'UTC'))))*1000 AS
-			  "aggr__quarter2__key_0"
-			ORDER BY "aggr__quarter2__key_0" ASC`,
-			`SELECT  toInt64(toUnixTimestamp(toStartOfWeek(toTimezone("@timestamp",'Europe/Warsaw'))))*1000 AS
-			  "aggr__week1__key_0", count(*) AS "aggr__week1__count"
-			FROM ` + TableName + `
-			GROUP BY toInt64(toUnixTimestamp(toStartOfWeek(toTimezone("@timestamp",'Europe/Warsaw'))))*1000
-			  AS "aggr__week1__key_0"
-			ORDER BY "aggr__week1__key_0" ASC`,
-			`SELECT toInt64(toUnixTimestamp(toStartOfWeek(toTimezone("@timestamp",'UTC'))))*1000 AS
-			  "aggr__week2__key_0", count(*) AS "aggr__week2__count"
-			FROM ` + TableName + `
-			GROUP BY toInt64(toUnixTimestamp(toStartOfWeek(toTimezone("@timestamp",'UTC'))))*1000 AS
-			  "aggr__week2__key_0"
-			ORDER BY "aggr__week2__key_0" ASC`,
-			`SELECT toInt64(toUnixTimestamp(toStartOfYear(toTimezone("@timestamp",'Europe/Warsaw'))))*1000
-    		  AS "aggr__year1__key_0", count(*) AS "aggr__year1__count"
-			FROM ` + TableName + `
-			GROUP BY toInt64(toUnixTimestamp(toStartOfYear(toTimezone("@timestamp",'Europe/Warsaw'))))*1000
-			  AS "aggr__year1__key_0"
-			ORDER BY "aggr__year1__key_0" ASC`,
-			`SELECT toInt64(toUnixTimestamp(toStartOfYear(toTimezone("@timestamp",'UTC'))))*1000 AS
-			  "aggr__year2__key_0", count(*) AS "aggr__year2__count"
-			FROM ` + TableName + `
-			GROUP BY toInt64(toUnixTimestamp(toStartOfYear(toTimezone("@timestamp",'UTC'))))*1000 AS
-			  "aggr__year2__key_0"
-			ORDER BY "aggr__year2__key_0" ASC`,
+			"SELECT toInt64(toUnixTimestamp64Milli(`@timestamp`) / 86400000) AS `aggr__day2__key_0`, count(*) AS `aggr__day2__count` FROM `__quesma_table_name` GROUP BY toInt64(toUnixTimestamp64Milli(`@timestamp`) / 86400000) AS `aggr__day2__key_0` ORDER BY `aggr__day2__key_0` ASC",
+			"SELECT toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone(`@timestamp`,'Europe/Warsaw'))*1000) / 3600000) AS `aggr__hour1__key_0`, count(*) AS `aggr__hour1__count` FROM `__quesma_table_name` GROUP BY toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone(`@timestamp`,'Europe/Warsaw'))*1000) / 3600000) AS `aggr__hour1__key_0` ORDER BY `aggr__hour1__key_0` ASC",
+			"SELECT toInt64(toUnixTimestamp64Milli(`@timestamp`) / 3600000) AS `aggr__hour2__key_0`, count(*) AS `aggr__hour2__count` FROM `__quesma_table_name` GROUP BY toInt64(toUnixTimestamp64Milli(`@timestamp`) / 3600000) AS `aggr__hour2__key_0` ORDER BY `aggr__hour2__key_0` ASC",
+			"SELECT toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone(`@timestamp`,'Europe/Warsaw'))*1000) / 60000) AS `aggr__minute1__key_0`, count(*) AS `aggr__minute1__count` FROM `__quesma_table_name` GROUP BY toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone(`@timestamp`,'Europe/Warsaw'))*1000) / 60000) AS `aggr__minute1__key_0` ORDER BY `aggr__minute1__key_0` ASC",
+			"SELECT toInt64(toUnixTimestamp64Milli(`@timestamp`) / 60000) AS `aggr__minute2__key_0`, count(*) AS `aggr__minute2__count` FROM `__quesma_table_name` GROUP BY toInt64(toUnixTimestamp64Milli(`@timestamp`) / 60000) AS `aggr__minute2__key_0` ORDER BY `aggr__minute2__key_0` ASC",
+			"SELECT toInt64(toUnixTimestamp(toStartOfMonth(toTimezone(`@timestamp`,'Europe/Warsaw'))))*1000 AS `aggr__month1__key_0`, count(*) AS `aggr__month1__count` FROM `__quesma_table_name` GROUP BY toInt64(toUnixTimestamp(toStartOfMonth(toTimezone(`@timestamp`,'Europe/Warsaw'))))*1000 AS `aggr__month1__key_0` ORDER BY `aggr__month1__key_0` ASC",
+			"SELECT toInt64(toUnixTimestamp(toStartOfMonth(toTimezone(`@timestamp`,'UTC'))))*1000 AS `aggr__month2__key_0`, count(*) AS `aggr__month2__count` FROM `__quesma_table_name` GROUP BY toInt64(toUnixTimestamp(toStartOfMonth(toTimezone(`@timestamp`,'UTC'))))*1000 AS `aggr__month2__key_0` ORDER BY `aggr__month2__key_0` ASC",
+			"SELECT toInt64(toUnixTimestamp(toStartOfQuarter(toTimezone(`@timestamp`,'Europe/Warsaw'))))*1000 AS `aggr__quarter1__key_0`, count(*) AS `aggr__quarter1__count` FROM `__quesma_table_name` GROUP BY toInt64(toUnixTimestamp(toStartOfQuarter(toTimezone(`@timestamp`,'Europe/Warsaw'))))*1000 AS `aggr__quarter1__key_0` ORDER BY `aggr__quarter1__key_0` ASC",
+			"SELECT toInt64(toUnixTimestamp(toStartOfQuarter(toTimezone(`@timestamp`,'UTC'))))*1000 AS `aggr__quarter2__key_0`, count(*) AS `aggr__quarter2__count` FROM `__quesma_table_name` GROUP BY toInt64(toUnixTimestamp(toStartOfQuarter(toTimezone(`@timestamp`,'UTC'))))*1000 AS `aggr__quarter2__key_0` ORDER BY `aggr__quarter2__key_0` ASC",
+			"SELECT toInt64(toUnixTimestamp(toStartOfWeek(toTimezone(`@timestamp`,'Europe/Warsaw'))))*1000 AS `aggr__week1__key_0`, count(*) AS `aggr__week1__count` FROM `__quesma_table_name` GROUP BY toInt64(toUnixTimestamp(toStartOfWeek(toTimezone(`@timestamp`,'Europe/Warsaw'))))*1000 AS `aggr__week1__key_0` ORDER BY `aggr__week1__key_0` ASC",
+			"SELECT toInt64(toUnixTimestamp(toStartOfWeek(toTimezone(`@timestamp`,'UTC'))))*1000 AS `aggr__week2__key_0`, count(*) AS `aggr__week2__count` FROM `__quesma_table_name` GROUP BY toInt64(toUnixTimestamp(toStartOfWeek(toTimezone(`@timestamp`,'UTC'))))*1000 AS `aggr__week2__key_0` ORDER BY `aggr__week2__key_0` ASC",
+			"SELECT toInt64(toUnixTimestamp(toStartOfYear(toTimezone(`@timestamp`,'Europe/Warsaw'))))*1000 AS `aggr__year1__key_0`, count(*) AS `aggr__year1__count` FROM `__quesma_table_name` GROUP BY toInt64(toUnixTimestamp(toStartOfYear(toTimezone(`@timestamp`,'Europe/Warsaw'))))*1000 AS `aggr__year1__key_0` ORDER BY `aggr__year1__key_0` ASC",
+			"SELECT toInt64(toUnixTimestamp(toStartOfYear(toTimezone(`@timestamp`,'UTC'))))*1000 AS `aggr__year2__key_0`, count(*) AS `aggr__year2__count` FROM `__quesma_table_name` GROUP BY toInt64(toUnixTimestamp(toStartOfYear(toTimezone(`@timestamp`,'UTC'))))*1000 AS `aggr__year2__key_0` ORDER BY `aggr__year2__key_0` ASC",
 		},
 	},
 	{ // [43]
@@ -600,17 +526,7 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("metric__2__2_col_0", 10),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-			  "response" AS "aggr__2__key_0", count(*) AS "aggr__2__count",
-			  quantiles(0.010000)("timestamp") AS "metric__2__1_col_0",
-			  quantiles(0.020000)("timestamp") AS "metric__2__1_col_1",
-			  sumOrNull("count") AS "metric__2__2_col_0"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1713401475845) AND "timestamp"<=fromUnixTimestamp64Milli(1714697475845))
-			GROUP BY "response" AS "aggr__2__key_0"
-			ORDER BY "aggr__2__count" DESC, "aggr__2__key_0" ASC
-			LIMIT 4`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`, `response` AS `aggr__2__key_0`, count(*) AS `aggr__2__count`, quantiles(0.010000)(`timestamp`) AS `metric__2__1_col_0`, quantiles(0.020000)(`timestamp`) AS `metric__2__1_col_1`, sumOrNull(`count`) AS `metric__2__2_col_0` FROM `__quesma_table_name` WHERE (`timestamp`>=fromUnixTimestamp64Milli(1713401475845) AND `timestamp`<=fromUnixTimestamp64Milli(1714697475845)) GROUP BY `response` AS `aggr__2__key_0` ORDER BY `aggr__2__count` DESC, `aggr__2__key_0` ASC LIMIT 4",
 	},
 	{ // [44]
 		TestName: "2x terms with nulls 1/4, nulls in second aggregation, with missing parameter",
@@ -751,30 +667,29 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__8__count", int64(17)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-			  "aggr__2__8__parent_count", "aggr__2__8__key_0", "aggr__2__8__count"
-			FROM (
-			  SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-				"aggr__2__8__parent_count", "aggr__2__8__key_0", "aggr__2__8__count",
-				dense_rank() OVER (ORDER BY "aggr__2__count" DESC, "aggr__2__key_0" ASC) AS
-				"aggr__2__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"aggr__2__8__count" DESC, "aggr__2__8__key_0" ASC) AS
-				"aggr__2__8__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-				  "surname" AS "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "aggr__2__8__parent_count",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__8__key_0",
-				  count(*) AS "aggr__2__8__count"
-				FROM __quesma_table_name
-				GROUP BY "surname" AS "aggr__2__key_0",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__8__key_0"))
-			WHERE ("aggr__2__order_1_rank"<=201 AND "aggr__2__8__order_1_rank"<=20)
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__8__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"  `aggr__2__8__parent_count`, `aggr__2__8__key_0`, `aggr__2__8__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"    `aggr__2__8__parent_count`, `aggr__2__8__key_0`, `aggr__2__8__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__count` DESC, `aggr__2__key_0` ASC) AS\n" +
+			"    `aggr__2__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+			"    `aggr__2__8__count` DESC, `aggr__2__8__key_0` ASC) AS\n" +
+			"    `aggr__2__8__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n" +
+			"      `surname` AS `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS\n" +
+			"      `aggr__2__8__parent_count`,\n" +
+			"      COALESCE(`limbName`, '__missing__') AS `aggr__2__8__key_0`,\n" +
+			"      count(*) AS `aggr__2__8__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY `surname` AS `aggr__2__key_0`,\n" +
+			"      COALESCE(`limbName`, '__missing__') AS `aggr__2__8__key_0`))\n" +
+			"WHERE (`aggr__2__order_1_rank`<=201 AND `aggr__2__8__order_1_rank`<=20)\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__8__order_1_rank` ASC",
 	},
 	{ // [45]
 		TestName: "2x terms with nulls 2/4, nulls in the second aggregation, but no missing parameter",
@@ -975,28 +890,27 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__8__count_1", int64(17)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-			  "aggr__2__8__parent_count", "aggr__2__8__key_0", "aggr__2__8__count"
-			FROM (
-			  SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-				"aggr__2__8__parent_count", "aggr__2__8__key_0", "aggr__2__8__count",
-				dense_rank() OVER (ORDER BY "aggr__2__count" DESC, "aggr__2__key_0" ASC) AS
-				"aggr__2__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"aggr__2__8__count" DESC, "aggr__2__8__key_0" ASC) AS
-				"aggr__2__8__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-				  "surname" AS "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "aggr__2__8__parent_count", "limbName" AS "aggr__2__8__key_0",
-				  count(*) AS "aggr__2__8__count"
-				FROM __quesma_table_name
-				GROUP BY "surname" AS "aggr__2__key_0", "limbName" AS "aggr__2__8__key_0"))
-			WHERE ("aggr__2__order_1_rank"<=201 AND "aggr__2__8__order_1_rank"<=21)
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__8__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"  `aggr__2__8__parent_count`, `aggr__2__8__key_0`, `aggr__2__8__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"    `aggr__2__8__parent_count`, `aggr__2__8__key_0`, `aggr__2__8__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__count` DESC, `aggr__2__key_0` ASC) AS\n" +
+			"    `aggr__2__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+			"    `aggr__2__8__count` DESC, `aggr__2__8__key_0` ASC) AS\n" +
+			"    `aggr__2__8__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n" +
+			"      `surname` AS `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS\n" +
+			"      `aggr__2__8__parent_count`, `limbName` AS `aggr__2__8__key_0`,\n" +
+			"      count(*) AS `aggr__2__8__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY `surname` AS `aggr__2__key_0`, `limbName` AS `aggr__2__8__key_0`))\n" +
+			"WHERE (`aggr__2__order_1_rank`<=201 AND `aggr__2__8__order_1_rank`<=21)\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__8__order_1_rank` ASC",
 	},
 	{ // [46]
 		TestName: "2x terms with nulls 3/4, nulls in the first aggregation, with missing parameter",
@@ -1138,30 +1052,29 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__8__count", int64(17)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-			  "aggr__2__8__parent_count", "aggr__2__8__key_0", "aggr__2__8__count"
-			FROM (
-			  SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-				"aggr__2__8__parent_count", "aggr__2__8__key_0", "aggr__2__8__count",
-				dense_rank() OVER (ORDER BY "aggr__2__count" DESC, "aggr__2__key_0" ASC) AS
-				"aggr__2__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"aggr__2__8__count" DESC, "aggr__2__8__key_0" ASC) AS
-				"aggr__2__8__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-				  COALESCE("surname", 'miss') AS "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "aggr__2__8__parent_count",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__8__key_0",
-				  count(*) AS "aggr__2__8__count"
-				FROM __quesma_table_name
-				GROUP BY COALESCE("surname", 'miss') AS "aggr__2__key_0",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__8__key_0"))
-			WHERE ("aggr__2__order_1_rank"<=200 AND "aggr__2__8__order_1_rank"<=20)
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__8__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"  `aggr__2__8__parent_count`, `aggr__2__8__key_0`, `aggr__2__8__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"    `aggr__2__8__parent_count`, `aggr__2__8__key_0`, `aggr__2__8__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__count` DESC, `aggr__2__key_0` ASC) AS\n" +
+			"    `aggr__2__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+			"    `aggr__2__8__count` DESC, `aggr__2__8__key_0` ASC) AS\n" +
+			"    `aggr__2__8__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n" +
+			"      COALESCE(`surname`, 'miss') AS `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS\n" +
+			"      `aggr__2__8__parent_count`,\n" +
+			"      COALESCE(`limbName`, '__missing__') AS `aggr__2__8__key_0`,\n" +
+			"      count(*) AS `aggr__2__8__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY COALESCE(`surname`, 'miss') AS `aggr__2__key_0`,\n" +
+			"      COALESCE(`limbName`, '__missing__') AS `aggr__2__8__key_0`))\n" +
+			"WHERE (`aggr__2__order_1_rank`<=200 AND `aggr__2__8__order_1_rank`<=20)\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__8__order_1_rank` ASC",
 	},
 	{ // [47]
 		TestName: "2x terms with nulls 4/4, nulls in the first aggregation, without missing parameter",
@@ -1325,28 +1238,27 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__8__count", int64(17)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-			  "aggr__2__8__parent_count", "aggr__2__8__key_0", "aggr__2__8__count"
-			FROM (
-			  SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-				"aggr__2__8__parent_count", "aggr__2__8__key_0", "aggr__2__8__count",
-				dense_rank() OVER (ORDER BY "aggr__2__count" DESC, "aggr__2__key_0" ASC) AS
-				"aggr__2__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"aggr__2__8__count" DESC, "aggr__2__8__key_0" ASC) AS
-				"aggr__2__8__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-				  "surname" AS "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "aggr__2__8__parent_count", "limbName" AS "aggr__2__8__key_0",
-				  count(*) AS "aggr__2__8__count"
-				FROM __quesma_table_name
-				GROUP BY "surname" AS "aggr__2__key_0", "limbName" AS "aggr__2__8__key_0"))
-			WHERE ("aggr__2__order_1_rank"<=201 AND "aggr__2__8__order_1_rank"<=21)
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__8__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"  `aggr__2__8__parent_count`, `aggr__2__8__key_0`, `aggr__2__8__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"    `aggr__2__8__parent_count`, `aggr__2__8__key_0`, `aggr__2__8__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__count` DESC, `aggr__2__key_0` ASC) AS\n" +
+			"    `aggr__2__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+			"    `aggr__2__8__count` DESC, `aggr__2__8__key_0` ASC) AS\n" +
+			"    `aggr__2__8__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n" +
+			"      `surname` AS `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS\n" +
+			"      `aggr__2__8__parent_count`, `limbName` AS `aggr__2__8__key_0`,\n" +
+			"      count(*) AS `aggr__2__8__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY `surname` AS `aggr__2__key_0`, `limbName` AS `aggr__2__8__key_0`))\n" +
+			"WHERE (`aggr__2__order_1_rank`<=201 AND `aggr__2__8__order_1_rank`<=21)\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__8__order_1_rank` ASC",
 	},
 	{ // [48], "old" test, also can be found in testdata/requests.go TestAsyncSearch[3]
 		// Copied it also here to be more sure we do not create some regression
@@ -1500,28 +1412,26 @@ var AggregationTests2 = []AggregationTestCase{
 					`ORDER BY ` + timestampGroupByClause,
 			},
 		*/
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__key_0", "aggr__2__count", "aggr__2__3__key_0",
-			  "aggr__2__3__count"
-			FROM (
-			  SELECT "aggr__2__key_0", "aggr__2__count", "aggr__2__3__key_0",
-				"aggr__2__3__count",
-				dense_rank() OVER (ORDER BY "aggr__2__key_0" ASC) AS "aggr__2__order_1_rank"
-				,
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"aggr__2__3__key_0" ASC) AS "aggr__2__3__order_1_rank"
-			  FROM (
-				SELECT toInt64(toUnixTimestamp64Milli("@timestamp") / 30000) AS
-				  "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  toInt64(toUnixTimestamp64Milli("@timestamp") / 40000) AS
-				  "aggr__2__3__key_0", count(*) AS "aggr__2__3__count"
-				FROM ` + TableName + `
-				GROUP BY toInt64(toUnixTimestamp64Milli("@timestamp") / 30000) AS
-				  "aggr__2__key_0",
-				  toInt64(toUnixTimestamp64Milli("@timestamp") / 40000) AS
-				  "aggr__2__3__key_0"))
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__3__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__key_0`, `aggr__2__count`, `aggr__2__3__key_0`,\n" +
+			"  `aggr__2__3__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__key_0`, `aggr__2__count`, `aggr__2__3__key_0`,\n" +
+			"    `aggr__2__3__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__key_0` ASC) AS `aggr__2__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+			"    `aggr__2__3__key_0` ASC) AS `aggr__2__3__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT toInt64(toUnixTimestamp64Milli(`@timestamp`) / 30000) AS\n" +
+			"      `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      toInt64(toUnixTimestamp64Milli(`@timestamp`) / 40000) AS\n" +
+			"      `aggr__2__3__key_0`, count(*) AS `aggr__2__3__count`\n" +
+			"    FROM `" + TableName + "`\n" +
+			"    GROUP BY toInt64(toUnixTimestamp64Milli(`@timestamp`) / 30000) AS\n" +
+			"      `aggr__2__key_0`,\n" +
+			"      toInt64(toUnixTimestamp64Milli(`@timestamp`) / 40000) AS\n" +
+			"      `aggr__2__3__key_0`))\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__3__order_1_rank` ASC",
 	},
 	{ // [49] TODO should null be in the response? Maybe try to replicate and see if it's fine as is.
 		TestName: "2x histogram",
@@ -1661,26 +1571,24 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__3__count", 1),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__key_0", "aggr__2__count", "aggr__2__3__key_0",
-			  "aggr__2__3__count"
-			FROM (
-			  SELECT "aggr__2__key_0", "aggr__2__count", "aggr__2__3__key_0",
-				"aggr__2__3__count",
-				dense_rank() OVER (ORDER BY "aggr__2__key_0" ASC) AS "aggr__2__order_1_rank"
-				,
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"aggr__2__3__key_0" ASC) AS "aggr__2__3__order_1_rank"
-			  FROM (
-				SELECT floor("bytes"/100)*100 AS "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  floor("bytes2"/5)*5 AS "aggr__2__3__key_0",
-				  count(*) AS "aggr__2__3__count"
-				FROM ` + TableName + `
-				WHERE ("timestamp">=fromUnixTimestamp64Milli(1715348876077) AND "timestamp"<=fromUnixTimestamp64Milli(1715349776077))
-				GROUP BY floor("bytes"/100)*100 AS "aggr__2__key_0",
-				  floor("bytes2"/5)*5 AS "aggr__2__3__key_0"))
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__3__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__key_0`, `aggr__2__count`, `aggr__2__3__key_0`,\n" +
+			"  `aggr__2__3__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__key_0`, `aggr__2__count`, `aggr__2__3__key_0`,\n" +
+			"    `aggr__2__3__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__key_0` ASC) AS `aggr__2__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+			"    `aggr__2__3__key_0` ASC) AS `aggr__2__3__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT floor(`bytes`/100)*100 AS `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      floor(`bytes2`/5)*5 AS `aggr__2__3__key_0`,\n" +
+			"      count(*) AS `aggr__2__3__count`\n" +
+			"    FROM `" + TableName + "`\n" +
+			"    WHERE (`timestamp`>=fromUnixTimestamp64Milli(1715348876077) AND `timestamp`<=fromUnixTimestamp64Milli(1715349776077))\n" +
+			"    GROUP BY floor(`bytes`/100)*100 AS `aggr__2__key_0`,\n" +
+			"      floor(`bytes2`/5)*5 AS `aggr__2__3__key_0`))\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__3__order_1_rank` ASC",
 	},
 	{ // [50] TODO: what about nulls in histogram? Maybe they should be treated like in terms?
 		TestName: "2x histogram with min_doc_count 0",
@@ -1855,26 +1763,24 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__3__count", 1),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__key_0", "aggr__2__count", "aggr__2__3__key_0",
-			  "aggr__2__3__count"
-			FROM (
-			  SELECT "aggr__2__key_0", "aggr__2__count", "aggr__2__3__key_0",
-				"aggr__2__3__count",
-				dense_rank() OVER (ORDER BY "aggr__2__key_0" ASC) AS "aggr__2__order_1_rank"
-				,
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"aggr__2__3__key_0" ASC) AS "aggr__2__3__order_1_rank"
-			  FROM (
-				SELECT floor("bytes"/100)*100 AS "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  floor("bytes2"/5)*5 AS "aggr__2__3__key_0",
-				  count(*) AS "aggr__2__3__count"
-				FROM ` + TableName + `
-				WHERE ("timestamp">=fromUnixTimestamp64Milli(1715348876077) AND "timestamp"<=fromUnixTimestamp64Milli(1715349776077))
-				GROUP BY floor("bytes"/100)*100 AS "aggr__2__key_0",
-				  floor("bytes2"/5)*5 AS "aggr__2__3__key_0"))
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__3__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__key_0`, `aggr__2__count`, `aggr__2__3__key_0`,\n" +
+			"  `aggr__2__3__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__key_0`, `aggr__2__count`, `aggr__2__3__key_0`,\n" +
+			"    `aggr__2__3__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__key_0` ASC) AS `aggr__2__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+			"    `aggr__2__3__key_0` ASC) AS `aggr__2__3__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT floor(`bytes`/100)*100 AS `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      floor(`bytes2`/5)*5 AS `aggr__2__3__key_0`,\n" +
+			"      count(*) AS `aggr__2__3__count`\n" +
+			"    FROM `" + TableName + "`\n" +
+			"    WHERE (`timestamp`>=fromUnixTimestamp64Milli(1715348876077) AND `timestamp`<=fromUnixTimestamp64Milli(1715349776077))\n" +
+			"    GROUP BY floor(`bytes`/100)*100 AS `aggr__2__key_0`,\n" +
+			"      floor(`bytes2`/5)*5 AS `aggr__2__3__key_0`))\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__3__order_1_rank` ASC",
 	},
 	{ // [51]
 		TestName: "2x terms with sampler in the middle",
@@ -2032,33 +1938,32 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__8__5__count", int64(17)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-			  "aggr__2__8__count", "aggr__2__8__5__parent_count", "aggr__2__8__5__key_0",
-			  "aggr__2__8__5__count"
-			FROM (
-			  SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-				"aggr__2__8__count", "aggr__2__8__5__parent_count", "aggr__2__8__5__key_0",
-				"aggr__2__8__5__count",
-				dense_rank() OVER (ORDER BY "aggr__2__count" DESC, "aggr__2__key_0" ASC) AS
-				"aggr__2__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"aggr__2__8__5__count" DESC, "aggr__2__8__5__key_0" ASC) AS
-				"aggr__2__8__5__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-				  "surname" AS "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__8__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "aggr__2__8__5__parent_count",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__8__5__key_0",
-				  count(*) AS "aggr__2__8__5__count"
-				FROM __quesma_table_name
-				GROUP BY "surname" AS "aggr__2__key_0",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__8__5__key_0"))
-			WHERE ("aggr__2__order_1_rank"<=201 AND "aggr__2__8__5__order_1_rank"<=20)
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__8__5__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"  `aggr__2__8__count`, `aggr__2__8__5__parent_count`, `aggr__2__8__5__key_0`,\n" +
+			"  `aggr__2__8__5__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"    `aggr__2__8__count`, `aggr__2__8__5__parent_count`, `aggr__2__8__5__key_0`,\n" +
+			"    `aggr__2__8__5__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__count` DESC, `aggr__2__key_0` ASC) AS\n" +
+			"    `aggr__2__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+			"    `aggr__2__8__5__count` DESC, `aggr__2__8__5__key_0` ASC) AS\n" +
+			"    `aggr__2__8__5__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n" +
+			"      `surname` AS `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__8__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS\n" +
+			"      `aggr__2__8__5__parent_count`,\n" +
+			"      COALESCE(`limbName`, '__missing__') AS `aggr__2__8__5__key_0`,\n" +
+			"      count(*) AS `aggr__2__8__5__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY `surname` AS `aggr__2__key_0`,\n" +
+			"      COALESCE(`limbName`, '__missing__') AS `aggr__2__8__5__key_0`))\n" +
+			"WHERE (`aggr__2__order_1_rank`<=201 AND `aggr__2__8__5__order_1_rank`<=20)\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__8__5__order_1_rank` ASC",
 	},
 	{ // [52]
 		TestName: "2x terms with random_sampler in the middle",
@@ -2221,33 +2126,32 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__8__5__count", int64(17)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-			  "aggr__2__8__count", "aggr__2__8__5__parent_count", "aggr__2__8__5__key_0",
-			  "aggr__2__8__5__count"
-			FROM (
-			  SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-				"aggr__2__8__count", "aggr__2__8__5__parent_count", "aggr__2__8__5__key_0",
-				"aggr__2__8__5__count",
-				dense_rank() OVER (ORDER BY "aggr__2__count" DESC, "aggr__2__key_0" ASC) AS
-				"aggr__2__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"aggr__2__8__5__count" DESC, "aggr__2__8__5__key_0" ASC) AS
-				"aggr__2__8__5__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-				  "surname" AS "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__8__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "aggr__2__8__5__parent_count",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__8__5__key_0",
-				  count(*) AS "aggr__2__8__5__count"
-				FROM __quesma_table_name
-				GROUP BY "surname" AS "aggr__2__key_0",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__8__5__key_0"))
-			WHERE ("aggr__2__order_1_rank"<=201 AND "aggr__2__8__5__order_1_rank"<=20)
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__8__5__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"  `aggr__2__8__count`, `aggr__2__8__5__parent_count`, `aggr__2__8__5__key_0`,\n" +
+			"  `aggr__2__8__5__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"    `aggr__2__8__count`, `aggr__2__8__5__parent_count`, `aggr__2__8__5__key_0`,\n" +
+			"    `aggr__2__8__5__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__count` DESC, `aggr__2__key_0` ASC) AS\n" +
+			"    `aggr__2__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+			"    `aggr__2__8__5__count` DESC, `aggr__2__8__5__key_0` ASC) AS\n" +
+			"    `aggr__2__8__5__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n" +
+			"      `surname` AS `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__8__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS\n" +
+			"      `aggr__2__8__5__parent_count`,\n" +
+			"      COALESCE(`limbName`, '__missing__') AS `aggr__2__8__5__key_0`,\n" +
+			"      count(*) AS `aggr__2__8__5__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY `surname` AS `aggr__2__key_0`,\n" +
+			"      COALESCE(`limbName`, '__missing__') AS `aggr__2__8__5__key_0`))\n" +
+			"WHERE (`aggr__2__order_1_rank`<=201 AND `aggr__2__8__5__order_1_rank`<=20)\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__8__5__order_1_rank` ASC",
 	},
 	{ // [53]
 		TestName: "terms order by quantile, simplest - only one percentile",
@@ -2452,33 +2356,29 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0__1__2_col_0", []float64{5}),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__key_0", "aggr__0__count", "aggr__0__1__parent_count",
-			  "aggr__0__1__key_0", "aggr__0__1__count", "metric__0__1__2_col_0"
-			FROM (
-			  SELECT "aggr__0__key_0", "aggr__0__count", "aggr__0__1__parent_count",
-				"aggr__0__1__key_0", "aggr__0__1__count", "metric__0__1__2_col_0",
-				dense_rank() OVER (ORDER BY "aggr__0__key_0" ASC) AS "aggr__0__order_1_rank"
-				,
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0" ORDER BY
-				"metric__0__1__2_col_0" DESC, "aggr__0__1__key_0" ASC) AS
-				"aggr__0__1__order_1_rank"
-			  FROM (
-				SELECT toInt64(toUnixTimestamp64Milli("@timestamp") / 43200000) AS
-				  "aggr__0__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS "aggr__0__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS
-				  "aggr__0__1__parent_count", "container.name" AS "aggr__0__1__key_0",
-				  count(*) AS "aggr__0__1__count",
-				  quantiles(0.750000)("docker.cpu.total.pct") AS "metric__0__1__2_col_0"
-				FROM __quesma_table_name
-				WHERE ("data_stream.dataset"='docker.cpu' AND ("@timestamp">=
-				  fromUnixTimestamp64Milli(1723967652291) AND "@timestamp"<=
-				  fromUnixTimestamp64Milli(1725263652291)))
-				GROUP BY toInt64(toUnixTimestamp64Milli("@timestamp") / 43200000) AS
-				  "aggr__0__key_0", "container.name" AS "aggr__0__1__key_0"))
-			WHERE "aggr__0__1__order_1_rank"<=6
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__key_0`, `aggr__0__count`, `aggr__0__1__parent_count`,\n" +
+			"  `aggr__0__1__key_0`, `aggr__0__1__count`, `metric__0__1__2_col_0`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__key_0`, `aggr__0__count`, `aggr__0__1__parent_count`,\n" +
+			"    `aggr__0__1__key_0`, `aggr__0__1__count`, `metric__0__1__2_col_0`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__0__key_0` ASC) AS `aggr__0__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0` ORDER BY\n" +
+			"    `metric__0__1__2_col_0` DESC, `aggr__0__1__key_0` ASC) AS\n" +
+			"    `aggr__0__1__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT toInt64(toUnixTimestamp64Milli(`@timestamp`) / 43200000) AS\n" +
+			"      `aggr__0__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS `aggr__0__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS\n" +
+			"      `aggr__0__1__parent_count`, `container.name` AS `aggr__0__1__key_0`,\n" +
+			"      count(*) AS `aggr__0__1__count`,\n" +
+			"      quantiles(0.750000)(`docker.cpu.total.pct`) AS `metric__0__1__2_col_0`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    WHERE (`data_stream.dataset`='docker.cpu' AND (`@timestamp`>=fromUnixTimestamp64Milli(1723967652291) AND `@timestamp`<=fromUnixTimestamp64Milli(1725263652291)))\n" +
+			"    GROUP BY toInt64(toUnixTimestamp64Milli(`@timestamp`) / 43200000) AS\n" +
+			"      `aggr__0__key_0`, `container.name` AS `aggr__0__1__key_0`))\n" +
+			"WHERE `aggr__0__1__order_1_rank`<=6\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC",
 	},
 	{ // [54]
 		TestName: "terms order by quantile - more percentiles",
@@ -2681,34 +2581,32 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0__1__2_col_2", []float64{22.2}),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__key_0", "aggr__0__count", "aggr__0__1__parent_count",
-			  "aggr__0__1__key_0", "aggr__0__1__count", "metric__0__1__2_col_0",
-			  "metric__0__1__2_col_1", "metric__0__1__2_col_2"
-			FROM (
-			  SELECT "aggr__0__key_0", "aggr__0__count", "aggr__0__1__parent_count",
-				"aggr__0__1__key_0", "aggr__0__1__count", "metric__0__1__2_col_0",
-				"metric__0__1__2_col_1", "metric__0__1__2_col_2",
-				dense_rank() OVER (ORDER BY "aggr__0__key_0" ASC) AS "aggr__0__order_1_rank"
-				,
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0" ORDER BY
-				"metric__0__1__2_col_1" DESC, "aggr__0__1__key_0" ASC) AS
-				"aggr__0__1__order_1_rank"
-			  FROM (
-				SELECT toInt64(toUnixTimestamp64Milli("@timestamp") / 43200000) AS
-				  "aggr__0__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS "aggr__0__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS
-				  "aggr__0__1__parent_count", "container.name" AS "aggr__0__1__key_0",
-				  count(*) AS "aggr__0__1__count",
-				  quantiles(0.100000)("docker.cpu.total.pct") AS "metric__0__1__2_col_0",
-				  quantiles(0.750000)("docker.cpu.total.pct") AS "metric__0__1__2_col_1",
-				  quantiles(0.990000)("docker.cpu.total.pct") AS "metric__0__1__2_col_2"
-				FROM __quesma_table_name
-				GROUP BY toInt64(toUnixTimestamp64Milli("@timestamp") / 43200000) AS
-				  "aggr__0__key_0", "container.name" AS "aggr__0__1__key_0"))
-			WHERE "aggr__0__1__order_1_rank"<=6
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__key_0`, `aggr__0__count`, `aggr__0__1__parent_count`,\n" +
+			"  `aggr__0__1__key_0`, `aggr__0__1__count`, `metric__0__1__2_col_0`,\n" +
+			"  `metric__0__1__2_col_1`, `metric__0__1__2_col_2`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__key_0`, `aggr__0__count`, `aggr__0__1__parent_count`,\n" +
+			"    `aggr__0__1__key_0`, `aggr__0__1__count`, `metric__0__1__2_col_0`,\n" +
+			"    `metric__0__1__2_col_1`, `metric__0__1__2_col_2`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__0__key_0` ASC) AS `aggr__0__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0` ORDER BY\n" +
+			"    `metric__0__1__2_col_1` DESC, `aggr__0__1__key_0` ASC) AS\n" +
+			"    `aggr__0__1__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT toInt64(toUnixTimestamp64Milli(`@timestamp`) / 43200000) AS\n" +
+			"      `aggr__0__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS `aggr__0__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS\n" +
+			"      `aggr__0__1__parent_count`, `container.name` AS `aggr__0__1__key_0`,\n" +
+			"      count(*) AS `aggr__0__1__count`,\n" +
+			"      quantiles(0.100000)(`docker.cpu.total.pct`) AS `metric__0__1__2_col_0`,\n" +
+			"      quantiles(0.750000)(`docker.cpu.total.pct`) AS `metric__0__1__2_col_1`,\n" +
+			"      quantiles(0.990000)(`docker.cpu.total.pct`) AS `metric__0__1__2_col_2`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY toInt64(toUnixTimestamp64Milli(`@timestamp`) / 43200000) AS\n" +
+			"      `aggr__0__key_0`, `container.name` AS `aggr__0__1__key_0`))\n" +
+			"WHERE `aggr__0__1__order_1_rank`<=6\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC",
 	},
 	{ // [55]
 		TestName: "terms order by percentile_ranks",
@@ -2816,15 +2714,14 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0__1_col_1", 9.813812484840025),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-			  "Cancelled" AS "aggr__0__key_0", count(*) AS "aggr__0__count",
-			  countIf("DistanceKilometers"<=0)/count(*)*100 AS "metric__0__1_col_0",
-			  countIf("DistanceKilometers"<=50)/count(*)*100 AS "metric__0__1_col_1"
-			FROM __quesma_table_name
-			GROUP BY "Cancelled" AS "aggr__0__key_0"
-			ORDER BY "metric__0__1_col_0" DESC, "aggr__0__key_0" ASC
-			LIMIT 6`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"  `Cancelled` AS `aggr__0__key_0`, count(*) AS `aggr__0__count`,\n" +
+			"  countIf(`DistanceKilometers`<=0)/count(*)*100 AS `metric__0__1_col_0`,\n" +
+			"  countIf(`DistanceKilometers`<=50)/count(*)*100 AS `metric__0__1_col_1`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY `Cancelled` AS `aggr__0__key_0`\n" +
+			"ORDER BY `metric__0__1_col_0` DESC, `aggr__0__key_0` ASC\n" +
+			"LIMIT 6",
 	},
 	{ // [56]
 		TestName: "simple histogram with null values, no missing parameter",
@@ -2911,17 +2808,16 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__sample__histo__count", 1),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__sample__count",
-			  floor("taxful_total_price"/224.19300000000004)*224.19300000000004 AS
-			  "aggr__sample__histo__key_0", count(*) AS "aggr__sample__histo__count"
-			FROM (
-			  SELECT "taxful_total_price"
-			  FROM __quesma_table_name
-			  LIMIT 20000)
-			GROUP BY floor("taxful_total_price"/224.19300000000004)*224.19300000000004 AS
-			  "aggr__sample__histo__key_0"
-			ORDER BY "aggr__sample__histo__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__sample__count`,\n" +
+			"  floor(`taxful_total_price`/224.19300000000004)*224.19300000000004 AS\n" +
+			"  `aggr__sample__histo__key_0`, count(*) AS `aggr__sample__histo__count`\n" +
+			"FROM (\n" +
+			"  SELECT `taxful_total_price`\n" +
+			"  FROM `__quesma_table_name`\n" +
+			"  LIMIT 20000)\n" +
+			"GROUP BY floor(`taxful_total_price`/224.19300000000004)*224.19300000000004 AS\n" +
+			"  `aggr__sample__histo__key_0`\n" +
+			"ORDER BY `aggr__sample__histo__key_0` ASC",
 	},
 	{ // [57]
 		TestName: "histogram with null values, no missing parameter, and some subaggregation",
@@ -3042,32 +2938,30 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__histo__0__count", int64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__histo__key_0", "aggr__histo__count",
-			  "aggr__histo__0__parent_count", "aggr__histo__0__key_0",
-			  "aggr__histo__0__count"
-			FROM (
-			  SELECT "aggr__histo__key_0", "aggr__histo__count",
-				"aggr__histo__0__parent_count", "aggr__histo__0__key_0",
-				"aggr__histo__0__count",
-				dense_rank() OVER (ORDER BY "aggr__histo__key_0" ASC) AS
-				"aggr__histo__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__histo__key_0" ORDER BY
-				"aggr__histo__0__count" DESC, "aggr__histo__0__key_0" ASC) AS
-				"aggr__histo__0__order_1_rank"
-			  FROM (
-				SELECT floor("taxful_total_price"/224.19300000000004)*224.19300000000004 AS
-				  "aggr__histo__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__histo__key_0") AS
-				  "aggr__histo__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__histo__key_0") AS
-				  "aggr__histo__0__parent_count", "type" AS "aggr__histo__0__key_0",
-				  count(*) AS "aggr__histo__0__count"
-				FROM __quesma_table_name
-				GROUP BY floor("taxful_total_price"/224.19300000000004)*224.19300000000004
-				  AS "aggr__histo__key_0", "type" AS "aggr__histo__0__key_0"))
-			WHERE "aggr__histo__0__order_1_rank"<=11
-			ORDER BY "aggr__histo__order_1_rank" ASC, "aggr__histo__0__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__histo__key_0`, `aggr__histo__count`,\n" +
+			"  `aggr__histo__0__parent_count`, `aggr__histo__0__key_0`,\n" +
+			"  `aggr__histo__0__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__histo__key_0`, `aggr__histo__count`,\n" +
+			"    `aggr__histo__0__parent_count`, `aggr__histo__0__key_0`,\n" +
+			"    `aggr__histo__0__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__histo__key_0` ASC) AS\n" +
+			"    `aggr__histo__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__histo__key_0` ORDER BY\n" +
+			"    `aggr__histo__0__count` DESC, `aggr__histo__0__key_0` ASC) AS\n" +
+			"    `aggr__histo__0__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT floor(`taxful_total_price`/224.19300000000004)*224.19300000000004 AS\n" +
+			"      `aggr__histo__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__histo__key_0`) AS `aggr__histo__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__histo__key_0`) AS\n" +
+			"      `aggr__histo__0__parent_count`, `type` AS `aggr__histo__0__key_0`,\n" +
+			"      count(*) AS `aggr__histo__0__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY floor(`taxful_total_price`/224.19300000000004)*224.19300000000004\n" +
+			"      AS `aggr__histo__key_0`, `type` AS `aggr__histo__0__key_0`))\n" +
+			"WHERE `aggr__histo__0__order_1_rank`<=11\n" +
+			"ORDER BY `aggr__histo__order_1_rank` ASC, `aggr__histo__0__order_1_rank` ASC",
 	},
 	{ // [58]
 		TestName: "simple histogram with null values and missing parameter",
@@ -3159,18 +3053,17 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__sample__histo__count", 1),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__sample__count",
-			  floor(COALESCE("taxful_total_price", 80)/224.19300000000004)*
-			  224.19300000000004 AS "aggr__sample__histo__key_0",
-			  count(*) AS "aggr__sample__histo__count"
-			FROM (
-			  SELECT "taxful_total_price"
-			  FROM __quesma_table_name
-			  LIMIT 20000)
-			GROUP BY floor(COALESCE("taxful_total_price", 80)/224.19300000000004)*
-			  224.19300000000004 AS "aggr__sample__histo__key_0"
-			ORDER BY "aggr__sample__histo__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__sample__count`,\n" +
+			"  floor(COALESCE(`taxful_total_price`, 80)/224.19300000000004)*\n" +
+			"  224.19300000000004 AS `aggr__sample__histo__key_0`,\n" +
+			"  count(*) AS `aggr__sample__histo__count`\n" +
+			"FROM (\n" +
+			"  SELECT `taxful_total_price`\n" +
+			"  FROM `__quesma_table_name`\n" +
+			"  LIMIT 20000)\n" +
+			"GROUP BY floor(COALESCE(`taxful_total_price`, 80)/224.19300000000004)*\n" +
+			"  224.19300000000004 AS `aggr__sample__histo__key_0`\n" +
+			"ORDER BY `aggr__sample__histo__key_0` ASC",
 	},
 	{ // [59]
 		TestName: "histogram with null values, missing parameter, and some subaggregation",
@@ -3310,33 +3203,31 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__histo__0__count", int64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__histo__key_0", "aggr__histo__count",
-			  "aggr__histo__0__parent_count", "aggr__histo__0__key_0",
-			  "aggr__histo__0__count"
-			FROM (
-			  SELECT "aggr__histo__key_0", "aggr__histo__count",
-				"aggr__histo__0__parent_count", "aggr__histo__0__key_0",
-				"aggr__histo__0__count",
-				dense_rank() OVER (ORDER BY "aggr__histo__key_0" ASC) AS
-				"aggr__histo__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__histo__key_0" ORDER BY
-				"aggr__histo__0__count" DESC, "aggr__histo__0__key_0" ASC) AS
-				"aggr__histo__0__order_1_rank"
-			  FROM (
-				SELECT floor(COALESCE("taxful_total_price", 800)/224.19300000000004)*
-				  224.19300000000004 AS "aggr__histo__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__histo__key_0") AS
-				  "aggr__histo__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__histo__key_0") AS
-				  "aggr__histo__0__parent_count", "type" AS "aggr__histo__0__key_0",
-				  count(*) AS "aggr__histo__0__count"
-				FROM __quesma_table_name
-				GROUP BY floor(COALESCE("taxful_total_price", 800)/224.19300000000004)*
-				  224.19300000000004 AS "aggr__histo__key_0",
-				  "type" AS "aggr__histo__0__key_0"))
-			WHERE "aggr__histo__0__order_1_rank"<=11
-			ORDER BY "aggr__histo__order_1_rank" ASC, "aggr__histo__0__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__histo__key_0`, `aggr__histo__count`,\n" +
+			"  `aggr__histo__0__parent_count`, `aggr__histo__0__key_0`,\n" +
+			"  `aggr__histo__0__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__histo__key_0`, `aggr__histo__count`,\n" +
+			"    `aggr__histo__0__parent_count`, `aggr__histo__0__key_0`,\n" +
+			"    `aggr__histo__0__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__histo__key_0` ASC) AS\n" +
+			"    `aggr__histo__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__histo__key_0` ORDER BY\n" +
+			"    `aggr__histo__0__count` DESC, `aggr__histo__0__key_0` ASC) AS\n" +
+			"    `aggr__histo__0__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT floor(COALESCE(`taxful_total_price`, 800)/224.19300000000004)*\n" +
+			"      224.19300000000004 AS `aggr__histo__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__histo__key_0`) AS `aggr__histo__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__histo__key_0`) AS\n" +
+			"      `aggr__histo__0__parent_count`, `type` AS `aggr__histo__0__key_0`,\n" +
+			"      count(*) AS `aggr__histo__0__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY floor(COALESCE(`taxful_total_price`, 800)/224.19300000000004)*\n" +
+			"      224.19300000000004 AS `aggr__histo__key_0`,\n" +
+			"      `type` AS `aggr__histo__0__key_0`))\n" +
+			"WHERE `aggr__histo__0__order_1_rank`<=11\n" +
+			"ORDER BY `aggr__histo__order_1_rank` ASC, `aggr__histo__0__order_1_rank` ASC",
 	},
 	{ // [60]
 		TestName: "simple date_histogram with null values, no missing parameter (DateTime)",
@@ -3429,17 +3320,16 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__sample__histo__count", 1),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__sample__count",
-			  toInt64(toUnixTimestamp("customer_birth_date") / 30) AS
-			  "aggr__sample__histo__key_0", count(*) AS "aggr__sample__histo__count"
-			FROM (
-			  SELECT "customer_birth_date"
-			  FROM __quesma_table_name
-			  LIMIT 20000)
-			GROUP BY toInt64(toUnixTimestamp("customer_birth_date") / 30) AS
-			  "aggr__sample__histo__key_0"
-			ORDER BY "aggr__sample__histo__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__sample__count`,\n" +
+			"  toInt64(toUnixTimestamp(`customer_birth_date`) / 30) AS\n" +
+			"  `aggr__sample__histo__key_0`, count(*) AS `aggr__sample__histo__count`\n" +
+			"FROM (\n" +
+			"  SELECT `customer_birth_date`\n" +
+			"  FROM `__quesma_table_name`\n" +
+			"  LIMIT 20000)\n" +
+			"GROUP BY toInt64(toUnixTimestamp(`customer_birth_date`) / 30) AS\n" +
+			"  `aggr__sample__histo__key_0`\n" +
+			"ORDER BY `aggr__sample__histo__key_0` ASC",
 	},
 	{ // [61]
 		TestName: "date_histogram with null values, no missing parameter, and some subaggregation",
@@ -3561,32 +3451,30 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__histo__0__count", int64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__histo__key_0", "aggr__histo__count",
-			  "aggr__histo__0__parent_count", "aggr__histo__0__key_0",
-			  "aggr__histo__0__count"
-			FROM (
-			  SELECT "aggr__histo__key_0", "aggr__histo__count",
-				"aggr__histo__0__parent_count", "aggr__histo__0__key_0",
-				"aggr__histo__0__count",
-				dense_rank() OVER (ORDER BY "aggr__histo__key_0" ASC) AS
-				"aggr__histo__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__histo__key_0" ORDER BY
-				"aggr__histo__0__count" DESC, "aggr__histo__0__key_0" ASC) AS
-				"aggr__histo__0__order_1_rank"
-			  FROM (
-				SELECT toInt64(toUnixTimestamp64Milli("customer_birth_date_datetime64") / 30000) AS
-				  "aggr__histo__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__histo__key_0") AS
-				  "aggr__histo__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__histo__key_0") AS
-				  "aggr__histo__0__parent_count", "type" AS "aggr__histo__0__key_0",
-				  count(*) AS "aggr__histo__0__count"
-				FROM __quesma_table_name
-				GROUP BY toInt64(toUnixTimestamp64Milli("customer_birth_date_datetime64") / 30000) AS
-				  "aggr__histo__key_0", "type" AS "aggr__histo__0__key_0"))
-			WHERE "aggr__histo__0__order_1_rank"<=11
-			ORDER BY "aggr__histo__order_1_rank" ASC, "aggr__histo__0__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__histo__key_0`, `aggr__histo__count`,\n" +
+			"  `aggr__histo__0__parent_count`, `aggr__histo__0__key_0`,\n" +
+			"  `aggr__histo__0__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__histo__key_0`, `aggr__histo__count`,\n" +
+			"    `aggr__histo__0__parent_count`, `aggr__histo__0__key_0`,\n" +
+			"    `aggr__histo__0__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__histo__key_0` ASC) AS\n" +
+			"    `aggr__histo__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__histo__key_0` ORDER BY\n" +
+			"    `aggr__histo__0__count` DESC, `aggr__histo__0__key_0` ASC) AS\n" +
+			"    `aggr__histo__0__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT toInt64(toUnixTimestamp64Milli(`customer_birth_date_datetime64`) / 30000) AS\n" +
+			"      `aggr__histo__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__histo__key_0`) AS `aggr__histo__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__histo__key_0`) AS\n" +
+			"      `aggr__histo__0__parent_count`, `type` AS `aggr__histo__0__key_0`,\n" +
+			"      count(*) AS `aggr__histo__0__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY toInt64(toUnixTimestamp64Milli(`customer_birth_date_datetime64`) / 30000) AS\n" +
+			"      `aggr__histo__key_0`, `type` AS `aggr__histo__0__key_0`))\n" +
+			"WHERE `aggr__histo__0__order_1_rank`<=11\n" +
+			"ORDER BY `aggr__histo__order_1_rank` ASC, `aggr__histo__0__order_1_rank` ASC",
 	},
 	{ // [62]
 		TestName: "date_histogram with null values, missing parameter (DateTime, not DateTime64), and some subaggregation",
@@ -3744,33 +3632,31 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__histo__0__count", int64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__histo__key_0", "aggr__histo__count",
-			  "aggr__histo__0__parent_count", "aggr__histo__0__key_0",
-			  "aggr__histo__0__count"
-			FROM (
-			  SELECT "aggr__histo__key_0", "aggr__histo__count",
-				"aggr__histo__0__parent_count", "aggr__histo__0__key_0",
-				"aggr__histo__0__count",
-				dense_rank() OVER (ORDER BY "aggr__histo__key_0" ASC) AS
-				"aggr__histo__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__histo__key_0" ORDER BY
-				"aggr__histo__0__count" DESC, "aggr__histo__0__key_0" ASC) AS
-				"aggr__histo__0__order_1_rank"
-			  FROM (
-				SELECT toInt64(toUnixTimestamp(COALESCE("customer_birth_date",
-				  fromUnixTimestamp(1706021760))) / 30) AS "aggr__histo__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__histo__key_0") AS
-				  "aggr__histo__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__histo__key_0") AS
-				  "aggr__histo__0__parent_count", "type" AS "aggr__histo__0__key_0",
-				  count(*) AS "aggr__histo__0__count"
-				FROM __quesma_table_name
-				GROUP BY toInt64(toUnixTimestamp(COALESCE("customer_birth_date",
-				  fromUnixTimestamp(1706021760))) / 30) AS "aggr__histo__key_0",
-				  "type" AS "aggr__histo__0__key_0"))
-			WHERE "aggr__histo__0__order_1_rank"<=11
-			ORDER BY "aggr__histo__order_1_rank" ASC, "aggr__histo__0__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__histo__key_0`, `aggr__histo__count`,\n" +
+			"  `aggr__histo__0__parent_count`, `aggr__histo__0__key_0`,\n" +
+			"  `aggr__histo__0__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__histo__key_0`, `aggr__histo__count`,\n" +
+			"    `aggr__histo__0__parent_count`, `aggr__histo__0__key_0`,\n" +
+			"    `aggr__histo__0__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__histo__key_0` ASC) AS\n" +
+			"    `aggr__histo__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__histo__key_0` ORDER BY\n" +
+			"    `aggr__histo__0__count` DESC, `aggr__histo__0__key_0` ASC) AS\n" +
+			"    `aggr__histo__0__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT toInt64(toUnixTimestamp(COALESCE(`customer_birth_date`,\n" +
+			"      fromUnixTimestamp(1706021760))) / 30) AS `aggr__histo__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__histo__key_0`) AS `aggr__histo__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__histo__key_0`) AS\n" +
+			"      `aggr__histo__0__parent_count`, `type` AS `aggr__histo__0__key_0`,\n" +
+			"      count(*) AS `aggr__histo__0__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY toInt64(toUnixTimestamp(COALESCE(`customer_birth_date`,\n" +
+			"      fromUnixTimestamp(1706021760))) / 30) AS `aggr__histo__key_0`,\n" +
+			"      `type` AS `aggr__histo__0__key_0`))\n" +
+			"WHERE `aggr__histo__0__order_1_rank`<=11\n" +
+			"ORDER BY `aggr__histo__order_1_rank` ASC, `aggr__histo__0__order_1_rank` ASC",
 	},
 	{ // [63]
 		TestName: "date_histogram with missing, different formats, and types (DateTime/DateTime64)",
@@ -3907,43 +3793,42 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__histo5__count", int64(4675)),
 			}}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp(COALESCE("customer_birth_date",
-			  fromUnixTimestamp(1706878800))) / 90) AS "aggr__histo1__key_0",
-			  count(*) AS "aggr__histo1__count"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp(COALESCE("customer_birth_date",
-			  fromUnixTimestamp(1706878800))) / 90) AS "aggr__histo1__key_0"
-			ORDER BY "aggr__histo1__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp(COALESCE(`customer_birth_date`,\n" +
+			"  fromUnixTimestamp(1706878800))) / 90) AS `aggr__histo1__key_0`,\n" +
+			"  count(*) AS `aggr__histo1__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY toInt64(toUnixTimestamp(COALESCE(`customer_birth_date`,\n" +
+			"  fromUnixTimestamp(1706878800))) / 90) AS `aggr__histo1__key_0`\n" +
+			"ORDER BY `aggr__histo1__key_0` ASC",
 		ExpectedAdditionalPancakeSQLs: []string{
-			`SELECT toInt64(toUnixTimestamp(COALESCE("customer_birth_date",
-			  fromUnixTimestamp(1706878800))) / 90) AS "aggr__histo2__key_0",
-			  count(*) AS "aggr__histo2__count"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp(COALESCE("customer_birth_date",
-			  fromUnixTimestamp(1706878800))) / 90) AS "aggr__histo2__key_0"
-			ORDER BY "aggr__histo2__key_0" ASC`,
-			`SELECT toInt64(toUnixTimestamp64Milli(COALESCE("customer_birth_date_datetime64",
-              fromUnixTimestamp64Milli(1706878800000))) / 90000) AS "aggr__histo3__key_0",
-			  count(*) AS "aggr__histo3__count"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp64Milli(COALESCE("customer_birth_date_datetime64",
-              fromUnixTimestamp64Milli(1706878800000))) / 90000) AS "aggr__histo3__key_0"
-			ORDER BY "aggr__histo3__key_0" ASC`,
-			`SELECT toInt64(toUnixTimestamp64Milli(COALESCE("customer_birth_date_datetime64",
-			  fromUnixTimestamp64Milli(1706853600000))) / 90000) AS "aggr__histo4__key_0",
-			  count(*) AS "aggr__histo4__count"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp64Milli(COALESCE("customer_birth_date_datetime64",
-			  fromUnixTimestamp64Milli(1706853600000))) / 90000) AS "aggr__histo4__key_0"
-			ORDER BY "aggr__histo4__key_0" ASC`,
-			`SELECT toInt64(toUnixTimestamp(COALESCE("customer_birth_date",
-			  fromUnixTimestamp(1706853600))) / 90) AS "aggr__histo5__key_0",
-			  count(*) AS "aggr__histo5__count"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp(COALESCE("customer_birth_date",
-			  fromUnixTimestamp(1706853600))) / 90) AS "aggr__histo5__key_0"
-			ORDER BY "aggr__histo5__key_0" ASC`,
+			"SELECT toInt64(toUnixTimestamp(COALESCE(`customer_birth_date`,\n" +
+				"  fromUnixTimestamp(1706878800))) / 90) AS `aggr__histo2__key_0`,\n" +
+				"  count(*) AS `aggr__histo2__count`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"GROUP BY toInt64(toUnixTimestamp(COALESCE(`customer_birth_date`,\n" +
+				"  fromUnixTimestamp(1706878800))) / 90) AS `aggr__histo2__key_0`\n" +
+				"ORDER BY `aggr__histo2__key_0` ASC",
+			"SELECT toInt64(toUnixTimestamp64Milli(COALESCE(`customer_birth_date_datetime64`,\n" +
+				"  fromUnixTimestamp64Milli(1706878800000))) / 90000) AS `aggr__histo3__key_0`,\n" +
+				"  count(*) AS `aggr__histo3__count`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"GROUP BY toInt64(toUnixTimestamp64Milli(COALESCE(`customer_birth_date_datetime64`,\n" +
+				"  fromUnixTimestamp64Milli(1706878800000))) / 90000) AS `aggr__histo3__key_0`\n" +
+				"ORDER BY `aggr__histo3__key_0` ASC",
+			"SELECT toInt64(toUnixTimestamp64Milli(COALESCE(`customer_birth_date_datetime64`,\n" +
+				"  fromUnixTimestamp64Milli(1706853600000))) / 90000) AS `aggr__histo4__key_0`,\n" +
+				"  count(*) AS `aggr__histo4__count`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"GROUP BY toInt64(toUnixTimestamp64Milli(COALESCE(`customer_birth_date_datetime64`,\n" +
+				"  fromUnixTimestamp64Milli(1706853600000))) / 90000) AS `aggr__histo4__key_0`\n" +
+				"ORDER BY `aggr__histo4__key_0` ASC",
+			"SELECT toInt64(toUnixTimestamp(COALESCE(`customer_birth_date`,\n" +
+				"  fromUnixTimestamp(1706853600))) / 90) AS `aggr__histo5__key_0`,\n" +
+				"  count(*) AS `aggr__histo5__count`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"GROUP BY toInt64(toUnixTimestamp(COALESCE(`customer_birth_date`,\n" +
+				"  fromUnixTimestamp(1706853600))) / 90) AS `aggr__histo5__key_0`\n" +
+				"ORDER BY `aggr__histo5__key_0` ASC",
 		},
 	},
 	{ // [64]
@@ -4101,28 +3986,27 @@ var AggregationTests2 = []AggregationTestCase{
 				}},
 			},
 		},
-		ExpectedPancakeSQL: `
-			SELECT floor("total_quantity"/0)*0 AS "aggr__interval-0__key_0",
-			  count(*) AS "aggr__interval-0__count"
-			FROM __quesma_table_name
-			GROUP BY floor("total_quantity"/0)*0 AS "aggr__interval-0__key_0"
-			ORDER BY "aggr__interval-0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT floor(`total_quantity`/0)*0 AS `aggr__interval-0__key_0`,\n" +
+			"  count(*) AS `aggr__interval-0__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY floor(`total_quantity`/0)*0 AS `aggr__interval-0__key_0`\n" +
+			"ORDER BY `aggr__interval-0__key_0` ASC",
 		ExpectedAdditionalPancakeSQLs: []string{
-			`SELECT floor("total_quantity"/0.5)*0.5 AS "aggr__interval-0.5__key_0",
-			  count(*) AS "aggr__interval-0.5__count"
-			FROM __quesma_table_name
-			GROUP BY floor("total_quantity"/0.5)*0.5 AS "aggr__interval-0.5__key_0"
-			ORDER BY "aggr__interval-0.5__key_0" ASC`,
-			`SELECT "total_quantity" AS "aggr__interval-1__key_0",
-			  count(*) AS "aggr__interval-1__count"
-			FROM __quesma_table_name
-			GROUP BY "total_quantity" AS "aggr__interval-1__key_0"
-			ORDER BY "aggr__interval-1__key_0" ASC`,
-			`SELECT floor("total_quantity"/2)*2 AS "aggr__interval-2__key_0",
-			  count(*) AS "aggr__interval-2__count"
-			FROM __quesma_table_name
-			GROUP BY floor("total_quantity"/2)*2 AS "aggr__interval-2__key_0"
-			ORDER BY "aggr__interval-2__key_0" ASC`,
+			"SELECT floor(`total_quantity`/0.5)*0.5 AS `aggr__interval-0.5__key_0`,\n" +
+				"  count(*) AS `aggr__interval-0.5__count`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"GROUP BY floor(`total_quantity`/0.5)*0.5 AS `aggr__interval-0.5__key_0`\n" +
+				"ORDER BY `aggr__interval-0.5__key_0` ASC",
+			"SELECT `total_quantity` AS `aggr__interval-1__key_0`,\n" +
+				"  count(*) AS `aggr__interval-1__count`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"GROUP BY `total_quantity` AS `aggr__interval-1__key_0`\n" +
+				"ORDER BY `aggr__interval-1__key_0` ASC",
+			"SELECT floor(`total_quantity`/2)*2 AS `aggr__interval-2__key_0`,\n" +
+				"  count(*) AS `aggr__interval-2__count`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"GROUP BY floor(`total_quantity`/2)*2 AS `aggr__interval-2__key_0`\n" +
+				"ORDER BY `aggr__interval-2__key_0` ASC",
 		},
 	},
 	{ // [65]
@@ -4280,13 +4164,12 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__my_buckets__count", int64(8)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "product" AS "aggr__my_buckets__key_0",
-              count(*) AS "aggr__my_buckets__count"
-            FROM __quesma_table_name
-            GROUP BY "product" AS "aggr__my_buckets__key_0"
-            ORDER BY "aggr__my_buckets__count" DESC, "aggr__my_buckets__key_0" ASC
-            LIMIT 11`,
+		ExpectedPancakeSQL: "SELECT `product` AS `aggr__my_buckets__key_0`,\n" +
+			"  count(*) AS `aggr__my_buckets__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY `product` AS `aggr__my_buckets__key_0`\n" +
+			"ORDER BY `aggr__my_buckets__count` DESC, `aggr__my_buckets__key_0` ASC\n" +
+			"LIMIT 11",
 	},
 	{ // [66]
 		TestName: "simplest composite: 1 histogram (with size)",
@@ -4375,13 +4258,12 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__my_buckets__count", 100000000),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT floor("price"/5)*5 AS "aggr__my_buckets__key_0",
-			  count(*) AS "aggr__my_buckets__count"
-			FROM __quesma_table_name
-			GROUP BY floor("price"/5)*5 AS "aggr__my_buckets__key_0"
-			ORDER BY "aggr__my_buckets__key_0" ASC
-			LIMIT 4`,
+		ExpectedPancakeSQL: "SELECT floor(`price`/5)*5 AS `aggr__my_buckets__key_0`,\n" +
+			"  count(*) AS `aggr__my_buckets__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY floor(`price`/5)*5 AS `aggr__my_buckets__key_0`\n" +
+			"ORDER BY `aggr__my_buckets__key_0` ASC\n" +
+			"LIMIT 4",
 	},
 	{ // [67]
 		TestName: "simplest composite: 1 date_histogram",
@@ -4460,14 +4342,13 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__my_buckets__count", int64(567)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp64Milli("timestamp") / 86400000) AS
-			  "aggr__my_buckets__key_0", count(*) AS "aggr__my_buckets__count"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp64Milli("timestamp") / 86400000) AS
-			  "aggr__my_buckets__key_0"
-			ORDER BY "aggr__my_buckets__key_0" ASC
-			LIMIT 3`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp64Milli(`timestamp`) / 86400000) AS\n" +
+			"  `aggr__my_buckets__key_0`, count(*) AS `aggr__my_buckets__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY toInt64(toUnixTimestamp64Milli(`timestamp`) / 86400000) AS\n" +
+			"  `aggr__my_buckets__key_0`\n" +
+			"ORDER BY `aggr__my_buckets__key_0` ASC\n" +
+			"LIMIT 3",
 	},
 	{ // [68]
 		TestName: "simplest composite: 1 geotile_grid",
@@ -4554,21 +4435,20 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__my_buckets__count", int64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT FLOOR(((__quesma_geo_lon("OriginLocation")+180)/360)*POWER(2, 8))
-              AS "aggr__my_buckets__key_0",
-			  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat("OriginLocation")))+(1/COS(RADIANS(
-			  __quesma_geo_lat("OriginLocation")))))/PI())/2*POWER(2, 8))
-			  AS "aggr__my_buckets__key_1", count(*) AS "aggr__my_buckets__count"
-			FROM __quesma_table_name
-			GROUP BY FLOOR(((__quesma_geo_lon("OriginLocation")+180)/360)*POWER(2, 8))
-			  AS "aggr__my_buckets__key_0",
-			  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat("OriginLocation")))+(1/COS(RADIANS(
-			  __quesma_geo_lat("OriginLocation")))))/PI())/2*POWER(2, 8))
-			  AS "aggr__my_buckets__key_1"
-			ORDER BY "aggr__my_buckets__count" DESC, "aggr__my_buckets__key_0" ASC,
-  			  "aggr__my_buckets__key_1" ASC
-			LIMIT 10`,
+		ExpectedPancakeSQL: "SELECT FLOOR(((__quesma_geo_lon(`OriginLocation`)+180)/360)*POWER(2, 8))\n" +
+			"  AS `aggr__my_buckets__key_0`,\n" +
+			"  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat(`OriginLocation`)))+(1/COS(RADIANS(\n" +
+			"  __quesma_geo_lat(`OriginLocation`)))))/PI())/2*POWER(2, 8))\n" +
+			"  AS `aggr__my_buckets__key_1`, count(*) AS `aggr__my_buckets__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY FLOOR(((__quesma_geo_lon(`OriginLocation`)+180)/360)*POWER(2, 8))\n" +
+			"  AS `aggr__my_buckets__key_0`,\n" +
+			"  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat(`OriginLocation`)))+(1/COS(RADIANS(\n" +
+			"  __quesma_geo_lat(`OriginLocation`)))))/PI())/2*POWER(2, 8))\n" +
+			"  AS `aggr__my_buckets__key_1`\n" +
+			"ORDER BY `aggr__my_buckets__count` DESC, `aggr__my_buckets__key_0` ASC,\n" +
+			"  `aggr__my_buckets__key_1` ASC\n" +
+			"LIMIT 10",
 	},
 	{ // [69]
 		TestName: "composite: 2 sources + 1 subaggregation",
@@ -4693,18 +4573,17 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("metric__my_buckets__the_avg_col_0", 100000000),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp64Milli("timestamp") / 86400000) AS
-			  "aggr__my_buckets__key_0", "product" AS "aggr__my_buckets__key_1",
-			  count(*) AS "aggr__my_buckets__count",
-			  avgOrNull("price") AS "metric__my_buckets__the_avg_col_0"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp64Milli("timestamp") / 86400000) AS
-			  "aggr__my_buckets__key_0",
-			  "product" AS "aggr__my_buckets__key_1"
-			ORDER BY "aggr__my_buckets__count" DESC, "aggr__my_buckets__key_0" ASC,
-			  "aggr__my_buckets__key_1" ASC
-			LIMIT 4`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp64Milli(`timestamp`) / 86400000) AS\n" +
+			"  `aggr__my_buckets__key_0`, `product` AS `aggr__my_buckets__key_1`,\n" +
+			"  count(*) AS `aggr__my_buckets__count`,\n" +
+			"  avgOrNull(`price`) AS `metric__my_buckets__the_avg_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY toInt64(toUnixTimestamp64Milli(`timestamp`) / 86400000) AS\n" +
+			"  `aggr__my_buckets__key_0`,\n" +
+			"  `product` AS `aggr__my_buckets__key_1`\n" +
+			"ORDER BY `aggr__my_buckets__count` DESC, `aggr__my_buckets__key_0` ASC,\n" +
+			"  `aggr__my_buckets__key_1` ASC\n" +
+			"LIMIT 4",
 	},
 	{ // [70]
 		TestName: "simplest terms with exclude (array of values)",
@@ -4754,14 +4633,13 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__1__count", int64(3261)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__1__parent_count",
-			  if("chess_goat" NOT IN tuple('Carlsen', 'Kasparov', 'Fis._er\'*'), "chess_goat", NULL)
-			  AS "aggr__1__key_0", count(*) AS "aggr__1__count"
-			FROM __quesma_table_name
-			GROUP BY if("chess_goat" NOT IN tuple('Carlsen', 'Kasparov', 'Fis._er\'*'), "chess_goat", NULL) AS "aggr__1__key_0"
-			ORDER BY "aggr__1__count" DESC, "aggr__1__key_0" ASC
-			LIMIT 3`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__1__parent_count`,\n" +
+			"  if(`chess_goat` NOT IN tuple('Carlsen', 'Kasparov', 'Fis._er\\'*'), `chess_goat`, NULL)\n" +
+			"  AS `aggr__1__key_0`, count(*) AS `aggr__1__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY if(`chess_goat` NOT IN tuple('Carlsen', 'Kasparov', 'Fis._er\\'*'), `chess_goat`, NULL) AS `aggr__1__key_0`\n" +
+			"ORDER BY `aggr__1__count` DESC, `aggr__1__key_0` ASC\n" +
+			"LIMIT 3",
 	},
 	{ // [71]
 		TestName: "simplest terms with exclude (single value, no regex)",
@@ -4807,14 +4685,13 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__1__count", int64(3300)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__1__parent_count",
-			  if("agi_birth_year"!=2025, "agi_birth_year", NULL) AS "aggr__1__key_0",
-			  count(*) AS "aggr__1__count"
-			FROM __quesma_table_name
-			GROUP BY if("agi_birth_year"!=2025, "agi_birth_year", NULL) AS "aggr__1__key_0"
-			ORDER BY "aggr__1__count" DESC, "aggr__1__key_0" ASC
-			LIMIT 2`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__1__parent_count`,\n" +
+			"  if(`agi_birth_year`!=2025, `agi_birth_year`, NULL) AS `aggr__1__key_0`,\n" +
+			"  count(*) AS `aggr__1__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY if(`agi_birth_year`!=2025, `agi_birth_year`, NULL) AS `aggr__1__key_0`\n" +
+			"ORDER BY `aggr__1__count` DESC, `aggr__1__key_0` ASC\n" +
+			"LIMIT 2",
 	},
 	{ // [72]
 		TestName: "simplest terms with exclude (empty array)",
@@ -4860,13 +4737,12 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__1__count", int64(300)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__1__parent_count",
-			  "agi_birth_year" AS "aggr__1__key_0", count(*) AS "aggr__1__count"
-			FROM __quesma_table_name
-			GROUP BY "agi_birth_year" AS "aggr__1__key_0"
-			ORDER BY "aggr__1__count" DESC, "aggr__1__key_0" ASC
-			LIMIT 2`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__1__parent_count`,\n" +
+			"  `agi_birth_year` AS `aggr__1__key_0`, count(*) AS `aggr__1__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY `agi_birth_year` AS `aggr__1__key_0`\n" +
+			"ORDER BY `aggr__1__count` DESC, `aggr__1__key_0` ASC\n" +
+			"LIMIT 2",
 	},
 	{ // [73]
 		TestName: "simplest terms with exclude (of strings), regression test",
@@ -4912,14 +4788,13 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__1__count", int64(300)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__1__parent_count",
-			  if("chess_goat" NOT IN 'abc', "chess_goat", NULL) AS "aggr__1__key_0",
-			  count(*) AS "aggr__1__count"
-			FROM __quesma_table_name
-			GROUP BY if("chess_goat" NOT IN 'abc', "chess_goat", NULL) AS "aggr__1__key_0"
-			ORDER BY "aggr__1__count" DESC, "aggr__1__key_0" ASC
-			LIMIT 2`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__1__parent_count`,\n" +
+			"  if(`chess_goat` NOT IN 'abc', `chess_goat`, NULL) AS `aggr__1__key_0`,\n" +
+			"  count(*) AS `aggr__1__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY if(`chess_goat` NOT IN 'abc', `chess_goat`, NULL) AS `aggr__1__key_0`\n" +
+			"ORDER BY `aggr__1__count` DESC, `aggr__1__key_0` ASC\n" +
+			"LIMIT 2",
 	},
 	{ // [74]
 		TestName: "terms with exclude (more complex, string field with exclude regex)",
@@ -4966,14 +4841,13 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__1__count", int64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__1__parent_count",
-			  if("chess_goat" NOT LIKE 'K%', "chess_goat", NULL) AS "aggr__1__key_0",
-			  count(*) AS "aggr__1__count"
-			FROM __quesma_table_name
-			GROUP BY if("chess_goat" NOT LIKE 'K%', "chess_goat", NULL) AS "aggr__1__key_0"
-			ORDER BY "aggr__1__count" DESC, "aggr__1__key_0" ASC
-			LIMIT 2`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__1__parent_count`,\n" +
+			"  if(`chess_goat` NOT LIKE 'K%', `chess_goat`, NULL) AS `aggr__1__key_0`,\n" +
+			"  count(*) AS `aggr__1__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY if(`chess_goat` NOT LIKE 'K%', `chess_goat`, NULL) AS `aggr__1__key_0`\n" +
+			"ORDER BY `aggr__1__count` DESC, `aggr__1__key_0` ASC\n" +
+			"LIMIT 2",
 	},
 	{ // [75]
 		TestName: "complex terms with exclude: nested terms + 2 metrics",
@@ -5118,40 +4992,38 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("metric__terms1__terms2__metric2_col_0", 99314.3501429406),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__terms1__parent_count", "aggr__terms1__key_0",
-			  "aggr__terms1__count", "metric__terms1__metric1_col_0",
-			  "aggr__terms1__terms2__parent_count", "aggr__terms1__terms2__key_0",
-			  "aggr__terms1__terms2__count", "metric__terms1__terms2__metric2_col_0"
-			FROM (
-			  SELECT "aggr__terms1__parent_count", "aggr__terms1__key_0",
-				"aggr__terms1__count", "metric__terms1__metric1_col_0",
-				"aggr__terms1__terms2__parent_count", "aggr__terms1__terms2__key_0",
-				"aggr__terms1__terms2__count", "metric__terms1__terms2__metric2_col_0",
-				dense_rank() OVER (ORDER BY "aggr__terms1__count" DESC,
-				"aggr__terms1__key_0" ASC) AS "aggr__terms1__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__terms1__key_0" ORDER BY
-				"aggr__terms1__terms2__count" DESC, "aggr__terms1__terms2__key_0" ASC) AS
-				"aggr__terms1__terms2__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__terms1__parent_count",
-				  if("Carrier" NOT IN tuple('a', 'b'), "Carrier", NULL) AS "aggr__terms1__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__terms1__key_0") AS
-				  "aggr__terms1__count",
-				  avgOrNullMerge(avgOrNullState("DistanceMiles")) OVER (PARTITION BY
-				  "aggr__terms1__key_0") AS "metric__terms1__metric1_col_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__terms1__key_0") AS
-				  "aggr__terms1__terms2__parent_count",
-				  "DestCityName" AS "aggr__terms1__terms2__key_0",
-				  count(*) AS "aggr__terms1__terms2__count",
-				  sumOrNull("AvgTicketPrice") AS "metric__terms1__terms2__metric2_col_0"
-				FROM __quesma_table_name
-				GROUP BY if("Carrier" NOT IN tuple('a', 'b'), "Carrier", NULL) AS
-				  "aggr__terms1__key_0", "DestCityName" AS "aggr__terms1__terms2__key_0"))
-			WHERE ("aggr__terms1__order_1_rank"<=3 AND "aggr__terms1__terms2__order_1_rank"
-			  <=2)
-			ORDER BY "aggr__terms1__order_1_rank" ASC,
-			  "aggr__terms1__terms2__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__terms1__parent_count`, `aggr__terms1__key_0`,\n" +
+			"  `aggr__terms1__count`, `metric__terms1__metric1_col_0`,\n" +
+			"  `aggr__terms1__terms2__parent_count`, `aggr__terms1__terms2__key_0`,\n" +
+			"  `aggr__terms1__terms2__count`, `metric__terms1__terms2__metric2_col_0`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__terms1__parent_count`, `aggr__terms1__key_0`,\n" +
+			"    `aggr__terms1__count`, `metric__terms1__metric1_col_0`,\n" +
+			"    `aggr__terms1__terms2__parent_count`, `aggr__terms1__terms2__key_0`,\n" +
+			"    `aggr__terms1__terms2__count`, `metric__terms1__terms2__metric2_col_0`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__terms1__count` DESC,\n" +
+			"    `aggr__terms1__key_0` ASC) AS `aggr__terms1__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__terms1__key_0` ORDER BY\n" +
+			"    `aggr__terms1__terms2__count` DESC, `aggr__terms1__terms2__key_0` ASC) AS\n" +
+			"    `aggr__terms1__terms2__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__terms1__parent_count`,\n" +
+			"      if(`Carrier` NOT IN tuple('a', 'b'), `Carrier`, NULL) AS `aggr__terms1__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__terms1__key_0`) AS\n" +
+			"      `aggr__terms1__count`,\n" +
+			"      avgOrNullMerge(avgOrNullState(`DistanceMiles`)) OVER (PARTITION BY\n" +
+			"      `aggr__terms1__key_0`) AS `metric__terms1__metric1_col_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__terms1__key_0`) AS\n" +
+			"      `aggr__terms1__terms2__parent_count`,\n" +
+			"      `DestCityName` AS `aggr__terms1__terms2__key_0`,\n" +
+			"      count(*) AS `aggr__terms1__terms2__count`,\n" +
+			"      sumOrNull(`AvgTicketPrice`) AS `metric__terms1__terms2__metric2_col_0`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY if(`Carrier` NOT IN tuple('a', 'b'), `Carrier`, NULL) AS\n" +
+			"      `aggr__terms1__key_0`, `DestCityName` AS `aggr__terms1__terms2__key_0`))\n" +
+			"WHERE (`aggr__terms1__order_1_rank`<=3 AND `aggr__terms1__terms2__order_1_rank`<=2)\n" +
+			"ORDER BY `aggr__terms1__order_1_rank` ASC,\n" +
+			"  `aggr__terms1__terms2__order_1_rank` ASC",
 	},
 	{ // [76]
 		TestName: "terms with exclude, but with branched off aggregation tree",
@@ -5265,16 +5137,15 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("metric__terms1__metric1_col_0", 6.20),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__terms1__parent_count",
-			  if("Carrier" NOT IN tuple('a', 'b'), "Carrier", NULL) AS "aggr__terms1__key_0"
-			  , count(*) AS "aggr__terms1__count",
-			  avgOrNull("DistanceMiles") AS "metric__terms1__metric1_col_0"
-			FROM __quesma_table_name
-			GROUP BY if("Carrier" NOT IN tuple('a', 'b'), "Carrier", NULL) AS
-			  "aggr__terms1__key_0"
-			ORDER BY "aggr__terms1__count" DESC, "aggr__terms1__key_0" ASC
-			LIMIT 2`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__terms1__parent_count`,\n" +
+			"  if(`Carrier` NOT IN tuple('a', 'b'), `Carrier`, NULL) AS `aggr__terms1__key_0`,\n" +
+			"  count(*) AS `aggr__terms1__count`,\n" +
+			"  avgOrNull(`DistanceMiles`) AS `metric__terms1__metric1_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY if(`Carrier` NOT IN tuple('a', 'b'), `Carrier`, NULL) AS\n" +
+			"  `aggr__terms1__key_0`\n" +
+			"ORDER BY `aggr__terms1__count` DESC, `aggr__terms1__key_0` ASC\n" +
+			"LIMIT 2",
 		ExpectedAdditionalPancakeResults: [][]model.QueryResultRow{{
 			{Cols: []model.QueryResultCol{
 				model.NewQueryResultCol("aggr__terms2__parent_count", int64(13014)),
@@ -5295,16 +5166,17 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("metric__terms2__metric1_col_0", 42),
 			}},
 		}},
-		ExpectedAdditionalPancakeSQLs: []string{`
-			SELECT sum(count(*)) OVER () AS "aggr__terms2__parent_count",
-			  if("Carrier" NOT IN tuple('Logstash Airways', '.*'), "Carrier", NULL) AS
-			  "aggr__terms2__key_0", count(*) AS "aggr__terms2__count",
-			  avgOrNull("DistanceMiles") AS "metric__terms2__metric1_col_0"
-			FROM __quesma_table_name
-			GROUP BY if("Carrier" NOT IN tuple('Logstash Airways', '.*'), "Carrier", NULL)
-			  AS "aggr__terms2__key_0"
-			ORDER BY "aggr__terms2__count" DESC, "aggr__terms2__key_0" ASC
-			LIMIT 3`},
+		ExpectedAdditionalPancakeSQLs: []string{
+			"SELECT sum(count(*)) OVER () AS `aggr__terms2__parent_count`,\n" +
+				"  if(`Carrier` NOT IN tuple('Logstash Airways', '.*'), `Carrier`, NULL) AS\n" +
+				"  `aggr__terms2__key_0`, count(*) AS `aggr__terms2__count`,\n" +
+				"  avgOrNull(`DistanceMiles`) AS `metric__terms2__metric1_col_0`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"GROUP BY if(`Carrier` NOT IN tuple('Logstash Airways', '.*'), `Carrier`, NULL)\n" +
+				"  AS `aggr__terms2__key_0`\n" +
+				"ORDER BY `aggr__terms2__count` DESC, `aggr__terms2__key_0` ASC\n" +
+				"LIMIT 3",
+		},
 	},
 	{ // [77]
 		TestName: "terms with bool field",
@@ -5360,13 +5232,12 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__terms__count", int64(2)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__terms__parent_count",
-			  "Cancelled" AS "aggr__terms__key_0", count(*) AS "aggr__terms__count"
-			FROM __quesma_table_name
-			GROUP BY "Cancelled" AS "aggr__terms__key_0"
-			ORDER BY "aggr__terms__count" DESC, "aggr__terms__key_0" ASC
-			LIMIT 3`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__terms__parent_count`,\n" +
+			"  `Cancelled` AS `aggr__terms__key_0`, count(*) AS `aggr__terms__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY `Cancelled` AS `aggr__terms__key_0`\n" +
+			"ORDER BY `aggr__terms__count` DESC, `aggr__terms__key_0` ASC\n" +
+			"LIMIT 3",
 	},
 	{ // [78]
 		TestName: `Escaping of ', \, \n, and \t in some example aggregations. No tests for other escape characters, e.g. \r or 'b. Add if needed.`,
@@ -5426,17 +5297,13 @@ var AggregationTests2 = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__terms__count", int64(5362)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT avgOrNullMerge(avgOrNullState("@timestamp's\\")) OVER () AS
-			  "metric__avg_col_0", sum(count(*)) OVER () AS "aggr__terms__parent_count",
-			  COALESCE("agent", 'quote \' and slash \\ Also
-') AS "aggr__terms__key_0",
-			  count(*) AS "aggr__terms__count"
-			FROM __quesma_table_name
-			GROUP BY COALESCE("agent", 'quote \' and slash \\ Also
-') AS
-			  "aggr__terms__key_0"
-			ORDER BY "aggr__terms__count" DESC, "aggr__terms__key_0" ASC
-			LIMIT 1`,
+		ExpectedPancakeSQL: "SELECT avgOrNullMerge(avgOrNullState(`@timestamp's\\`)) OVER () AS `metric__avg_col_0`, sum(count(*)) OVER () AS `aggr__terms__parent_count`, COALESCE(`agent`,'quote \\' and slash \\\\ Also\n') " +
+			"AS `aggr__terms__key_0`, count(*) AS `aggr__terms__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY COALESCE(`agent`, 'quote \\' and slash \\\\ Also\n" +
+			"') AS `\n" +
+			"aggr__terms__key_0` ORDER BY `aggr__terms__count` DESC, `aggr__terms__key_0`\n" +
+			" ASC \n" +
+			"LIMIT 1",
 	},
 }
diff --git a/platform/testdata/clients/clover.go b/platform/testdata/clients/clover.go
index 52ab9b493..14fdcb8de 100644
--- a/platform/testdata/clients/clover.go
+++ b/platform/testdata/clients/clover.go
@@ -156,31 +156,29 @@ var CloverTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__1__timeseries__count", int64(301)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__1__parent_count", "aggr__1__key_0", "aggr__1__count",
-			  "aggr__1__timeseries__key_0", "aggr__1__timeseries__count"
-			FROM (
-			  SELECT "aggr__1__parent_count", "aggr__1__key_0", "aggr__1__count",
-				"aggr__1__timeseries__key_0", "aggr__1__timeseries__count",
-				dense_rank() OVER (ORDER BY "aggr__1__count" DESC, "aggr__1__key_0" ASC) AS
-				"aggr__1__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__1__key_0" ORDER BY
-				"aggr__1__timeseries__key_0" ASC) AS "aggr__1__timeseries__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__1__parent_count",
-				  "nobel_laureate" AS "aggr__1__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__1__key_0") AS "aggr__1__count",
-				  toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone(
-				  "@timestamp", 'Europe/Warsaw'))*1000) / 2592000000) AS
-				  "aggr__1__timeseries__key_0", count(*) AS "aggr__1__timeseries__count"
-				FROM __quesma_table_name
-				GROUP BY "nobel_laureate" AS "aggr__1__key_0",
-				  toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone(
-				  "@timestamp", 'Europe/Warsaw'))*1000) / 2592000000) AS
-				  "aggr__1__timeseries__key_0"))
-			WHERE "aggr__1__order_1_rank"<=11
-			ORDER BY "aggr__1__order_1_rank" ASC, "aggr__1__timeseries__order_1_rank" ASC
-			`,
+		ExpectedPancakeSQL: "SELECT `aggr__1__parent_count`, `aggr__1__key_0`, `aggr__1__count`,\n" +
+			"  `aggr__1__timeseries__key_0`, `aggr__1__timeseries__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__1__parent_count`, `aggr__1__key_0`, `aggr__1__count`,\n" +
+			"    `aggr__1__timeseries__key_0`, `aggr__1__timeseries__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__1__count` DESC, `aggr__1__key_0` ASC) AS\n" +
+			"    `aggr__1__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__1__key_0` ORDER BY\n" +
+			"    `aggr__1__timeseries__key_0` ASC) AS `aggr__1__timeseries__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__1__parent_count`,\n" +
+			"      `nobel_laureate` AS `aggr__1__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__1__key_0`) AS `aggr__1__count`,\n" +
+			"      toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"      `@timestamp`, 'Europe/Warsaw'))*1000) / 2592000000) AS\n" +
+			"      `aggr__1__timeseries__key_0`, count(*) AS `aggr__1__timeseries__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY `nobel_laureate` AS `aggr__1__key_0`,\n" +
+			"      toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"      `@timestamp`, 'Europe/Warsaw'))*1000) / 2592000000) AS\n" +
+			"      `aggr__1__timeseries__key_0`))\n" +
+			"WHERE `aggr__1__order_1_rank`<=11\n" +
+			"ORDER BY `aggr__1__order_1_rank` ASC, `aggr__1__timeseries__order_1_rank` ASC",
 	},
 	{ // [1]
 		TestName: "multiple buckets_path",
@@ -344,14 +342,13 @@ var CloverTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__timeseries__a2-numerator_col_0", int64(202)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT count(*) AS "aggr__timeseries__count",
-			  countIf(true) AS "metric__timeseries__a2-denominator_col_0",
-			  countIf(NOT ("table.flower" __quesma_match 'clover')) AS
-			  "metric__timeseries__a2-numerator_col_0"
-			FROM __quesma_table_name
-			WHERE ("@timestamp">=fromUnixTimestamp64Milli(1728640683723) AND "@timestamp"<=
-			  fromUnixTimestamp64Milli(1728641583723))`,
+		ExpectedPancakeSQL: "SELECT count(*) AS `aggr__timeseries__count`,\n" +
+			"  countIf(true) AS `metric__timeseries__a2-denominator_col_0`,\n" +
+			"  countIf(NOT (`table.flower` __quesma_match 'clover')) AS\n" +
+			"  `metric__timeseries__a2-numerator_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`@timestamp`>=fromUnixTimestamp64Milli(1728640683723) AND `@timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1728641583723))",
 	},
 	{ // [2]
 		TestName: "simplest auto_date_histogram",
@@ -474,11 +471,10 @@ var CloverTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__timeseries__count", int64(202)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT count(*) AS "aggr__timeseries__count"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1728581627125) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1728635627125))`,
+		ExpectedPancakeSQL: "SELECT count(*) AS `aggr__timeseries__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1728581627125) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1728635627125))",
 		AdditionalAcceptableDifference: []string{"key_as_string"}, // timezone differences between local and github runs... There's always 2h difference between those, need to investigate. Maybe come back to .UTC() so there's no "+timezone" (e.g. +02:00)?
 	},
 	{ // [3]
@@ -642,14 +638,13 @@ var CloverTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__timeseries__f2-numerator_col_0", int64(178)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT count(*) AS "aggr__timeseries__count",
-			  countIf(true) AS "metric__timeseries__f2-denominator_col_0",
-			  countIf(NOT ("a.b_str" IS NOT NULL)) AS
-			  "metric__timeseries__f2-numerator_col_0"
-			FROM __quesma_table_name
-			WHERE ("@timestamp">=fromUnixTimestamp64Milli(1721399904783) AND "@timestamp"<=
-			  fromUnixTimestamp64Milli(1730475504783))`,
+		ExpectedPancakeSQL: "SELECT count(*) AS `aggr__timeseries__count`,\n" +
+			"  countIf(true) AS `metric__timeseries__f2-denominator_col_0`,\n" +
+			"  countIf(NOT (`a.b_str` IS NOT NULL)) AS\n" +
+			"  `metric__timeseries__f2-numerator_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`@timestamp`>=fromUnixTimestamp64Milli(1721399904783) AND `@timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1730475504783))",
 	},
 	{ // [4]
 		TestName: "todo",
@@ -760,17 +755,16 @@ var CloverTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__other-filter__3__count", int64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__other-filter__count",
-			  sum(count(*)) OVER () AS "aggr__other-filter__3__parent_count",
-			  "field" AS "aggr__other-filter__3__key_0",
-			  count(*) AS "aggr__other-filter__3__count"
-			FROM __quesma_table_name
-			WHERE ("a" __quesma_match '%b%' AND "c" __quesma_match '%d%')
-			GROUP BY "field" AS "aggr__other-filter__3__key_0"
-			ORDER BY "aggr__other-filter__3__count" DESC,
-			  "aggr__other-filter__3__key_0" ASC
-			LIMIT 16`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__other-filter__count`,\n" +
+			"  sum(count(*)) OVER () AS `aggr__other-filter__3__parent_count`,\n" +
+			"  `field` AS `aggr__other-filter__3__key_0`,\n" +
+			"  count(*) AS `aggr__other-filter__3__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`a` __quesma_match '%b%' AND `c` __quesma_match '%d%')\n" +
+			"GROUP BY `field` AS `aggr__other-filter__3__key_0`\n" +
+			"ORDER BY `aggr__other-filter__3__count` DESC,\n" +
+			"  `aggr__other-filter__3__key_0` ASC\n" +
+			"LIMIT 16",
 	},
 	{ // [5]
 		TestName: "todo",
@@ -944,17 +938,16 @@ var CloverTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__q__time_buckets__count", int64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__q__count",
-			  toInt64(toUnixTimestamp64Milli("@timestamp") / 604800000) AS
-			  "aggr__q__time_buckets__key_0", count(*) AS "aggr__q__time_buckets__count"
-			FROM __quesma_table_name
-			WHERE (("@timestamp">=fromUnixTimestamp64Milli(1728507729621) AND "@timestamp"<=
-			  fromUnixTimestamp64Milli(1728507732621)) AND "__quesma_fulltext_field_name"
-			  __quesma_match '%')
-			GROUP BY toInt64(toUnixTimestamp64Milli("@timestamp") / 604800000) AS
-			  "aggr__q__time_buckets__key_0"
-			ORDER BY "aggr__q__time_buckets__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__q__count`,\n" +
+			"  toInt64(toUnixTimestamp64Milli(`@timestamp`) / 604800000) AS\n" +
+			"  `aggr__q__time_buckets__key_0`, count(*) AS `aggr__q__time_buckets__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE ((`@timestamp`>=fromUnixTimestamp64Milli(1728507729621) AND `@timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1728507732621)) AND `__quesma_fulltext_field_name`\n" +
+			"  __quesma_match '%')\n" +
+			"GROUP BY toInt64(toUnixTimestamp64Milli(`@timestamp`) / 604800000) AS\n" +
+			"  `aggr__q__time_buckets__key_0`\n" +
+			"ORDER BY `aggr__q__time_buckets__key_0` ASC",
 	},
 	{ // [6]
 		TestName: "Clover",
@@ -1108,18 +1101,17 @@ var CloverTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__q__time_buckets__sum(count)_col_0", int64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__q__count",
-			  toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone(
-			  "@timestamp", 'Europe/Warsaw'))*1000) / 1800000) AS
-			  "aggr__q__time_buckets__key_0", count(*) AS "aggr__q__time_buckets__count",
-			  sumOrNull("count") AS "metric__q__time_buckets__sum(count)_col_0"
-			FROM __quesma_table_name
-			WHERE NOT ("str_field" __quesma_match 'CRASH')
-			GROUP BY toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone
-			  ("@timestamp", 'Europe/Warsaw'))*1000) / 1800000) AS
-			  "aggr__q__time_buckets__key_0"
-			ORDER BY "aggr__q__time_buckets__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__q__count`,\n" +
+			"  toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `@timestamp`, 'Europe/Warsaw'))*1000) / 1800000) AS\n" +
+			"  `aggr__q__time_buckets__key_0`, count(*) AS `aggr__q__time_buckets__count`,\n" +
+			"  sumOrNull(`count`) AS `metric__q__time_buckets__sum(count)_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE NOT (`str_field` __quesma_match 'CRASH')\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone\n" +
+			"  (`@timestamp`, 'Europe/Warsaw'))*1000) / 1800000) AS\n" +
+			"  `aggr__q__time_buckets__key_0`\n" +
+			"ORDER BY `aggr__q__time_buckets__key_0` ASC",
 	},
 	{
 		TestName: "Weird aggregation and filter names",
@@ -1208,16 +1200,15 @@ var CloverTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__q__time__cardinality(a.b.keyword)_col_0", int64(672)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__q__count",
-			  toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone(
-			  "@timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS "aggr__q__time__key_0",
-			  count(*) AS "aggr__q__time__count",
-			  uniq("a.b") AS "metric__q__time__cardinality(a.b.keyword)_col_0"
-			FROM __quesma_table_name
-			WHERE (("a.b" __quesma_match '%c%') OR "a.b" __quesma_match '%d%')
-			GROUP BY toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone
-			  ("@timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS "aggr__q__time__key_0"
-			ORDER BY "aggr__q__time__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__q__count`,\n" +
+			"  toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `@timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS `aggr__q__time__key_0`,\n" +
+			"  count(*) AS `aggr__q__time__count`,\n" +
+			"  uniq(`a.b`) AS `metric__q__time__cardinality(a.b.keyword)_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE ((`a.b` __quesma_match '%c%') OR `a.b` __quesma_match '%d%')\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone\n" +
+			"  (`@timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS `aggr__q__time__key_0`\n" +
+			"ORDER BY `aggr__q__time__key_0` ASC",
 	},
 }
diff --git a/platform/testdata/clients/kunkka.go b/platform/testdata/clients/kunkka.go
index 95d0df44b..4ce6ec23b 100644
--- a/platform/testdata/clients/kunkka.go
+++ b/platform/testdata/clients/kunkka.go
@@ -8,7 +8,7 @@ import (
 )
 
 const TableName = model.SingleTableNamePlaceHolder
-const fullTextFieldName = `"` + model.FullTextFieldNamePlaceHolder + `"`
+const fullTextFieldName = "`" + model.FullTextFieldNamePlaceHolder + "`"
 
 var KunkkaTests = []testdata.AggregationTestCase{
 	{ // [0]
@@ -169,17 +169,16 @@ var KunkkaTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__0__2-bucket__2-metric_col_0", 1.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp64Milli("@timestamp") / 3600000) AS
-			  "aggr__0__key_0", count(*) AS "aggr__0__count",
-			  sumOrNull("spent") AS "metric__0__1_col_0",
-			  countIf(` + fullTextFieldName + ` iLIKE '%started%') AS "aggr__0__2-bucket__count",
-			  sumOrNullIf("multiplier", ` + fullTextFieldName + ` iLIKE '%started%') AS
-			  "metric__0__2-bucket__2-metric_col_0"
-			FROM ` + TableName + `
-			GROUP BY toInt64(toUnixTimestamp64Milli("@timestamp") / 3600000) AS
-			  "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp64Milli(`@timestamp`) / 3600000) AS\n" +
+			"  `aggr__0__key_0`, count(*) AS `aggr__0__count`,\n" +
+			"  sumOrNull(`spent`) AS `metric__0__1_col_0`,\n" +
+			"  countIf(" + fullTextFieldName + " iLIKE '%started%') AS `aggr__0__2-bucket__count`,\n" +
+			"  sumOrNullIf(`multiplier`, " + fullTextFieldName + " iLIKE '%started%') AS\n" +
+			"  `metric__0__2-bucket__2-metric_col_0`\n" +
+			"FROM `" + TableName + "`\n" +
+			"GROUP BY toInt64(toUnixTimestamp64Milli(`@timestamp`) / 3600000) AS\n" +
+			"  `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__key_0` ASC",
 	},
 	{ // [1]
 		TestName: "it's the same input as in previous test, but with the original output from Elastic." +
@@ -352,18 +351,17 @@ var KunkkaTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__0__2-bucket__2-metric_col_0", 1.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone(
-  			  "@timestamp", 'Europe/Warsaw'))*1000) / 3600000) AS "aggr__0__key_0",
-			  count(*) AS "aggr__0__count",
-			  sumOrNull("spent") AS "metric__0__1_col_0",
-			  countIf(` + fullTextFieldName + ` iLIKE '%started%') AS "aggr__0__2-bucket__count",
-			  sumOrNullIf("multiplier", ` + fullTextFieldName + ` iLIKE '%started%') AS
-			  "metric__0__2-bucket__2-metric_col_0"
-			FROM ` + TableName + `
-			GROUP BY toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone(
-  			  "@timestamp", 'Europe/Warsaw'))*1000) / 3600000) AS "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `@timestamp`, 'Europe/Warsaw'))*1000) / 3600000) AS `aggr__0__key_0`,\n" +
+			"  count(*) AS `aggr__0__count`,\n" +
+			"  sumOrNull(`spent`) AS `metric__0__1_col_0`,\n" +
+			"  countIf(" + fullTextFieldName + " iLIKE '%started%') AS `aggr__0__2-bucket__count`,\n" +
+			"  sumOrNullIf(`multiplier`, " + fullTextFieldName + " iLIKE '%started%') AS\n" +
+			"  `metric__0__2-bucket__2-metric_col_0`\n" +
+			"FROM `" + TableName + "`\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `@timestamp`, 'Europe/Warsaw'))*1000) / 3600000) AS `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__key_0` ASC",
 	},
 	{ // [2]
 		TestName: "clients/kunkka/test_1, used to be broken before aggregations merge fix",
diff --git a/platform/testdata/clients/ophelia.go b/platform/testdata/clients/ophelia.go
index 662cc862b..ee0fae6cc 100644
--- a/platform/testdata/clients/ophelia.go
+++ b/platform/testdata/clients/ophelia.go
@@ -229,43 +229,42 @@ var OpheliaTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__8__4__count", int64(17)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-			  "aggr__2__8__parent_count", "aggr__2__8__key_0", "aggr__2__8__count",
-			  "aggr__2__8__4__parent_count", "aggr__2__8__4__key_0", "aggr__2__8__4__count"
-			FROM (
-			  SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-				"aggr__2__8__parent_count", "aggr__2__8__key_0", "aggr__2__8__count",
-				"aggr__2__8__4__parent_count", "aggr__2__8__4__key_0",
-				"aggr__2__8__4__count",
-				dense_rank() OVER (ORDER BY "aggr__2__count" DESC, "aggr__2__key_0" ASC) AS
-				"aggr__2__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"aggr__2__8__count" DESC, "aggr__2__8__key_0" ASC) AS
-				"aggr__2__8__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0", "aggr__2__8__key_0" ORDER
-				BY "aggr__2__8__4__count" DESC, "aggr__2__8__4__key_0" ASC) AS
-				"aggr__2__8__4__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-				  "surname" AS "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "aggr__2__8__parent_count",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__8__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__8__key_0") AS
-				  "aggr__2__8__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__8__key_0") AS
-				  "aggr__2__8__4__parent_count", "organName" AS "aggr__2__8__4__key_0",
-				  count(*) AS "aggr__2__8__4__count"
-				FROM __quesma_table_name
-				GROUP BY "surname" AS "aggr__2__key_0",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__8__key_0",
-				  "organName" AS "aggr__2__8__4__key_0"))
-			WHERE (("aggr__2__order_1_rank"<=201 AND "aggr__2__8__order_1_rank"<=20) AND
-			  "aggr__2__8__4__order_1_rank"<=2)
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__8__order_1_rank" ASC,
-			  "aggr__2__8__4__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"  `aggr__2__8__parent_count`, `aggr__2__8__key_0`, `aggr__2__8__count`,\n" +
+			"  `aggr__2__8__4__parent_count`, `aggr__2__8__4__key_0`, `aggr__2__8__4__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"    `aggr__2__8__parent_count`, `aggr__2__8__key_0`, `aggr__2__8__count`,\n" +
+			"    `aggr__2__8__4__parent_count`, `aggr__2__8__4__key_0`,\n" +
+			"    `aggr__2__8__4__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__count` DESC, `aggr__2__key_0` ASC) AS\n" +
+			"    `aggr__2__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+			"    `aggr__2__8__count` DESC, `aggr__2__8__key_0` ASC) AS\n" +
+			"    `aggr__2__8__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__8__key_0` ORDER\n" +
+			"    BY `aggr__2__8__4__count` DESC, `aggr__2__8__4__key_0` ASC) AS\n" +
+			"    `aggr__2__8__4__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n" +
+			"      `surname` AS `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS\n" +
+			"      `aggr__2__8__parent_count`,\n" +
+			"      COALESCE(`limbName`, '__missing__') AS `aggr__2__8__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__8__key_0`) AS\n" +
+			"      `aggr__2__8__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__8__key_0`) AS\n" +
+			"      `aggr__2__8__4__parent_count`, `organName` AS `aggr__2__8__4__key_0`,\n" +
+			"      count(*) AS `aggr__2__8__4__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY `surname` AS `aggr__2__key_0`,\n" +
+			"      COALESCE(`limbName`, '__missing__') AS `aggr__2__8__key_0`,\n" +
+			"      `organName` AS `aggr__2__8__4__key_0`))\n" +
+			"WHERE ((`aggr__2__order_1_rank`<=201 AND `aggr__2__8__order_1_rank`<=20) AND\n" +
+			"  `aggr__2__8__4__order_1_rank`<=2)\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__8__order_1_rank` ASC,\n" +
+			"  `aggr__2__8__4__order_1_rank` ASC",
 	},
 	{ // [1]
 		TestName: "Ophelia Test 2: triple terms + other aggregations + default order",
@@ -568,52 +567,51 @@ var OpheliaTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__8__4__5_col_0", 205408.48849999998),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-			  "metric__2__1_col_0", "aggr__2__8__parent_count", "aggr__2__8__key_0",
-			  "aggr__2__8__count", "metric__2__8__1_col_0", "aggr__2__8__4__parent_count",
-			  "aggr__2__8__4__key_0", "aggr__2__8__4__count", "metric__2__8__4__1_col_0",
-			  "metric__2__8__4__5_col_0"
-			FROM (
-			  SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-				"metric__2__1_col_0", "aggr__2__8__parent_count", "aggr__2__8__key_0",
-				"aggr__2__8__count", "metric__2__8__1_col_0", "aggr__2__8__4__parent_count",
-				"aggr__2__8__4__key_0", "aggr__2__8__4__count", "metric__2__8__4__1_col_0",
-				"metric__2__8__4__5_col_0",
-				dense_rank() OVER (ORDER BY "aggr__2__count" DESC, "aggr__2__key_0" ASC) AS
-				"aggr__2__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"aggr__2__8__count" DESC, "aggr__2__8__key_0" ASC) AS
-				"aggr__2__8__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0", "aggr__2__8__key_0" ORDER
-				BY "aggr__2__8__4__count" DESC, "aggr__2__8__4__key_0" ASC) AS
-				"aggr__2__8__4__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-				  "surname" AS "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  sumOrNull(sumOrNull("total")) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "metric__2__1_col_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "aggr__2__8__parent_count",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__8__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__8__key_0") AS
-				  "aggr__2__8__count",
-				  sumOrNull(sumOrNull("total")) OVER (PARTITION BY "aggr__2__key_0",
-				  "aggr__2__8__key_0") AS "metric__2__8__1_col_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__8__key_0") AS
-				  "aggr__2__8__4__parent_count", "organName" AS "aggr__2__8__4__key_0",
-				  count(*) AS "aggr__2__8__4__count",
-				  sumOrNull("total") AS "metric__2__8__4__1_col_0",
-				  sumOrNull("some") AS "metric__2__8__4__5_col_0"
-				FROM __quesma_table_name
-				GROUP BY "surname" AS "aggr__2__key_0",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__8__key_0",
-				  "organName" AS "aggr__2__8__4__key_0"))
-			WHERE (("aggr__2__order_1_rank"<=201 AND "aggr__2__8__order_1_rank"<=20) AND
-			  "aggr__2__8__4__order_1_rank"<=2)
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__8__order_1_rank" ASC,
-			  "aggr__2__8__4__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"  `metric__2__1_col_0`, `aggr__2__8__parent_count`, `aggr__2__8__key_0`,\n" +
+			"  `aggr__2__8__count`, `metric__2__8__1_col_0`, `aggr__2__8__4__parent_count`,\n" +
+			"  `aggr__2__8__4__key_0`, `aggr__2__8__4__count`, `metric__2__8__4__1_col_0`,\n" +
+			"  `metric__2__8__4__5_col_0`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"    `metric__2__1_col_0`, `aggr__2__8__parent_count`, `aggr__2__8__key_0`,\n" +
+			"    `aggr__2__8__count`, `metric__2__8__1_col_0`, `aggr__2__8__4__parent_count`,\n" +
+			"    `aggr__2__8__4__key_0`, `aggr__2__8__4__count`, `metric__2__8__4__1_col_0`,\n" +
+			"    `metric__2__8__4__5_col_0`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__count` DESC, `aggr__2__key_0` ASC) AS\n" +
+			"    `aggr__2__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+			"    `aggr__2__8__count` DESC, `aggr__2__8__key_0` ASC) AS\n" +
+			"    `aggr__2__8__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__8__key_0` ORDER\n" +
+			"    BY `aggr__2__8__4__count` DESC, `aggr__2__8__4__key_0` ASC) AS\n" +
+			"    `aggr__2__8__4__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n" +
+			"      `surname` AS `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      sumOrNull(sumOrNull(`total`)) OVER (PARTITION BY `aggr__2__key_0`) AS\n" +
+			"      `metric__2__1_col_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS\n" +
+			"      `aggr__2__8__parent_count`,\n" +
+			"      COALESCE(`limbName`, '__missing__') AS `aggr__2__8__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__8__key_0`) AS\n" +
+			"      `aggr__2__8__count`,\n" +
+			"      sumOrNull(sumOrNull(`total`)) OVER (PARTITION BY `aggr__2__key_0`,\n" +
+			"      `aggr__2__8__key_0`) AS `metric__2__8__1_col_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__8__key_0`) AS\n" +
+			"      `aggr__2__8__4__parent_count`, `organName` AS `aggr__2__8__4__key_0`,\n" +
+			"      count(*) AS `aggr__2__8__4__count`,\n" +
+			"      sumOrNull(`total`) AS `metric__2__8__4__1_col_0`,\n" +
+			"      sumOrNull(`some`) AS `metric__2__8__4__5_col_0`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY `surname` AS `aggr__2__key_0`,\n" +
+			"      COALESCE(`limbName`, '__missing__') AS `aggr__2__8__key_0`,\n" +
+			"      `organName` AS `aggr__2__8__4__key_0`))\n" +
+			"WHERE ((`aggr__2__order_1_rank`<=201 AND `aggr__2__8__order_1_rank`<=20) AND\n" +
+			"  `aggr__2__8__4__order_1_rank`<=2)\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__8__order_1_rank` ASC,\n" +
+			"  `aggr__2__8__4__order_1_rank` ASC",
 	},
 	{ // [2]
 		TestName: "Ophelia Test 3: 5x terms + a lot of other aggregations",
@@ -1003,91 +1001,7 @@ var OpheliaTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__7__8__4__3__6_col_0", -0.6),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-			  "metric__2__1_col_0", "aggr__2__7__parent_count", "aggr__2__7__key_0",
-			  "aggr__2__7__count", "metric__2__7__1_col_0", "aggr__2__7__8__parent_count",
-			  "aggr__2__7__8__key_0", "aggr__2__7__8__count", "metric__2__7__8__1_col_0",
-			  "aggr__2__7__8__4__parent_count", "aggr__2__7__8__4__key_0",
-			  "aggr__2__7__8__4__count", "metric__2__7__8__4__1_col_0",
-			  "aggr__2__7__8__4__3__parent_count", "aggr__2__7__8__4__3__key_0",
-			  "aggr__2__7__8__4__3__count", "metric__2__7__8__4__3__1_col_0",
-			  "metric__2__7__8__4__3__5_col_0", "metric__2__7__8__4__3__6_col_0"
-			FROM (
-			  SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-				"metric__2__1_col_0", "aggr__2__7__parent_count", "aggr__2__7__key_0",
-				"aggr__2__7__count", "metric__2__7__1_col_0", "aggr__2__7__8__parent_count",
-				"aggr__2__7__8__key_0", "aggr__2__7__8__count", "metric__2__7__8__1_col_0",
-				"aggr__2__7__8__4__parent_count", "aggr__2__7__8__4__key_0",
-				"aggr__2__7__8__4__count", "metric__2__7__8__4__1_col_0",
-				"aggr__2__7__8__4__3__parent_count", "aggr__2__7__8__4__3__key_0",
-				"aggr__2__7__8__4__3__count", "metric__2__7__8__4__3__1_col_0",
-				"metric__2__7__8__4__3__5_col_0", "metric__2__7__8__4__3__6_col_0",
-				dense_rank() OVER (ORDER BY "metric__2__1_col_0" DESC, "aggr__2__key_0" ASC)
-				AS "aggr__2__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"metric__2__7__1_col_0" DESC, "aggr__2__7__key_0" ASC) AS
-				"aggr__2__7__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0", "aggr__2__7__key_0" ORDER
-				BY "metric__2__7__8__1_col_0" DESC, "aggr__2__7__8__key_0" ASC) AS
-				"aggr__2__7__8__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0", "aggr__2__7__key_0",
-				"aggr__2__7__8__key_0" ORDER BY "metric__2__7__8__4__1_col_0" DESC,
-				"aggr__2__7__8__4__key_0" ASC) AS "aggr__2__7__8__4__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0", "aggr__2__7__key_0",
-				"aggr__2__7__8__key_0", "aggr__2__7__8__4__key_0" ORDER BY
-				"metric__2__7__8__4__3__1_col_0" DESC, "aggr__2__7__8__4__3__key_0" ASC) AS
-				"aggr__2__7__8__4__3__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-				  "surname" AS "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  sumOrNull(sumOrNull("total")) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "metric__2__1_col_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "aggr__2__7__parent_count",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__7__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__7__key_0") AS
-				  "aggr__2__7__count",
-				  sumOrNull(sumOrNull("total")) OVER (PARTITION BY "aggr__2__key_0",
-				  "aggr__2__7__key_0") AS "metric__2__7__1_col_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__7__key_0") AS
-				  "aggr__2__7__8__parent_count",
-				  COALESCE("organName", '__missing__') AS "aggr__2__7__8__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__7__key_0",
-				  "aggr__2__7__8__key_0") AS "aggr__2__7__8__count",
-				  sumOrNull(sumOrNull("total")) OVER (PARTITION BY "aggr__2__key_0",
-				  "aggr__2__7__key_0", "aggr__2__7__8__key_0") AS "metric__2__7__8__1_col_0"
-				  ,
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__7__key_0",
-				  "aggr__2__7__8__key_0") AS "aggr__2__7__8__4__parent_count",
-				  "doctorName" AS "aggr__2__7__8__4__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__7__key_0",
-				  "aggr__2__7__8__key_0", "aggr__2__7__8__4__key_0") AS
-				  "aggr__2__7__8__4__count",
-				  sumOrNull(sumOrNull("total")) OVER (PARTITION BY "aggr__2__key_0",
-				  "aggr__2__7__key_0", "aggr__2__7__8__key_0", "aggr__2__7__8__4__key_0") AS
-				  "metric__2__7__8__4__1_col_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__7__key_0",
-				  "aggr__2__7__8__key_0", "aggr__2__7__8__4__key_0") AS
-				  "aggr__2__7__8__4__3__parent_count",
-				  "height" AS "aggr__2__7__8__4__3__key_0",
-				  count(*) AS "aggr__2__7__8__4__3__count",
-				  sumOrNull("total") AS "metric__2__7__8__4__3__1_col_0",
-				  sumOrNull("some") AS "metric__2__7__8__4__3__5_col_0",
-				  sumOrNull("cost") AS "metric__2__7__8__4__3__6_col_0"
-				FROM __quesma_table_name
-				GROUP BY "surname" AS "aggr__2__key_0",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__7__key_0",
-				  COALESCE("organName", '__missing__') AS "aggr__2__7__8__key_0",
-				  "doctorName" AS "aggr__2__7__8__4__key_0",
-				  "height" AS "aggr__2__7__8__4__3__key_0"))
-			WHERE (((("aggr__2__order_1_rank"<=101 AND "aggr__2__7__order_1_rank"<=10) AND
-			  "aggr__2__7__8__order_1_rank"<=10) AND "aggr__2__7__8__4__order_1_rank"<=7)
-			  AND "aggr__2__7__8__4__3__order_1_rank"<=2)
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__7__order_1_rank" ASC,
-			  "aggr__2__7__8__order_1_rank" ASC, "aggr__2__7__8__4__order_1_rank" ASC,
-			  "aggr__2__7__8__4__3__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n          `metric__2__1_col_0`, `aggr__2__7__parent_count`, `aggr__2__7__key_0`,\n          `aggr__2__7__count`, `metric__2__7__1_col_0`, `aggr__2__7__8__parent_count`,\n          `aggr__2__7__8__key_0`, `aggr__2__7__8__count`, `metric__2__7__8__1_col_0`,\n          `aggr__2__7__8__4__parent_count`, `aggr__2__7__8__4__key_0`,\n          `aggr__2__7__8__4__count`, `metric__2__7__8__4__1_col_0`,\n          `aggr__2__7__8__4__3__parent_count`, `aggr__2__7__8__4__3__key_0`,\n          `aggr__2__7__8__4__3__count`, `metric__2__7__8__4__3__1_col_0`,\n          `metric__2__7__8__4__3__5_col_0`, `metric__2__7__8__4__3__6_col_0`\n        FROM (\n          SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n            `metric__2__1_col_0`, `aggr__2__7__parent_count`, `aggr__2__7__key_0`,\n            `aggr__2__7__count`, `metric__2__7__1_col_0`, `aggr__2__7__8__parent_count`,\n            `aggr__2__7__8__key_0`, `aggr__2__7__8__count`, `metric__2__7__8__1_col_0`,\n            `aggr__2__7__8__4__parent_count`, `aggr__2__7__8__4__key_0`,\n            `aggr__2__7__8__4__count`, `metric__2__7__8__4__1_col_0`,\n            `aggr__2__7__8__4__3__parent_count`, `aggr__2__7__8__4__3__key_0`,\n            `aggr__2__7__8__4__3__count`, `metric__2__7__8__4__3__1_col_0`,\n            `metric__2__7__8__4__3__5_col_0`, `metric__2__7__8__4__3__6_col_0`,\n            dense_rank() OVER (ORDER BY `metric__2__1_col_0` DESC, `aggr__2__key_0` ASC)\n            AS `aggr__2__order_1_rank`,\n            dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY `\n            metric__2__7__1_col_0` DESC, `aggr__2__7__key_0` ASC) AS `\n            aggr__2__7__order_1_rank`,\n            dense_rank() OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__7__key_0` ORDER\n            BY `metric__2__7__8__1_col_0` DESC, `aggr__2__7__8__key_0` ASC) AS `\n            aggr__2__7__8__order_1_rank`,\n            dense_rank() OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__7__key_0`, `\n            aggr__2__7__8__key_0` ORDER BY `metric__2__7__8__4__1_col_0` DESC, `\n            aggr__2__7__8__4__key_0` ASC) AS `aggr__2__7__8__4__order_1_rank`,\n            dense_rank() OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__7__key_0`, `\n            aggr__2__7__8__key_0`, `aggr__2__7__8__4__key_0` ORDER BY `\n            metric__2__7__8__4__3__1_col_0` DESC, `aggr__2__7__8__4__3__key_0` ASC) AS `\n            aggr__2__7__8__4__3__order_1_rank`\n          FROM (\n            SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n              `surname` AS `aggr__2__key_0`,\n              sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n              sumOrNull(sumOrNull(`total`)) OVER (PARTITION BY `aggr__2__key_0`) AS `\n              metric__2__1_col_0`,\n              sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `\n              aggr__2__7__parent_count`,\n              COALESCE(`limbName`, '__missing__') AS `aggr__2__7__key_0`,\n              sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__7__key_0`) AS\n              `aggr__2__7__count`,\n              sumOrNull(sumOrNull(`total`)) OVER (PARTITION BY `aggr__2__key_0`, `\n              aggr__2__7__key_0`) AS `metric__2__7__1_col_0`,\n              sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__7__key_0`) AS\n              `aggr__2__7__8__parent_count`,\n              COALESCE(`organName`, '__missing__') AS `aggr__2__7__8__key_0`,\n              sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__7__key_0`, `\n              aggr__2__7__8__key_0`) AS `aggr__2__7__8__count`,\n              sumOrNull(sumOrNull(`total`)) OVER (PARTITION BY `aggr__2__key_0`, `\n              aggr__2__7__key_0`, `aggr__2__7__8__key_0`) AS `metric__2__7__8__1_col_0`,\n              sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__7__key_0`, `\n              aggr__2__7__8__key_0`) AS `aggr__2__7__8__4__parent_count`,\n              `doctorName` AS `aggr__2__7__8__4__key_0`,\n              sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__7__key_0`, `\n              aggr__2__7__8__key_0`, `aggr__2__7__8__4__key_0`) AS `\n              aggr__2__7__8__4__count`,\n              sumOrNull(sumOrNull(`total`)) OVER (PARTITION BY `aggr__2__key_0`, `\n              aggr__2__7__key_0`, `aggr__2__7__8__key_0`, `aggr__2__7__8__4__key_0`) AS\n              `metric__2__7__8__4__1_col_0`,\n              sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__7__key_0`, `\n              aggr__2__7__8__key_0`, `aggr__2__7__8__4__key_0`) AS `\n              aggr__2__7__8__4__3__parent_count`,\n              `height` AS `aggr__2__7__8__4__3__key_0`,\n              count(*) AS `aggr__2__7__8__4__3__count`,\n              sumOrNull(`total`) AS `metric__2__7__8__4__3__1_col_0`,\n              sumOrNull(`some`) AS `metric__2__7__8__4__3__5_col_0`,\n              sumOrNull(`cost`) AS `metric__2__7__8__4__3__6_col_0`\n            FROM `__quesma_table_name`\n            GROUP BY `surname` AS `aggr__2__key_0`,\n              COALESCE(`limbName`, '__missing__') AS `aggr__2__7__key_0`,\n              COALESCE(`organName`, '__missing__') AS `aggr__2__7__8__key_0`,\n              `doctorName` AS `aggr__2__7__8__4__key_0`,\n              `height` AS `aggr__2__7__8__4__3__key_0`))\n        WHERE ((((`aggr__2__order_1_rank`<=101 AND `aggr__2__7__order_1_rank`<=10) AND `\n          aggr__2__7__8__order_1_rank`<=10) AND `aggr__2__7__8__4__order_1_rank`<=7) AND\n          `aggr__2__7__8__4__3__order_1_rank`<=2)\n        ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__7__order_1_rank` ASC,\n          `aggr__2__7__8__order_1_rank` ASC, `aggr__2__7__8__4__order_1_rank` ASC,\n          `aggr__2__7__8__4__3__order_1_rank` ASC",
 	},
 	{ // [3]
 		TestName: "Ophelia Test 4: triple terms + order by another aggregations",
@@ -1355,47 +1269,46 @@ var OpheliaTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__8__4__count", int64(17)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			 SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-			  "metric__2__1_col_0", "aggr__2__8__parent_count", "aggr__2__8__key_0",
-			  "aggr__2__8__count", "metric__2__8__1_col_0", "aggr__2__8__4__parent_count",
-			  "aggr__2__8__4__key_0", "aggr__2__8__4__count"
-			FROM (
-			  SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-				"metric__2__1_col_0", "aggr__2__8__parent_count", "aggr__2__8__key_0",
-				"aggr__2__8__count", "metric__2__8__1_col_0", "aggr__2__8__4__parent_count",
-				"aggr__2__8__4__key_0", "aggr__2__8__4__count",
-				dense_rank() OVER (ORDER BY "metric__2__1_col_0" DESC, "aggr__2__key_0" ASC)
-				AS "aggr__2__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"metric__2__8__1_col_0" ASC, "aggr__2__8__key_0" ASC) AS
-				"aggr__2__8__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0", "aggr__2__8__key_0" ORDER
-				BY "aggr__2__8__4__key_0" DESC) AS "aggr__2__8__4__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-				  "surname" AS "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  avgOrNullMerge(avgOrNullState("total")) OVER (PARTITION BY
-				  "aggr__2__key_0") AS "metric__2__1_col_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "aggr__2__8__parent_count",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__8__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__8__key_0") AS
-				  "aggr__2__8__count",
-				  sumOrNull(sumOrNull("total")) OVER (PARTITION BY "aggr__2__key_0",
-				  "aggr__2__8__key_0") AS "metric__2__8__1_col_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__8__key_0") AS
-				  "aggr__2__8__4__parent_count", "organName" AS "aggr__2__8__4__key_0",
-				  count(*) AS "aggr__2__8__4__count"
-				FROM __quesma_table_name
-				GROUP BY "surname" AS "aggr__2__key_0",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__8__key_0",
-				  "organName" AS "aggr__2__8__4__key_0"))
-			WHERE (("aggr__2__order_1_rank"<=201 AND "aggr__2__8__order_1_rank"<=20) AND
-			  "aggr__2__8__4__order_1_rank"<=2)
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__8__order_1_rank" ASC,
-			  "aggr__2__8__4__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"  `metric__2__1_col_0`, `aggr__2__8__parent_count`, `aggr__2__8__key_0`,\n" +
+			"  `aggr__2__8__count`, `metric__2__8__1_col_0`, `aggr__2__8__4__parent_count`,\n" +
+			"  `aggr__2__8__4__key_0`, `aggr__2__8__4__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"    `metric__2__1_col_0`, `aggr__2__8__parent_count`, `aggr__2__8__key_0`,\n" +
+			"    `aggr__2__8__count`, `metric__2__8__1_col_0`, `aggr__2__8__4__parent_count`,\n" +
+			"    `aggr__2__8__4__key_0`, `aggr__2__8__4__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `metric__2__1_col_0` DESC, `aggr__2__key_0` ASC)\n" +
+			"    AS `aggr__2__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+			"    `metric__2__8__1_col_0` ASC, `aggr__2__8__key_0` ASC) AS\n" +
+			"    `aggr__2__8__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__8__key_0` ORDER\n" +
+			"    BY `aggr__2__8__4__key_0` DESC) AS `aggr__2__8__4__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n" +
+			"      `surname` AS `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      avgOrNullMerge(avgOrNullState(`total`)) OVER (PARTITION BY\n" +
+			"      `aggr__2__key_0`) AS `metric__2__1_col_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS\n" +
+			"      `aggr__2__8__parent_count`,\n" +
+			"      COALESCE(`limbName`, '__missing__') AS `aggr__2__8__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__8__key_0`) AS\n" +
+			"      `aggr__2__8__count`,\n" +
+			"      sumOrNull(sumOrNull(`total`)) OVER (PARTITION BY `aggr__2__key_0`,\n" +
+			"      `aggr__2__8__key_0`) AS `metric__2__8__1_col_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__8__key_0`) AS\n" +
+			"      `aggr__2__8__4__parent_count`, `organName` AS `aggr__2__8__4__key_0`,\n" +
+			"      count(*) AS `aggr__2__8__4__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY `surname` AS `aggr__2__key_0`,\n" +
+			"      COALESCE(`limbName`, '__missing__') AS `aggr__2__8__key_0`,\n" +
+			"      `organName` AS `aggr__2__8__4__key_0`))\n" +
+			"WHERE ((`aggr__2__order_1_rank`<=201 AND `aggr__2__8__order_1_rank`<=20) AND\n" +
+			"  `aggr__2__8__4__order_1_rank`<=2)\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__8__order_1_rank` ASC,\n" +
+			"  `aggr__2__8__4__order_1_rank` ASC",
 	},
 	{ // [4]
 		TestName: "Ophelia Test 5: 4x terms + order by another aggregations",
@@ -1704,58 +1617,57 @@ var OpheliaTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__8__4__5__count", int64(21)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-			  "aggr__2__8__parent_count", "aggr__2__8__key_0", "aggr__2__8__count",
-			  "metric__2__8__1_col_0", "aggr__2__8__4__parent_count",
-			  "aggr__2__8__4__key_0", "aggr__2__8__4__count",
-			  "aggr__2__8__4__5__parent_count", "aggr__2__8__4__5__key_0",
-			  "aggr__2__8__4__5__count"
-			FROM (
-			  SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-				"aggr__2__8__parent_count", "aggr__2__8__key_0", "aggr__2__8__count",
-				"metric__2__8__1_col_0", "aggr__2__8__4__parent_count",
-				"aggr__2__8__4__key_0", "aggr__2__8__4__count",
-				"aggr__2__8__4__5__parent_count", "aggr__2__8__4__5__key_0",
-				"aggr__2__8__4__5__count",
-				dense_rank() OVER (ORDER BY "aggr__2__key_0" DESC) AS
-				"aggr__2__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"metric__2__8__1_col_0" ASC, "aggr__2__8__key_0" ASC) AS
-				"aggr__2__8__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0", "aggr__2__8__key_0" ORDER
-				BY "aggr__2__8__4__key_0" DESC) AS "aggr__2__8__4__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0", "aggr__2__8__key_0",
-				"aggr__2__8__4__key_0" ORDER BY "aggr__2__8__4__5__count" DESC,
-				"aggr__2__8__4__5__key_0" ASC) AS "aggr__2__8__4__5__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-				  "surname" AS "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "aggr__2__8__parent_count",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__8__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__8__key_0") AS
-				  "aggr__2__8__count",
-				  sumOrNull(sumOrNull("total")) OVER (PARTITION BY "aggr__2__key_0",
-				  "aggr__2__8__key_0") AS "metric__2__8__1_col_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__8__key_0") AS
-				  "aggr__2__8__4__parent_count", "organName" AS "aggr__2__8__4__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__8__key_0",
-				  "aggr__2__8__4__key_0") AS "aggr__2__8__4__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__8__key_0",
-				  "aggr__2__8__4__key_0") AS "aggr__2__8__4__5__parent_count",
-				  "organName" AS "aggr__2__8__4__5__key_0",
-				  count(*) AS "aggr__2__8__4__5__count"
-				FROM __quesma_table_name
-				GROUP BY "surname" AS "aggr__2__key_0",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__8__key_0",
-				  "organName" AS "aggr__2__8__4__key_0",
-				  "organName" AS "aggr__2__8__4__5__key_0"))
-			WHERE ((("aggr__2__order_1_rank"<=201 AND "aggr__2__8__order_1_rank"<=20) AND
-			  "aggr__2__8__4__order_1_rank"<=2) AND "aggr__2__8__4__5__order_1_rank"<=3)
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__8__order_1_rank" ASC,
-			  "aggr__2__8__4__order_1_rank" ASC, "aggr__2__8__4__5__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"  `aggr__2__8__parent_count`, `aggr__2__8__key_0`, `aggr__2__8__count`,\n" +
+			"  `metric__2__8__1_col_0`, `aggr__2__8__4__parent_count`,\n" +
+			"  `aggr__2__8__4__key_0`, `aggr__2__8__4__count`,\n" +
+			"  `aggr__2__8__4__5__parent_count`, `aggr__2__8__4__5__key_0`,\n" +
+			"  `aggr__2__8__4__5__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"    `aggr__2__8__parent_count`, `aggr__2__8__key_0`, `aggr__2__8__count`,\n" +
+			"    `metric__2__8__1_col_0`, `aggr__2__8__4__parent_count`,\n" +
+			"    `aggr__2__8__4__key_0`, `aggr__2__8__4__count`,\n" +
+			"    `aggr__2__8__4__5__parent_count`, `aggr__2__8__4__5__key_0`,\n" +
+			"    `aggr__2__8__4__5__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__key_0` DESC) AS\n" +
+			"    `aggr__2__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+			"    `metric__2__8__1_col_0` ASC, `aggr__2__8__key_0` ASC) AS\n" +
+			"    `aggr__2__8__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__8__key_0` ORDER\n" +
+			"    BY `aggr__2__8__4__key_0` DESC) AS `aggr__2__8__4__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__8__key_0`,\n" +
+			"    `aggr__2__8__4__key_0` ORDER BY `aggr__2__8__4__5__count` DESC,\n" +
+			"    `aggr__2__8__4__5__key_0` ASC) AS `aggr__2__8__4__5__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n" +
+			"      `surname` AS `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS\n" +
+			"      `aggr__2__8__parent_count`,\n" +
+			"      COALESCE(`limbName`, '__missing__') AS `aggr__2__8__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__8__key_0`) AS\n" +
+			"      `aggr__2__8__count`,\n" +
+			"      sumOrNull(sumOrNull(`total`)) OVER (PARTITION BY `aggr__2__key_0`,\n" +
+			"      `aggr__2__8__key_0`) AS `metric__2__8__1_col_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__8__key_0`) AS\n" +
+			"      `aggr__2__8__4__parent_count`, `organName` AS `aggr__2__8__4__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__8__key_0`,\n" +
+			"      `aggr__2__8__4__key_0`) AS `aggr__2__8__4__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__8__key_0`,\n" +
+			"      `aggr__2__8__4__key_0`) AS `aggr__2__8__4__5__parent_count`,\n" +
+			"      `organName` AS `aggr__2__8__4__5__key_0`,\n" +
+			"      count(*) AS `aggr__2__8__4__5__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY `surname` AS `aggr__2__key_0`,\n" +
+			"      COALESCE(`limbName`, '__missing__') AS `aggr__2__8__key_0`,\n" +
+			"      `organName` AS `aggr__2__8__4__key_0`,\n" +
+			"      `organName` AS `aggr__2__8__4__5__key_0`))\n" +
+			"WHERE (((`aggr__2__order_1_rank`<=201 AND `aggr__2__8__order_1_rank`<=20) AND\n" +
+			"  `aggr__2__8__4__order_1_rank`<=2) AND `aggr__2__8__4__5__order_1_rank`<=3)\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__8__order_1_rank` ASC,\n" +
+			"  `aggr__2__8__4__order_1_rank` ASC, `aggr__2__8__4__5__order_1_rank` ASC",
 	},
 	{ // [5]
 		TestName: "Ophelia Test 6: triple terms + other aggregations + order by another aggregations",
@@ -2067,52 +1979,51 @@ var OpheliaTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__8__4__5_col_0", 205408.48849999998),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-			  "metric__2__1_col_0", "aggr__2__8__parent_count", "aggr__2__8__key_0",
-			  "aggr__2__8__count", "metric__2__8__1_col_0", "aggr__2__8__4__parent_count",
-			  "aggr__2__8__4__key_0", "aggr__2__8__4__count", "metric__2__8__4__1_col_0",
-			  "metric__2__8__4__5_col_0"
-			FROM (
-			  SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-				"metric__2__1_col_0", "aggr__2__8__parent_count", "aggr__2__8__key_0",
-				"aggr__2__8__count", "metric__2__8__1_col_0", "aggr__2__8__4__parent_count",
-				"aggr__2__8__4__key_0", "aggr__2__8__4__count", "metric__2__8__4__1_col_0",
-				"metric__2__8__4__5_col_0",
-				dense_rank() OVER (ORDER BY "metric__2__1_col_0" DESC, "aggr__2__key_0" ASC)
-				AS "aggr__2__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"metric__2__8__1_col_0" DESC, "aggr__2__8__key_0" ASC) AS
-				"aggr__2__8__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0", "aggr__2__8__key_0" ORDER
-				BY "metric__2__8__4__1_col_0" DESC, "aggr__2__8__4__key_0" ASC) AS
-				"aggr__2__8__4__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-				  "surname" AS "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  sumOrNull(sumOrNull("total")) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "metric__2__1_col_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "aggr__2__8__parent_count",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__8__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__8__key_0") AS
-				  "aggr__2__8__count",
-				  sumOrNull(sumOrNull("total")) OVER (PARTITION BY "aggr__2__key_0",
-				  "aggr__2__8__key_0") AS "metric__2__8__1_col_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__8__key_0") AS
-				  "aggr__2__8__4__parent_count", "organName" AS "aggr__2__8__4__key_0",
-				  count(*) AS "aggr__2__8__4__count",
-				  sumOrNull("total") AS "metric__2__8__4__1_col_0",
-				  sumOrNull("some") AS "metric__2__8__4__5_col_0"
-				FROM __quesma_table_name
-				GROUP BY "surname" AS "aggr__2__key_0",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__8__key_0",
-				  "organName" AS "aggr__2__8__4__key_0"))
-			WHERE (("aggr__2__order_1_rank"<=201 AND "aggr__2__8__order_1_rank"<=20) AND
-			  "aggr__2__8__4__order_1_rank"<=2)
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__8__order_1_rank" ASC,
-			  "aggr__2__8__4__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"  `metric__2__1_col_0`, `aggr__2__8__parent_count`, `aggr__2__8__key_0`,\n" +
+			"  `aggr__2__8__count`, `metric__2__8__1_col_0`, `aggr__2__8__4__parent_count`,\n" +
+			"  `aggr__2__8__4__key_0`, `aggr__2__8__4__count`, `metric__2__8__4__1_col_0`,\n" +
+			"  `metric__2__8__4__5_col_0`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"    `metric__2__1_col_0`, `aggr__2__8__parent_count`, `aggr__2__8__key_0`,\n" +
+			"    `aggr__2__8__count`, `metric__2__8__1_col_0`, `aggr__2__8__4__parent_count`,\n" +
+			"    `aggr__2__8__4__key_0`, `aggr__2__8__4__count`, `metric__2__8__4__1_col_0`,\n" +
+			"    `metric__2__8__4__5_col_0`,\n" +
+			"    dense_rank() OVER (ORDER BY `metric__2__1_col_0` DESC, `aggr__2__key_0` ASC) AS\n" +
+			"    `aggr__2__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+			"    `metric__2__8__1_col_0` DESC, `aggr__2__8__key_0` ASC) AS\n" +
+			"    `aggr__2__8__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__8__key_0` ORDER\n" +
+			"    BY `metric__2__8__4__1_col_0` DESC, `aggr__2__8__4__key_0` ASC) AS\n" +
+			"    `aggr__2__8__4__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n" +
+			"      `surname` AS `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      sumOrNull(sumOrNull(`total`)) OVER (PARTITION BY `aggr__2__key_0`) AS\n" +
+			"      `metric__2__1_col_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS\n" +
+			"      `aggr__2__8__parent_count`,\n" +
+			"      COALESCE(`limbName`, '__missing__') AS `aggr__2__8__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__8__key_0`) AS\n" +
+			"      `aggr__2__8__count`,\n" +
+			"      sumOrNull(sumOrNull(`total`)) OVER (PARTITION BY `aggr__2__key_0`,\n" +
+			"      `aggr__2__8__key_0`) AS `metric__2__8__1_col_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__8__key_0`) AS\n" +
+			"      `aggr__2__8__4__parent_count`, `organName` AS `aggr__2__8__4__key_0`,\n" +
+			"      count(*) AS `aggr__2__8__4__count`,\n" +
+			"      sumOrNull(`total`) AS `metric__2__8__4__1_col_0`,\n" +
+			"      sumOrNull(`some`) AS `metric__2__8__4__5_col_0`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY `surname` AS `aggr__2__key_0`,\n" +
+			"      COALESCE(`limbName`, '__missing__') AS `aggr__2__8__key_0`,\n" +
+			"      `organName` AS `aggr__2__8__4__key_0`))\n" +
+			"WHERE ((`aggr__2__order_1_rank`<=201 AND `aggr__2__8__order_1_rank`<=20) AND\n" +
+			"  `aggr__2__8__4__order_1_rank`<=2)\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__8__order_1_rank` ASC,\n" +
+			"  `aggr__2__8__4__order_1_rank` ASC",
 	},
 	{ // [6]
 		TestName: "Ophelia Test 7: 5x terms + a lot of other aggregations + different order bys",
@@ -2502,89 +2413,6 @@ var OpheliaTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__7__8__4__3__6_col_0", -0.6),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-			  "metric__2__1_col_0", "aggr__2__7__parent_count", "aggr__2__7__key_0",
-			  "aggr__2__7__count", "metric__2__7__1_col_0", "aggr__2__7__8__parent_count",
-			  "aggr__2__7__8__key_0", "aggr__2__7__8__count", "metric__2__7__8__1_col_0",
-			  "aggr__2__7__8__4__parent_count", "aggr__2__7__8__4__key_0",
-			  "aggr__2__7__8__4__count", "metric__2__7__8__4__1_col_0",
-			  "aggr__2__7__8__4__3__parent_count", "aggr__2__7__8__4__3__key_0",
-			  "aggr__2__7__8__4__3__count", "metric__2__7__8__4__3__1_col_0",
-			  "metric__2__7__8__4__3__5_col_0", "metric__2__7__8__4__3__6_col_0"
-			FROM (
-			  SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-				"metric__2__1_col_0", "aggr__2__7__parent_count", "aggr__2__7__key_0",
-				"aggr__2__7__count", "metric__2__7__1_col_0", "aggr__2__7__8__parent_count",
-				"aggr__2__7__8__key_0", "aggr__2__7__8__count", "metric__2__7__8__1_col_0",
-				"aggr__2__7__8__4__parent_count", "aggr__2__7__8__4__key_0",
-				"aggr__2__7__8__4__count", "metric__2__7__8__4__1_col_0",
-				"aggr__2__7__8__4__3__parent_count", "aggr__2__7__8__4__3__key_0",
-				"aggr__2__7__8__4__3__count", "metric__2__7__8__4__3__1_col_0",
-				"metric__2__7__8__4__3__5_col_0", "metric__2__7__8__4__3__6_col_0",
-				dense_rank() OVER (ORDER BY "metric__2__1_col_0" DESC, "aggr__2__key_0" ASC)
-				AS "aggr__2__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"aggr__2__7__key_0" ASC) AS "aggr__2__7__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0", "aggr__2__7__key_0" ORDER
-				BY "aggr__2__7__8__count" DESC, "aggr__2__7__8__key_0" ASC) AS
-				"aggr__2__7__8__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0", "aggr__2__7__key_0",
-				"aggr__2__7__8__key_0" ORDER BY "metric__2__7__8__4__1_col_0" DESC,
-				"aggr__2__7__8__4__key_0" ASC) AS "aggr__2__7__8__4__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0", "aggr__2__7__key_0",
-				"aggr__2__7__8__key_0", "aggr__2__7__8__4__key_0" ORDER BY
-				"metric__2__7__8__4__3__6_col_0" ASC, "aggr__2__7__8__4__3__key_0" ASC) AS
-				"aggr__2__7__8__4__3__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-				  "surname" AS "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  sumOrNull(sumOrNull("total")) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "metric__2__1_col_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "aggr__2__7__parent_count",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__7__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__7__key_0") AS
-				  "aggr__2__7__count",
-				  sumOrNull(sumOrNull("total")) OVER (PARTITION BY "aggr__2__key_0",
-				  "aggr__2__7__key_0") AS "metric__2__7__1_col_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__7__key_0") AS
-				  "aggr__2__7__8__parent_count",
-				  COALESCE("organName", '__missing__') AS "aggr__2__7__8__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__7__key_0",
-				  "aggr__2__7__8__key_0") AS "aggr__2__7__8__count",
-				  sumOrNull(sumOrNull("total")) OVER (PARTITION BY "aggr__2__key_0",
-				  "aggr__2__7__key_0", "aggr__2__7__8__key_0") AS "metric__2__7__8__1_col_0"
-				  ,
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__7__key_0",
-				  "aggr__2__7__8__key_0") AS "aggr__2__7__8__4__parent_count",
-				  "doctorName" AS "aggr__2__7__8__4__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__7__key_0",
-				  "aggr__2__7__8__key_0", "aggr__2__7__8__4__key_0") AS
-				  "aggr__2__7__8__4__count",
-				  sumOrNull(sumOrNull("total")) OVER (PARTITION BY "aggr__2__key_0",
-				  "aggr__2__7__key_0", "aggr__2__7__8__key_0", "aggr__2__7__8__4__key_0") AS
-				  "metric__2__7__8__4__1_col_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0", "aggr__2__7__key_0",
-				  "aggr__2__7__8__key_0", "aggr__2__7__8__4__key_0") AS
-				  "aggr__2__7__8__4__3__parent_count",
-				  "height" AS "aggr__2__7__8__4__3__key_0",
-				  count(*) AS "aggr__2__7__8__4__3__count",
-				  sumOrNull("total") AS "metric__2__7__8__4__3__1_col_0",
-				  sumOrNull("some") AS "metric__2__7__8__4__3__5_col_0",
-				  sumOrNull("cost") AS "metric__2__7__8__4__3__6_col_0"
-				FROM __quesma_table_name
-				GROUP BY "surname" AS "aggr__2__key_0",
-				  COALESCE("limbName", '__missing__') AS "aggr__2__7__key_0",
-				  COALESCE("organName", '__missing__') AS "aggr__2__7__8__key_0",
-				  "doctorName" AS "aggr__2__7__8__4__key_0",
-				  "height" AS "aggr__2__7__8__4__3__key_0"))
-			WHERE (((("aggr__2__order_1_rank"<=101 AND "aggr__2__7__order_1_rank"<=10) AND
-			  "aggr__2__7__8__order_1_rank"<=10) AND "aggr__2__7__8__4__order_1_rank"<=7)
-			  AND "aggr__2__7__8__4__3__order_1_rank"<=2)
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__7__order_1_rank" ASC,
-			  "aggr__2__7__8__order_1_rank" ASC, "aggr__2__7__8__4__order_1_rank" ASC,
-			  "aggr__2__7__8__4__3__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n          `metric__2__1_col_0`, `aggr__2__7__parent_count`, `aggr__2__7__key_0`,\n          `aggr__2__7__count`, `metric__2__7__1_col_0`, `aggr__2__7__8__parent_count`,\n          `aggr__2__7__8__key_0`, `aggr__2__7__8__count`, `metric__2__7__8__1_col_0`,\n          `aggr__2__7__8__4__parent_count`, `aggr__2__7__8__4__key_0`,\n          `aggr__2__7__8__4__count`, `metric__2__7__8__4__1_col_0`,\n          `aggr__2__7__8__4__3__parent_count`, `aggr__2__7__8__4__3__key_0`,\n          `aggr__2__7__8__4__3__count`, `metric__2__7__8__4__3__1_col_0`,\n          `metric__2__7__8__4__3__5_col_0`, `metric__2__7__8__4__3__6_col_0`\n        FROM (\n          SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n            `metric__2__1_col_0`, `aggr__2__7__parent_count`, `aggr__2__7__key_0`,\n            `aggr__2__7__count`, `metric__2__7__1_col_0`, `aggr__2__7__8__parent_count`,\n            `aggr__2__7__8__key_0`, `aggr__2__7__8__count`, `metric__2__7__8__1_col_0`,\n            `aggr__2__7__8__4__parent_count`, `aggr__2__7__8__4__key_0`,\n            `aggr__2__7__8__4__count`, `metric__2__7__8__4__1_col_0`,\n            `aggr__2__7__8__4__3__parent_count`, `aggr__2__7__8__4__3__key_0`,\n            `aggr__2__7__8__4__3__count`, `metric__2__7__8__4__3__1_col_0`,\n            `metric__2__7__8__4__3__5_col_0`, `metric__2__7__8__4__3__6_col_0`,\n            dense_rank() OVER (ORDER BY `metric__2__1_col_0` DESC, `aggr__2__key_0` ASC)\n            AS `aggr__2__order_1_rank`,\n            dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY `aggr__2__7__key_0\n            ` ASC) AS `aggr__2__7__order_1_rank`,\n            dense_rank() OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__7__key_0` ORDER\n            BY `aggr__2__7__8__count` DESC, `aggr__2__7__8__key_0` ASC) AS `\n            aggr__2__7__8__order_1_rank`,\n            dense_rank() OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__7__key_0`, `\n            aggr__2__7__8__key_0` ORDER BY `metric__2__7__8__4__1_col_0` DESC, `\n            aggr__2__7__8__4__key_0` ASC) AS `aggr__2__7__8__4__order_1_rank`,\n            dense_rank() OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__7__key_0`, `\n            aggr__2__7__8__key_0`, `aggr__2__7__8__4__key_0` ORDER BY `\n            metric__2__7__8__4__3__6_col_0` ASC, `aggr__2__7__8__4__3__key_0` ASC) AS `\n            aggr__2__7__8__4__3__order_1_rank`\n          FROM (\n            SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n              `surname` AS `aggr__2__key_0`,\n              sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n              sumOrNull(sumOrNull(`total`)) OVER (PARTITION BY `aggr__2__key_0`) AS `\n              metric__2__1_col_0`,\n              sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `\n              aggr__2__7__parent_count`,\n              COALESCE(`limbName`, '__missing__') AS `aggr__2__7__key_0`,\n              sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__7__key_0`) AS\n              `aggr__2__7__count`,\n              sumOrNull(sumOrNull(`total`)) OVER (PARTITION BY `aggr__2__key_0`, `\n              aggr__2__7__key_0`) AS `metric__2__7__1_col_0`,\n              sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__7__key_0`) AS\n              `aggr__2__7__8__parent_count`,\n              COALESCE(`organName`, '__missing__') AS `aggr__2__7__8__key_0`,\n              sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__7__key_0`, `\n              aggr__2__7__8__key_0`) AS `aggr__2__7__8__count`,\n              sumOrNull(sumOrNull(`total`)) OVER (PARTITION BY `aggr__2__key_0`, `\n              aggr__2__7__key_0`, `aggr__2__7__8__key_0`) AS `metric__2__7__8__1_col_0`,\n              sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__7__key_0`, `\n              aggr__2__7__8__key_0`) AS `aggr__2__7__8__4__parent_count`,\n              `doctorName` AS `aggr__2__7__8__4__key_0`,\n              sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__7__key_0`, `\n              aggr__2__7__8__key_0`, `aggr__2__7__8__4__key_0`) AS `\n              aggr__2__7__8__4__count`,\n              sumOrNull(sumOrNull(`total`)) OVER (PARTITION BY `aggr__2__key_0`, `\n              aggr__2__7__key_0`, `aggr__2__7__8__key_0`, `aggr__2__7__8__4__key_0`) AS\n              `metric__2__7__8__4__1_col_0`,\n              sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`, `aggr__2__7__key_0`, `\n              aggr__2__7__8__key_0`, `aggr__2__7__8__4__key_0`) AS `\n              aggr__2__7__8__4__3__parent_count`,\n              `height` AS `aggr__2__7__8__4__3__key_0`,\n              count(*) AS `aggr__2__7__8__4__3__count`,\n              sumOrNull(`total`) AS `metric__2__7__8__4__3__1_col_0`,\n              sumOrNull(`some`) AS `metric__2__7__8__4__3__5_col_0`,\n              sumOrNull(`cost`) AS `metric__2__7__8__4__3__6_col_0`\n            FROM `__quesma_table_name`\n            GROUP BY `surname` AS `aggr__2__key_0`,\n              COALESCE(`limbName`, '__missing__') AS `aggr__2__7__key_0`,\n              COALESCE(`organName`, '__missing__') AS `aggr__2__7__8__key_0`,\n              `doctorName` AS `aggr__2__7__8__4__key_0`,\n              `height` AS `aggr__2__7__8__4__3__key_0`))\n        WHERE ((((`aggr__2__order_1_rank`<=101 AND `aggr__2__7__order_1_rank`<=10) AND `\n          aggr__2__7__8__order_1_rank`<=10) AND `aggr__2__7__8__4__order_1_rank`<=7) AND\n          `aggr__2__7__8__4__3__order_1_rank`<=2)\n        ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__7__order_1_rank` ASC,\n          `aggr__2__7__8__order_1_rank` ASC, `aggr__2__7__8__4__order_1_rank` ASC,\n          `aggr__2__7__8__4__3__order_1_rank` ASC",
 	},
 }
diff --git a/platform/testdata/clients/turing.go b/platform/testdata/clients/turing.go
index 8534c612b..d324e65cd 100644
--- a/platform/testdata/clients/turing.go
+++ b/platform/testdata/clients/turing.go
@@ -45,30 +45,28 @@ var TuringTests = []testdata.AggregationTestCase{
 			}
 		}`,
 		ExpectedPancakeResults: []model.QueryResultRow{},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__key_0", "aggr__2__count", "aggr__2__3__parent_count",
-			  "aggr__2__3__key_0", "aggr__2__3__count"
-			FROM (
-			  SELECT "aggr__2__key_0", "aggr__2__count", "aggr__2__3__parent_count",
-				"aggr__2__3__key_0", "aggr__2__3__count",
-				dense_rank() OVER (ORDER BY "aggr__2__key_0" ASC) AS "aggr__2__order_1_rank"
-				,
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"aggr__2__3__count" DESC, "aggr__2__3__key_0" ASC) AS
-				"aggr__2__3__order_1_rank"
-			  FROM (
-				SELECT toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(
-				  toTimezone("@timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS
-				  "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "aggr__2__3__parent_count", "score" AS "aggr__2__3__key_0",
-				  count(*) AS "aggr__2__3__count"
-				FROM __quesma_table_name
-				GROUP BY toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(
-				  toTimezone("@timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS
-				  "aggr__2__key_0", "score" AS "aggr__2__3__key_0"))
-			WHERE "aggr__2__3__order_1_rank"<=6
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__3__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__key_0`, `aggr__2__count`, `aggr__2__3__parent_count`,\n" +
+			"  `aggr__2__3__key_0`, `aggr__2__3__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__key_0`, `aggr__2__count`, `aggr__2__3__parent_count`,\n" +
+			"    `aggr__2__3__key_0`, `aggr__2__3__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__key_0` ASC) AS `aggr__2__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+			"    `aggr__2__3__count` DESC, `aggr__2__3__key_0` ASC) AS\n" +
+			"    `aggr__2__3__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(\n" +
+			"      toTimezone(`@timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS\n" +
+			"      `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS\n" +
+			"      `aggr__2__3__parent_count`, `score` AS `aggr__2__3__key_0`,\n" +
+			"      count(*) AS `aggr__2__3__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(\n" +
+			"      toTimezone(`@timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS\n" +
+			"      `aggr__2__key_0`, `score` AS `aggr__2__3__key_0`))\n" +
+			"WHERE `aggr__2__3__order_1_rank`<=6\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__3__order_1_rank` ASC",
 	},
 }
diff --git a/platform/testdata/dashboard-1/aggregation_requests.go b/platform/testdata/dashboard-1/aggregation_requests.go
index b71b79937..8afcf148a 100644
--- a/platform/testdata/dashboard-1/aggregation_requests.go
+++ b/platform/testdata/dashboard-1/aggregation_requests.go
@@ -182,28 +182,27 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__0__1__2_col_0", 658654099),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__key_0", "aggr__0__count", "aggr__0__1__key_0",
-			  "aggr__0__1__count", "metric__0__1__2_col_0"
-			FROM (
-			  SELECT "aggr__0__key_0", "aggr__0__count", "aggr__0__1__key_0",
-				"aggr__0__1__count", "metric__0__1__2_col_0",
-				dense_rank() OVER (ORDER BY "aggr__0__key_0" ASC) AS "aggr__0__order_1_rank"
-				,
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0" ORDER BY "aggr__0__key_0"
-				ASC, "aggr__0__1__key_0" ASC) AS "aggr__0__1__order_1_rank"
-			  FROM (
-				SELECT floor("rspContentLen"/2e+06)*2e+06 AS "aggr__0__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS "aggr__0__count",
-				  floor("rspContentLen"/2e+06)*2e+06 AS "aggr__0__1__key_0",
-				  count(*) AS "aggr__0__1__count",
-				  avgOrNull("rspContentLen") AS "metric__0__1__2_col_0"
-				FROM __quesma_table_name
-				WHERE ("reqTimeSec">='2024-04-24T10:55:23.606Z' AND "reqTimeSec"<=
-				  '2024-04-24T11:10:23.606Z')
-				GROUP BY floor("rspContentLen"/2e+06)*2e+06 AS "aggr__0__key_0",
-				  floor("rspContentLen"/2e+06)*2e+06 AS "aggr__0__1__key_0"))
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__key_0`, `aggr__0__count`, `aggr__0__1__key_0`,\n" +
+			"  `aggr__0__1__count`, `metric__0__1__2_col_0`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__key_0`, `aggr__0__count`, `aggr__0__1__key_0`,\n" +
+			"    `aggr__0__1__count`, `metric__0__1__2_col_0`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__0__key_0` ASC) AS `aggr__0__order_1_rank`\n" +
+			"    ,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0` ORDER BY `aggr__0__key_0`\n" +
+			"    ASC, `aggr__0__1__key_0` ASC) AS `aggr__0__1__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT floor(`rspContentLen`/2e+06)*2e+06 AS `aggr__0__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS `aggr__0__count`,\n" +
+			"      floor(`rspContentLen`/2e+06)*2e+06 AS `aggr__0__1__key_0`,\n" +
+			"      count(*) AS `aggr__0__1__count`,\n" +
+			"      avgOrNull(`rspContentLen`) AS `metric__0__1__2_col_0`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    WHERE (`reqTimeSec`>='2024-04-24T10:55:23.606Z' AND `reqTimeSec`<= \n" +
+			"      '2024-04-24T11:10:23.606Z')\n" +
+			"    GROUP BY floor(`rspContentLen`/2e+06)*2e+06 AS `aggr__0__key_0`,\n" +
+			"      floor(`rspContentLen`/2e+06)*2e+06 AS `aggr__0__1__key_0`))\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC",
 	},
 	{ // [1]
 		TestName: "dashboard-1: bug, used to be infinite loop",
@@ -441,30 +440,29 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__0__1__2_col_0", []float64{83.8}),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__key_0", "aggr__0__count", "aggr__0__1__key_0",
-			  "aggr__0__1__count", "metric__0__1__2_col_0"
-			FROM (
-			  SELECT "aggr__0__key_0", "aggr__0__count", "aggr__0__1__key_0",
-				"aggr__0__1__count", "metric__0__1__2_col_0",
-				dense_rank() OVER (ORDER BY "aggr__0__key_0" ASC) AS "aggr__0__order_1_rank"
-				,
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0" ORDER BY
-				"aggr__0__1__key_0" ASC) AS "aggr__0__1__order_1_rank"
-			  FROM (
-				SELECT toInt64((toUnixTimestamp64Milli("reqTimeSec")+timeZoneOffset(
-				  toTimezone("reqTimeSec", 'Europe/Warsaw'))*1000) / 30000) AS
-				  "aggr__0__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS "aggr__0__count",
-				  floor("billingRegion"/0.5)*0.5 AS "aggr__0__1__key_0",
-				  count(*) AS "aggr__0__1__count",
-				  quantiles(0.950000)("latency") AS "metric__0__1__2_col_0"
-				FROM __quesma_table_name
-				WHERE ("reqTimeSec">='2024-04-24T11:15:46.279Z' AND "reqTimeSec"<=
-				  '2024-04-24T11:30:46.279Z')
-				GROUP BY toInt64((toUnixTimestamp64Milli("reqTimeSec")+timeZoneOffset(
-				  toTimezone("reqTimeSec", 'Europe/Warsaw'))*1000) / 30000) AS
-				  "aggr__0__key_0", floor("billingRegion"/0.5)*0.5 AS "aggr__0__1__key_0"))
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__key_0`, `aggr__0__count`, `aggr__0__1__key_0`,\n" +
+			"  `aggr__0__1__count`, `metric__0__1__2_col_0`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__key_0`, `aggr__0__count`, `aggr__0__1__key_0`,\n" +
+			"    `aggr__0__1__count`, `metric__0__1__2_col_0`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__0__key_0` ASC) AS `aggr__0__order_1_rank`\n" +
+			"    ,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0` ORDER BY\n" +
+			"    `aggr__0__1__key_0` ASC) AS `aggr__0__1__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT toInt64((toUnixTimestamp64Milli(`reqTimeSec`)+timeZoneOffset(\n" +
+			"      toTimezone(`reqTimeSec`, 'Europe/Warsaw'))*1000) / 30000) AS\n" +
+			"      `aggr__0__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS `aggr__0__count`,\n" +
+			"      floor(`billingRegion`/0.5)*0.5 AS `aggr__0__1__key_0`,\n" +
+			"      count(*) AS `aggr__0__1__count`,\n" +
+			"      quantiles(0.950000)(`latency`) AS `metric__0__1__2_col_0`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    WHERE (`reqTimeSec`>='2024-04-24T11:15:46.279Z' AND `reqTimeSec`<= \n" +
+			"      '2024-04-24T11:30:46.279Z')\n" +
+			"    GROUP BY toInt64((toUnixTimestamp64Milli(`reqTimeSec`)+timeZoneOffset(\n" +
+			"      toTimezone(`reqTimeSec`, 'Europe/Warsaw'))*1000) / 30000) AS\n" +
+			"      `aggr__0__key_0`, floor(`billingRegion`/0.5)*0.5 AS `aggr__0__1__key_0`))\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC",
 	},
 }
diff --git a/platform/testdata/dates.go b/platform/testdata/dates.go
index 301c89c4a..d597f0aea 100644
--- a/platform/testdata/dates.go
+++ b/platform/testdata/dates.go
@@ -105,18 +105,17 @@ var AggregationTestsWithDates = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__sampler__eventRate__count", int64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__sampler__count",
-			  toInt64(toUnixTimestamp(toStartOfWeek(toTimezone("order_date", 'UTC'))))*1000
-			  AS "aggr__sampler__eventRate__key_0",
-			  count(*) AS "aggr__sampler__eventRate__count"
-			FROM (
-			  SELECT "order_date"
-			  FROM __quesma_table_name
-			  LIMIT 20000)
-			GROUP BY toInt64(toUnixTimestamp(toStartOfWeek(toTimezone("order_date", 'UTC')))
-			  )*1000 AS "aggr__sampler__eventRate__key_0"
-			ORDER BY "aggr__sampler__eventRate__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__sampler__count`,\n" +
+			"  toInt64(toUnixTimestamp(toStartOfWeek(toTimezone(`order_date`, 'UTC'))))*1000\n" +
+			"  AS `aggr__sampler__eventRate__key_0`,\n" +
+			"  count(*) AS `aggr__sampler__eventRate__count`\n" +
+			"FROM (\n" +
+			"  SELECT `order_date`\n" +
+			"  FROM `__quesma_table_name`\n" +
+			"  LIMIT 20000)\n" +
+			"GROUP BY toInt64(toUnixTimestamp(toStartOfWeek(toTimezone(`order_date`, 'UTC')))\n" +
+			"  )*1000 AS `aggr__sampler__eventRate__key_0`\n" +
+			"ORDER BY `aggr__sampler__eventRate__key_0` ASC",
 	},
 	{ // [1]
 		TestName: "extended_bounds pre keys (timezone calculations most tricky to get right)",
@@ -312,16 +311,15 @@ var AggregationTestsWithDates = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__timeseries__count", int64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone(
-			  "@timestamp", 'Europe/Warsaw'))*1000) / 10000) AS "aggr__timeseries__key_0",
-			  count(*) AS "aggr__timeseries__count"
-			FROM __quesma_table_name
-			WHERE ("@timestamp">=fromUnixTimestamp64Milli(1730370296174) AND "@timestamp"<=
-			  fromUnixTimestamp64Milli(1730370596174))
-			GROUP BY toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone
-			  ("@timestamp", 'Europe/Warsaw'))*1000) / 10000) AS "aggr__timeseries__key_0"
-			ORDER BY "aggr__timeseries__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `@timestamp`, 'Europe/Warsaw'))*1000) / 10000) AS `aggr__timeseries__key_0`,\n" +
+			"  count(*) AS `aggr__timeseries__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`@timestamp`>=fromUnixTimestamp64Milli(1730370296174) AND `@timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1730370596174))\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone\n" +
+			"  (`@timestamp`, 'Europe/Warsaw'))*1000) / 10000) AS `aggr__timeseries__key_0`\n" +
+			"ORDER BY `aggr__timeseries__key_0` ASC",
 	},
 	{ // [2]
 		TestName: "extended_bounds post keys (timezone calculations most tricky to get right)",
@@ -493,16 +491,15 @@ var AggregationTestsWithDates = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__timeseries__count", int64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone(
-			  "@timestamp", 'Europe/Warsaw'))*1000) / 10000) AS "aggr__timeseries__key_0",
-			  count(*) AS "aggr__timeseries__count"
-			FROM __quesma_table_name
-			WHERE ("@timestamp">=fromUnixTimestamp64Milli(1730370296174) AND "@timestamp"<=
-			  fromUnixTimestamp64Milli(1730370596174))
-			GROUP BY toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone
-			  ("@timestamp", 'Europe/Warsaw'))*1000) / 10000) AS "aggr__timeseries__key_0"
-			ORDER BY "aggr__timeseries__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `@timestamp`, 'Europe/Warsaw'))*1000) / 10000) AS `aggr__timeseries__key_0`,\n" +
+			"  count(*) AS `aggr__timeseries__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`@timestamp`>=fromUnixTimestamp64Milli(1730370296174) AND `@timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1730370596174))\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone\n" +
+			"  (`@timestamp`, 'Europe/Warsaw'))*1000) / 10000) AS `aggr__timeseries__key_0`\n" +
+			"ORDER BY `aggr__timeseries__key_0` ASC",
 	},
 	{ // [3]
 		TestName: "empty results, we still should add empty buckets, because of the extended_bounds and min_doc_count defaulting to 0",
@@ -622,17 +619,16 @@ var AggregationTestsWithDates = []AggregationTestCase{
 			"start_time_in_millis": 1707486436397
 		}`,
 		ExpectedPancakeResults: []model.QueryResultRow{},
-		ExpectedPancakeSQL: `
-			SELECT toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone(
-			  "@timestamp", 'Europe/Warsaw'))*1000) / 86400000) AS "aggr__0__key_0",
-			  count(*) AS "aggr__0__count",
-			  sumOrNull("body_bytes_sent") AS "metric__0__1_col_0"
-			FROM __quesma_table_name
-			WHERE ("@timestamp">=fromUnixTimestamp64Milli(1259327903466) AND "@timestamp"<=
-			  fromUnixTimestamp64Milli(1732713503466))
-			GROUP BY toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone
-			  ("@timestamp", 'Europe/Warsaw'))*1000) / 86400000) AS "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `@timestamp`, 'Europe/Warsaw'))*1000) / 86400000) AS `aggr__0__key_0`,\n" +
+			"  count(*) AS `aggr__0__count`,\n" +
+			"  sumOrNull(`body_bytes_sent`) AS `metric__0__1_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`@timestamp`>=fromUnixTimestamp64Milli(1259327903466) AND `@timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1732713503466))\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone\n" +
+			"  (`@timestamp`, 'Europe/Warsaw'))*1000) / 86400000) AS `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__key_0` ASC",
 	},
 	{ // [4]
 		TestName: "date_histogram add in-between rows, calendar_interval: >= month (regression test)",
@@ -706,13 +702,12 @@ var AggregationTestsWithDates = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__sales_per_month__count", int64(2)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp(toStartOfMonth(toTimezone("date", 'UTC'))))*1000
-			  AS "aggr__sales_per_month__key_0", count(*) AS "aggr__sales_per_month__count"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp(toStartOfMonth(toTimezone("date", 'UTC'))))*
-			  1000 AS "aggr__sales_per_month__key_0"
-			ORDER BY "aggr__sales_per_month__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp(toStartOfMonth(toTimezone(`date`, 'UTC'))))*1000\n" +
+			"  AS `aggr__sales_per_month__key_0`, count(*) AS `aggr__sales_per_month__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY toInt64(toUnixTimestamp(toStartOfMonth(toTimezone(`date`, 'UTC'))))*\n" +
+			"  1000 AS `aggr__sales_per_month__key_0`\n" +
+			"ORDER BY `aggr__sales_per_month__key_0` ASC",
 	},
 	{ // [5]
 		TestName: "date_histogram add in-between rows, calendar_interval: >= month (regression test)",
@@ -762,14 +757,13 @@ var AggregationTestsWithDates = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__sales_per_quarter__count", int64(2)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp(toStartOfQuarter(toTimezone("date", 'UTC'))))*
-			  1000 AS "aggr__sales_per_quarter__key_0",
-			  count(*) AS "aggr__sales_per_quarter__count"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp(toStartOfQuarter(toTimezone("date", 'UTC'))))*
-			  1000 AS "aggr__sales_per_quarter__key_0"
-			ORDER BY "aggr__sales_per_quarter__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp(toStartOfQuarter(toTimezone(`date`, 'UTC'))))*\n" +
+			"  1000 AS `aggr__sales_per_quarter__key_0`,\n" +
+			"  count(*) AS `aggr__sales_per_quarter__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY toInt64(toUnixTimestamp(toStartOfQuarter(toTimezone(`date`, 'UTC'))))*\n" +
+			"  1000 AS `aggr__sales_per_quarter__key_0`\n" +
+			"ORDER BY `aggr__sales_per_quarter__key_0` ASC",
 	},
 	{ // [6]
 		TestName: "date_histogram add in-between rows, calendar_interval: >= month (regression test)",
@@ -819,14 +813,13 @@ var AggregationTestsWithDates = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__sales_per_year__count", int64(2)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp(toStartOfYear(toTimezone("date", 'UTC'))))*1000
-			  AS "aggr__sales_per_year__key_0",
-			  count(*) AS "aggr__sales_per_year__count"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp(toStartOfYear(toTimezone("date", 'UTC'))))*1000
-			  AS "aggr__sales_per_year__key_0"
-			ORDER BY "aggr__sales_per_year__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp(toStartOfYear(toTimezone(`date`, 'UTC'))))*1000\n" +
+			"  AS `aggr__sales_per_year__key_0`,\n" +
+			"  count(*) AS `aggr__sales_per_year__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY toInt64(toUnixTimestamp(toStartOfYear(toTimezone(`date`, 'UTC'))))*1000\n" +
+			"  AS `aggr__sales_per_year__key_0`\n" +
+			"ORDER BY `aggr__sales_per_year__key_0` ASC",
 	},
 	{ // [7]
 		TestName: "turing 1 - painless script in terms",
@@ -941,30 +934,29 @@ var AggregationTestsWithDates = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__1__2__count", int64(6844)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__1__key_0", "aggr__1__count", "aggr__1__2__parent_count",
-              "aggr__1__2__key_0", "aggr__1__2__count"
-            FROM (
-              SELECT "aggr__1__key_0", "aggr__1__count", "aggr__1__2__parent_count",
-                "aggr__1__2__key_0", "aggr__1__2__count",
-                dense_rank() OVER (ORDER BY "aggr__1__key_0" ASC) AS "aggr__1__order_1_rank",
-                dense_rank() OVER (PARTITION BY "aggr__1__key_0" ORDER BY
-                "aggr__1__2__count" DESC, "aggr__1__2__key_0" ASC) AS
-                "aggr__1__2__order_1_rank"
-              FROM (
-                SELECT toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(
-                  toTimezone("@timestamp", 'Europe/Warsaw'))*1000) / 2592000000) AS
-                  "aggr__1__key_0",
-                  sum(count(*)) OVER (PARTITION BY "aggr__1__key_0") AS "aggr__1__count",
-                  sum(count(*)) OVER (PARTITION BY "aggr__1__key_0") AS
-                  "aggr__1__2__parent_count",
-                  "request_id"="origin_request_id" AS "aggr__1__2__key_0",
-                  count(*) AS "aggr__1__2__count"
-                FROM __quesma_table_name
-                GROUP BY toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(
-                  toTimezone("@timestamp", 'Europe/Warsaw'))*1000) / 2592000000) AS
-                  "aggr__1__key_0", "request_id"="origin_request_id" AS "aggr__1__2__key_0"))
-            WHERE "aggr__1__2__order_1_rank"<=6
-            ORDER BY "aggr__1__order_1_rank" ASC, "aggr__1__2__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__1__key_0`, `aggr__1__count`, `aggr__1__2__parent_count`,\n" +
+			"  `aggr__1__2__key_0`, `aggr__1__2__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__1__key_0`, `aggr__1__count`, `aggr__1__2__parent_count`,\n" +
+			"    `aggr__1__2__key_0`, `aggr__1__2__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__1__key_0` ASC) AS `aggr__1__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__1__key_0` ORDER BY\n" +
+			"    `aggr__1__2__count` DESC, `aggr__1__2__key_0` ASC) AS\n" +
+			"    `aggr__1__2__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(\n" +
+			"      toTimezone(`@timestamp`, 'Europe/Warsaw'))*1000) / 2592000000) AS\n" +
+			"      `aggr__1__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__1__key_0`) AS `aggr__1__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__1__key_0`) AS\n" +
+			"      `aggr__1__2__parent_count`,\n" +
+			"      `request_id`=`origin_request_id` AS `aggr__1__2__key_0`,\n" +
+			"      count(*) AS `aggr__1__2__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(\n" +
+			"      toTimezone(`@timestamp`, 'Europe/Warsaw'))*1000) / 2592000000) AS\n" +
+			"      `aggr__1__key_0`, `request_id`=`origin_request_id` AS `aggr__1__2__key_0`))\n" +
+			"WHERE `aggr__1__2__order_1_rank`<=6\n" +
+			"ORDER BY `aggr__1__order_1_rank` ASC, `aggr__1__2__order_1_rank` ASC",
 	},
 }
diff --git a/platform/testdata/facets_requests.go b/platform/testdata/facets_requests.go
index 834ea9a4d..c37dbf4c1 100644
--- a/platform/testdata/facets_requests.go
+++ b/platform/testdata/facets_requests.go
@@ -132,25 +132,24 @@ var TestsNumericFacets = []struct {
 			},
 			"start_time_in_millis": 0
 		}`,
-		ExpectedSql: `
-SELECT sum(count(*)) OVER () AS "metric____quesma_total_count_col_0",
-  sum(count(*)) OVER () AS "aggr__sample__count",
-  maxOrNull(maxOrNull("int64-field")) OVER () AS
-  "metric__sample__max_value_col_0",
-  minOrNull(minOrNull("int64-field")) OVER () AS
-  "metric__sample__min_value_col_0",
-  sum(count("int64-field")) OVER () AS "metric__sample__sample_count_col_0",
-  sum(count(*)) OVER () AS "aggr__sample__top_values__parent_count",
-  "int64-field" AS "aggr__sample__top_values__key_0",
-  count(*) AS "aggr__sample__top_values__count"
-FROM (
-  SELECT "int64-field"
-  FROM __quesma_table_name
-  LIMIT 20000)
-GROUP BY "int64-field" AS "aggr__sample__top_values__key_0"
-ORDER BY "aggr__sample__top_values__count" DESC,
-  "aggr__sample__top_values__key_0" ASC
-LIMIT 11`,
+		ExpectedSql: "SELECT sum(count(*)) OVER () AS `metric____quesma_total_count_col_0`,\n" +
+			"  sum(count(*)) OVER () AS `aggr__sample__count`,\n" +
+			"  maxOrNull(maxOrNull(`int64-field`)) OVER () AS\n" +
+			"  `metric__sample__max_value_col_0`,\n" +
+			"  minOrNull(minOrNull(`int64-field`)) OVER () AS\n" +
+			"  `metric__sample__min_value_col_0`,\n" +
+			"  sum(count(`int64-field`)) OVER () AS `metric__sample__sample_count_col_0`,\n" +
+			"  sum(count(*)) OVER () AS `aggr__sample__top_values__parent_count`,\n" +
+			"  `int64-field` AS `aggr__sample__top_values__key_0`,\n" +
+			"  count(*) AS `aggr__sample__top_values__count`\n" +
+			"FROM (\n" +
+			"  SELECT `int64-field`\n" +
+			"  FROM `__quesma_table_name`\n" +
+			"  LIMIT 20000)\n" +
+			"GROUP BY `int64-field` AS `aggr__sample__top_values__key_0`\n" +
+			"ORDER BY `aggr__sample__top_values__count` DESC,\n" +
+			"  `aggr__sample__top_values__key_0` ASC\n" +
+			"LIMIT 11",
 		NewResultRows: []model.QueryResultRow{
 			{Cols: []model.QueryResultCol{
 				model.NewQueryResultCol("metric____quesma_total_count_col_0", 2693),
diff --git a/platform/testdata/full_search_requests.go b/platform/testdata/full_search_requests.go
index 8b6d41f55..f8313b747 100644
--- a/platform/testdata/full_search_requests.go
+++ b/platform/testdata/full_search_requests.go
@@ -8,13 +8,13 @@ import (
 )
 
 func selectCnt(limit int) string {
-	return fmt.Sprintf(`SELECT count(*) AS "column_0" FROM (SELECT 1 FROM %s LIMIT %d)`, TableName, limit)
+	return fmt.Sprintf("SELECT count(*) AS `column_0` FROM (SELECT 1 FROM `%s` LIMIT %d)", TableName, limit)
 }
 func selectTotalCnt() string {
-	return fmt.Sprintf(`SELECT count(*) AS "column_0" FROM %s`, TableName)
+	return fmt.Sprintf("SELECT count(*) AS `column_0` FROM `%s`", TableName)
 }
 func selectStar(limit int) string {
-	return fmt.Sprintf("SELECT \"@timestamp\", \"message\" FROM %s LIMIT %d", TableName, limit)
+	return fmt.Sprintf("SELECT `@timestamp`, `message` FROM `%s` LIMIT %d", TableName, limit)
 }
 
 func resultCount(cnt int) []model.QueryResultRow {
@@ -423,31 +423,31 @@ var FullSearchRequests = []FullSearchTestCase{
 			}
 		}`,
 		ExpectedSQLs: []string{
-			`SELECT "metric____quesma_total_count_col_0", "aggr__2__key_0", "aggr__2__count",
-			  "aggr__2__3__parent_count", "aggr__2__3__key_0", "aggr__2__3__count"
-			FROM (
-			  SELECT "metric____quesma_total_count_col_0", "aggr__2__key_0",
-				"aggr__2__count", "aggr__2__3__parent_count", "aggr__2__3__key_0",
-				"aggr__2__3__count",
-				dense_rank() OVER (ORDER BY "aggr__2__key_0" ASC) AS "aggr__2__order_1_rank"
-				,
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"aggr__2__3__count" DESC, "aggr__2__3__key_0" ASC) AS
-				"aggr__2__3__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "metric____quesma_total_count_col_0",
-				  toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(toTimezone(
-				  "@timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "aggr__2__3__parent_count", NULL AS "aggr__2__3__key_0",
-				  count(*) AS "aggr__2__3__count"
-				FROM __quesma_table_name
-				GROUP BY toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(
-				  toTimezone("@timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS
-				  "aggr__2__key_0", NULL AS "aggr__2__3__key_0"))
-			WHERE "aggr__2__3__order_1_rank"<=6
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__3__order_1_rank" ASC`,
+			"SELECT `metric____quesma_total_count_col_0`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+				"  `aggr__2__3__parent_count`, `aggr__2__3__key_0`, `aggr__2__3__count`\n" +
+				"FROM (\n" +
+				"  SELECT `metric____quesma_total_count_col_0`, `aggr__2__key_0`,\n" +
+				"    `aggr__2__count`, `aggr__2__3__parent_count`, `aggr__2__3__key_0`,\n" +
+				"    `aggr__2__3__count`,\n" +
+				"    dense_rank() OVER (ORDER BY `aggr__2__key_0` ASC) AS `aggr__2__order_1_rank`\n" +
+				"    ,\n" +
+				"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+				"    `aggr__2__3__count` DESC, `aggr__2__3__key_0` ASC) AS\n" +
+				"    `aggr__2__3__order_1_rank`\n" +
+				"  FROM (\n" +
+				"    SELECT sum(count(*)) OVER () AS `metric____quesma_total_count_col_0`,\n" +
+				"      toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(toTimezone(\n" +
+				"      `@timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS `aggr__2__key_0`,\n" +
+				"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+				"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS\n" +
+				"      `aggr__2__3__parent_count`, NULL AS `aggr__2__3__key_0`,\n" +
+				"      count(*) AS `aggr__2__3__count`\n" +
+				"    FROM `__quesma_table_name`\n" +
+				"    GROUP BY toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(\n" +
+				"      toTimezone(`@timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS\n" +
+				"      `aggr__2__key_0`, NULL AS `aggr__2__3__key_0`))\n" +
+				"WHERE `aggr__2__3__order_1_rank`<=6\n" +
+				"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__3__order_1_rank` ASC",
 		},
 		ExpectedSQLResults: [][]model.QueryResultRow{{}},
 	},
@@ -485,13 +485,13 @@ var FullSearchRequests = []FullSearchTestCase{
 			}
 		}`,
 		ExpectedSQLs: []string{
-			`SELECT sum(count(*)) OVER () AS "metric____quesma_total_count_col_0",
-			  sum(count(*)) OVER () AS "aggr__2__parent_count", NULL AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY NULL AS "aggr__2__key_0"
-			ORDER BY "aggr__2__count" DESC, "aggr__2__key_0" ASC
-			LIMIT 21`,
+			"SELECT sum(count(*)) OVER () AS `metric____quesma_total_count_col_0`,\n" +
+				"  sum(count(*)) OVER () AS `aggr__2__parent_count`, NULL AS `aggr__2__key_0`,\n" +
+				"  count(*) AS `aggr__2__count`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"GROUP BY NULL AS `aggr__2__key_0`\n" +
+				"ORDER BY `aggr__2__count` DESC, `aggr__2__key_0` ASC\n" +
+				"LIMIT 21",
 		},
 		ExpectedSQLResults: [][]model.QueryResultRow{{}},
 	},
diff --git a/platform/testdata/geo.go b/platform/testdata/geo.go
index 9ca1dc336..2eebd05d6 100644
--- a/platform/testdata/geo.go
+++ b/platform/testdata/geo.go
@@ -73,21 +73,20 @@ var AggregationTestsWithGeographicalCoordinates = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__large-grid__count", 283),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT FLOOR(((__quesma_geo_lon("OriginLocation")+180)/360)*POWER(2, 8)) AS
-			  "aggr__large-grid__key_0",
-			  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat("OriginLocation")))+(1/COS(RADIANS(
-			  __quesma_geo_lat("OriginLocation")))))/PI())/2*POWER(2, 8)) AS
-			  "aggr__large-grid__key_1", count(*) AS "aggr__large-grid__count"
-			FROM __quesma_table_name
-			GROUP BY FLOOR(((__quesma_geo_lon("OriginLocation")+180)/360)*POWER(2, 8)) AS
-			  "aggr__large-grid__key_0",
-			  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat("OriginLocation")))+(1/COS(RADIANS(
-			  __quesma_geo_lat("OriginLocation")))))/PI())/2*POWER(2, 8)) AS
-			  "aggr__large-grid__key_1"
-			ORDER BY "aggr__large-grid__count" DESC, "aggr__large-grid__key_0" ASC,
-			  "aggr__large-grid__key_1" ASC
-			LIMIT 10000`,
+		ExpectedPancakeSQL: "SELECT FLOOR(((__quesma_geo_lon(`OriginLocation`)+180)/360)*POWER(2, 8)) AS\n" +
+			"  `aggr__large-grid__key_0`,\n" +
+			"  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat(`OriginLocation`)))+(1/COS(RADIANS(\n" +
+			"  __quesma_geo_lat(`OriginLocation`)))))/PI())/2*POWER(2, 8)) AS\n" +
+			"  `aggr__large-grid__key_1`, count(*) AS `aggr__large-grid__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY FLOOR(((__quesma_geo_lon(`OriginLocation`)+180)/360)*POWER(2, 8)) AS\n" +
+			"  `aggr__large-grid__key_0`,\n" +
+			"  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat(`OriginLocation`)))+(1/COS(RADIANS(\n" +
+			"  __quesma_geo_lat(`OriginLocation`)))))/PI())/2*POWER(2, 8)) AS\n" +
+			"  `aggr__large-grid__key_1`\n" +
+			"ORDER BY `aggr__large-grid__count` DESC, `aggr__large-grid__key_0` ASC,\n" +
+			"  `aggr__large-grid__key_1` ASC\n" +
+			"LIMIT 10000",
 	},
 	{ // [1]
 		TestName: "geotile_grid with size",
@@ -158,20 +157,19 @@ var AggregationTestsWithGeographicalCoordinates = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__large-grid__count", 283),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT FLOOR(((__quesma_geo_lon("OriginLocation")+180)/360)*POWER(2, 8))
-			  AS "aggr__large-grid__key_0",
-			  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat("OriginLocation")))+(1/COS(RADIANS(
-			  __quesma_geo_lat("OriginLocation")))))/PI())/2*POWER(2, 8))
-			  AS "aggr__large-grid__key_1", count(*) AS "aggr__large-grid__count"
-			FROM __quesma_table_name
-			GROUP BY FLOOR(((__quesma_geo_lon("OriginLocation")+180)/360)*POWER(2, 8))
-			  AS "aggr__large-grid__key_0",
-			  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat("OriginLocation")))+(1/COS(RADIANS(
-			  __quesma_geo_lat("OriginLocation")))))/PI())/2*POWER(2, 8)) AS "aggr__large-grid__key_1"
-			ORDER BY "aggr__large-grid__count" DESC, "aggr__large-grid__key_0" ASC,
-			  "aggr__large-grid__key_1" ASC
-			LIMIT 3`,
+		ExpectedPancakeSQL: "SELECT FLOOR(((__quesma_geo_lon(`OriginLocation`)+180)/360)*POWER(2, 8))\n" +
+			"  AS `aggr__large-grid__key_0`,\n" +
+			"  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat(`OriginLocation`)))+(1/COS(RADIANS(\n" +
+			"  __quesma_geo_lat(`OriginLocation`)))))/PI())/2*POWER(2, 8))\n" +
+			"  AS `aggr__large-grid__key_1`, count(*) AS `aggr__large-grid__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY FLOOR(((__quesma_geo_lon(`OriginLocation`)+180)/360)*POWER(2, 8))\n" +
+			"  AS `aggr__large-grid__key_0`,\n" +
+			"  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat(`OriginLocation`)))+(1/COS(RADIANS(\n" +
+			"  __quesma_geo_lat(`OriginLocation`)))))/PI())/2*POWER(2, 8)) AS `aggr__large-grid__key_1`\n" +
+			"ORDER BY `aggr__large-grid__count` DESC, `aggr__large-grid__key_0` ASC,\n" +
+			"  `aggr__large-grid__key_1` ASC\n" +
+			"LIMIT 3",
 	},
 	{ // [2]
 		TestName: "geotile_grid with some other aggregations",
@@ -296,43 +294,42 @@ var AggregationTestsWithGeographicalCoordinates = []AggregationTestCase{
 				model.NewQueryResultCol("metric__terms__large-grid__avg_col_0", 50.5),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__terms__parent_count", "aggr__terms__key_0", "aggr__terms__count",
-			  "aggr__terms__large-grid__key_0", "aggr__terms__large-grid__key_1",
-			  "aggr__terms__large-grid__count", "metric__terms__large-grid__avg_col_0"
-			FROM (
-			  SELECT "aggr__terms__parent_count", "aggr__terms__key_0",
-				"aggr__terms__count", "aggr__terms__large-grid__key_0",
-				"aggr__terms__large-grid__key_1", "aggr__terms__large-grid__count",
-				"metric__terms__large-grid__avg_col_0",
-				dense_rank() OVER (ORDER BY "aggr__terms__count" DESC, "aggr__terms__key_0"
-				ASC) AS "aggr__terms__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__terms__key_0" ORDER BY
-				"aggr__terms__large-grid__count" DESC, "aggr__terms__large-grid__key_0" ASC,
-				"aggr__terms__large-grid__key_1" ASC) AS
-				"aggr__terms__large-grid__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__terms__parent_count",
-				  COALESCE("AvgTicketPrice", 'N/A') AS "aggr__terms__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__terms__key_0") AS
-				  "aggr__terms__count",
-				  FLOOR(((__quesma_geo_lon("OriginLocation")+180)/360)*POWER(2, 8)) AS
-				  "aggr__terms__large-grid__key_0",
-				  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat("OriginLocation")))+(1/COS(
-				  RADIANS(__quesma_geo_lat("OriginLocation")))))/PI())/2*POWER(2, 8)) AS
-				  "aggr__terms__large-grid__key_1",
-				  count(*) AS "aggr__terms__large-grid__count",
-				  avgOrNull("DistanceKilometers") AS "metric__terms__large-grid__avg_col_0"
-				FROM __quesma_table_name
-				GROUP BY COALESCE("AvgTicketPrice", 'N/A') AS "aggr__terms__key_0",
-				  FLOOR(((__quesma_geo_lon("OriginLocation")+180)/360)*POWER(2, 8)) AS
-				  "aggr__terms__large-grid__key_0",
-				  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat("OriginLocation")))+(1/COS(
-				  RADIANS(__quesma_geo_lat("OriginLocation")))))/PI())/2*POWER(2, 8)) AS
-				  "aggr__terms__large-grid__key_1"))
-			WHERE ("aggr__terms__order_1_rank"<=2 AND
-			  "aggr__terms__large-grid__order_1_rank"<=3)
-			ORDER BY "aggr__terms__order_1_rank" ASC,
-			  "aggr__terms__large-grid__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__terms__parent_count`, `aggr__terms__key_0`, `aggr__terms__count`,\n" +
+			"  `aggr__terms__large-grid__key_0`, `aggr__terms__large-grid__key_1`,\n" +
+			"  `aggr__terms__large-grid__count`, `metric__terms__large-grid__avg_col_0`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__terms__parent_count`, `aggr__terms__key_0`,\n" +
+			"    `aggr__terms__count`, `aggr__terms__large-grid__key_0`,\n" +
+			"    `aggr__terms__large-grid__key_1`, `aggr__terms__large-grid__count`,\n" +
+			"    `metric__terms__large-grid__avg_col_0`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__terms__count` DESC, `aggr__terms__key_0`\n" +
+			"    ASC) AS `aggr__terms__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__terms__key_0` ORDER BY\n" +
+			"    `aggr__terms__large-grid__count` DESC, `aggr__terms__large-grid__key_0` ASC,\n" +
+			"    `aggr__terms__large-grid__key_1` ASC) AS\n" +
+			"    `aggr__terms__large-grid__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__terms__parent_count`,\n" +
+			"      COALESCE(`AvgTicketPrice`, 'N/A') AS `aggr__terms__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__terms__key_0`) AS\n" +
+			"      `aggr__terms__count`,\n" +
+			"      FLOOR(((__quesma_geo_lon(`OriginLocation`)+180)/360)*POWER(2, 8)) AS\n" +
+			"      `aggr__terms__large-grid__key_0`,\n" +
+			"      FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat(`OriginLocation`)))+(1/COS(\n" +
+			"      RADIANS(__quesma_geo_lat(`OriginLocation`)))))/PI())/2*POWER(2, 8)) AS\n" +
+			"      `aggr__terms__large-grid__key_1`,\n" +
+			"      count(*) AS `aggr__terms__large-grid__count`,\n" +
+			"      avgOrNull(`DistanceKilometers`) AS `metric__terms__large-grid__avg_col_0`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY COALESCE(`AvgTicketPrice`, 'N/A') AS `aggr__terms__key_0`,\n" +
+			"      FLOOR(((__quesma_geo_lon(`OriginLocation`)+180)/360)*POWER(2, 8)) AS\n" +
+			"      `aggr__terms__large-grid__key_0`,\n" +
+			"      FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat(`OriginLocation`)))+(1/COS(\n" +
+			"      RADIANS(__quesma_geo_lat(`OriginLocation`)))))/PI())/2*POWER(2, 8)) AS\n" +
+			"      `aggr__terms__large-grid__key_1`))\n" +
+			"WHERE (`aggr__terms__order_1_rank`<=2 AND\n" +
+			"  `aggr__terms__large-grid__order_1_rank`<=3)\n" +
+			"ORDER BY `aggr__terms__order_1_rank` ASC,\n" +
+			"  `aggr__terms__large-grid__order_1_rank` ASC",
 	},
 }
diff --git a/platform/testdata/grafana.go b/platform/testdata/grafana.go
index 1a641ab2b..596ca1362 100644
--- a/platform/testdata/grafana.go
+++ b/platform/testdata/grafana.go
@@ -75,13 +75,12 @@ var GrafanaAggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", int64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp64Milli("@timestamp") / 2000) AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp64Milli("@timestamp") / 2000) AS
-			  "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp64Milli(`@timestamp`) / 2000) AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY toInt64(toUnixTimestamp64Milli(`@timestamp`) / 2000) AS\n" +
+			"  `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [1]
 		TestName: "1x terms with min_doc_count, need to erase some rows with count < min_doc_count",
@@ -141,14 +140,13 @@ var GrafanaAggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__order_1", int64(0)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-		  	SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-			  "extension" AS "aggr__2__key_0", count(*) AS "aggr__2__count",
-			  count(*)>=40 AS "aggr__2__order_1"
-			FROM __quesma_table_name
-			GROUP BY "extension" AS "aggr__2__key_0"
-			ORDER BY "aggr__2__order_1" DESC, "aggr__2__key_0" DESC
-			LIMIT 5`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n" +
+			"  `extension` AS `aggr__2__key_0`, count(*) AS `aggr__2__count`,\n" +
+			"  count(*)>=40 AS `aggr__2__order_1`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY `extension` AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__order_1` DESC, `aggr__2__key_0` DESC\n" +
+			"LIMIT 5",
 	},
 	{ // [2]
 		TestName: "2x terms with min_doc_count",
@@ -271,32 +269,31 @@ var GrafanaAggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__3__count", int64(2)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-		  	SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-			  "aggr__2__order_1", "aggr__2__3__parent_count", "aggr__2__3__key_0",
-			  "aggr__2__3__count"
-			FROM (
-			  SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-				"aggr__2__order_1", "aggr__2__3__parent_count", "aggr__2__3__key_0",
-				"aggr__2__3__count",
-				dense_rank() OVER (ORDER BY "aggr__2__order_1" DESC, "aggr__2__key_0" DESC)
-				AS "aggr__2__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"aggr__2__3__count" DESC, "aggr__2__3__key_0" ASC) AS
-				"aggr__2__3__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-				  "extension" AS "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  sum(count(*)>=30) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "aggr__2__order_1",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "aggr__2__3__parent_count", "message" AS "aggr__2__3__key_0",
-				  count(*) AS "aggr__2__3__count"
-				FROM __quesma_table_name
-				GROUP BY "extension" AS "aggr__2__key_0", "message" AS "aggr__2__3__key_0"))
-			WHERE ("aggr__2__order_1_rank"<=5 AND "aggr__2__3__order_1_rank"<=11)
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__3__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"  `aggr__2__order_1`, `aggr__2__3__parent_count`, `aggr__2__3__key_0`,\n" +
+			"  `aggr__2__3__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"    `aggr__2__order_1`, `aggr__2__3__parent_count`, `aggr__2__3__key_0`,\n" +
+			"    `aggr__2__3__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__order_1` DESC, `aggr__2__key_0` DESC)\n" +
+			"    AS `aggr__2__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+			"    `aggr__2__3__count` DESC, `aggr__2__3__key_0` ASC) AS\n" +
+			"    `aggr__2__3__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n" +
+			"      `extension` AS `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      sum(count(*)>=30) OVER (PARTITION BY `aggr__2__key_0`) AS\n" +
+			"      `aggr__2__order_1`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS\n" +
+			"      `aggr__2__3__parent_count`, `message` AS `aggr__2__3__key_0`,\n" +
+			"      count(*) AS `aggr__2__3__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY `extension` AS `aggr__2__key_0`, `message` AS `aggr__2__3__key_0`))\n" +
+			"WHERE (`aggr__2__order_1_rank`<=5 AND `aggr__2__3__order_1_rank`<=11)\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__3__order_1_rank` ASC",
 	},
 	{ // [3]
 		TestName: "simplest geotile_grid",
@@ -358,13 +355,12 @@ var GrafanaAggregationTests = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", int64(21)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT geohashEncode(__quesma_geo_lon("geo.coordinates"), __quesma_geo_lat(
-			  "geo.coordinates"), 2) AS "aggr__2__key_0", count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY geohashEncode(__quesma_geo_lon("geo.coordinates"), __quesma_geo_lat(
-			  "geo.coordinates"), 2) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__count" DESC, "aggr__2__key_0" ASC
-			LIMIT 10000`,
+		ExpectedPancakeSQL: "SELECT geohashEncode(__quesma_geo_lon(`geo.coordinates`), __quesma_geo_lat(\n" +
+			"  `geo.coordinates`), 2) AS `aggr__2__key_0`, count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY geohashEncode(__quesma_geo_lon(`geo.coordinates`), __quesma_geo_lat(\n" +
+			"  `geo.coordinates`), 2) AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__count` DESC, `aggr__2__key_0` ASC\n" +
+			"LIMIT 10000",
 	},
 }
diff --git a/platform/testdata/kibana-visualize/aggregation_requests.go b/platform/testdata/kibana-visualize/aggregation_requests.go
index b58d2994c..70d982149 100644
--- a/platform/testdata/kibana-visualize/aggregation_requests.go
+++ b/platform/testdata/kibana-visualize/aggregation_requests.go
@@ -231,34 +231,32 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__0__1__count", 1),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__key_0", "aggr__0__count", "aggr__0__1__parent_count",
-			  "aggr__0__1__key_0", "aggr__0__1__key_1", "aggr__0__1__count"
-			FROM (
-			  SELECT "aggr__0__key_0", "aggr__0__count", "aggr__0__1__parent_count",
-				"aggr__0__1__key_0", "aggr__0__1__key_1", "aggr__0__1__count",
-				dense_rank() OVER (ORDER BY "aggr__0__key_0" ASC) AS "aggr__0__order_1_rank"
-				,
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0" ORDER BY
-				"aggr__0__1__count" DESC, "aggr__0__1__key_0" ASC, "aggr__0__1__key_1" ASC)
-				AS "aggr__0__1__order_1_rank"
-			  FROM (
-				SELECT toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(
-				  toTimezone("@timestamp", 'Europe/Warsaw'))*1000) / 30000) AS
-				  "aggr__0__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS "aggr__0__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS
-				  "aggr__0__1__parent_count", "severity" AS "aggr__0__1__key_0",
-				  "source" AS "aggr__0__1__key_1", count(*) AS "aggr__0__1__count"
-				FROM __quesma_table_name
-				WHERE ("@timestamp">=fromUnixTimestamp64Milli(1716811196627) AND
-				  "@timestamp"<=fromUnixTimestamp64Milli(1716812096627))
-				GROUP BY toInt64((toUnixTimestamp64Milli("@timestamp")+timeZoneOffset(
-				  toTimezone("@timestamp", 'Europe/Warsaw'))*1000) / 30000) AS
-				  "aggr__0__key_0", "severity" AS "aggr__0__1__key_0",
-				  "source" AS "aggr__0__1__key_1"))
-			WHERE "aggr__0__1__order_1_rank"<=3
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__key_0`, `aggr__0__count`, `aggr__0__1__parent_count`,\n" +
+			"  `aggr__0__1__key_0`, `aggr__0__1__key_1`, `aggr__0__1__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__key_0`, `aggr__0__count`, `aggr__0__1__parent_count`,\n" +
+			"    `aggr__0__1__key_0`, `aggr__0__1__key_1`, `aggr__0__1__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__0__key_0` ASC) AS `aggr__0__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0` ORDER BY\n" +
+			"    `aggr__0__1__count` DESC, `aggr__0__1__key_0` ASC, `aggr__0__1__key_1` ASC)\n" +
+			"    AS `aggr__0__1__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(\n" +
+			"      toTimezone(`@timestamp`, 'Europe/Warsaw'))*1000) / 30000) AS\n" +
+			"      `aggr__0__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS `aggr__0__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS\n" +
+			"      `aggr__0__1__parent_count`, `severity` AS `aggr__0__1__key_0`,\n" +
+			"      `source` AS `aggr__0__1__key_1`, count(*) AS `aggr__0__1__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    WHERE (`@timestamp`>=fromUnixTimestamp64Milli(1716811196627) AND\n" +
+			"      `@timestamp`<=fromUnixTimestamp64Milli(1716812096627))\n" +
+			"    GROUP BY toInt64((toUnixTimestamp64Milli(`@timestamp`)+timeZoneOffset(\n" +
+			"      toTimezone(`@timestamp`, 'Europe/Warsaw'))*1000) / 30000) AS\n" +
+			"      `aggr__0__key_0`, `severity` AS `aggr__0__1__key_0`,\n" +
+			"      `source` AS `aggr__0__1__key_1`))\n" +
+			"WHERE `aggr__0__1__order_1_rank`<=3\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC",
 	},
 	{ // [1]
 		TestName: "Multi_terms with simple count. Visualize: Bar Vertical: Horizontal Axis: Top values (2 values), Vertical: Count of records, Breakdown: @timestamp",
@@ -412,30 +410,29 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__0__1__order_1", int64(1716834510000/30000)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__key_1",
-			  "aggr__0__count", "aggr__0__1__key_0", "aggr__0__1__count"
-			FROM (
-			  SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__key_1",
-				"aggr__0__count", "aggr__0__1__key_0",
-				"aggr__0__1__count",
-				dense_rank() OVER (ORDER BY "aggr__0__count" DESC, "aggr__0__key_0" ASC,
-				"aggr__0__key_1" ASC) AS "aggr__0__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0", "aggr__0__key_1" ORDER BY
-				"aggr__0__1__key_0" ASC) AS "aggr__0__1__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-				  "message" AS "aggr__0__key_0", "host.name" AS "aggr__0__key_1",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0", "aggr__0__key_1") AS
-				  "aggr__0__count",
-				  toInt64(toUnixTimestamp64Milli("@timestamp") / 30000) AS
-				  "aggr__0__1__key_0", count(*) AS "aggr__0__1__count"
-				FROM ` + TableName + `
-				GROUP BY "message" AS "aggr__0__key_0", "host.name" AS "aggr__0__key_1",
-				  toInt64(toUnixTimestamp64Milli("@timestamp") / 30000) AS
-				  "aggr__0__1__key_0"))
-			WHERE "aggr__0__order_1_rank"<=3
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__key_1`,\n" +
+			"  `aggr__0__count`, `aggr__0__1__key_0`, `aggr__0__1__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__key_1`,\n" +
+			"    `aggr__0__count`, `aggr__0__1__key_0`,\n" +
+			"    `aggr__0__1__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__0__count` DESC, `aggr__0__key_0` ASC,\n" +
+			"    `aggr__0__key_1` ASC) AS `aggr__0__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0`, `aggr__0__key_1` ORDER BY\n" +
+			"    `aggr__0__1__key_0` ASC) AS `aggr__0__1__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"      `message` AS `aggr__0__key_0`, `host.name` AS `aggr__0__key_1`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`, `aggr__0__key_1`) AS\n" +
+			"      `aggr__0__count`,\n" +
+			"      toInt64(toUnixTimestamp64Milli(`@timestamp`) / 30000) AS\n" +
+			"      `aggr__0__1__key_0`, count(*) AS `aggr__0__1__count`\n" +
+			"    FROM `" + TableName + "`\n" +
+			"    GROUP BY `message` AS `aggr__0__key_0`, `host.name` AS `aggr__0__key_1`,\n" +
+			"      toInt64(toUnixTimestamp64Milli(`@timestamp`) / 30000) AS\n" +
+			"      `aggr__0__1__key_0`))\n" +
+			"WHERE `aggr__0__order_1_rank`<=3\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC",
 	},
 	{ //[2],
 		TestName: "Multi_terms with double-nested subaggregations. Visualize: Bar Vertical: Horizontal Axis: Top values (2 values), Vertical: Unique count, Breakdown: @timestamp",
@@ -631,34 +628,33 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__0__1__2_col_0", 1),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__key_1",
-			  "aggr__0__count", "metric__0__2_col_0", "aggr__0__1__key_0",
-			  "aggr__0__1__count", "metric__0__1__2_col_0"
-			FROM (
-			  SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__key_1",
-				"aggr__0__count", "metric__0__2_col_0", "aggr__0__1__key_0",
-				"aggr__0__1__count", "metric__0__1__2_col_0",
-				dense_rank() OVER (ORDER BY "metric__0__2_col_0" DESC, "aggr__0__key_0" ASC,
-				"aggr__0__key_1" ASC) AS "aggr__0__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0", "aggr__0__key_1" ORDER BY
-				"aggr__0__1__key_0" ASC) AS "aggr__0__1__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-				  "severity" AS "aggr__0__key_0", "source" AS "aggr__0__key_1",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0", "aggr__0__key_1") AS
-				  "aggr__0__count",
-				  uniqMerge(uniqState("severity")) OVER (PARTITION BY "aggr__0__key_0",
-				  "aggr__0__key_1") AS "metric__0__2_col_0",
-				  toInt64(toUnixTimestamp64Milli("@timestamp") / 30000) AS
-				  "aggr__0__1__key_0", count(*) AS "aggr__0__1__count",
-				  uniq("severity") AS "metric__0__1__2_col_0"
-				FROM __quesma_table_name
-				GROUP BY "severity" AS "aggr__0__key_0", "source" AS "aggr__0__key_1",
-				  toInt64(toUnixTimestamp64Milli("@timestamp") / 30000) AS
-				  "aggr__0__1__key_0"))
-			WHERE "aggr__0__order_1_rank"<=3
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__key_1`,\n" +
+			"  `aggr__0__count`, `metric__0__2_col_0`, `aggr__0__1__key_0`,\n" +
+			"  `aggr__0__1__count`, `metric__0__1__2_col_0`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__key_1`,\n" +
+			"    `aggr__0__count`, `metric__0__2_col_0`, `aggr__0__1__key_0`,\n" +
+			"    `aggr__0__1__count`, `metric__0__1__2_col_0`,\n" +
+			"    dense_rank() OVER (ORDER BY `metric__0__2_col_0` DESC, `aggr__0__key_0` ASC,\n" +
+			"    `aggr__0__key_1` ASC) AS `aggr__0__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0`, `aggr__0__key_1` ORDER BY\n" +
+			"    `aggr__0__1__key_0` ASC) AS `aggr__0__1__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"      `severity` AS `aggr__0__key_0`, `source` AS `aggr__0__key_1`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`, `aggr__0__key_1`) AS\n" +
+			"      `aggr__0__count`,\n" +
+			"      uniqMerge(uniqState(`severity`)) OVER (PARTITION BY `aggr__0__key_0`,\n" +
+			"      `aggr__0__key_1`) AS `metric__0__2_col_0`,\n" +
+			"      toInt64(toUnixTimestamp64Milli(`@timestamp`) / 30000) AS\n" +
+			"      `aggr__0__1__key_0`, count(*) AS `aggr__0__1__count`,\n" +
+			"      uniq(`severity`) AS `metric__0__1__2_col_0`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY `severity` AS `aggr__0__key_0`, `source` AS `aggr__0__key_1`,\n" +
+			"      toInt64(toUnixTimestamp64Milli(`@timestamp`) / 30000) AS\n" +
+			"      `aggr__0__1__key_0`))\n" +
+			"WHERE `aggr__0__order_1_rank`<=3\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC",
 	},
 	{ // [3]
 		TestName: "Quite simple multi_terms, but with non-string keys. Visualize: Bar Vertical: Horizontal Axis: Date Histogram, Vertical Axis: Count of records, Breakdown: Top values (2 values)",
@@ -831,31 +827,30 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__0__1__order_1", int64(1716838500000/30000)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__key_1",
-			  "aggr__0__count", "aggr__0__1__key_0", "aggr__0__1__count"
-			FROM (
-			  SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__key_1",
-				"aggr__0__count", "aggr__0__1__key_0",
-				"aggr__0__1__count",
-				dense_rank() OVER (ORDER BY "aggr__0__count" DESC, "aggr__0__key_0" ASC,
-				"aggr__0__key_1" ASC) AS "aggr__0__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0", "aggr__0__key_1" ORDER BY
-				"aggr__0__1__key_0" ASC) AS "aggr__0__1__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-				  "Cancelled" AS "aggr__0__key_0", "AvgTicketPrice" AS "aggr__0__key_1",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0", "aggr__0__key_1") AS
-				  "aggr__0__count",
-				  toInt64(toUnixTimestamp64Milli("@timestamp") / 30000) AS
-				  "aggr__0__1__key_0", count(*) AS "aggr__0__1__count"
-				FROM ` + TableName + `
-				GROUP BY "Cancelled" AS "aggr__0__key_0",
-				  "AvgTicketPrice" AS "aggr__0__key_1",
-				  toInt64(toUnixTimestamp64Milli("@timestamp") / 30000) AS
-				  "aggr__0__1__key_0"))
-			WHERE "aggr__0__order_1_rank"<=3
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__key_1`,\n" +
+			"  `aggr__0__count`, `aggr__0__1__key_0`, `aggr__0__1__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__key_1`,\n" +
+			"    `aggr__0__count`, `aggr__0__1__key_0`,\n" +
+			"    `aggr__0__1__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__0__count` DESC, `aggr__0__key_0` ASC,\n" +
+			"    `aggr__0__key_1` ASC) AS `aggr__0__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0`, `aggr__0__key_1` ORDER BY\n" +
+			"    `aggr__0__1__key_0` ASC) AS `aggr__0__1__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"      `Cancelled` AS `aggr__0__key_0`, `AvgTicketPrice` AS `aggr__0__key_1`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`, `aggr__0__key_1`) AS\n" +
+			"      `aggr__0__count`,\n" +
+			"      toInt64(toUnixTimestamp64Milli(`@timestamp`) / 30000) AS\n" +
+			"      `aggr__0__1__key_0`, count(*) AS `aggr__0__1__count`\n" +
+			"    FROM `" + TableName + "`\n" +
+			"    GROUP BY `Cancelled` AS `aggr__0__key_0`,\n" +
+			"      `AvgTicketPrice` AS `aggr__0__key_1`,\n" +
+			"      toInt64(toUnixTimestamp64Milli(`@timestamp`) / 30000) AS\n" +
+			"      `aggr__0__1__key_0`))\n" +
+			"WHERE `aggr__0__order_1_rank`<=3\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC",
 	},
 	{ // [4]
 		TestName: "percentile with subaggregation (so, combinator). Visualize, Pie, Slice by: top5 of Cancelled, DistanceKilometers, Metric: 95th Percentile",
@@ -1030,32 +1025,31 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__0__1__2_col_0", []float64{9842.6279296875}),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-			  "metric__0__2_col_0", "aggr__0__1__key_0", "aggr__0__1__count",
-			  "metric__0__1__2_col_0"
-			FROM (
-			  SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-				"metric__0__2_col_0", "aggr__0__1__key_0", "aggr__0__1__count",
-				"metric__0__1__2_col_0",
-				dense_rank() OVER (ORDER BY "metric__0__2_col_0" DESC, "aggr__0__key_0" ASC)
-				AS "aggr__0__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0" ORDER BY
-				"aggr__0__1__key_0" ASC) AS "aggr__0__1__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-				  "Cancelled" AS "aggr__0__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS "aggr__0__count",
-				  quantilesMerge(0.950000)(quantilesState(0.950000)("DistanceKilometers"))
-				  OVER (PARTITION BY "aggr__0__key_0") AS "metric__0__2_col_0",
-				  floor("DistanceKilometers"/5000)*5000 AS "aggr__0__1__key_0",
-				  count(*) AS "aggr__0__1__count",
-				  quantiles(0.950000)("DistanceKilometers") AS "metric__0__1__2_col_0"
-				FROM __quesma_table_name
-				GROUP BY "Cancelled" AS "aggr__0__key_0",
-				  floor("DistanceKilometers"/5000)*5000 AS "aggr__0__1__key_0"))
-			WHERE "aggr__0__order_1_rank"<=6
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"  `metric__0__2_col_0`, `aggr__0__1__key_0`, `aggr__0__1__count`,\n" +
+			"  `metric__0__1__2_col_0`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"    `metric__0__2_col_0`, `aggr__0__1__key_0`, `aggr__0__1__count`,\n" +
+			"    `metric__0__1__2_col_0`,\n" +
+			"    dense_rank() OVER (ORDER BY `metric__0__2_col_0` DESC, `aggr__0__key_0` ASC)\n" +
+			"    AS `aggr__0__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0` ORDER BY\n" +
+			"    `aggr__0__1__key_0` ASC) AS `aggr__0__1__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"      `Cancelled` AS `aggr__0__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS `aggr__0__count`,\n" +
+			"      quantilesMerge(0.950000)(quantilesState(0.950000)(`DistanceKilometers`))\n" +
+			"      OVER (PARTITION BY `aggr__0__key_0`) AS `metric__0__2_col_0`,\n" +
+			"      floor(`DistanceKilometers`/5000)*5000 AS `aggr__0__1__key_0`,\n" +
+			"      count(*) AS `aggr__0__1__count`,\n" +
+			"      quantiles(0.950000)(`DistanceKilometers`) AS `metric__0__1__2_col_0`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY `Cancelled` AS `aggr__0__key_0`,\n" +
+			"      floor(`DistanceKilometers`/5000)*5000 AS `aggr__0__1__key_0`))\n" +
+			"WHERE `aggr__0__order_1_rank`<=6\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC",
 	},
 	{ // [5]
 		TestName: "terms with order by agg1>agg2 (multiple aggregations)",
@@ -1177,16 +1171,15 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__0__2-bucket__2-metric_col_0", 19285.5078125),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-			  "AvgTicketPrice" AS "aggr__0__key_0", count(*) AS "aggr__0__count",
-			  countIf("bytes_gauge" IS NOT NULL) AS "aggr__0__2-bucket__count",
-			  maxOrNullIf("DistanceKilometers", "bytes_gauge" IS NOT NULL) AS
-			  "metric__0__2-bucket__2-metric_col_0"
-			FROM __quesma_table_name
-			GROUP BY "AvgTicketPrice" AS "aggr__0__key_0"
-			ORDER BY "metric__0__2-bucket__2-metric_col_0" DESC, "aggr__0__key_0" ASC
-			LIMIT 3`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"  `AvgTicketPrice` AS `aggr__0__key_0`, count(*) AS `aggr__0__count`,\n" +
+			"  countIf(`bytes_gauge` IS NOT NULL) AS `aggr__0__2-bucket__count`,\n" +
+			"  maxOrNullIf(`DistanceKilometers`, `bytes_gauge` IS NOT NULL) AS\n" +
+			"  `metric__0__2-bucket__2-metric_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY `AvgTicketPrice` AS `aggr__0__key_0`\n" +
+			"ORDER BY `metric__0__2-bucket__2-metric_col_0` DESC, `aggr__0__key_0` ASC\n" +
+			"LIMIT 3",
 	},
 	{ // [6]
 		TestName: "terms with order by stats, easily reproducible in Kibana Visualize",
@@ -1328,22 +1321,21 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__0__1_col_4", 25545.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-			  "Carrier" AS "aggr__0__key_0", count(*) AS "aggr__0__count",
-			  count("FlightDelayMin") AS "metric__0__1_col_0",
-			  minOrNull("FlightDelayMin") AS "metric__0__1_col_1",
-			  maxOrNull("FlightDelayMin") AS "metric__0__1_col_2",
-			  avgOrNull("FlightDelayMin") AS "metric__0__1_col_3",
-			  sumOrNull("FlightDelayMin") AS "metric__0__1_col_4"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1725723024239) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1727019024239))
-			GROUP BY "Carrier" AS "aggr__0__key_0"
-			ORDER BY "metric__0__1_col_1" DESC, "metric__0__1_col_0" DESC,
-			  "metric__0__1_col_3" DESC, "metric__0__1_col_2" ASC,
-			  "metric__0__1_col_4" DESC, "aggr__0__key_0" ASC
-			LIMIT 4`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"  `Carrier` AS `aggr__0__key_0`, count(*) AS `aggr__0__count`,\n" +
+			"  count(`FlightDelayMin`) AS `metric__0__1_col_0`,\n" +
+			"  minOrNull(`FlightDelayMin`) AS `metric__0__1_col_1`,\n" +
+			"  maxOrNull(`FlightDelayMin`) AS `metric__0__1_col_2`,\n" +
+			"  avgOrNull(`FlightDelayMin`) AS `metric__0__1_col_3`,\n" +
+			"  sumOrNull(`FlightDelayMin`) AS `metric__0__1_col_4`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1725723024239) AND `timestamp`<=\n" +
+			"  fromUnixTimestamp64Milli(1727019024239))\n" +
+			"GROUP BY `Carrier` AS `aggr__0__key_0`\n" +
+			"ORDER BY `metric__0__1_col_1` DESC, `metric__0__1_col_0` DESC,\n" +
+			"  `metric__0__1_col_3` DESC, `metric__0__1_col_2` ASC,\n" +
+			"  `metric__0__1_col_4` DESC, `aggr__0__key_0` ASC\n" +
+			"LIMIT 4",
 	},
 	{ // [7]
 		TestName: "terms with order by extended_stats (easily reproducible in Kibana Visualize)",
@@ -1491,30 +1483,29 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__0__1_col_9", 98.1100106105745),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-			  "Carrier" AS "aggr__0__key_0", count(*) AS "aggr__0__count",
-			  count("FlightDelayMin") AS "metric__0__1_col_0",
-			  minOrNull("FlightDelayMin") AS "metric__0__1_col_1",
-			  maxOrNull("FlightDelayMin") AS "metric__0__1_col_2",
-			  avgOrNull("FlightDelayMin") AS "metric__0__1_col_3",
-			  sumOrNull("FlightDelayMin") AS "metric__0__1_col_4",
-			  sumOrNull("FlightDelayMin"*"FlightDelayMin") AS "metric__0__1_col_5",
-			  varPop("FlightDelayMin") AS "metric__0__1_col_6",
-			  varSamp("FlightDelayMin") AS "metric__0__1_col_7",
-			  stddevPop("FlightDelayMin") AS "metric__0__1_col_8",
-			  stddevSamp("FlightDelayMin") AS "metric__0__1_col_9"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1725723024239) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1727019024239))
-			GROUP BY "Carrier" AS "aggr__0__key_0"
-			ORDER BY "metric__0__1_col_1" DESC, "metric__0__1_col_0" DESC,
-			  "metric__0__1_col_3" DESC, "metric__0__1_col_2" ASC,
-			  "metric__0__1_col_4" DESC, "metric__0__1_col_5" DESC,
-			  "metric__0__1_col_6" DESC, "metric__0__1_col_6" DESC,
-			  "metric__0__1_col_7" DESC, "metric__0__1_col_8" DESC,
-			  "metric__0__1_col_8" DESC, "metric__0__1_col_9" DESC, "aggr__0__key_0" ASC
-			LIMIT 4`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"  `Carrier` AS `aggr__0__key_0`, count(*) AS `aggr__0__count`,\n" +
+			"  count(`FlightDelayMin`) AS `metric__0__1_col_0`,\n" +
+			"  minOrNull(`FlightDelayMin`) AS `metric__0__1_col_1`,\n" +
+			"  maxOrNull(`FlightDelayMin`) AS `metric__0__1_col_2`,\n" +
+			"  avgOrNull(`FlightDelayMin`) AS `metric__0__1_col_3`,\n" +
+			"  sumOrNull(`FlightDelayMin`) AS `metric__0__1_col_4`,\n" +
+			"  sumOrNull(`FlightDelayMin`*`FlightDelayMin`) AS `metric__0__1_col_5`,\n" +
+			"  varPop(`FlightDelayMin`) AS `metric__0__1_col_6`,\n" +
+			"  varSamp(`FlightDelayMin`) AS `metric__0__1_col_7`,\n" +
+			"  stddevPop(`FlightDelayMin`) AS `metric__0__1_col_8`,\n" +
+			"  stddevSamp(`FlightDelayMin`) AS `metric__0__1_col_9`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1725723024239) AND `timestamp`<=\n" +
+			"  fromUnixTimestamp64Milli(1727019024239))\n" +
+			"GROUP BY `Carrier` AS `aggr__0__key_0`\n" +
+			"ORDER BY `metric__0__1_col_1` DESC, `metric__0__1_col_0` DESC,\n" +
+			"  `metric__0__1_col_3` DESC, `metric__0__1_col_2` ASC,\n" +
+			"  `metric__0__1_col_4` DESC, `metric__0__1_col_5` DESC,\n" +
+			"  `metric__0__1_col_6` DESC, `metric__0__1_col_6` DESC,\n" +
+			"  `metric__0__1_col_7` DESC, `metric__0__1_col_8` DESC,\n" +
+			"  `metric__0__1_col_8` DESC, `metric__0__1_col_9` DESC, `aggr__0__key_0` ASC\n" +
+			"LIMIT 4",
 	},
 	{ // [8]
 		TestName: "Terms with order by top metrics",
@@ -1773,33 +1764,32 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__0__1__count", 1),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-			  "aggr__0__order_1", "aggr__0__1__key_0", "aggr__0__1__count",
-			  "aggr__0__1__2-bucket__count"
-			FROM (
-			  SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-				"aggr__0__order_1", "aggr__0__1__key_0", "aggr__0__1__count",
-				"aggr__0__1__2-bucket__count",
-				dense_rank() OVER (ORDER BY "aggr__0__order_1" DESC, "aggr__0__key_0" ASC)
-				AS "aggr__0__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0" ORDER BY
-				"aggr__0__1__key_0" ASC) AS "aggr__0__1__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-				  "AvgTicketPrice" AS "aggr__0__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS "aggr__0__count",
-				  "top_metrics__0__2-bucket__2-metric_col_0" AS "aggr__0__order_1",
-				  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-				  "timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS "aggr__0__1__key_0",
-				  count(*) AS "aggr__0__1__count",
-				  countIf("bytes_gauge" IS NOT NULL) AS "aggr__0__1__2-bucket__count"
-				FROM __quesma_table_name
-				GROUP BY "AvgTicketPrice" AS "aggr__0__key_0",
-				  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-				  "timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS "aggr__0__1__key_0"))
-			WHERE "aggr__0__order_1_rank"<=13
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"  `aggr__0__order_1`, `aggr__0__1__key_0`, `aggr__0__1__count`,\n" +
+			"  `aggr__0__1__2-bucket__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"    `aggr__0__order_1`, `aggr__0__1__key_0`, `aggr__0__1__count`,\n" +
+			"    `aggr__0__1__2-bucket__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__0__order_1` DESC, `aggr__0__key_0` ASC)\n" +
+			"    AS `aggr__0__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0` ORDER BY\n" +
+			"    `aggr__0__1__key_0` ASC) AS `aggr__0__1__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"      `AvgTicketPrice` AS `aggr__0__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS `aggr__0__count`,\n" +
+			"      `top_metrics__0__2-bucket__2-metric_col_0` AS `aggr__0__order_1`,\n" +
+			"      toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"      `timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS `aggr__0__1__key_0`,\n" +
+			"      count(*) AS `aggr__0__1__count`,\n" +
+			"      countIf(`bytes_gauge` IS NOT NULL) AS `aggr__0__1__2-bucket__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY `AvgTicketPrice` AS `aggr__0__key_0`,\n" +
+			"      toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"      `timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS `aggr__0__1__key_0`))\n" +
+			"WHERE `aggr__0__order_1_rank`<=13\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC",
 	},
 	{ // [9]
 		TestName: "Line, Y-axis: Min, Buckets: Date Range, X-Axis: Terms, Split Chart: Date Histogram",
@@ -2061,93 +2051,88 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__3__4__1_col_0", 360.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__count", "aggr__2__3__parent_count", "aggr__2__3__key_0",
-			  "aggr__2__3__count", "metric__2__3__1_col_0", "aggr__2__3__4__key_0",
-			  "aggr__2__3__4__count", "metric__2__3__4__1_col_0"
-			FROM (
-			  SELECT "aggr__2__count", "aggr__2__3__parent_count", "aggr__2__3__key_0",
-				"aggr__2__3__count", "metric__2__3__1_col_0", "aggr__2__3__4__key_0",
-				"aggr__2__3__4__count", "metric__2__3__4__1_col_0",
-				dense_rank() OVER (ORDER BY "metric__2__3__1_col_0" DESC,
-				"aggr__2__3__key_0" ASC) AS "aggr__2__3__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__3__key_0" ORDER BY
-				"aggr__2__3__4__key_0" ASC) AS "aggr__2__3__4__order_1_rank"
-			  FROM (
-				SELECT sum(countIf(("timestamp">=toInt64(toUnixTimestamp(toStartOfWeek(
-				  subDate(now(), INTERVAL 1 week)))) AND "timestamp"<toInt64(toUnixTimestamp
-				  (now()))))) OVER () AS "aggr__2__count",
-				  sum(countIf(("timestamp">=toInt64(toUnixTimestamp(toStartOfWeek(subDate(
-				  now(), INTERVAL 1 week)))) AND "timestamp"<toInt64(toUnixTimestamp(now()))
-				  ))) OVER () AS "aggr__2__3__parent_count",
-				  "DistanceKilometers" AS "aggr__2__3__key_0",
-				  sum(countIf(("timestamp">=toInt64(toUnixTimestamp(toStartOfWeek(subDate(
-				  now(), INTERVAL 1 week)))) AND "timestamp"<toInt64(toUnixTimestamp(now()))
-				  ))) OVER (PARTITION BY "aggr__2__3__key_0") AS "aggr__2__3__count",
-				  minOrNull(minOrNullIf("FlightDelayMin", ("timestamp">=toInt64(
-				  toUnixTimestamp(toStartOfWeek(subDate(now(), INTERVAL 1 week)))) AND
-				  "timestamp"<toInt64(toUnixTimestamp(now()))))) OVER (PARTITION BY
-				  "aggr__2__3__key_0") AS "metric__2__3__1_col_0",
-				  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-				  "timestamp", 'Europe/Warsaw'))*1000) / 2592000000) AS
-				  "aggr__2__3__4__key_0",
-				  countIf(("timestamp">=toInt64(toUnixTimestamp(toStartOfWeek(subDate(now(),
-				  INTERVAL 1 week)))) AND "timestamp"<toInt64(toUnixTimestamp(now())))) AS
-				  "aggr__2__3__4__count",
-				  minOrNullIf("FlightDelayMin", ("timestamp">=toInt64(toUnixTimestamp(
-				  toStartOfWeek(subDate(now(), INTERVAL 1 week)))) AND "timestamp"<toInt64(
-				  toUnixTimestamp(now())))) AS "metric__2__3__4__1_col_0"
-				FROM __quesma_table_name
-				WHERE (("timestamp">=fromUnixTimestamp64Milli(1258014686584) AND "timestamp"
-				  <=fromUnixTimestamp64Milli(1731400286584)) AND ("timestamp">=toInt64(
-				  toUnixTimestamp(toStartOfWeek(subDate(now(), INTERVAL 1 week)))) AND
-				  "timestamp"<toInt64(toUnixTimestamp(now()))))
-				GROUP BY "DistanceKilometers" AS "aggr__2__3__key_0",
-				  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-				  "timestamp", 'Europe/Warsaw'))*1000) / 2592000000) AS
-				  "aggr__2__3__4__key_0"))
-			WHERE "aggr__2__3__order_1_rank"<=6
-			ORDER BY "aggr__2__3__order_1_rank" ASC, "aggr__2__3__4__order_1_rank" ASC`,
-		ExpectedAdditionalPancakeSQLs: []string{`
-			SELECT "aggr__2__count", "aggr__2__3__parent_count", "aggr__2__3__key_0",
-			  "aggr__2__3__count", "metric__2__3__1_col_0", "aggr__2__3__4__key_0",
-			  "aggr__2__3__4__count", "metric__2__3__4__1_col_0"
-			FROM (
-			  SELECT "aggr__2__count", "aggr__2__3__parent_count", "aggr__2__3__key_0",
-				"aggr__2__3__count", "metric__2__3__1_col_0", "aggr__2__3__4__key_0",
-				"aggr__2__3__4__count", "metric__2__3__4__1_col_0",
-				dense_rank() OVER (ORDER BY "metric__2__3__1_col_0" DESC,
-				"aggr__2__3__key_0" ASC) AS "aggr__2__3__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__3__key_0" ORDER BY
-				"aggr__2__3__4__key_0" ASC) AS "aggr__2__3__4__order_1_rank"
-			  FROM (
-				SELECT sum(countIf("timestamp">=toInt64(toUnixTimestamp(subDate(now(),
-				  INTERVAL 1 day))))) OVER () AS "aggr__2__count",
-				  sum(countIf("timestamp">=toInt64(toUnixTimestamp(subDate(now(), INTERVAL 1
-				  day))))) OVER () AS "aggr__2__3__parent_count",
-				  "DistanceKilometers" AS "aggr__2__3__key_0",
-				  sum(countIf("timestamp">=toInt64(toUnixTimestamp(subDate(now(), INTERVAL 1
-				  day))))) OVER (PARTITION BY "aggr__2__3__key_0") AS "aggr__2__3__count",
-				  minOrNull(minOrNullIf("FlightDelayMin", "timestamp">=toInt64(
-				  toUnixTimestamp(subDate(now(), INTERVAL 1 day))))) OVER (PARTITION BY
-				  "aggr__2__3__key_0") AS "metric__2__3__1_col_0",
-				  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-				  "timestamp", 'Europe/Warsaw'))*1000) / 2592000000) AS
-				  "aggr__2__3__4__key_0",
-				  countIf("timestamp">=toInt64(toUnixTimestamp(subDate(now(), INTERVAL 1 day
-				  )))) AS "aggr__2__3__4__count",
-				  minOrNullIf("FlightDelayMin", "timestamp">=toInt64(toUnixTimestamp(subDate
-				  (now(), INTERVAL 1 day)))) AS "metric__2__3__4__1_col_0"
-				FROM __quesma_table_name
-				WHERE (("timestamp">=fromUnixTimestamp64Milli(1258014686584) AND "timestamp"
-				  <=fromUnixTimestamp64Milli(1731400286584)) AND "timestamp">=toInt64(
-				  toUnixTimestamp(subDate(now(), INTERVAL 1 day))))
-				GROUP BY "DistanceKilometers" AS "aggr__2__3__key_0",
-				  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-				  "timestamp", 'Europe/Warsaw'))*1000) / 2592000000) AS
-				  "aggr__2__3__4__key_0"))
-			WHERE "aggr__2__3__order_1_rank"<=6
-			ORDER BY "aggr__2__3__order_1_rank" ASC, "aggr__2__3__4__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__count`, `aggr__2__3__parent_count`, `aggr__2__3__key_0`,\n" +
+			"  `aggr__2__3__count`, `metric__2__3__1_col_0`, `aggr__2__3__4__key_0`,\n" +
+			"  `aggr__2__3__4__count`, `metric__2__3__4__1_col_0`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__count`, `aggr__2__3__parent_count`, `aggr__2__3__key_0`,\n" +
+			"    `aggr__2__3__count`, `metric__2__3__1_col_0`, `aggr__2__3__4__key_0`,\n" +
+			"    `aggr__2__3__4__count`, `metric__2__3__4__1_col_0`,\n" +
+			"    dense_rank() OVER (ORDER BY `metric__2__3__1_col_0` DESC,\n" +
+			"    `aggr__2__3__key_0` ASC) AS `aggr__2__3__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__3__key_0` ORDER BY\n" +
+			"    `aggr__2__3__4__key_0` ASC) AS `aggr__2__3__4__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(countIf((`timestamp`>=toInt64(toUnixTimestamp(toStartOfWeek(\n" +
+			"      subDate(now(), INTERVAL 1 week)))) AND `timestamp`<toInt64(toUnixTimestamp\n" +
+			"      (now()))))) OVER () AS `aggr__2__count`,\n" +
+			"      sum(countIf((`timestamp`>=toInt64(toUnixTimestamp(toStartOfWeek(subDate(\n" +
+			"      now(), INTERVAL 1 week)))) AND `timestamp`<toInt64(toUnixTimestamp(now())\n" +
+			"      ))) OVER () AS `aggr__2__3__parent_count`,\n" +
+			"      `DistanceKilometers` AS `aggr__2__3__key_0`,\n" +
+			"      sum(countIf((`timestamp`>=toInt64(toUnixTimestamp(toStartOfWeek(subDate(\n" +
+			"      now(), INTERVAL 1 week)))) AND `timestamp`<toInt64(toUnixTimestamp(now())\n" +
+			"      ))) OVER (PARTITION BY `aggr__2__3__key_0`) AS `aggr__2__3__count`,\n" +
+			"      minOrNull(minOrNullIf(`FlightDelayMin`, (`timestamp`>=toInt64(\n" +
+			"      toUnixTimestamp(toStartOfWeek(subDate(now(), INTERVAL 1 week)))) AND\n" +
+			"      `timestamp`<toInt64(toUnixTimestamp(now()))))) OVER (PARTITION BY\n" +
+			"      `aggr__2__3__key_0`) AS `metric__2__3__1_col_0`,\n" +
+			"      toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"      `timestamp`, 'Europe/Warsaw'))*1000) / 2592000000) AS `aggr__2__3__4__key_0`,\n" +
+			"      countIf((`timestamp`>=toInt64(toUnixTimestamp(toStartOfWeek(subDate(now(),\n" +
+			"      INTERVAL 1 week)))) AND `timestamp`<toInt64(toUnixTimestamp(now())))) AS\n" +
+			"      `aggr__2__3__4__count`,\n" +
+			"      minOrNullIf(`FlightDelayMin`, (`timestamp`>=toInt64(toUnixTimestamp(\n" +
+			"      toStartOfWeek(subDate(now(), INTERVAL 1 week)))) AND `timestamp`<toInt64(\n" +
+			"      toUnixTimestamp(now())))) AS `metric__2__3__4__1_col_0`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1258014686584) AND `timestamp`\n" +
+			"      <=fromUnixTimestamp64Milli(1731400286584)) AND (`timestamp`>=toInt64(\n" +
+			"      toUnixTimestamp(toStartOfWeek(subDate(now(), INTERVAL 1 week)))) AND\n" +
+			"      `timestamp`<toInt64(toUnixTimestamp(now()))))\n" +
+			"    GROUP BY `DistanceKilometers` AS `aggr__2__3__key_0`,\n" +
+			"      toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"      `timestamp`, 'Europe/Warsaw'))*1000) / 2592000000) AS `aggr__2__3__4__key_0`))\n" +
+			"WHERE `aggr__2__3__order_1_rank`<=6\n" +
+			"ORDER BY `aggr__2__3__order_1_rank` ASC, `aggr__2__3__4__order_1_rank` ASC",
+		ExpectedAdditionalPancakeSQLs: []string{
+			"SELECT `aggr__2__count`, `aggr__2__3__parent_count`, `aggr__2__3__key_0`,\n" +
+				"  `aggr__2__3__count`, `metric__2__3__1_col_0`, `aggr__2__3__4__key_0`,\n" +
+				"  `aggr__2__3__4__count`, `metric__2__3__4__1_col_0`\n" +
+				"FROM (\n" +
+				"  SELECT `aggr__2__count`, `aggr__2__3__parent_count`, `aggr__2__3__key_0`,\n" +
+				"    `aggr__2__3__count`, `metric__2__3__1_col_0`, `aggr__2__3__4__key_0`,\n" +
+				"    `aggr__2__3__4__count`, `metric__2__3__4__1_col_0`,\n" +
+				"    dense_rank() OVER (ORDER BY `metric__2__3__1_col_0` DESC,\n" +
+				"    `aggr__2__3__key_0` ASC) AS `aggr__2__3__order_1_rank`,\n" +
+				"    dense_rank() OVER (PARTITION BY `aggr__2__3__key_0` ORDER BY\n" +
+				"    `aggr__2__3__4__key_0` ASC) AS `aggr__2__3__4__order_1_rank`\n" +
+				"  FROM (\n" +
+				"    SELECT sum(countIf(`timestamp`>=toInt64(toUnixTimestamp(subDate(now(),\n" +
+				"      INTERVAL 1 day))))) OVER () AS `aggr__2__count`,\n" +
+				"      sum(countIf(`timestamp`>=toInt64(toUnixTimestamp(subDate(now(), INTERVAL 1\n" +
+				"      day))))) OVER () AS `aggr__2__3__parent_count`,\n" +
+				"      `DistanceKilometers` AS `aggr__2__3__key_0`,\n" +
+				"      sum(countIf(`timestamp`>=toInt64(toUnixTimestamp(subDate(now(), INTERVAL 1\n" +
+				"      day))))) OVER (PARTITION BY `aggr__2__3__key_0`) AS `aggr__2__3__count`,\n" +
+				"      minOrNull(minOrNullIf(`FlightDelayMin`, `timestamp`>=toInt64(\n" +
+				"      toUnixTimestamp(subDate(now(), INTERVAL 1 day))))) OVER (PARTITION BY\n" +
+				"      `aggr__2__3__key_0`) AS `metric__2__3__1_col_0`,\n" +
+				"      toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+				"      `timestamp`, 'Europe/Warsaw'))*1000) / 2592000000) AS `aggr__2__3__4__key_0`,\n" +
+				"      countIf(`timestamp`>=toInt64(toUnixTimestamp(subDate(now(), INTERVAL 1 day\n" +
+				"      )))) AS `aggr__2__3__4__count`,\n" +
+				"      minOrNullIf(`FlightDelayMin`, `timestamp`>=toInt64(toUnixTimestamp(subDate\n" +
+				"      (now(), INTERVAL 1 day)))) AS `metric__2__3__4__1_col_0`\n" +
+				"    FROM `__quesma_table_name`\n" +
+				"    WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1258014686584) AND `timestamp`\n" +
+				"      <=fromUnixTimestamp64Milli(1731400286584)) AND `timestamp`>=toInt64(\n" +
+				"      toUnixTimestamp(subDate(now(), INTERVAL 1 day))))\n" +
+				"    GROUP BY `DistanceKilometers` AS `aggr__2__3__key_0`,\n" +
+				"      toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+				"      `timestamp`, 'Europe/Warsaw'))*1000) / 2592000000) AS `aggr__2__3__4__key_0`))\n" +
+				"WHERE `aggr__2__3__order_1_rank`<=6\n" +
+				"ORDER BY `aggr__2__3__order_1_rank` ASC, `aggr__2__3__4__order_1_rank` ASC",
 		},
 		ExpectedAdditionalPancakeResults: [][]model.QueryResultRow{
 			{
@@ -2212,9 +2197,8 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", 14074),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT count(*) AS "aggr__2__count"
-			FROM __quesma_table_name`,
+		ExpectedPancakeSQL: "SELECT count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`",
 	},
 	{ // [11]
 		TestName: "simplest IP Prefix (Kibana 8.13+), ipv4 field, prefix_length=1",
@@ -2276,12 +2260,11 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", 6784),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT intDiv("clientip", 2147483648) AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY intDiv("clientip", 2147483648) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT intDiv(`clientip`, 2147483648) AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY intDiv(`clientip`, 2147483648) AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [12]
 		TestName: "simplest IP Prefix (Kibana 8.13+), ipv4 field, prefix_length=10",
@@ -2387,12 +2370,11 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", 1),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT intDiv("clientip", 4194304) AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY intDiv("clientip", 4194304) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT intDiv(`clientip`, 4194304) AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY intDiv(`clientip`, 4194304) AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [13]
 		TestName: "simplest IP Prefix (Kibana 8.13+), ipv4 field, prefix_length=32",
@@ -2499,12 +2481,11 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", 1),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT intDiv("clientip", 1) AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY intDiv("clientip", 1) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT intDiv(`clientip`, 1) AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY intDiv(`clientip`, 1) AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [14]
 		TestName: "simplest IP Prefix (Kibana 8.13+), ipv4 field, keyed=true",
@@ -2565,12 +2546,11 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", 6784),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT intDiv("clientip", 8192) AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY intDiv("clientip", 8192) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT intDiv(`clientip`, 8192) AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY intDiv(`clientip`, 8192) AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [15]
 		TestName: "simplest IP Prefix (Kibana 8.13+), ipv4 field, append_prefix_length=true",
@@ -2633,12 +2613,11 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", 6784),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT intDiv("clientip", 128) AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY intDiv("clientip", 128) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT intDiv(`clientip`, 128) AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY intDiv(`clientip`, 128) AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [16]
 		TestName: "simplest IP Prefix (Kibana 8.13+), ipv4 field, keyed=true, append_prefix_length=true",
@@ -2700,11 +2679,10 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", 6784),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT intDiv("clientip", 2) AS "aggr__2__key_0", count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY intDiv("clientip", 2) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT intDiv(`clientip`, 2) AS `aggr__2__key_0`, count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY intDiv(`clientip`, 2) AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [17]
 		TestName: "IP Prefix with other aggregations",
@@ -2903,28 +2881,27 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__3__4_col_0", 1831),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-			  "aggr__2__3__key_0", "aggr__2__3__count", "metric__2__3__4_col_0"
-			FROM (
-			  SELECT "aggr__2__parent_count", "aggr__2__key_0", "aggr__2__count",
-				"aggr__2__3__key_0", "aggr__2__3__count", "metric__2__3__4_col_0",
-				dense_rank() OVER (ORDER BY "aggr__2__count" DESC, "aggr__2__key_0" ASC) AS
-				"aggr__2__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"aggr__2__3__key_0" ASC) AS "aggr__2__3__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-				  "bytes" AS "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  intDiv("clientip", 1073741824) AS "aggr__2__3__key_0",
-				  count(*) AS "aggr__2__3__count",
-				  sumOrNull("bytes") AS "metric__2__3__4_col_0"
-				FROM __quesma_table_name
-				GROUP BY "bytes" AS "aggr__2__key_0",
-				  intDiv("clientip", 1073741824) AS "aggr__2__3__key_0"))
-			WHERE "aggr__2__order_1_rank"<=3
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__3__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"  `aggr__2__3__key_0`, `aggr__2__3__count`, `metric__2__3__4_col_0`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__parent_count`, `aggr__2__key_0`, `aggr__2__count`,\n" +
+			"    `aggr__2__3__key_0`, `aggr__2__3__count`, `metric__2__3__4_col_0`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__count` DESC, `aggr__2__key_0` ASC) AS\n" +
+			"    `aggr__2__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+			"    `aggr__2__3__key_0` ASC) AS `aggr__2__3__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n" +
+			"      `bytes` AS `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      intDiv(`clientip`, 1073741824) AS `aggr__2__3__key_0`,\n" +
+			"      count(*) AS `aggr__2__3__count`,\n" +
+			"      sumOrNull(`bytes`) AS `metric__2__3__4_col_0`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY `bytes` AS `aggr__2__key_0`,\n" +
+			"      intDiv(`clientip`, 1073741824) AS `aggr__2__3__key_0`))\n" +
+			"WHERE `aggr__2__order_1_rank`<=3\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__3__order_1_rank` ASC",
 	},
 	{ // [18]
 		TestName: "simplest IP Prefix (Kibana 8.13+), ipv6 field, prefix_length=0",
@@ -2972,9 +2949,8 @@ var AggregationTests = []testdata.AggregationTestCase{
 		ExpectedPancakeResults: []model.QueryResultRow{
 			{Cols: []model.QueryResultCol{model.NewQueryResultCol("aggr__2__count", 14074)}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT count(*) AS "aggr__2__count"
-			FROM __quesma_table_name`,
+		ExpectedPancakeSQL: "SELECT count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`",
 	},
 	{ // [19]
 		TestName: "simplest IP Prefix (Kibana 8.13+), ipv6 field, prefix_length=128",
@@ -3035,11 +3011,10 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", 2),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT intDiv("clientip", 1) AS "aggr__2__key_0", count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY intDiv("clientip", 1) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT intDiv(`clientip`, 1) AS `aggr__2__key_0`, count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY intDiv(`clientip`, 1) AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [20]
 		TestName: "IP Prefix (Kibana 8.13+), ipv6 field, keyed=true and overflow of 1<<(128-prefix_length)",
@@ -3090,12 +3065,11 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", 14074),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT intDiv("clientip", 1237940039285380274899124224) AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY intDiv("clientip", 1237940039285380274899124224) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT intDiv(`clientip`, 1237940039285380274899124224) AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY intDiv(`clientip`, 1237940039285380274899124224) AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [21]
 		TestName: "simple IP Prefix (Kibana 8.13+), ipv6 field, non-zero& and non-ipv4 key",
@@ -3146,12 +3120,11 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", 14074),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT intDiv("clientip", 8589934592) AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY intDiv("clientip", 8589934592) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT intDiv(`clientip`, 8589934592) AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY intDiv(`clientip`, 8589934592) AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [22]
 		TestName: "IP Prefix (Kibana 8.13+), ipv6 field, multiple keys and append_prefix_length=true",
@@ -3202,12 +3175,11 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", 14074),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT intDiv("clientip", 8589934592) AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY intDiv("clientip", 8589934592) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT intDiv(`clientip`, 8589934592) AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY intDiv(`clientip`, 8589934592) AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [23]
 		TestName: "IP Prefix (Kibana 8.13+), ipv6 field, multiple keys and append_prefix_length=true",
@@ -3269,12 +3241,11 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", 6784),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT intDiv("clientip", 2147483648) AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY intDiv("clientip", 2147483648) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT intDiv(`clientip`, 2147483648) AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY intDiv(`clientip`, 2147483648) AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [24]
 		TestName: "Simplest IP range. In Kibana: Add panel > Aggregation Based > Area. Buckets: X-asis: IP Range",
@@ -3363,13 +3334,12 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("range_3__aggr__2__count", int64(534)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT countIf(("clientip">='0.0.0.0' AND "clientip"<'127.255.255.255')) AS
-			  "range_0__aggr__2__count",
-			  countIf("clientip">='128.0.0.0') AS "range_1__aggr__2__count",
-			  countIf("clientip">='128.129.130.131') AS "range_2__aggr__2__count",
-			  countIf("clientip"<'10.0.0.5') AS "range_3__aggr__2__count"
-			FROM __quesma_table_name`,
+		ExpectedPancakeSQL: "SELECT countIf((`clientip`>='0.0.0.0' AND `clientip`<'127.255.255.255')) AS\n" +
+			"  `range_0__aggr__2__count`,\n" +
+			"  countIf(`clientip`>='128.0.0.0') AS `range_1__aggr__2__count`,\n" +
+			"  countIf(`clientip`>='128.129.130.131') AS `range_2__aggr__2__count`,\n" +
+			"  countIf(`clientip`<'10.0.0.5') AS `range_3__aggr__2__count`\n" +
+			"FROM `__quesma_table_name`",
 	},
 	{ // [25]
 		TestName: "IP range, with ranges as CIDR masks. In Kibana: Add panel > Aggregation Based > Area. Buckets: X-asis: IP Range",
@@ -3447,12 +3417,11 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("range_2__aggr__2__count", int64(3)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT countIf(("clientip">='255.255.255.252' AND "clientip"<'::1:0:0:0')) AS "range_0__aggr__2__count",
-			  countIf("clientip">='128.129.130.131') AS "range_1__aggr__2__count",
-			  countIf(("clientip">='10.0.7.96' AND "clientip"<'10.0.7.128')) AS
-			  "range_2__aggr__2__count"
-			FROM __quesma_table_name`,
+		ExpectedPancakeSQL: "SELECT countIf((`clientip`>='255.255.255.252' AND `clientip`<'::1:0:0:0')) AS `range_0__aggr__2__count`,\n" +
+			"  countIf(`clientip`>='128.129.130.131') AS `range_1__aggr__2__count`,\n" +
+			"  countIf((`clientip`>='10.0.7.96' AND `clientip`<'10.0.7.128')) AS\n" +
+			"  `range_2__aggr__2__count`\n" +
+			"FROM `__quesma_table_name`",
 	},
 	{ // [26]
 		TestName: "IP range, with ranges as CIDR masks, keyed=true. In Kibana: Add panel > Aggregation Based > Area. Buckets: X-asis: IP Range",
@@ -3528,12 +3497,11 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("range_2__aggr__2__count", int64(3)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT countIf(("clientip">='255.255.255.254' AND "clientip"<'::1:0:0:0')) AS "range_0__aggr__2__count",
-			  countIf("clientip">='128.129.130.131') AS "range_1__aggr__2__count",
-			  countIf(("clientip">='10.0.7.96' AND "clientip"<'10.0.7.128')) AS
-			  "range_2__aggr__2__count"
-			FROM __quesma_table_name`,
+		ExpectedPancakeSQL: "SELECT countIf((`clientip`>='255.255.255.254' AND `clientip`<'::1:0:0:0')) AS `range_0__aggr__2__count`,\n" +
+			"  countIf(`clientip`>='128.129.130.131') AS `range_1__aggr__2__count`,\n" +
+			"  countIf((`clientip`>='10.0.7.96' AND `clientip`<'10.0.7.128')) AS\n" +
+			"  `range_2__aggr__2__count`\n" +
+			"FROM `__quesma_table_name`",
 	},
 	{ // [27]
 		TestName: "IP range ipv6",
@@ -3591,13 +3559,12 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("range_2__aggr__2__count", int64(999999)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT countIf("clientip">='1::132:13:21:23:122:22') AS
-			  "range_0__aggr__2__count",
-			  countIf("clientip"<'1::132:13:21:23:122:22') AS "range_1__aggr__2__count",
-			  countIf("clientip"<'ffff:ffff:ffff:ffff:ffff:ffff:ffff:ffff') AS
-			  "range_2__aggr__2__count"
-			FROM __quesma_table_name`,
+		ExpectedPancakeSQL: "SELECT countIf(`clientip`>='1::132:13:21:23:122:22') AS\n" +
+			"  `range_0__aggr__2__count`,\n" +
+			"  countIf(`clientip`<'1::132:13:21:23:122:22') AS `range_1__aggr__2__count`,\n" +
+			"  countIf(`clientip`<'ffff:ffff:ffff:ffff:ffff:ffff:ffff:ffff') AS\n" +
+			"  `range_2__aggr__2__count`\n" +
+			"FROM `__quesma_table_name`",
 	},
 	{ // [28]
 		TestName: "IP range ipv6 with mask",
@@ -3646,11 +3613,10 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("range_1__aggr__2__count", int64(0)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT countIf("clientip"<'4000::') AS "range_0__aggr__2__count",
-			  countIf("clientip">='ffff:ffff:ffff:ffff:ffff:ffff:ffff:fffe') AS
-			  "range_1__aggr__2__count"
-			FROM __quesma_table_name`,
+		ExpectedPancakeSQL: "SELECT countIf(`clientip`<'4000::') AS `range_0__aggr__2__count`,\n" +
+			"  countIf(`clientip`>='ffff:ffff:ffff:ffff:ffff:ffff:ffff:fffe') AS\n" +
+			"  `range_1__aggr__2__count`\n" +
+			"FROM `__quesma_table_name`",
 	},
 	{ // [29]
 		TestName: `Simplest Rate aggregation: only 'unit' present (all possible units), with date_histogram's calendar_interval,
@@ -3790,13 +3756,12 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", 5011),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp(toStartOfMonth(toTimezone("timestamp",
-			  'Europe/Warsaw'))))*1000 AS "aggr__2__key_0", count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp(toStartOfMonth(toTimezone("timestamp",
-			  'Europe/Warsaw'))))*1000 AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp(toStartOfMonth(toTimezone(`timestamp`,\n" +
+			"  'Europe/Warsaw'))))*1000 AS `aggr__2__key_0`, count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY toInt64(toUnixTimestamp(toStartOfMonth(toTimezone(`timestamp`,\n" +
+			"  'Europe/Warsaw'))))*1000 AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [30]
 		TestName: "Rate aggregation: all possible units with date_histogram's fixed_interval ('field' present)",
@@ -3906,21 +3871,20 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__week_col_0", 1958.13671875),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-			  "timestamp", 'Europe/Warsaw'))*1000) / 30000) AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count",
-			  sumOrNull("DistanceKilometers") AS "metric__2__day_col_0",
-			  sumOrNull("DistanceKilometers") AS "metric__2__hour_col_0",
-			  sumOrNull("DistanceKilometers") AS "metric__2__minute_col_0",
-			  sumOrNull("DistanceKilometers") AS "metric__2__second_col_0",
-			  sumOrNull("DistanceKilometers") AS "metric__2__week_col_0"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1731584141864) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1731585041864))
-			GROUP BY toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-			  "timestamp", 'Europe/Warsaw'))*1000) / 30000) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 30000) AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`,\n" +
+			"  sumOrNull(`DistanceKilometers`) AS `metric__2__day_col_0`,\n" +
+			"  sumOrNull(`DistanceKilometers`) AS `metric__2__hour_col_0`,\n" +
+			"  sumOrNull(`DistanceKilometers`) AS `metric__2__minute_col_0`,\n" +
+			"  sumOrNull(`DistanceKilometers`) AS `metric__2__second_col_0`,\n" +
+			"  sumOrNull(`DistanceKilometers`) AS `metric__2__week_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1731584141864) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1731585041864))\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 30000) AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [31]
 		TestName: "Rate aggregation: all possible units with date_histogram's calendar_interval ('field' present)",
@@ -4067,23 +4031,22 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__year_col_0", 5011),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp(toStartOfMonth(toTimezone("timestamp",
-			  'Europe/Warsaw'))))*1000 AS "aggr__2__key_0", count(*) AS "aggr__2__count",
-			  count("DistanceKilometers") AS "metric__2__day_col_0",
-			  count("DistanceKilometers") AS "metric__2__hour_col_0",
-			  count("DistanceKilometers") AS "metric__2__minute_col_0",
-			  count("DistanceKilometers") AS "metric__2__month_col_0",
-			  count("DistanceKilometers") AS "metric__2__quarter_col_0",
-			  count("DistanceKilometers") AS "metric__2__second_col_0",
-			  sumOrNull("DistanceKilometers") AS "metric__2__week_col_0",
-			  count("DistanceKilometers") AS "metric__2__year_col_0"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1668427553316) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1731585953316))
-			GROUP BY toInt64(toUnixTimestamp(toStartOfMonth(toTimezone("timestamp",
-			  'Europe/Warsaw'))))*1000 AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp(toStartOfMonth(toTimezone(`timestamp`,\n" +
+			"  'Europe/Warsaw'))))*1000 AS `aggr__2__key_0`, count(*) AS `aggr__2__count`,\n" +
+			"  count(`DistanceKilometers`) AS `metric__2__day_col_0`,\n" +
+			"  count(`DistanceKilometers`) AS `metric__2__hour_col_0`,\n" +
+			"  count(`DistanceKilometers`) AS `metric__2__minute_col_0`,\n" +
+			"  count(`DistanceKilometers`) AS `metric__2__month_col_0`,\n" +
+			"  count(`DistanceKilometers`) AS `metric__2__quarter_col_0`,\n" +
+			"  count(`DistanceKilometers`) AS `metric__2__second_col_0`,\n" +
+			"  sumOrNull(`DistanceKilometers`) AS `metric__2__week_col_0`,\n" +
+			"  count(`DistanceKilometers`) AS `metric__2__year_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1668427553316) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1731585953316))\n" +
+			"GROUP BY toInt64(toUnixTimestamp(toStartOfMonth(toTimezone(`timestamp`,\n" +
+			"  'Europe/Warsaw'))))*1000 AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [32]
 		TestName: "Rate aggregation with some bigger aggregation tree",
@@ -4178,32 +4141,31 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__dh1__dh2__count", 9),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__dh1__key_0", "aggr__dh1__count", "metric__dh1__rate1_col_0",
-			  "aggr__dh1__dh2__key_0", "aggr__dh1__dh2__count"
-			FROM (
-			  SELECT "aggr__dh1__key_0", "aggr__dh1__count", "metric__dh1__rate1_col_0",
-				"aggr__dh1__dh2__key_0", "aggr__dh1__dh2__count",
-				dense_rank() OVER (ORDER BY "aggr__dh1__key_0" ASC) AS
-				"aggr__dh1__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__dh1__key_0" ORDER BY
-				"aggr__dh1__dh2__key_0" ASC) AS "aggr__dh1__dh2__order_1_rank"
-			  FROM (
-				SELECT toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(
-				  toTimezone("timestamp", 'Europe/Warsaw'))*1000) / 86400000) AS
-				  "aggr__dh1__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__dh1__key_0") AS "aggr__dh1__count"
-				  ,
-				  sum(count("DistanceKilometers")) OVER (PARTITION BY "aggr__dh1__key_0") AS
-				  "metric__dh1__rate1_col_0",
-				  toInt64(toUnixTimestamp64Milli("timestamp") / 3600000) AS
-				  "aggr__dh1__dh2__key_0", count(*) AS "aggr__dh1__dh2__count"
-				FROM __quesma_table_name
-				GROUP BY toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(
-				  toTimezone("timestamp", 'Europe/Warsaw'))*1000) / 86400000) AS
-				  "aggr__dh1__key_0",
-				  toInt64(toUnixTimestamp64Milli("timestamp") / 3600000) AS
-				  "aggr__dh1__dh2__key_0"))
-			ORDER BY "aggr__dh1__order_1_rank" ASC, "aggr__dh1__dh2__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__dh1__key_0`, `aggr__dh1__count`, `metric__dh1__rate1_col_0`,\n" +
+			"  `aggr__dh1__dh2__key_0`, `aggr__dh1__dh2__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__dh1__key_0`, `aggr__dh1__count`, `metric__dh1__rate1_col_0`,\n" +
+			"    `aggr__dh1__dh2__key_0`, `aggr__dh1__dh2__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__dh1__key_0` ASC) AS\n" +
+			"    `aggr__dh1__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__dh1__key_0` ORDER BY\n" +
+			"    `aggr__dh1__dh2__key_0` ASC) AS `aggr__dh1__dh2__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(\n" +
+			"      toTimezone(`timestamp`, 'Europe/Warsaw'))*1000) / 86400000) AS\n" +
+			"      `aggr__dh1__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__dh1__key_0`) AS `aggr__dh1__count`\n" +
+			"      ,\n" +
+			"      sum(count(`DistanceKilometers`)) OVER (PARTITION BY `aggr__dh1__key_0`) AS\n" +
+			"      `metric__dh1__rate1_col_0`,\n" +
+			"      toInt64(toUnixTimestamp64Milli(`timestamp`) / 3600000) AS\n" +
+			"      `aggr__dh1__dh2__key_0`, count(*) AS `aggr__dh1__dh2__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(\n" +
+			"      toTimezone(`timestamp`, 'Europe/Warsaw'))*1000) / 86400000) AS\n" +
+			"      `aggr__dh1__key_0`,\n" +
+			"      toInt64(toUnixTimestamp64Milli(`timestamp`) / 3600000) AS\n" +
+			"      `aggr__dh1__dh2__key_0`))\n" +
+			"ORDER BY `aggr__dh1__order_1_rank` ASC, `aggr__dh1__dh2__order_1_rank` ASC",
 	},
 }
diff --git a/platform/testdata/kibana-visualize/pipeline_aggregation_requests.go b/platform/testdata/kibana-visualize/pipeline_aggregation_requests.go
index 00863aab6..12c9140c5 100644
--- a/platform/testdata/kibana-visualize/pipeline_aggregation_requests.go
+++ b/platform/testdata/kibana-visualize/pipeline_aggregation_requests.go
@@ -178,35 +178,33 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__1-bucket__1-metric_col_0", time.UnixMilli(1727128681000)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__key_0", "aggr__2__count", "aggr__2__1-bucket__key_0",
-			  "aggr__2__1-bucket__count", "metric__2__1-bucket__1-metric_col_0"
-			FROM (
-			  SELECT "aggr__2__key_0", "aggr__2__count", "aggr__2__1-bucket__key_0",
-				"aggr__2__1-bucket__count", "metric__2__1-bucket__1-metric_col_0",
-				dense_rank() OVER (ORDER BY "aggr__2__key_0" ASC) AS "aggr__2__order_1_rank"
-				,
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY "aggr__2__key_0"
-				ASC, "aggr__2__1-bucket__key_0" ASC) AS "aggr__2__1-bucket__order_1_rank"
-			  FROM (
-				SELECT toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(
-				  toTimezone("timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS
-				  "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-				  "timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS
-				  "aggr__2__1-bucket__key_0", count(*) AS "aggr__2__1-bucket__count",
-				  maxOrNull("timestamp") AS "metric__2__1-bucket__1-metric_col_0"
-				FROM __quesma_table_name
-				WHERE ("timestamp">=fromUnixTimestamp64Milli(1726848963807) AND "timestamp"
-				  <=fromUnixTimestamp64Milli(1728144963807))
-				GROUP BY toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(
-				  toTimezone("timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS
-				  "aggr__2__key_0",
-				  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-				  "timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS
-				  "aggr__2__1-bucket__key_0"))
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__1-bucket__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__key_0`, `aggr__2__count`, `aggr__2__1-bucket__key_0`,\n" +
+			"  `aggr__2__1-bucket__count`, `metric__2__1-bucket__1-metric_col_0`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__key_0`, `aggr__2__count`, `aggr__2__1-bucket__key_0`,\n" +
+			"    `aggr__2__1-bucket__count`, `metric__2__1-bucket__1-metric_col_0`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__key_0` ASC) AS `aggr__2__order_1_rank`\n" +
+			"    ,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY `aggr__2__key_0`\n" +
+			"    ASC, `aggr__2__1-bucket__key_0` ASC) AS `aggr__2__1-bucket__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(\n" +
+			"      toTimezone(`timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS\n" +
+			"      `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"      `timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS\n" +
+			"      `aggr__2__1-bucket__key_0`, count(*) AS `aggr__2__1-bucket__count`,\n" +
+			"      maxOrNull(`timestamp`) AS `metric__2__1-bucket__1-metric_col_0`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    WHERE (`timestamp`>=fromUnixTimestamp64Milli(1726848963807) AND `timestamp`<=fromUnixTimestamp64Milli(1728144963807))\n" +
+			"    GROUP BY toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(\n" +
+			"      toTimezone(`timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS\n" +
+			"      `aggr__2__key_0`,\n" +
+			"      toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"      `timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS\n" +
+			"      `aggr__2__1-bucket__key_0`))\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__1-bucket__order_1_rank` ASC",
 	},
 	{ // [1]
 		TestName: "Reproduce: Visualize -> Vertical Bar: Metrics: Cumulative Sum (Aggregation: Avg), Buckets: Date Histogram",
@@ -356,15 +354,14 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__1-metric_col_0", 7.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-			  "timestamp", 'Europe/Warsaw'))*1000) / 60000) AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count",
-			  avgOrNull("dayOfWeek") AS "metric__2__1-metric_col_0"
-			FROM __quesma_table_name
-			GROUP BY toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-			  "timestamp", 'Europe/Warsaw'))*1000) / 60000) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 60000) AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`,\n" +
+			"  avgOrNull(`dayOfWeek`) AS `metric__2__1-metric_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 60000) AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [2]
 		TestName: "Reproduce: Visualize -> Vertical Bar: Metrics: Cumulative Sum (Aggregation: Cumulative Sum (Aggregation: Count)), Buckets: Date Histogram",
@@ -527,33 +524,32 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__3-bucket__3-metric_col_0", 165),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__key_0", "aggr__2__count", "aggr__2__3-bucket__key_0",
-			  "aggr__2__3-bucket__count", "metric__2__3-bucket__3-metric_col_0"
-			FROM (
-			  SELECT "aggr__2__key_0", "aggr__2__count", "aggr__2__3-bucket__key_0",
-				"aggr__2__3-bucket__count", "metric__2__3-bucket__3-metric_col_0",
-				dense_rank() OVER (ORDER BY "aggr__2__key_0" ASC) AS "aggr__2__order_1_rank"
-				,
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY "aggr__2__key_0"
-				ASC, "aggr__2__3-bucket__key_0" ASC) AS "aggr__2__3-bucket__order_1_rank"
-			  FROM (
-				SELECT toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(
-				  toTimezone("timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS
-				  "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-				  "timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS
-				  "aggr__2__3-bucket__key_0", count(*) AS "aggr__2__3-bucket__count",
-				  uniq("timestamp") AS "metric__2__3-bucket__3-metric_col_0"
-				FROM __quesma_table_name
-				GROUP BY toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(
-				  toTimezone("timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS
-				  "aggr__2__key_0",
-				  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-				  "timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS
-				  "aggr__2__3-bucket__key_0"))
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__3-bucket__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__key_0`, `aggr__2__count`, `aggr__2__3-bucket__key_0`,\n" +
+			"  `aggr__2__3-bucket__count`, `metric__2__3-bucket__3-metric_col_0`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__key_0`, `aggr__2__count`, `aggr__2__3-bucket__key_0`,\n" +
+			"    `aggr__2__3-bucket__count`, `metric__2__3-bucket__3-metric_col_0`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__key_0` ASC) AS `aggr__2__order_1_rank`\n" +
+			"    ,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY `aggr__2__key_0`\n" +
+			"    ASC, `aggr__2__3-bucket__key_0` ASC) AS `aggr__2__3-bucket__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(\n" +
+			"      toTimezone(`timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS\n" +
+			"      `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"      `timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS\n" +
+			"      `aggr__2__3-bucket__key_0`, count(*) AS `aggr__2__3-bucket__count`,\n" +
+			"      uniq(`timestamp`) AS `metric__2__3-bucket__3-metric_col_0`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(\n" +
+			"      toTimezone(`timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS\n" +
+			"      `aggr__2__key_0`,\n" +
+			"      toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"      `timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS\n" +
+			"      `aggr__2__3-bucket__key_0`))\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__3-bucket__order_1_rank` ASC",
 	},
 	{ // [3]
 		TestName: "Reproduce: Visualize -> Vertical Bar: Metrics: Cumulative Sum (Aggregation: Count), Buckets: Histogram" +
@@ -668,11 +664,10 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", int64(3)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT floor("DistanceMiles"/10)*10 AS "aggr__2__key_0",
-              count(*) AS "aggr__2__count"
-            FROM __quesma_table_name
-            GROUP BY floor("DistanceMiles"/10)*10 AS "aggr__2__key_0"
-            ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT floor(`DistanceMiles`/10)*10 AS `aggr__2__key_0`,\n" +
+			"              count(*) AS `aggr__2__count`\n" +
+			"            FROM `__quesma_table_name`\n" +
+			"            GROUP BY floor(`DistanceMiles`/10)*10 AS `aggr__2__key_0`\n" +
+			"            ORDER BY `aggr__2__key_0` ASC",
 	},
 }
diff --git a/platform/testdata/kibana_sample_data_ecommerce.go b/platform/testdata/kibana_sample_data_ecommerce.go
index 30a55beef..602a19eca 100644
--- a/platform/testdata/kibana_sample_data_ecommerce.go
+++ b/platform/testdata/kibana_sample_data_ecommerce.go
@@ -104,12 +104,11 @@ var KibanaSampleDataEcommerce = []AggregationTestCase{
 				model.NewQueryResultCol("metric__minAgg_col_0", 2.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT maxOrNull("total_quantity") AS "metric__maxAgg_col_0",
-			  minOrNull("total_quantity") AS "metric__minAgg_col_0"
-			FROM __quesma_table_name
-			WHERE ("order_date">=fromUnixTimestamp64Milli(1739980133594) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1740584933594))`,
+		ExpectedPancakeSQL: "SELECT maxOrNull(`total_quantity`) AS `metric__maxAgg_col_0`,\n" +
+			"  minOrNull(`total_quantity`) AS `metric__minAgg_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`order_date`>=fromUnixTimestamp64Milli(1739980133594) AND `order_date`<= \n" +
+			"  fromUnixTimestamp64Milli(1740584933594))",
 	},
 	{ // [1]
 		TestName: "Promotions tracking (request 1/3)",
@@ -376,81 +375,74 @@ var KibanaSampleDataEcommerce = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__1__2__count", int64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__1__count",
-			  toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone(
-			  "order_date", 'Europe/Warsaw'))*1000) / 43200000) AS "aggr__1__2__key_0",
-			  count(*) AS "aggr__1__2__count"
-			FROM __quesma_table_name
-			WHERE (("order_date">=fromUnixTimestamp64Milli(1739980133594) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1740584933594)) AND "taxful_total_price" > '250')
-			GROUP BY toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone
-			  ("order_date", 'Europe/Warsaw'))*1000) / 43200000) AS "aggr__1__2__key_0"
-			ORDER BY "aggr__1__2__key_0" ASC`,
-		ExpectedAdditionalPancakeSQLs: []string{`
-				WITH quesma_top_hits_group_table AS (
-				  SELECT sum(count(*)) OVER () AS "aggr__1__count",
-					toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone(
-					"order_date", 'Europe/Warsaw'))*1000) / 43200000) AS "aggr__1__2__key_0",
-					count(*) AS "aggr__1__2__count"
-				  FROM __quesma_table_name
-				  WHERE (("order_date">=fromUnixTimestamp64Milli(1739980133594) AND "order_date"
-					<=fromUnixTimestamp64Milli(1740584933594)) AND "taxful_total_price" > '250')
-				  GROUP BY toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(
-					toTimezone("order_date", 'Europe/Warsaw'))*1000) / 43200000) AS
-					"aggr__1__2__key_0"
-				  ORDER BY "aggr__1__2__key_0" ASC) ,
-				quesma_top_hits_join AS (
-				  SELECT "group_table"."aggr__1__count" AS "aggr__1__count",
-					"group_table"."aggr__1__2__key_0" AS "aggr__1__2__key_0",
-					"group_table"."aggr__1__2__count" AS "aggr__1__2__count",
-					"hit_table"."order_date" AS "top_metrics__1__2__4_col_0",
-					"hit_table"."order_date" AS "top_metrics__1__2__4_col_1",
-					ROW_NUMBER() OVER (PARTITION BY "group_table"."aggr__1__2__key_0" ORDER BY
-					"order_date" ASC) AS "top_hits_rank"
-				  FROM quesma_top_hits_group_table AS "group_table" LEFT OUTER JOIN
-					__quesma_table_name AS "hit_table" ON ("group_table"."aggr__1__2__key_0"=
-					toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone(
-					"order_date", 'Europe/Warsaw'))*1000) / 43200000))
-				  WHERE (("order_date">=fromUnixTimestamp64Milli(1739980133594) AND "order_date"
-					<=fromUnixTimestamp64Milli(1740584933594)) AND "taxful_total_price" > '250'))
-				SELECT "aggr__1__count", "aggr__1__2__key_0", "aggr__1__2__count",
-				  "top_metrics__1__2__4_col_0", "top_metrics__1__2__4_col_1", "top_hits_rank"
-				FROM "quesma_top_hits_join"
-				WHERE "top_hits_rank"<=10
-				ORDER BY "aggr__1__2__key_0" ASC, "top_hits_rank" ASC`,
-			`
-				WITH quesma_top_hits_group_table AS (
-				  SELECT sum(count(*)) OVER () AS "aggr__1__count",
-					toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone(
-					"order_date", 'Europe/Warsaw'))*1000) / 43200000) AS "aggr__1__2__key_0",
-					count(*) AS "aggr__1__2__count"
-				  FROM __quesma_table_name
-				  WHERE (("order_date">=fromUnixTimestamp64Milli(1739980133594) AND "order_date"
-					<=fromUnixTimestamp64Milli(1740584933594)) AND "taxful_total_price" > '250')
-				  GROUP BY toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(
-					toTimezone("order_date", 'Europe/Warsaw'))*1000) / 43200000) AS
-					"aggr__1__2__key_0"
-				  ORDER BY "aggr__1__2__key_0" ASC) ,
-				quesma_top_hits_join AS (
-				  SELECT "group_table"."aggr__1__count" AS "aggr__1__count",
-					"group_table"."aggr__1__2__key_0" AS "aggr__1__2__key_0",
-					"group_table"."aggr__1__2__count" AS "aggr__1__2__count",
-					"hit_table"."taxful_total_price" AS "top_metrics__1__2__5_col_0",
-					"hit_table"."order_date" AS "top_metrics__1__2__5_col_1",
-					ROW_NUMBER() OVER (PARTITION BY "group_table"."aggr__1__2__key_0" ORDER BY
-					"order_date" ASC) AS "top_hits_rank"
-				  FROM quesma_top_hits_group_table AS "group_table" LEFT OUTER JOIN
-					__quesma_table_name AS "hit_table" ON ("group_table"."aggr__1__2__key_0"=
-					toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone(
-					"order_date", 'Europe/Warsaw'))*1000) / 43200000))
-				  WHERE (("order_date">=fromUnixTimestamp64Milli(1739980133594) AND "order_date"
-					<=fromUnixTimestamp64Milli(1740584933594)) AND "taxful_total_price" > '250'))
-				SELECT "aggr__1__count", "aggr__1__2__key_0", "aggr__1__2__count",
-				  "top_metrics__1__2__5_col_0", "top_metrics__1__2__5_col_1", "top_hits_rank"
-				FROM "quesma_top_hits_join"
-				WHERE "top_hits_rank"<=10
-				ORDER BY "aggr__1__2__key_0" ASC, "top_hits_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__1__count`,\n" +
+			"  toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone(\n" +
+			"  `order_date`, 'Europe/Warsaw'))*1000) / 43200000) AS `aggr__1__2__key_0`,\n" +
+			"  count(*) AS `aggr__1__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE ((`order_date`>=fromUnixTimestamp64Milli(1739980133594) AND `order_date`<=fromUnixTimestamp64Milli(1740584933594)) AND `taxful_total_price` > '250')\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone\n" +
+			"  (`order_date`, 'Europe/Warsaw'))*1000) / 43200000) AS `aggr__1__2__key_0`\n" +
+			"ORDER BY `aggr__1__2__key_0` ASC",
+		ExpectedAdditionalPancakeSQLs: []string{
+			" WITH quesma_top_hits_group_table AS (\n" +
+				"  SELECT sum(count(*)) OVER () AS `aggr__1__count`,\n" +
+				"    toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone(\n" +
+				"    `order_date`, 'Europe/Warsaw'))*1000) / 43200000) AS `aggr__1__2__key_0`,\n" +
+				"    count(*) AS `aggr__1__2__count`\n" +
+				"  FROM `__quesma_table_name`\n" +
+				"  WHERE ((`order_date`>=fromUnixTimestamp64Milli(1739980133594) AND `order_date`<=fromUnixTimestamp64Milli(1740584933594)) AND `taxful_total_price` > '250')\n" +
+				"  GROUP BY toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(\n" +
+				"    toTimezone(`order_date`, 'Europe/Warsaw'))*1000) / 43200000) AS\n" +
+				"    `aggr__1__2__key_0`\n" +
+				"  ORDER BY `aggr__1__2__key_0` ASC) ,\n" +
+				"quesma_top_hits_join AS (\n" +
+				"  SELECT `group_table`.`aggr__1__count` AS `aggr__1__count`,\n" +
+				"    `group_table`.`aggr__1__2__key_0` AS `aggr__1__2__key_0`,\n" +
+				"    `group_table`.`aggr__1__2__count` AS `aggr__1__2__count`,\n" +
+				"    `hit_table`.`order_date` AS `top_metrics__1__2__4_col_0`,\n" +
+				"    `hit_table`.`order_date` AS `top_metrics__1__2__4_col_1`,\n" +
+				"    ROW_NUMBER() OVER (PARTITION BY `group_table`.`aggr__1__2__key_0` ORDER BY\n" +
+				"    `order_date` ASC) AS `top_hits_rank`\n" +
+				"  FROM quesma_top_hits_group_table AS `group_table` LEFT OUTER JOIN\n" +
+				"    `__quesma_table_name` AS `hit_table` ON (`group_table`.`aggr__1__2__key_0`=\n" +
+				"    toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone(\n" +
+				"    `order_date`, 'Europe/Warsaw'))*1000) / 43200000))\n" +
+				"  WHERE ((`order_date`>=fromUnixTimestamp64Milli(1739980133594) AND `order_date`<=fromUnixTimestamp64Milli(1740584933594)) AND `taxful_total_price` > '250'))\n" +
+				"SELECT `aggr__1__count`, `aggr__1__2__key_0`, `aggr__1__2__count`,\n" +
+				"  `top_metrics__1__2__4_col_0`, `top_metrics__1__2__4_col_1`, `top_hits_rank`\n" +
+				"FROM `quesma_top_hits_join`\n" +
+				"WHERE `top_hits_rank`<=10\n" +
+				"ORDER BY `aggr__1__2__key_0` ASC, `top_hits_rank` ASC",
+			" WITH quesma_top_hits_group_table AS (\n" +
+				"  SELECT sum(count(*)) OVER () AS `aggr__1__count`,\n" +
+				"    toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone(\n" +
+				"    `order_date`, 'Europe/Warsaw'))*1000) / 43200000) AS `aggr__1__2__key_0`,\n" +
+				"    count(*) AS `aggr__1__2__count`\n" +
+				"  FROM `__quesma_table_name`\n" +
+				"  WHERE ((`order_date`>=fromUnixTimestamp64Milli(1739980133594) AND `order_date`<=fromUnixTimestamp64Milli(1740584933594)) AND `taxful_total_price` > '250')\n" +
+				"  GROUP BY toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(\n" +
+				"    toTimezone(`order_date`, 'Europe/Warsaw'))*1000) / 43200000) AS\n" +
+				"    `aggr__1__2__key_0`\n" +
+				"  ORDER BY `aggr__1__2__key_0` ASC) ,\n" +
+				"quesma_top_hits_join AS (\n" +
+				"  SELECT `group_table`.`aggr__1__count` AS `aggr__1__count`,\n" +
+				"    `group_table`.`aggr__1__2__key_0` AS `aggr__1__2__key_0`,\n" +
+				"    `group_table`.`aggr__1__2__count` AS `aggr__1__2__count`,\n" +
+				"    `hit_table`.`taxful_total_price` AS `top_metrics__1__2__5_col_0`,\n" +
+				"    `hit_table`.`order_date` AS `top_metrics__1__2__5_col_1`,\n" +
+				"    ROW_NUMBER() OVER (PARTITION BY `group_table`.`aggr__1__2__key_0` ORDER BY\n" +
+				"    `order_date` ASC) AS `top_hits_rank`\n" +
+				"  FROM quesma_top_hits_group_table AS `group_table` LEFT OUTER JOIN\n" +
+				"    `__quesma_table_name` AS `hit_table` ON (`group_table`.`aggr__1__2__key_0`=\n" +
+				"    toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone(\n" +
+				"    `order_date`, 'Europe/Warsaw'))*1000) / 43200000))\n" +
+				"  WHERE ((`order_date`>=fromUnixTimestamp64Milli(1739980133594) AND `order_date`<=fromUnixTimestamp64Milli(1740584933594)) AND `taxful_total_price` > '250'))\n" +
+				"SELECT `aggr__1__count`, `aggr__1__2__key_0`, `aggr__1__2__count`,\n" +
+				"  `top_metrics__1__2__5_col_0`, `top_metrics__1__2__5_col_1`, `top_hits_rank`\n" +
+				"FROM `quesma_top_hits_join`\n" +
+				"WHERE `top_hits_rank`<=10\n" +
+				"ORDER BY `aggr__1__2__key_0` ASC, `top_hits_rank` ASC",
 		},
 		ExpectedAdditionalPancakeResults: [][]model.QueryResultRow{
 			{
@@ -683,20 +675,19 @@ var KibanaSampleDataEcommerce = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0__1-bucket__1-metric_col_0", 0.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone(
-			  "order_date", 'Europe/Warsaw'))*1000) / 43200000) AS "aggr__0__key_0",
-			  count(*) AS "aggr__0__count",
-			  countIf("products.product_name" __quesma_match '%trouser%') AS
-			  "aggr__0__1-bucket__count",
-			  sumOrNullIf("taxful_total_price", "products.product_name" __quesma_match '%trouser%')
-			  AS "metric__0__1-bucket__1-metric_col_0"
-			FROM __quesma_table_name
-			WHERE ("order_date">=fromUnixTimestamp64Milli(1739979776601) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1740584576601))
-			GROUP BY toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone
-			  ("order_date", 'Europe/Warsaw'))*1000) / 43200000) AS "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone(\n" +
+			"  `order_date`, 'Europe/Warsaw'))*1000) / 43200000) AS `aggr__0__key_0`,\n" +
+			"  count(*) AS `aggr__0__count`,\n" +
+			"  countIf(`products.product_name` __quesma_match '%trouser%') AS\n" +
+			"  `aggr__0__1-bucket__count`,\n" +
+			"  sumOrNullIf(`taxful_total_price`, `products.product_name` __quesma_match '%trouser%')\n" +
+			"  AS `metric__0__1-bucket__1-metric_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`order_date`>=fromUnixTimestamp64Milli(1739979776601) AND `order_date`<= \n" +
+			"  fromUnixTimestamp64Milli(1740584576601))\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone\n" +
+			"  (`order_date`, 'Europe/Warsaw'))*1000) / 43200000) AS `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__key_0` ASC",
 	},
 	{ // [3]
 		TestName: "Promotions tracking (request 3/3)",
@@ -854,22 +845,21 @@ var KibanaSampleDataEcommerce = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0__1-bucket__1-metric_col_0", 0.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone(
-			  "order_date", 'Europe/Warsaw'))*1000) / 43200000) AS "aggr__0__key_0",
-			  count(*) AS "aggr__0__count",
-			  countIf(("products.product_name" __quesma_match '%cocktail' OR
-			  "__quesma_fulltext_field_name" __quesma_match 'dress%')) AS
-			  "aggr__0__1-bucket__count",
-			  sumOrNullIf("taxful_total_price", ("products.product_name" __quesma_match '%cocktail'
-			  OR "__quesma_fulltext_field_name" __quesma_match 'dress%')) AS
-			  "metric__0__1-bucket__1-metric_col_0"
-			FROM __quesma_table_name
-			WHERE ("order_date">=fromUnixTimestamp64Milli(1740234098238) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1740838898238))
-			GROUP BY toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone
-			  ("order_date", 'Europe/Warsaw'))*1000) / 43200000) AS "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone(\n" +
+			"  `order_date`, 'Europe/Warsaw'))*1000) / 43200000) AS `aggr__0__key_0`,\n" +
+			"  count(*) AS `aggr__0__count`,\n" +
+			"  countIf((`products.product_name` __quesma_match '%cocktail' OR\n" +
+			"  `__quesma_fulltext_field_name` __quesma_match 'dress%')) AS\n" +
+			"  `aggr__0__1-bucket__count`,\n" +
+			"  sumOrNullIf(`taxful_total_price`, (`products.product_name` __quesma_match '%cocktail'\n" +
+			"  OR `__quesma_fulltext_field_name` __quesma_match 'dress%')) AS\n" +
+			"  `metric__0__1-bucket__1-metric_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`order_date`>=fromUnixTimestamp64Milli(1740234098238) AND `order_date`<= \n" +
+			"  fromUnixTimestamp64Milli(1740838898238))\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone\n" +
+			"  (`order_date`, 'Europe/Warsaw'))*1000) / 43200000) AS `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__key_0` ASC",
 	},
 	{ // [4]
 		TestName: "Sum of revenue",
@@ -962,11 +952,10 @@ var KibanaSampleDataEcommerce = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0_col_0", 77112.984375),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sumOrNull("taxful_total_price") AS "metric__0_col_0"
-			FROM __quesma_table_name
-			WHERE ("order_date">=fromUnixTimestamp64Milli(1739980133594) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1740584933594))`,
+		ExpectedPancakeSQL: "SELECT sumOrNull(`taxful_total_price`) AS `metric__0_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`order_date`>=fromUnixTimestamp64Milli(1739980133594) AND `order_date`<= \n" +
+			"  fromUnixTimestamp64Milli(1740584933594))",
 	},
 	{ // [5]
 		TestName: "Median spending",
@@ -1064,11 +1053,10 @@ var KibanaSampleDataEcommerce = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0_col_0", []float64{67.0}),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT quantiles(0.500000)("taxful_total_price") AS "metric__0_col_0"
-			FROM __quesma_table_name
-			WHERE ("order_date">=fromUnixTimestamp64Milli(1739980133594) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1740584933594))`,
+		ExpectedPancakeSQL: "SELECT quantiles(0.500000)(`taxful_total_price`) AS `metric__0_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`order_date`>=fromUnixTimestamp64Milli(1739980133594) AND `order_date`<= \n" +
+			"  fromUnixTimestamp64Milli(1740584933594))",
 	},
 	{ // [6]
 		TestName: "Avg. items sold",
@@ -1161,11 +1149,10 @@ var KibanaSampleDataEcommerce = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0_col_0", 2.164569215876089),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT avgOrNull("total_quantity") AS "metric__0_col_0"
-			FROM __quesma_table_name
-			WHERE ("order_date">=fromUnixTimestamp64Milli(1739980133594) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1740584933594))`,
+		ExpectedPancakeSQL: "SELECT avgOrNull(`total_quantity`) AS `metric__0_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`order_date`>=fromUnixTimestamp64Milli(1739980133594) AND `order_date`<= \n" +
+			"  fromUnixTimestamp64Milli(1740584933594))",
 	},
 	{ // [7]
 		TestName: "TODO Transactions per day",
@@ -1386,25 +1373,23 @@ var KibanaSampleDataEcommerce = []AggregationTestCase{
 				model.NewQueryResultCol("metric__time_offset_split__0__2_col_0", 218.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__time_offset_split__count",
-			  toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone(
-			  "order_date", 'Europe/Warsaw'))*1000) / 86400000) AS
-			  "aggr__time_offset_split__0__key_0",
-			  count(*) AS "aggr__time_offset_split__0__count",
-			  sumOrNull("products.quantity") AS "metric__time_offset_split__0__1_col_0",
-			  sumOrNull("products.quantity") AS "metric__time_offset_split__0__2_col_0"
-			FROM __quesma_table_name
-			WHERE ((("order_date">=fromUnixTimestamp64Milli(1740234098238) AND "order_date"
-			  <=fromUnixTimestamp64Milli(1740838898238)) OR ("order_date">=
-			  fromUnixTimestamp64Milli(1739629298238) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1740234098238))) AND ("order_date">=
-			  fromUnixTimestamp64Milli(1740234098238) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1740838898238)))
-			GROUP BY toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone
-			  ("order_date", 'Europe/Warsaw'))*1000) / 86400000) AS
-			  "aggr__time_offset_split__0__key_0"
-			ORDER BY "aggr__time_offset_split__0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__time_offset_split__count`,\n" +
+			"  toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone(\n" +
+			"  `order_date`, 'Europe/Warsaw'))*1000) / 86400000) AS\n" +
+			"  `aggr__time_offset_split__0__key_0`,\n" +
+			"  count(*) AS `aggr__time_offset_split__0__count`,\n" +
+			"  sumOrNull(`products.quantity`) AS `metric__time_offset_split__0__1_col_0`,\n" +
+			"  sumOrNull(`products.quantity`) AS `metric__time_offset_split__0__2_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (((`order_date`>=fromUnixTimestamp64Milli(1740234098238) AND `order_date`<=fromUnixTimestamp64Milli(1740838898238)) OR (`order_date`>= \n" +
+			"  fromUnixTimestamp64Milli(1739629298238) AND `order_date`<= \n" +
+			"  fromUnixTimestamp64Milli(1740234098238))) AND (`order_date`>= \n" +
+			"  fromUnixTimestamp64Milli(1740234098238) AND `order_date`<= \n" +
+			"  fromUnixTimestamp64Milli(1740838898238)))\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone\n" +
+			"  (`order_date`, 'Europe/Warsaw'))*1000) / 86400000) AS\n" +
+			"  `aggr__time_offset_split__0__key_0`\n" +
+			"ORDER BY `aggr__time_offset_split__0__key_0` ASC",
 		ExpectedAdditionalPancakeResults: [][]model.QueryResultRow{
 			{
 				{Cols: []model.QueryResultCol{
@@ -1416,25 +1401,24 @@ var KibanaSampleDataEcommerce = []AggregationTestCase{
 				}},
 			},
 		},
-		ExpectedAdditionalPancakeSQLs: []string{`
-				SELECT sum(count(*)) OVER () AS "aggr__time_offset_split__count",
-				  toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone(
-				  "order_date", 'Europe/Warsaw'))*1000) / 86400000) AS
-				  "aggr__time_offset_split__0__key_0",
-				  count(*) AS "aggr__time_offset_split__0__count",
-				  sumOrNull("products.quantity") AS "metric__time_offset_split__0__1_col_0",
-				  sumOrNull("products.quantity") AS "metric__time_offset_split__0__2_col_0"
-				FROM __quesma_table_name
-				WHERE ((("order_date">=fromUnixTimestamp64Milli(1740234098238) AND "order_date"
-				  <=fromUnixTimestamp64Milli(1740838898238)) OR ("order_date">=
-				  fromUnixTimestamp64Milli(1739629298238) AND "order_date"<=
-				  fromUnixTimestamp64Milli(1740234098238))) AND ("order_date">=
-				  fromUnixTimestamp64Milli(1739629298238) AND "order_date"<=
-				  fromUnixTimestamp64Milli(1740234098238)))
-				GROUP BY toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone
-				  ("order_date", 'Europe/Warsaw'))*1000) / 86400000) AS
-				  "aggr__time_offset_split__0__key_0"
-				ORDER BY "aggr__time_offset_split__0__key_0" ASC`},
+		ExpectedAdditionalPancakeSQLs: []string{
+			"SELECT sum(count(*)) OVER () AS `aggr__time_offset_split__count`,\n" +
+				"  toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone(\n" +
+				"  `order_date`, 'Europe/Warsaw'))*1000) / 86400000) AS\n" +
+				"  `aggr__time_offset_split__0__key_0`,\n" +
+				"  count(*) AS `aggr__time_offset_split__0__count`,\n" +
+				"  sumOrNull(`products.quantity`) AS `metric__time_offset_split__0__1_col_0`,\n" +
+				"  sumOrNull(`products.quantity`) AS `metric__time_offset_split__0__2_col_0`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"WHERE (((`order_date`>=fromUnixTimestamp64Milli(1740234098238) AND `order_date`<=fromUnixTimestamp64Milli(1740838898238)) OR (`order_date`>= \n" +
+				"  fromUnixTimestamp64Milli(1739629298238) AND `order_date`<= \n" +
+				"  fromUnixTimestamp64Milli(1740234098238))) AND (`order_date`>= \n" +
+				"  fromUnixTimestamp64Milli(1739629298238) AND `order_date`<= \n" +
+				"  fromUnixTimestamp64Milli(1740234098238)))\n" +
+				"GROUP BY toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone\n" +
+				"  (`order_date`, 'Europe/Warsaw'))*1000) / 86400000) AS\n" +
+				"  `aggr__time_offset_split__0__key_0`\n" +
+				"ORDER BY `aggr__time_offset_split__0__key_0` ASC"},
 	},
 	{ // [8]
 		TestName: "TODO Daily comparison",
@@ -1648,25 +1632,23 @@ var KibanaSampleDataEcommerce = []AggregationTestCase{
 				model.NewQueryResultCol("metric__time_offset_split__0__2_col_0", 4033.34375),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__time_offset_split__count",
-			  toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone(
-			  "order_date", 'Europe/Warsaw'))*1000) / 86400000) AS
-			  "aggr__time_offset_split__0__key_0",
-			  count(*) AS "aggr__time_offset_split__0__count",
-			  sumOrNull("taxful_total_price") AS "metric__time_offset_split__0__1_col_0",
-			  sumOrNull("taxful_total_price") AS "metric__time_offset_split__0__2_col_0"
-			FROM __quesma_table_name
-			WHERE ((("order_date">=fromUnixTimestamp64Milli(1740234098238) AND "order_date"
-			  <=fromUnixTimestamp64Milli(1740838898238)) OR ("order_date">=
-			  fromUnixTimestamp64Milli(1739629298238) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1740234098238))) AND ("order_date">=
-			  fromUnixTimestamp64Milli(1740234098238) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1740838898238)))
-			GROUP BY toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone
-			  ("order_date", 'Europe/Warsaw'))*1000) / 86400000) AS
-			  "aggr__time_offset_split__0__key_0"
-			ORDER BY "aggr__time_offset_split__0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__time_offset_split__count`,\n" +
+			"  toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone(\n" +
+			"  `order_date`, 'Europe/Warsaw'))*1000) / 86400000) AS\n" +
+			"  `aggr__time_offset_split__0__key_0`,\n" +
+			"  count(*) AS `aggr__time_offset_split__0__count`,\n" +
+			"  sumOrNull(`taxful_total_price`) AS `metric__time_offset_split__0__1_col_0`,\n" +
+			"  sumOrNull(`taxful_total_price`) AS `metric__time_offset_split__0__2_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (((`order_date`>=fromUnixTimestamp64Milli(1740234098238) AND `order_date`<=fromUnixTimestamp64Milli(1740838898238)) OR (`order_date`>= \n" +
+			"  fromUnixTimestamp64Milli(1739629298238) AND `order_date`<= \n" +
+			"  fromUnixTimestamp64Milli(1740234098238))) AND (`order_date`>= \n" +
+			"  fromUnixTimestamp64Milli(1740234098238) AND `order_date`<= \n" +
+			"  fromUnixTimestamp64Milli(1740838898238)))\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone\n" +
+			"  (`order_date`, 'Europe/Warsaw'))*1000) / 86400000) AS\n" +
+			"  `aggr__time_offset_split__0__key_0`\n" +
+			"ORDER BY `aggr__time_offset_split__0__key_0` ASC",
 		ExpectedAdditionalPancakeResults: [][]model.QueryResultRow{
 			{
 				{Cols: []model.QueryResultCol{
@@ -1685,26 +1667,24 @@ var KibanaSampleDataEcommerce = []AggregationTestCase{
 				}},
 			},
 		},
-		ExpectedAdditionalPancakeSQLs: []string{`
-			SELECT sum(count(*)) OVER () AS "aggr__time_offset_split__count",
-			  toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone(
-			  "order_date", 'Europe/Warsaw'))*1000) / 86400000) AS
-			  "aggr__time_offset_split__0__key_0",
-			  count(*) AS "aggr__time_offset_split__0__count",
-			  sumOrNull("taxful_total_price") AS "metric__time_offset_split__0__1_col_0",
-			  sumOrNull("taxful_total_price") AS "metric__time_offset_split__0__2_col_0"
-			FROM __quesma_table_name
-			WHERE ((("order_date">=fromUnixTimestamp64Milli(1740234098238) AND "order_date"
-			  <=fromUnixTimestamp64Milli(1740838898238)) OR ("order_date">=
-			  fromUnixTimestamp64Milli(1739629298238) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1740234098238))) AND ("order_date">=
-			  fromUnixTimestamp64Milli(1739629298238) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1740234098238)))
-			GROUP BY toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone
-			  ("order_date", 'Europe/Warsaw'))*1000) / 86400000) AS
-			  "aggr__time_offset_split__0__key_0"
-			ORDER BY "aggr__time_offset_split__0__key_0" ASC`,
-		},
+		ExpectedAdditionalPancakeSQLs: []string{
+			"SELECT sum(count(*)) OVER () AS `aggr__time_offset_split__count`,\n" +
+				"  toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone(\n" +
+				"  `order_date`, 'Europe/Warsaw'))*1000) / 86400000) AS\n" +
+				"  `aggr__time_offset_split__0__key_0`,\n" +
+				"  count(*) AS `aggr__time_offset_split__0__count`,\n" +
+				"  sumOrNull(`taxful_total_price`) AS `metric__time_offset_split__0__1_col_0`,\n" +
+				"  sumOrNull(`taxful_total_price`) AS `metric__time_offset_split__0__2_col_0`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"WHERE (((`order_date`>=fromUnixTimestamp64Milli(1740234098238) AND `order_date`<=fromUnixTimestamp64Milli(1740838898238)) OR (`order_date`>= \n" +
+				"  fromUnixTimestamp64Milli(1739629298238) AND `order_date`<= \n" +
+				"  fromUnixTimestamp64Milli(1740234098238))) AND (`order_date`>= \n" +
+				"  fromUnixTimestamp64Milli(1739629298238) AND `order_date`<= \n" +
+				"  fromUnixTimestamp64Milli(1740234098238)))\n" +
+				"GROUP BY toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone\n" +
+				"  (`order_date`, 'Europe/Warsaw'))*1000) / 86400000) AS\n" +
+				"  `aggr__time_offset_split__0__key_0`\n" +
+				"ORDER BY `aggr__time_offset_split__0__key_0` ASC"},
 	},
 	{ // [9]
 		TestName: "TODO Top products this/last week",
@@ -2039,31 +2019,30 @@ var KibanaSampleDataEcommerce = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__0__1__count", int64(52)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-			  "aggr__0__1__key_0", "aggr__0__1__count"
-			FROM (
-			  SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-				"aggr__0__1__key_0", "aggr__0__1__count",
-				dense_rank() OVER (ORDER BY "aggr__0__count" DESC, "aggr__0__key_0" ASC) AS
-				"aggr__0__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0" ORDER BY
-				"aggr__0__1__key_0" ASC) AS "aggr__0__1__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-				  "category" AS "aggr__0__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS "aggr__0__count",
-				  toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone(
-				  "order_date", 'Europe/Warsaw'))*1000) / 86400000) AS "aggr__0__1__key_0",
-				  count(*) AS "aggr__0__1__count"
-				FROM __quesma_table_name
-				WHERE ("order_date">=fromUnixTimestamp64Milli(1740234098238) AND
-				  "order_date"<=fromUnixTimestamp64Milli(1740838898238))
-				GROUP BY "category" AS "aggr__0__key_0",
-				  toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone(
-				  "order_date", 'Europe/Warsaw'))*1000) / 86400000) AS "aggr__0__1__key_0"))
-			WHERE "aggr__0__order_1_rank"<=11
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"  `aggr__0__1__key_0`, `aggr__0__1__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"    `aggr__0__1__key_0`, `aggr__0__1__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__0__count` DESC, `aggr__0__key_0` ASC) AS\n" +
+			"    `aggr__0__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0` ORDER BY\n" +
+			"    `aggr__0__1__key_0` ASC) AS `aggr__0__1__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"      `category` AS `aggr__0__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS `aggr__0__count`,\n" +
+			"      toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone(\n" +
+			"      `order_date`, 'Europe/Warsaw'))*1000) / 86400000) AS `aggr__0__1__key_0`,\n" +
+			"      count(*) AS `aggr__0__1__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    WHERE (`order_date`>=fromUnixTimestamp64Milli(1740234098238) AND\n" +
+			"      `order_date`<=fromUnixTimestamp64Milli(1740838898238))\n" +
+			"    GROUP BY `category` AS `aggr__0__key_0`,\n" +
+			"      toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone(\n" +
+			"      `order_date`, 'Europe/Warsaw'))*1000) / 86400000) AS `aggr__0__1__key_0`))\n" +
+			"WHERE `aggr__0__order_1_rank`<=11\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC",
 	},
 	{ // [11]
 		TestName: "% of target revenue ($10k)",
@@ -2271,17 +2250,16 @@ var KibanaSampleDataEcommerce = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0__1_col_0", 7541.5),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone(
-			  "order_date", 'Europe/Warsaw'))*1000) / 86400000) AS "aggr__0__key_0",
-			  count(*) AS "aggr__0__count",
-			  sumOrNull("taxful_total_price") AS "metric__0__1_col_0"
-			FROM __quesma_table_name
-			WHERE ("order_date">=fromUnixTimestamp64Milli(1739980133594) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1740584933594))
-			GROUP BY toInt64((toUnixTimestamp64Milli("order_date")+timeZoneOffset(toTimezone
-			  ("order_date", 'Europe/Warsaw'))*1000) / 86400000) AS "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone(\n" +
+			"  `order_date`, 'Europe/Warsaw'))*1000) / 86400000) AS `aggr__0__key_0`,\n" +
+			"  count(*) AS `aggr__0__count`,\n" +
+			"  sumOrNull(`taxful_total_price`) AS `metric__0__1_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`order_date`>=fromUnixTimestamp64Milli(1739980133594) AND `order_date`<= \n" +
+			"  fromUnixTimestamp64Milli(1740584933594))\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`order_date`)+timeZoneOffset(toTimezone\n" +
+			"  (`order_date`, 'Europe/Warsaw'))*1000) / 86400000) AS `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__key_0` ASC",
 	},
 	{ // [12]
 		TestName: "Orders by Country (request 1/3)",
@@ -2464,24 +2442,23 @@ var KibanaSampleDataEcommerce = []AggregationTestCase{
 				model.NewQueryResultCol("metric__gridSplit__sum_of_taxful_total_price_col_0", 14978.84375),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT FLOOR(((__quesma_geo_lon("geoip.location")+180)/360)*POWER(2, 5)) AS "aggr__gridSplit__key_0",
-			  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat("geoip.location")))+(1/COS(RADIANS(
-			    __quesma_geo_lat("geoip.location")))))/PI())/2*POWER(2, 5)) AS "aggr__gridSplit__key_1",
-			  count(*) AS "aggr__gridSplit__count",
-			  avgOrNull(__quesma_geo_lat("geoip_location")) AS "metric__gridSplit__gridCentroid_col_0",
-			  avgOrNull(__quesma_geo_lon("geoip_location")) AS "metric__gridSplit__gridCentroid_col_1",
-			  count(*) AS "metric__gridSplit__gridCentroid_col_2",
-			  sumOrNull("taxful_total_price") AS "metric__gridSplit__sum_of_taxful_total_price_col_0"
-			FROM __quesma_table_name
-			WHERE ("geoip.location" IS NOT NULL AND ("order_date">=fromUnixTimestamp64Milli(
-			  1740143222223) AND "order_date"<=fromUnixTimestamp64Milli(1740748022223)))
-			GROUP BY FLOOR(((__quesma_geo_lon("geoip.location")+180)/360)*POWER(2, 5)) AS "aggr__gridSplit__key_0",
-			  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat("geoip.location")))+(1/COS(RADIANS(
-			    __quesma_geo_lat("geoip.location")))))/PI())/2*POWER(2, 5)) AS "aggr__gridSplit__key_1"
-			ORDER BY "aggr__gridSplit__count" DESC, "aggr__gridSplit__key_0" ASC,
-              "aggr__gridSplit__key_1" ASC
-            LIMIT 65535`,
+		ExpectedPancakeSQL: "SELECT FLOOR(((__quesma_geo_lon(`geoip.location`)+180)/360)*POWER(2, 5)) AS `aggr__gridSplit__key_0`,\n" +
+			"  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat(`geoip.location`)))+(1/COS(RADIANS(\n" +
+			"    __quesma_geo_lat(`geoip.location`)))))/PI())/2*POWER(2, 5)) AS `aggr__gridSplit__key_1`,\n" +
+			"  count(*) AS `aggr__gridSplit__count`,\n" +
+			"  avgOrNull(__quesma_geo_lat(`geoip_location`)) AS `metric__gridSplit__gridCentroid_col_0`,\n" +
+			"  avgOrNull(__quesma_geo_lon(`geoip_location`)) AS `metric__gridSplit__gridCentroid_col_1`,\n" +
+			"  count(*) AS `metric__gridSplit__gridCentroid_col_2`,\n" +
+			"  sumOrNull(`taxful_total_price`) AS `metric__gridSplit__sum_of_taxful_total_price_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`geoip.location` IS NOT NULL AND (`order_date`>=fromUnixTimestamp64Milli(\n" +
+			"  1740143222223) AND `order_date`<=fromUnixTimestamp64Milli(1740748022223)))\n" +
+			"GROUP BY FLOOR(((__quesma_geo_lon(`geoip.location`)+180)/360)*POWER(2, 5)) AS `aggr__gridSplit__key_0`,\n" +
+			"  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat(`geoip.location`)))+(1/COS(RADIANS(\n" +
+			"    __quesma_geo_lat(`geoip.location`)))))/PI())/2*POWER(2, 5)) AS `aggr__gridSplit__key_1`\n" +
+			"ORDER BY `aggr__gridSplit__count` DESC, `aggr__gridSplit__key_0` ASC,\n" +
+			"  `aggr__gridSplit__key_1` ASC\n" +
+			"LIMIT 65535",
 	},
 	{ // [13]
 		TestName: "Orders by Country (request 2/3)",
@@ -2613,15 +2590,14 @@ var KibanaSampleDataEcommerce = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__join__count", int64(5)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__join__parent_count",
-			  "geoip.region_name" AS "aggr__join__key_0", count(*) AS "aggr__join__count"
-			FROM __quesma_table_name
-			WHERE ("order_date">=fromUnixTimestamp64Milli(1740234098238) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1740838898238))
-			GROUP BY "geoip.region_name" AS "aggr__join__key_0"
-			ORDER BY "aggr__join__count" DESC, "aggr__join__key_0" ASC
-			LIMIT 5`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__join__parent_count`,\n" +
+			"  `geoip.region_name` AS `aggr__join__key_0`, count(*) AS `aggr__join__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`order_date`>=fromUnixTimestamp64Milli(1740234098238) AND `order_date`<= \n" +
+			"  fromUnixTimestamp64Milli(1740838898238))\n" +
+			"GROUP BY `geoip.region_name` AS `aggr__join__key_0`\n" +
+			"ORDER BY `aggr__join__count` DESC, `aggr__join__key_0` ASC\n" +
+			"LIMIT 5",
 	},
 	{ // [14]
 		TestName: "Orders by Country (request 3/3)",
@@ -2802,16 +2778,15 @@ var KibanaSampleDataEcommerce = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__join__count", int64(41)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__join__parent_count",
-			  "geoip.country_iso_code" AS "aggr__join__key_0",
-			  count(*) AS "aggr__join__count"
-			FROM __quesma_table_name
-			WHERE ("order_date">=fromUnixTimestamp64Milli(1740234098238) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1740838898238))
-			GROUP BY "geoip.country_iso_code" AS "aggr__join__key_0"
-			ORDER BY "aggr__join__count" DESC, "aggr__join__key_0" ASC
-			LIMIT 65536`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__join__parent_count`,\n" +
+			"  `geoip.country_iso_code` AS `aggr__join__key_0`,\n" +
+			"  count(*) AS `aggr__join__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`order_date`>=fromUnixTimestamp64Milli(1740234098238) AND `order_date`<= \n" +
+			"  fromUnixTimestamp64Milli(1740838898238))\n" +
+			"GROUP BY `geoip.country_iso_code` AS `aggr__join__key_0`\n" +
+			"ORDER BY `aggr__join__count` DESC, `aggr__join__key_0` ASC\n" +
+			"LIMIT 65536",
 	},
 	{ // [15]
 		TestName: "weird",
@@ -2915,16 +2890,15 @@ var KibanaSampleDataEcommerce = []AggregationTestCase{
 				model.NewQueryResultCol("metric__fitToBounds_col_3", 153.11700434423983),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT minOrNull(__quesma_geo_lon("originlocation")) AS
-			  "metric__fitToBounds_col_0",
-			  argMinOrNull(__quesma_geo_lat("originlocation"), __quesma_geo_lon(
-			  "originlocation")) AS "metric__fitToBounds_col_1",
-			  minOrNull(__quesma_geo_lat("originlocation")) AS "metric__fitToBounds_col_2",
-			  argMinOrNull(__quesma_geo_lon("originlocation"), __quesma_geo_lat(
-			  "originlocation")) AS "metric__fitToBounds_col_3"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1740924992069) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1741529792069))`,
+		ExpectedPancakeSQL: "SELECT minOrNull(__quesma_geo_lon(`originlocation`)) AS\n" +
+			"  `metric__fitToBounds_col_0`,\n" +
+			"  argMinOrNull(__quesma_geo_lat(`originlocation`), __quesma_geo_lon(\n" +
+			"  `originlocation`)) AS `metric__fitToBounds_col_1`,\n" +
+			"  minOrNull(__quesma_geo_lat(`originlocation`)) AS `metric__fitToBounds_col_2`,\n" +
+			"  argMinOrNull(__quesma_geo_lon(`originlocation`), __quesma_geo_lat(\n" +
+			"  `originlocation`)) AS `metric__fitToBounds_col_3`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740924992069) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1741529792069))",
 	},
 }
diff --git a/platform/testdata/kibana_sample_data_flights.go b/platform/testdata/kibana_sample_data_flights.go
index 845f13921..33fa932c7 100644
--- a/platform/testdata/kibana_sample_data_flights.go
+++ b/platform/testdata/kibana_sample_data_flights.go
@@ -103,12 +103,11 @@ var KibanaSampleDataFlights = []AggregationTestCase{
 				model.NewQueryResultCol("metric__minAgg_col_0", 100.14596557617188),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT maxOrNull("AvgTicketPrice") AS "metric__maxAgg_col_0",
-			  minOrNull("AvgTicketPrice") AS "metric__minAgg_col_0"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740835408853))`,
+		ExpectedPancakeSQL: "SELECT maxOrNull(`AvgTicketPrice`) AS `metric__maxAgg_col_0`,\n" +
+			"  minOrNull(`AvgTicketPrice`) AS `metric__minAgg_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1740835408853))",
 	},
 	{ // [1]
 		TestName: "fill out when panel starts missing - don't know which panel it is",
@@ -217,12 +216,11 @@ var KibanaSampleDataFlights = []AggregationTestCase{
 				model.NewQueryResultCol("metric__minAgg_col_0", 15.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT maxOrNull("FlightDelayMin") AS "metric__maxAgg_col_0",
-			  minOrNull("FlightDelayMin") AS "metric__minAgg_col_0"
-			FROM __quesma_table_name
-			WHERE (("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740835408853)) AND NOT ("FlightDelayMin" __quesma_match 0))`,
+		ExpectedPancakeSQL: "SELECT maxOrNull(`FlightDelayMin`) AS `metric__maxAgg_col_0`,\n" +
+			"  minOrNull(`FlightDelayMin`) AS `metric__minAgg_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1740835408853)) AND NOT (`FlightDelayMin` __quesma_match 0))",
 	},
 	{ // [2]
 		TestName: "Delays & Cancellations (request 1/2)",
@@ -793,154 +791,150 @@ var KibanaSampleDataFlights = []AggregationTestCase{
 				}},
 			},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__1__count",
-			  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-			  "timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__1__2__key_0",
-			  count(*) AS "aggr__1__2__count"
-			FROM __quesma_table_name
-			WHERE (("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740835408853)) AND ("FlightDelay" __quesma_match 'true' AND
-			  "Cancelled" __quesma_match 'true'))
-			GROUP BY toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-			  "timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__1__2__key_0"
-			ORDER BY "aggr__1__2__key_0" ASC`,
-		ExpectedAdditionalPancakeSQLs: []string{`
-				WITH quesma_top_hits_group_table AS (
-				  SELECT sum(count(*)) OVER () AS "aggr__1__count",
-					toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-					"timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__1__2__key_0",
-					count(*) AS "aggr__1__2__count"
-				  FROM __quesma_table_name
-				  WHERE (("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"<=
-					fromUnixTimestamp64Milli(1740835408853)) AND ("FlightDelay" __quesma_match 'true'
-					AND "Cancelled" __quesma_match 'true'))
-				  GROUP BY toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(
-					toTimezone("timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS
-					"aggr__1__2__key_0"
-				  ORDER BY "aggr__1__2__key_0" ASC) ,
-				quesma_top_hits_join AS (
-				  SELECT "group_table"."aggr__1__count" AS "aggr__1__count",
-					"group_table"."aggr__1__2__key_0" AS "aggr__1__2__key_0",
-					"group_table"."aggr__1__2__count" AS "aggr__1__2__count",
-					"hit_table"."timestamp" AS "top_metrics__1__2__4_col_0",
-					"hit_table"."timestamp" AS "top_metrics__1__2__4_col_1",
-					ROW_NUMBER() OVER (PARTITION BY "group_table"."aggr__1__2__key_0" ORDER BY
-					"timestamp" ASC) AS "top_hits_rank"
-				  FROM quesma_top_hits_group_table AS "group_table" LEFT OUTER JOIN
-					__quesma_table_name AS "hit_table" ON ("group_table"."aggr__1__2__key_0"=
-					toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-					"timestamp", 'Europe/Warsaw'))*1000) / 10800000))
-				  WHERE (("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"<=
-					fromUnixTimestamp64Milli(1740835408853)) AND ("FlightDelay" __quesma_match 'true'
-					AND "Cancelled" __quesma_match 'true')))
-				SELECT "aggr__1__count", "aggr__1__2__key_0", "aggr__1__2__count",
-				  "top_metrics__1__2__4_col_0", "top_metrics__1__2__4_col_1", "top_hits_rank"
-				FROM "quesma_top_hits_join"
-				WHERE "top_hits_rank"<=10
-				ORDER BY "aggr__1__2__key_0" ASC, "top_hits_rank" ASC`,
-			`
-				WITH quesma_top_hits_group_table AS (
-				  SELECT sum(count(*)) OVER () AS "aggr__1__count",
-					toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-					"timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__1__2__key_0",
-					count(*) AS "aggr__1__2__count"
-				  FROM __quesma_table_name
-				  WHERE (("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"<=
-					fromUnixTimestamp64Milli(1740835408853)) AND ("FlightDelay" __quesma_match 'true'
-					AND "Cancelled" __quesma_match 'true'))
-				  GROUP BY toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(
-					toTimezone("timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS
-					"aggr__1__2__key_0"
-				  ORDER BY "aggr__1__2__key_0" ASC) ,
-				quesma_top_hits_join AS (
-				  SELECT "group_table"."aggr__1__count" AS "aggr__1__count",
-					"group_table"."aggr__1__2__key_0" AS "aggr__1__2__key_0",
-					"group_table"."aggr__1__2__count" AS "aggr__1__2__count",
-					"hit_table"."FlightDelay" AS "top_metrics__1__2__5_col_0",
-					"hit_table"."timestamp" AS "top_metrics__1__2__5_col_1",
-					ROW_NUMBER() OVER (PARTITION BY "group_table"."aggr__1__2__key_0" ORDER BY
-					"timestamp" ASC) AS "top_hits_rank"
-				  FROM quesma_top_hits_group_table AS "group_table" LEFT OUTER JOIN
-					__quesma_table_name AS "hit_table" ON ("group_table"."aggr__1__2__key_0"=
-					toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-					"timestamp", 'Europe/Warsaw'))*1000) / 10800000))
-				  WHERE (("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"<=
-					fromUnixTimestamp64Milli(1740835408853)) AND ("FlightDelay" __quesma_match 'true'
-					AND "Cancelled" __quesma_match 'true')))
-				SELECT "aggr__1__count", "aggr__1__2__key_0", "aggr__1__2__count",
-				  "top_metrics__1__2__5_col_0", "top_metrics__1__2__5_col_1", "top_hits_rank"
-				FROM "quesma_top_hits_join"
-				WHERE "top_hits_rank"<=10
-				ORDER BY "aggr__1__2__key_0" ASC, "top_hits_rank" ASC`,
-			`
-				WITH quesma_top_hits_group_table AS (
-				  SELECT sum(count(*)) OVER () AS "aggr__1__count",
-					toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-					"timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__1__2__key_0",
-					count(*) AS "aggr__1__2__count"
-				  FROM __quesma_table_name
-				  WHERE (("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"<=
-					fromUnixTimestamp64Milli(1740835408853)) AND ("FlightDelay" __quesma_match 'true'
-					AND "Cancelled" __quesma_match 'true'))
-				  GROUP BY toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(
-					toTimezone("timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS
-					"aggr__1__2__key_0"
-				  ORDER BY "aggr__1__2__key_0" ASC) ,
-				quesma_top_hits_join AS (
-				  SELECT "group_table"."aggr__1__count" AS "aggr__1__count",
-					"group_table"."aggr__1__2__key_0" AS "aggr__1__2__key_0",
-					"group_table"."aggr__1__2__count" AS "aggr__1__2__count",
-					"hit_table"."Cancelled" AS "top_metrics__1__2__6_col_0",
-					"hit_table"."timestamp" AS "top_metrics__1__2__6_col_1",
-					ROW_NUMBER() OVER (PARTITION BY "group_table"."aggr__1__2__key_0" ORDER BY
-					"timestamp" ASC) AS "top_hits_rank"
-				  FROM quesma_top_hits_group_table AS "group_table" LEFT OUTER JOIN
-					__quesma_table_name AS "hit_table" ON ("group_table"."aggr__1__2__key_0"=
-					toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-					"timestamp", 'Europe/Warsaw'))*1000) / 10800000))
-				  WHERE (("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"<=
-					fromUnixTimestamp64Milli(1740835408853)) AND ("FlightDelay" __quesma_match 'true'
-					AND "Cancelled" __quesma_match 'true')))
-				SELECT "aggr__1__count", "aggr__1__2__key_0", "aggr__1__2__count",
-				  "top_metrics__1__2__6_col_0", "top_metrics__1__2__6_col_1", "top_hits_rank"
-				FROM "quesma_top_hits_join"
-				WHERE "top_hits_rank"<=10
-				ORDER BY "aggr__1__2__key_0" ASC, "top_hits_rank" ASC`,
-			`
-				WITH quesma_top_hits_group_table AS (
-				  SELECT sum(count(*)) OVER () AS "aggr__1__count",
-					toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-					"timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__1__2__key_0",
-					count(*) AS "aggr__1__2__count"
-				  FROM __quesma_table_name
-				  WHERE (("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"<=
-					fromUnixTimestamp64Milli(1740835408853)) AND ("FlightDelay" __quesma_match 'true'
-					AND "Cancelled" __quesma_match 'true'))
-				  GROUP BY toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(
-					toTimezone("timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS
-					"aggr__1__2__key_0"
-				  ORDER BY "aggr__1__2__key_0" ASC) ,
-				quesma_top_hits_join AS (
-				  SELECT "group_table"."aggr__1__count" AS "aggr__1__count",
-					"group_table"."aggr__1__2__key_0" AS "aggr__1__2__key_0",
-					"group_table"."aggr__1__2__count" AS "aggr__1__2__count",
-					"hit_table"."Carrier" AS "top_metrics__1__2__7_col_0",
-					"hit_table"."timestamp" AS "top_metrics__1__2__7_col_1",
-					ROW_NUMBER() OVER (PARTITION BY "group_table"."aggr__1__2__key_0" ORDER BY
-					"timestamp" ASC) AS "top_hits_rank"
-				  FROM quesma_top_hits_group_table AS "group_table" LEFT OUTER JOIN
-					__quesma_table_name AS "hit_table" ON ("group_table"."aggr__1__2__key_0"=
-					toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-					"timestamp", 'Europe/Warsaw'))*1000) / 10800000))
-				  WHERE (("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"<=
-					fromUnixTimestamp64Milli(1740835408853)) AND ("FlightDelay" __quesma_match 'true'
-					AND "Cancelled" __quesma_match 'true')))
-				SELECT "aggr__1__count", "aggr__1__2__key_0", "aggr__1__2__count",
-				  "top_metrics__1__2__7_col_0", "top_metrics__1__2__7_col_1", "top_hits_rank"
-				FROM "quesma_top_hits_join"
-				WHERE "top_hits_rank"<=10
-				ORDER BY "aggr__1__2__key_0" ASC, "top_hits_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__1__count`,\n" +
+			"  toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__1__2__key_0`,\n" +
+			"  count(*) AS `aggr__1__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1740835408853)) AND (`FlightDelay` __quesma_match 'true' AND\n" +
+			"  `Cancelled` __quesma_match 'true'))\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__1__2__key_0`\n" +
+			"ORDER BY `aggr__1__2__key_0` ASC",
+		ExpectedAdditionalPancakeSQLs: []string{
+			" WITH quesma_top_hits_group_table AS (\n" +
+				"  SELECT sum(count(*)) OVER () AS `aggr__1__count`,\n" +
+				"    toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+				"    `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__1__2__key_0`,\n" +
+				"    count(*) AS `aggr__1__2__count`\n" +
+				"  FROM `__quesma_table_name`\n" +
+				"  WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<= \n" +
+				"    fromUnixTimestamp64Milli(1740835408853)) AND (`FlightDelay` __quesma_match 'true'\n" +
+				"    AND `Cancelled` __quesma_match 'true'))\n" +
+				"  GROUP BY toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(\n" +
+				"    toTimezone(`timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS\n" +
+				"    `aggr__1__2__key_0`\n" +
+				"  ORDER BY `aggr__1__2__key_0` ASC) ,\n" +
+				"quesma_top_hits_join AS (\n" +
+				"  SELECT `group_table`.`aggr__1__count` AS `aggr__1__count`,\n" +
+				"    `group_table`.`aggr__1__2__key_0` AS `aggr__1__2__key_0`,\n" +
+				"    `group_table`.`aggr__1__2__count` AS `aggr__1__2__count`,\n" +
+				"    `hit_table`.`timestamp` AS `top_metrics__1__2__4_col_0`,\n" +
+				"    `hit_table`.`timestamp` AS `top_metrics__1__2__4_col_1`,\n" +
+				"    ROW_NUMBER() OVER (PARTITION BY `group_table`.`aggr__1__2__key_0` ORDER BY\n" +
+				"    `timestamp` ASC) AS `top_hits_rank`\n" +
+				"  FROM quesma_top_hits_group_table AS `group_table` LEFT OUTER JOIN\n" +
+				"    `__quesma_table_name` AS `hit_table` ON (`group_table`.`aggr__1__2__key_0`=\n" +
+				"    toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+				"    `timestamp`, 'Europe/Warsaw'))*1000) / 10800000))\n" +
+				"  WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<= \n" +
+				"    fromUnixTimestamp64Milli(1740835408853)) AND (`FlightDelay` __quesma_match 'true'\n" +
+				"    AND `Cancelled` __quesma_match 'true')))\n" +
+				"SELECT `aggr__1__count`, `aggr__1__2__key_0`, `aggr__1__2__count`,\n" +
+				"  `top_metrics__1__2__4_col_0`, `top_metrics__1__2__4_col_1`, `top_hits_rank`\n" +
+				"FROM `quesma_top_hits_join`\n" +
+				"WHERE `top_hits_rank`<=10\n" +
+				"ORDER BY `aggr__1__2__key_0` ASC, `top_hits_rank` ASC",
+			" WITH quesma_top_hits_group_table AS (\n" +
+				"  SELECT sum(count(*)) OVER () AS `aggr__1__count`,\n" +
+				"    toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+				"    `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__1__2__key_0`,\n" +
+				"    count(*) AS `aggr__1__2__count`\n" +
+				"  FROM `__quesma_table_name`\n" +
+				"  WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<= \n" +
+				"    fromUnixTimestamp64Milli(1740835408853)) AND (`FlightDelay` __quesma_match 'true'\n" +
+				"    AND `Cancelled` __quesma_match 'true'))\n" +
+				"  GROUP BY toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(\n" +
+				"    toTimezone(`timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS\n" +
+				"    `aggr__1__2__key_0`\n" +
+				"  ORDER BY `aggr__1__2__key_0` ASC) ,\n" +
+				"quesma_top_hits_join AS (\n" +
+				"  SELECT `group_table`.`aggr__1__count` AS `aggr__1__count`,\n" +
+				"    `group_table`.`aggr__1__2__key_0` AS `aggr__1__2__key_0`,\n" +
+				"    `group_table`.`aggr__1__2__count` AS `aggr__1__2__count`,\n" +
+				"    `hit_table`.`FlightDelay` AS `top_metrics__1__2__5_col_0`,\n" +
+				"    `hit_table`.`timestamp` AS `top_metrics__1__2__5_col_1`,\n" +
+				"    ROW_NUMBER() OVER (PARTITION BY `group_table`.`aggr__1__2__key_0` ORDER BY\n" +
+				"    `timestamp` ASC) AS `top_hits_rank`\n" +
+				"  FROM quesma_top_hits_group_table AS `group_table` LEFT OUTER JOIN\n" +
+				"    `__quesma_table_name` AS `hit_table` ON (`group_table`.`aggr__1__2__key_0`=\n" +
+				"    toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+				"    `timestamp`, 'Europe/Warsaw'))*1000) / 10800000))\n" +
+				"  WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<= \n" +
+				"    fromUnixTimestamp64Milli(1740835408853)) AND (`FlightDelay` __quesma_match 'true'\n" +
+				"    AND `Cancelled` __quesma_match 'true')))\n" +
+				"SELECT `aggr__1__count`, `aggr__1__2__key_0`, `aggr__1__2__count`,\n" +
+				"  `top_metrics__1__2__5_col_0`, `top_metrics__1__2__5_col_1`, `top_hits_rank`\n" +
+				"FROM `quesma_top_hits_join`\n" +
+				"WHERE `top_hits_rank`<=10\n" +
+				"ORDER BY `aggr__1__2__key_0` ASC, `top_hits_rank` ASC",
+			" WITH quesma_top_hits_group_table AS (\n" +
+				"  SELECT sum(count(*)) OVER () AS `aggr__1__count`,\n" +
+				"    toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+				"    `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__1__2__key_0`,\n" +
+				"    count(*) AS `aggr__1__2__count`\n" +
+				"  FROM `__quesma_table_name`\n" +
+				"  WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<= \n" +
+				"    fromUnixTimestamp64Milli(1740835408853)) AND (`FlightDelay` __quesma_match 'true'\n" +
+				"    AND `Cancelled` __quesma_match 'true'))\n" +
+				"  GROUP BY toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(\n" +
+				"    toTimezone(`timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS\n" +
+				"    `aggr__1__2__key_0`\n" +
+				"  ORDER BY `aggr__1__2__key_0` ASC) ,\n" +
+				"quesma_top_hits_join AS (\n" +
+				"  SELECT `group_table`.`aggr__1__count` AS `aggr__1__count`,\n" +
+				"    `group_table`.`aggr__1__2__key_0` AS `aggr__1__2__key_0`,\n" +
+				"    `group_table`.`aggr__1__2__count` AS `aggr__1__2__count`,\n" +
+				"    `hit_table`.`Cancelled` AS `top_metrics__1__2__6_col_0`,\n" +
+				"    `hit_table`.`timestamp` AS `top_metrics__1__2__6_col_1`,\n" +
+				"    ROW_NUMBER() OVER (PARTITION BY `group_table`.`aggr__1__2__key_0` ORDER BY\n" +
+				"    `timestamp` ASC) AS `top_hits_rank`\n" +
+				"  FROM quesma_top_hits_group_table AS `group_table` LEFT OUTER JOIN\n" +
+				"    `__quesma_table_name` AS `hit_table` ON (`group_table`.`aggr__1__2__key_0`=\n" +
+				"    toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+				"    `timestamp`, 'Europe/Warsaw'))*1000) / 10800000))\n" +
+				"  WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<= \n" +
+				"    fromUnixTimestamp64Milli(1740835408853)) AND (`FlightDelay` __quesma_match 'true'\n" +
+				"    AND `Cancelled` __quesma_match 'true')))\n" +
+				"SELECT `aggr__1__count`, `aggr__1__2__key_0`, `aggr__1__2__count`,\n" +
+				"  `top_metrics__1__2__6_col_0`, `top_metrics__1__2__6_col_1`, `top_hits_rank`\n" +
+				"FROM `quesma_top_hits_join`\n" +
+				"WHERE `top_hits_rank`<=10\n" +
+				"ORDER BY `aggr__1__2__key_0` ASC, `top_hits_rank` ASC",
+			" WITH quesma_top_hits_group_table AS (\n" +
+				"  SELECT sum(count(*)) OVER () AS `aggr__1__count`,\n" +
+				"    toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+				"    `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__1__2__key_0`,\n" +
+				"    count(*) AS `aggr__1__2__count`\n" +
+				"  FROM `__quesma_table_name`\n" +
+				"  WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<= \n" +
+				"    fromUnixTimestamp64Milli(1740835408853)) AND (`FlightDelay` __quesma_match 'true'\n" +
+				"    AND `Cancelled` __quesma_match 'true'))\n" +
+				"  GROUP BY toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(\n" +
+				"    toTimezone(`timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS\n" +
+				"    `aggr__1__2__key_0`\n" +
+				"  ORDER BY `aggr__1__2__key_0` ASC) ,\n" +
+				"quesma_top_hits_join AS (\n" +
+				"  SELECT `group_table`.`aggr__1__count` AS `aggr__1__count`,\n" +
+				"    `group_table`.`aggr__1__2__key_0` AS `aggr__1__2__key_0`,\n" +
+				"    `group_table`.`aggr__1__2__count` AS `aggr__1__2__count`,\n" +
+				"    `hit_table`.`Carrier` AS `top_metrics__1__2__7_col_0`,\n" +
+				"    `hit_table`.`timestamp` AS `top_metrics__1__2__7_col_1`,\n" +
+				"    ROW_NUMBER() OVER (PARTITION BY `group_table`.`aggr__1__2__key_0` ORDER BY\n" +
+				"    `timestamp` ASC) AS `top_hits_rank`\n" +
+				"  FROM quesma_top_hits_group_table AS `group_table` LEFT OUTER JOIN\n" +
+				"    `__quesma_table_name` AS `hit_table` ON (`group_table`.`aggr__1__2__key_0`=\n" +
+				"    toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+				"    `timestamp`, 'Europe/Warsaw'))*1000) / 10800000))\n" +
+				"  WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<= \n" +
+				"    fromUnixTimestamp64Milli(1740835408853)) AND (`FlightDelay` __quesma_match 'true'\n" +
+				"    AND `Cancelled` __quesma_match 'true')))\n" +
+				"SELECT `aggr__1__count`, `aggr__1__2__key_0`, `aggr__1__2__count`,\n" +
+				"  `top_metrics__1__2__7_col_0`, `top_metrics__1__2__7_col_1`, `top_hits_rank`\n" +
+				"FROM `quesma_top_hits_join`\n" +
+				"WHERE `top_hits_rank`<=10\n" +
+				"ORDER BY `aggr__1__2__key_0` ASC, `top_hits_rank` ASC",
 		},
 	},
 	{ // [3]
@@ -1052,11 +1046,10 @@ var KibanaSampleDataFlights = []AggregationTestCase{
 			}},
 		},
 		// TODO Sprawdz boola
-		ExpectedPancakeSQL: `
-			SELECT countIf("FlightDelay" __quesma_match true) AS "metric__0-bucket_col_0"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740835408853))`,
+		ExpectedPancakeSQL: "SELECT countIf(`FlightDelay` __quesma_match true) AS `metric__0-bucket_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1740835408853))",
 	},
 	{ // [4]
 		TestName: "Cancelled",
@@ -1166,11 +1159,10 @@ var KibanaSampleDataFlights = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0-bucket_col_0", int64(278)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT countIf("Cancelled" __quesma_match true) AS "metric__0-bucket_col_0"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740835408853))`,
+		ExpectedPancakeSQL: "SELECT countIf(`Cancelled` __quesma_match true) AS `metric__0-bucket_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1740835408853))",
 	},
 	{ // [5]
 		TestName: "Delayed/Cancelled vs 1 week earlier",
@@ -1323,18 +1315,16 @@ var KibanaSampleDataFlights = []AggregationTestCase{
 				model.NewQueryResultCol("filter_1__aggr__time_offset_split__count", int64(222)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT countIf(("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND
-			  "timestamp"<=fromUnixTimestamp64Milli(1740835408853))) AS
-			  "filter_0__aggr__time_offset_split__count",
-			  countIf(("timestamp">=fromUnixTimestamp64Milli(1739625808853) AND "timestamp"
-			  <=fromUnixTimestamp64Milli(1740230608853))) AS
-			  "filter_1__aggr__time_offset_split__count"
-			FROM __quesma_table_name
-			WHERE ("Cancelled" __quesma_match true AND (("timestamp">=fromUnixTimestamp64Milli(
-			  1740230608853) AND "timestamp"<=fromUnixTimestamp64Milli(1740835408853)) OR (
-			  "timestamp">=fromUnixTimestamp64Milli(1739625808853) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740230608853))))`,
+		ExpectedPancakeSQL: "SELECT countIf((`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND\n" +
+			"  `timestamp`<=fromUnixTimestamp64Milli(1740835408853))) AS\n" +
+			"  `filter_0__aggr__time_offset_split__count`,\n" +
+			"  countIf((`timestamp`>=fromUnixTimestamp64Milli(1739625808853) AND `timestamp`<=fromUnixTimestamp64Milli(1740230608853))) AS\n" +
+			"  `filter_1__aggr__time_offset_split__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`Cancelled` __quesma_match true AND ((`timestamp`>=fromUnixTimestamp64Milli(\n" +
+			"  1740230608853) AND `timestamp`<=fromUnixTimestamp64Milli(1740835408853)) OR (\n" +
+			"  `timestamp`>=fromUnixTimestamp64Milli(1739625808853) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1740230608853))))",
 	},
 	{ // [6]
 		TestName: "Flight count",
@@ -1448,16 +1438,14 @@ var KibanaSampleDataFlights = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__0__count", int64(31)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-			  "timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__0__key_0",
-			  count(*) AS "aggr__0__count"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740835408853))
-			GROUP BY toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-			  "timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__0__key_0`,\n" +
+			"  count(*) AS `aggr__0__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<=fromUnixTimestamp64Milli(1740835408853))\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__key_0` ASC",
 	},
 	{ // [7]
 		TestName: "Delays & Cancellations (request 2/2)",
@@ -1634,18 +1622,16 @@ var KibanaSampleDataFlights = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0__1-bucket_col_0", int64(7)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-			  "timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__0__key_0",
-			  count(*) AS "aggr__0__count",
-			  countIf("FlightDelay" __quesma_match 'true') AS "metric__0__1-bucket_col_0",
-			  countIf("__quesma_fulltext_field_name" __quesma_match '%') AS "metric__0__2-bucket_col_0"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740835408853))
-			GROUP BY toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-			  "timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__0__key_0`,\n" +
+			"  count(*) AS `aggr__0__count`,\n" +
+			"  countIf(`FlightDelay` __quesma_match 'true') AS `metric__0__1-bucket_col_0`,\n" +
+			"  countIf(`__quesma_fulltext_field_name` __quesma_match '%') AS `metric__0__2-bucket_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<=fromUnixTimestamp64Milli(1740835408853))\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__key_0` ASC",
 	},
 	{ // [8]
 		TestName: "Most delayed cities",
@@ -1840,17 +1826,15 @@ var KibanaSampleDataFlights = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0__1-bucket_col_0", int64(2)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-			  "OriginCityName" AS "aggr__0__key_0", count(*) AS "aggr__0__count",
-			  countIf("FlightDelay" __quesma_match true) AS "metric__0__1-bucket_col_0",
-			  countIf("Cancelled" __quesma_match true) AS "metric__0__3-bucket_col_0"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740835408853))
-			GROUP BY "OriginCityName" AS "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC
-			LIMIT 1001`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"  `OriginCityName` AS `aggr__0__key_0`, count(*) AS `aggr__0__count`,\n" +
+			"  countIf(`FlightDelay` __quesma_match true) AS `metric__0__1-bucket_col_0`,\n" +
+			"  countIf(`Cancelled` __quesma_match true) AS `metric__0__3-bucket_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<=fromUnixTimestamp64Milli(1740835408853))\n" +
+			"GROUP BY `OriginCityName` AS `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__key_0` ASC\n" +
+			"LIMIT 1001",
 	},
 	{ // [9]
 		TestName: "Delay Type",
@@ -2027,31 +2011,29 @@ var KibanaSampleDataFlights = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__0__1__count", int64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-			  "aggr__0__1__key_0", "aggr__0__1__count"
-			FROM (
-			  SELECT "aggr__0__parent_count", "aggr__0__key_0", "aggr__0__count",
-				"aggr__0__1__key_0", "aggr__0__1__count",
-				dense_rank() OVER (ORDER BY "aggr__0__count" DESC, "aggr__0__key_0" ASC) AS
-				"aggr__0__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__0__key_0" ORDER BY
-				"aggr__0__1__key_0" ASC) AS "aggr__0__1__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-				  "FlightDelayType" AS "aggr__0__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__0__key_0") AS "aggr__0__count",
-				  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-				  "timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__0__1__key_0",
-				  count(*) AS "aggr__0__1__count"
-				FROM __quesma_table_name
-				WHERE ("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"
-				  <=fromUnixTimestamp64Milli(1740835408853))
-				GROUP BY "FlightDelayType" AS "aggr__0__key_0",
-				  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-				  "timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__0__1__key_0"))
-			WHERE "aggr__0__order_1_rank"<=11
-			ORDER BY "aggr__0__order_1_rank" ASC, "aggr__0__1__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"  `aggr__0__1__key_0`, `aggr__0__1__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__0__parent_count`, `aggr__0__key_0`, `aggr__0__count`,\n" +
+			"    `aggr__0__1__key_0`, `aggr__0__1__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__0__count` DESC, `aggr__0__key_0` ASC) AS\n" +
+			"    `aggr__0__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__0__key_0` ORDER BY\n" +
+			"    `aggr__0__1__key_0` ASC) AS `aggr__0__1__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"      `FlightDelayType` AS `aggr__0__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__0__key_0`) AS `aggr__0__count`,\n" +
+			"      toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"      `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__0__1__key_0`,\n" +
+			"      count(*) AS `aggr__0__1__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<=fromUnixTimestamp64Milli(1740835408853))\n" +
+			"    GROUP BY `FlightDelayType` AS `aggr__0__key_0`,\n" +
+			"      toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"      `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__0__1__key_0`))\n" +
+			"WHERE `aggr__0__order_1_rank`<=11\n" +
+			"ORDER BY `aggr__0__order_1_rank` ASC, `aggr__0__1__order_1_rank` ASC",
 	},
 	{ // [10]
 		TestName: "Count of records by DestWeather (bottom right)",
@@ -2171,15 +2153,14 @@ var KibanaSampleDataFlights = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__0__count", int64(373)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-			  "DestWeather" AS "aggr__0__key_0", count(*) AS "aggr__0__count"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740835408853))
-			GROUP BY "DestWeather" AS "aggr__0__key_0"
-			ORDER BY "aggr__0__count" DESC, "aggr__0__key_0" ASC
-			LIMIT 3`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"  `DestWeather` AS `aggr__0__key_0`, count(*) AS `aggr__0__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1740835408853))\n" +
+			"GROUP BY `DestWeather` AS `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__count` DESC, `aggr__0__key_0` ASC\n" +
+			"LIMIT 3",
 	},
 	{ // [11]
 		TestName: "Delay Type",
@@ -2305,16 +2286,15 @@ var KibanaSampleDataFlights = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__0__count", int64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-			  "FlightDelayType" AS "aggr__0__key_0", count(*) AS "aggr__0__count"
-			FROM __quesma_table_name
-			WHERE (("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740835408853)) AND NOT ("FlightDelayType"
-			  __quesma_match '%No Delay%'))
-			GROUP BY "FlightDelayType" AS "aggr__0__key_0"
-			ORDER BY "aggr__0__count" DESC, "aggr__0__key_0" ASC
-			LIMIT 3`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"  `FlightDelayType` AS `aggr__0__key_0`, count(*) AS `aggr__0__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1740835408853)) AND NOT (`FlightDelayType`\n" +
+			"  __quesma_match '%No Delay%'))\n" +
+			"GROUP BY `FlightDelayType` AS `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__count` DESC, `aggr__0__key_0` ASC\n" +
+			"LIMIT 3",
 	},
 	{ // [12]
 		TestName: "Origin Time Delayed",
@@ -2496,25 +2476,24 @@ var KibanaSampleDataFlights = []AggregationTestCase{
 				model.NewQueryResultCol("metric__gridSplit__sum_of_FlightDelayMin_col_0", 1185.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT FLOOR(((__quesma_geo_lon("OriginLocation")+180)/360)*POWER(2, 7)) AS "aggr__gridSplit__key_0",
-			  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat("OriginLocation")))+(1/COS(RADIANS(
-			  __quesma_geo_lat("OriginLocation")))))/PI())/2*POWER(2, 7))
-			  AS "aggr__gridSplit__key_1",
-			  count(*) AS "aggr__gridSplit__count",
-			  avgOrNull(__quesma_geo_lat("originlocation")) AS "metric__gridSplit__gridCentroid_col_0",
-			  avgOrNull(__quesma_geo_lon("originlocation")) AS "metric__gridSplit__gridCentroid_col_1",
-			  count(*) AS "metric__gridSplit__gridCentroid_col_2",
-			  sumOrNull("FlightDelayMin") AS "metric__gridSplit__sum_of_FlightDelayMin_col_0"
-			FROM __quesma_table_name
-			WHERE ("OriginLocation" IS NOT NULL AND ("timestamp">=fromUnixTimestamp64Milli(
-			  1740230608853) AND "timestamp"<=fromUnixTimestamp64Milli(1740835408853)))
-			GROUP BY FLOOR(((__quesma_geo_lon("OriginLocation")+180)/360)*POWER(2, 7)) AS "aggr__gridSplit__key_0",
-			  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat("OriginLocation")))+(1/COS(RADIANS(
-			  __quesma_geo_lat("OriginLocation")))))/PI())/2*POWER(2, 7)) AS "aggr__gridSplit__key_1"
-			ORDER BY "aggr__gridSplit__count" DESC, "aggr__gridSplit__key_0" ASC,
-              "aggr__gridSplit__key_1" ASC
-            LIMIT 65535`,
+		ExpectedPancakeSQL: "SELECT FLOOR(((__quesma_geo_lon(`OriginLocation`)+180)/360)*POWER(2, 7)) AS `aggr__gridSplit__key_0`,\n" +
+			"  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat(`OriginLocation`)))+(1/COS(RADIANS(\n" +
+			"  __quesma_geo_lat(`OriginLocation`)))))/PI())/2*POWER(2, 7))\n" +
+			"  AS `aggr__gridSplit__key_1`,\n" +
+			"  count(*) AS `aggr__gridSplit__count`,\n" +
+			"  avgOrNull(__quesma_geo_lat(`originlocation`)) AS `metric__gridSplit__gridCentroid_col_0`,\n" +
+			"  avgOrNull(__quesma_geo_lon(`originlocation`)) AS `metric__gridSplit__gridCentroid_col_1`,\n" +
+			"  count(*) AS `metric__gridSplit__gridCentroid_col_2`,\n" +
+			"  sumOrNull(`FlightDelayMin`) AS `metric__gridSplit__sum_of_FlightDelayMin_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`OriginLocation` IS NOT NULL AND (`timestamp`>=fromUnixTimestamp64Milli(\n" +
+			"  1740230608853) AND `timestamp`<=fromUnixTimestamp64Milli(1740835408853)))\n" +
+			"GROUP BY FLOOR(((__quesma_geo_lon(`OriginLocation`)+180)/360)*POWER(2, 7)) AS `aggr__gridSplit__key_0`,\n" +
+			"  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat(`OriginLocation`)))+(1/COS(RADIANS(\n" +
+			"  __quesma_geo_lat(`OriginLocation`)))))/PI())/2*POWER(2, 7)) AS `aggr__gridSplit__key_1`\n" +
+			"ORDER BY `aggr__gridSplit__count` DESC, `aggr__gridSplit__key_0` ASC,\n" +
+			"  `aggr__gridSplit__key_1` ASC\n" +
+			"LIMIT 65535",
 	},
 	{ // [13]
 		TestName: "Delay Buckets",
@@ -2630,13 +2609,12 @@ var KibanaSampleDataFlights = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__0__count", int64(32)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "FlightDelayMin" AS "aggr__0__key_0", count(*) AS "aggr__0__count"
-			FROM __quesma_table_name
-			WHERE (("timestamp">=fromUnixTimestamp64Milli(1740230608853) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740835408853)) AND NOT ("FlightDelayMin" __quesma_match 0))
-			GROUP BY "FlightDelayMin" AS "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT `FlightDelayMin` AS `aggr__0__key_0`, count(*) AS `aggr__0__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1740230608853) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1740835408853)) AND NOT (`FlightDelayMin` __quesma_match 0))\n" +
+			"GROUP BY `FlightDelayMin` AS `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__key_0` ASC",
 	},
 	{ // [14]
 		TestName: "TODO Airport Connections (Hover Over Airport)",
@@ -2972,99 +2950,95 @@ var KibanaSampleDataFlights = []AggregationTestCase{
 				}},
 			},
 		},
-		ExpectedPancakeSQL: `
-			WITH quesma_top_hits_group_table AS (
-			  SELECT "aggr__origins__parent_count", "aggr__origins__key_0",
-				"aggr__origins__count", "aggr__origins__distinations__parent_count",
-				"aggr__origins__distinations__key_0", "aggr__origins__distinations__count",
-				"aggr__origins__order_1_rank", "aggr__origins__distinations__order_1_rank"
-			  FROM (
-				SELECT "aggr__origins__parent_count", "aggr__origins__key_0",
-				  "aggr__origins__count", "aggr__origins__distinations__parent_count",
-				  "aggr__origins__distinations__key_0",
-				  "aggr__origins__distinations__count",
-				  dense_rank() OVER (ORDER BY "aggr__origins__count" DESC,
-				  "aggr__origins__key_0" ASC) AS "aggr__origins__order_1_rank",
-				  dense_rank() OVER (PARTITION BY "aggr__origins__key_0" ORDER BY
-				  "aggr__origins__distinations__count" DESC,
-				  "aggr__origins__distinations__key_0" ASC) AS
-				  "aggr__origins__distinations__order_1_rank"
-				FROM (
-				  SELECT sum(count(*)) OVER () AS "aggr__origins__parent_count",
-					"OriginAirportID" AS "aggr__origins__key_0",
-					sum(count(*)) OVER (PARTITION BY "aggr__origins__key_0") AS
-					"aggr__origins__count",
-					sum(count(*)) OVER (PARTITION BY "aggr__origins__key_0") AS
-					"aggr__origins__distinations__parent_count",
-					"DestAirportID" AS "aggr__origins__distinations__key_0",
-					count(*) AS "aggr__origins__distinations__count"
-				  FROM __quesma_table_name
-				  GROUP BY "OriginAirportID" AS "aggr__origins__key_0",
-					"DestAirportID" AS "aggr__origins__distinations__key_0"))
-			  WHERE ("aggr__origins__order_1_rank"<=10001 AND
-				"aggr__origins__distinations__order_1_rank"<=10001)
-			  ORDER BY "aggr__origins__order_1_rank" ASC,
-				"aggr__origins__distinations__order_1_rank" ASC) ,
-			quesma_top_hits_join AS (
-			  SELECT "group_table"."aggr__origins__parent_count" AS
-				"aggr__origins__parent_count",
-				"group_table"."aggr__origins__key_0" AS "aggr__origins__key_0",
-				"group_table"."aggr__origins__count" AS "aggr__origins__count",
-				"group_table"."aggr__origins__distinations__parent_count" AS
-				"aggr__origins__distinations__parent_count",
-				"group_table"."aggr__origins__distinations__key_0" AS
-				"aggr__origins__distinations__key_0",
-				"group_table"."aggr__origins__distinations__count" AS
-				"aggr__origins__distinations__count",
-				"hit_table"."DestLocation" AS
-				"top_hits__origins__distinations__destLocation_col_0",
-				ROW_NUMBER() OVER (PARTITION BY "group_table"."aggr__origins__key_0",
-				"group_table"."aggr__origins__distinations__key_0") AS "top_hits_rank",
-				"group_table"."aggr__origins__order_1_rank" AS "aggr__origins__order_1_rank"
-				,
-				"group_table"."aggr__origins__distinations__order_1_rank" AS
-				"aggr__origins__distinations__order_1_rank"
-			  FROM quesma_top_hits_group_table AS "group_table" LEFT OUTER JOIN
-				__quesma_table_name AS "hit_table" ON (("group_table"."aggr__origins__key_0"
-				="hit_table"."OriginAirportID" AND
-				"group_table"."aggr__origins__distinations__key_0"=
-				"hit_table"."DestAirportID")))
-			SELECT "aggr__origins__parent_count", "aggr__origins__key_0",
-			  "aggr__origins__count", "aggr__origins__distinations__parent_count",
-			  "aggr__origins__distinations__key_0", "aggr__origins__distinations__count",
-			  "top_hits__origins__distinations__destLocation_col_0", "top_hits_rank"
-			FROM "quesma_top_hits_join"
-			WHERE "top_hits_rank"<=1
-			ORDER BY "aggr__origins__order_1_rank" ASC,
-			  "aggr__origins__distinations__order_1_rank" ASC, "top_hits_rank" ASC`,
-		ExpectedAdditionalPancakeSQLs: []string{`
-			WITH quesma_top_hits_group_table AS (
-			  SELECT sum(count(*)) OVER () AS "aggr__origins__parent_count",
-				"OriginAirportID" AS "aggr__origins__key_0",
-				count(*) AS "aggr__origins__count"
-			  FROM __quesma_table_name
-			  GROUP BY "OriginAirportID" AS "aggr__origins__key_0"
-			  ORDER BY "aggr__origins__count" DESC, "aggr__origins__key_0" ASC
-			  LIMIT 10001) ,
-			quesma_top_hits_join AS (
-			  SELECT "group_table"."aggr__origins__parent_count" AS
-				"aggr__origins__parent_count",
-				"group_table"."aggr__origins__key_0" AS "aggr__origins__key_0",
-				"group_table"."aggr__origins__count" AS "aggr__origins__count",
-				"hit_table"."OriginLocation" AS "top_hits__origins__originLocation_col_0",
-				"hit_table"."Origin" AS "top_hits__origins__originLocation_col_1",
-				ROW_NUMBER() OVER (PARTITION BY "group_table"."aggr__origins__key_0") AS
-				"top_hits_rank"
-			  FROM quesma_top_hits_group_table AS "group_table" LEFT OUTER JOIN
-				__quesma_table_name AS "hit_table" ON ("group_table"."aggr__origins__key_0"=
-				"hit_table"."OriginAirportID"))
-			SELECT "aggr__origins__parent_count", "aggr__origins__key_0",
-			  "aggr__origins__count", "top_hits__origins__originLocation_col_0",
-			  "top_hits__origins__originLocation_col_1", "top_hits_rank"
-			FROM "quesma_top_hits_join"
-			WHERE "top_hits_rank"<=1
-			ORDER BY "aggr__origins__count" DESC, "aggr__origins__key_0" ASC,
-			  "top_hits_rank" ASC`,
+		ExpectedPancakeSQL: "WITH quesma_top_hits_group_table AS (\n" +
+			"  SELECT `aggr__origins__parent_count`, `aggr__origins__key_0`,\n" +
+			"    `aggr__origins__count`, `aggr__origins__distinations__parent_count`,\n" +
+			"    `aggr__origins__distinations__key_0`, `aggr__origins__distinations__count`,\n" +
+			"    `aggr__origins__order_1_rank`, `aggr__origins__distinations__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT `aggr__origins__parent_count`, `aggr__origins__key_0`,\n" +
+			"      `aggr__origins__count`, `aggr__origins__distinations__parent_count`,\n" +
+			"      `aggr__origins__distinations__key_0`,\n" +
+			"      `aggr__origins__distinations__count`,\n" +
+			"      dense_rank() OVER (ORDER BY `aggr__origins__count` DESC,\n" +
+			"      `aggr__origins__key_0` ASC) AS `aggr__origins__order_1_rank`,\n" +
+			"      dense_rank() OVER (PARTITION BY `aggr__origins__key_0` ORDER BY\n" +
+			"      `aggr__origins__distinations__count` DESC,\n" +
+			"      `aggr__origins__distinations__key_0` ASC) AS\n" +
+			"      `aggr__origins__distinations__order_1_rank`\n" +
+			"    FROM (\n" +
+			"      SELECT sum(count(*)) OVER () AS `aggr__origins__parent_count`,\n" +
+			"        `OriginAirportID` AS `aggr__origins__key_0`,\n" +
+			"        sum(count(*)) OVER (PARTITION BY `aggr__origins__key_0`) AS\n" +
+			"        `aggr__origins__count`,\n" +
+			"        sum(count(*)) OVER (PARTITION BY `aggr__origins__key_0`) AS\n" +
+			"        `aggr__origins__distinations__parent_count`,\n" +
+			"        `DestAirportID` AS `aggr__origins__distinations__key_0`,\n" +
+			"        count(*) AS `aggr__origins__distinations__count`\n" +
+			"      FROM `__quesma_table_name`\n" +
+			"      GROUP BY `OriginAirportID` AS `aggr__origins__key_0`,\n" +
+			"        `DestAirportID` AS `aggr__origins__distinations__key_0`))\n" +
+			"  WHERE (`aggr__origins__order_1_rank`<=10001 AND\n" +
+			"    `aggr__origins__distinations__order_1_rank`<=10001)\n" +
+			"  ORDER BY `aggr__origins__order_1_rank` ASC,\n" +
+			"    `aggr__origins__distinations__order_1_rank` ASC) ,\n" +
+			"quesma_top_hits_join AS (\n" +
+			"  SELECT `group_table`.`aggr__origins__parent_count` AS\n" +
+			"    `aggr__origins__parent_count`,\n" +
+			"    `group_table`.`aggr__origins__key_0` AS `aggr__origins__key_0`,\n" +
+			"    `group_table`.`aggr__origins__count` AS `aggr__origins__count`,\n" +
+			"    `group_table`.`aggr__origins__distinations__parent_count` AS\n" +
+			"    `aggr__origins__distinations__parent_count`,\n" +
+			"    `group_table`.`aggr__origins__distinations__key_0` AS\n" +
+			"    `aggr__origins__distinations__key_0`,\n" +
+			"    `group_table`.`aggr__origins__distinations__count` AS\n" +
+			"    `aggr__origins__distinations__count`,\n" +
+			"    `hit_table`.`DestLocation` AS\n" +
+			"    `top_hits__origins__distinations__destLocation_col_0`,\n" +
+			"    ROW_NUMBER() OVER (PARTITION BY `group_table`.`aggr__origins__key_0`,\n" +
+			"    `group_table`.`aggr__origins__distinations__key_0`) AS `top_hits_rank`,\n" +
+			"    `group_table`.`aggr__origins__order_1_rank` AS `aggr__origins__order_1_rank`\n" +
+			"    ,\n" +
+			"    `group_table`.`aggr__origins__distinations__order_1_rank` AS\n" +
+			"    `aggr__origins__distinations__order_1_rank`\n" +
+			"  FROM quesma_top_hits_group_table AS `group_table` LEFT OUTER JOIN\n" +
+			"    `__quesma_table_name` AS `hit_table` ON ((`group_table`.`aggr__origins__key_0`=`hit_table`.`OriginAirportID` AND\n" +
+			"    `group_table`.`aggr__origins__distinations__key_0`=`hit_table`.`DestAirportID`)))\n" +
+			"SELECT `aggr__origins__parent_count`, `aggr__origins__key_0`,\n" +
+			"  `aggr__origins__count`, `aggr__origins__distinations__parent_count`,\n" +
+			"  `aggr__origins__distinations__key_0`, `aggr__origins__distinations__count`,\n" +
+			"  `top_hits__origins__distinations__destLocation_col_0`, `top_hits_rank`\n" +
+			"FROM `quesma_top_hits_join`\n" +
+			"WHERE `top_hits_rank`<=1\n" +
+			"ORDER BY `aggr__origins__order_1_rank` ASC,\n" +
+			"  `aggr__origins__distinations__order_1_rank` ASC, `top_hits_rank` ASC",
+		ExpectedAdditionalPancakeSQLs: []string{
+			" WITH quesma_top_hits_group_table AS (\n" +
+				"  SELECT sum(count(*)) OVER () AS `aggr__origins__parent_count`,\n" +
+				"    `OriginAirportID` AS `aggr__origins__key_0`,\n" +
+				"    count(*) AS `aggr__origins__count`\n" +
+				"  FROM `__quesma_table_name`\n" +
+				"  GROUP BY `OriginAirportID` AS `aggr__origins__key_0`\n" +
+				"  ORDER BY `aggr__origins__count` DESC, `aggr__origins__key_0` ASC\n" +
+				"  LIMIT 10001) ,\n" +
+				"quesma_top_hits_join AS (\n" +
+				"  SELECT `group_table`.`aggr__origins__parent_count` AS\n" +
+				"    `aggr__origins__parent_count`,\n" +
+				"    `group_table`.`aggr__origins__key_0` AS `aggr__origins__key_0`,\n" +
+				"    `group_table`.`aggr__origins__count` AS `aggr__origins__count`,\n" +
+				"    `hit_table`.`OriginLocation` AS `top_hits__origins__originLocation_col_0`,\n" +
+				"    `hit_table`.`Origin` AS `top_hits__origins__originLocation_col_1`,\n" +
+				"    ROW_NUMBER() OVER (PARTITION BY `group_table`.`aggr__origins__key_0`) AS\n" +
+				"    `top_hits_rank`\n" +
+				"  FROM quesma_top_hits_group_table AS `group_table` LEFT OUTER JOIN\n" +
+				"    `__quesma_table_name` AS `hit_table` ON (`group_table`.`aggr__origins__key_0`=`hit_table`.`OriginAirportID`))\n" +
+				"SELECT `aggr__origins__parent_count`, `aggr__origins__key_0`,\n" +
+				"  `aggr__origins__count`, `top_hits__origins__originLocation_col_0`,\n" +
+				"  `top_hits__origins__originLocation_col_1`, `top_hits_rank`\n" +
+				"FROM `quesma_top_hits_join`\n" +
+				"WHERE `top_hits_rank`<=1\n" +
+				"ORDER BY `aggr__origins__count` DESC, `aggr__origins__key_0` ASC,\n" +
+				"  `top_hits_rank` ASC",
 		},
 	},
 }
diff --git a/platform/testdata/kibana_sample_data_logs.go b/platform/testdata/kibana_sample_data_logs.go
index 925a833b8..8f6181325 100644
--- a/platform/testdata/kibana_sample_data_logs.go
+++ b/platform/testdata/kibana_sample_data_logs.go
@@ -111,12 +111,11 @@ var KibanaSampleDataLogs = []AggregationTestCase{
 				model.NewQueryResultCol("metric__minAgg_col_0", 0.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT maxOrNull("bytes") AS "metric__maxAgg_col_0",
-			  minOrNull("bytes") AS "metric__minAgg_col_0"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1740178800000) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740831278103))`,
+		ExpectedPancakeSQL: "SELECT maxOrNull(`bytes`) AS `metric__maxAgg_col_0`,\n" +
+			"  minOrNull(`bytes`) AS `metric__minAgg_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740178800000) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1740831278103))",
 	},
 	{ // [1]
 		TestName: "Response Codes Over Time + Annotations (1/2 request, Annotations part)",
@@ -341,87 +340,84 @@ var KibanaSampleDataLogs = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__1__2__count", int64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__1__count",
-			  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-			  "timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__1__2__key_0",
-			  count(*) AS "aggr__1__2__count"
-			FROM __quesma_table_name
-			WHERE (("timestamp">=fromUnixTimestamp64Milli(1740178800000) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740831278103)) AND ("tags" __quesma_match 'error' AND
-			  "tags" __quesma_match 'security'))
-			GROUP BY toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-			  "timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__1__2__key_0"
-			ORDER BY "aggr__1__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__1__count`,\n" +
+			"  toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__1__2__key_0`,\n" +
+			"  count(*) AS `aggr__1__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1740178800000) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1740831278103)) AND (`tags` __quesma_match 'error' AND\n" +
+			"  `tags` __quesma_match 'security'))\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__1__2__key_0`\n" +
+			"ORDER BY `aggr__1__2__key_0` ASC",
 		ExpectedAdditionalPancakeSQLs: []string{
-			`
-			WITH quesma_top_hits_group_table AS (
-			  SELECT sum(count(*)) OVER () AS "aggr__1__count",
-				toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-				"timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__1__2__key_0",
-				count(*) AS "aggr__1__2__count"
-			  FROM __quesma_table_name
-			  WHERE (("timestamp">=fromUnixTimestamp64Milli(1740178800000) AND "timestamp"<=
-				fromUnixTimestamp64Milli(1740831278103)) AND ("tags" __quesma_match 'error' AND
-				"tags" __quesma_match 'security'))
-			  GROUP BY toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(
-				toTimezone("timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS
-				"aggr__1__2__key_0"
-			  ORDER BY "aggr__1__2__key_0" ASC) ,
-			quesma_top_hits_join AS (
-			  SELECT "group_table"."aggr__1__count" AS "aggr__1__count",
-				"group_table"."aggr__1__2__key_0" AS "aggr__1__2__key_0",
-				"group_table"."aggr__1__2__count" AS "aggr__1__2__count",
-				"hit_table"."timestamp" AS "top_metrics__1__2__4_col_0",
-				"hit_table"."timestamp" AS "top_metrics__1__2__4_col_1",
-				ROW_NUMBER() OVER (PARTITION BY "group_table"."aggr__1__2__key_0" ORDER BY
-				"timestamp" ASC) AS "top_hits_rank"
-			  FROM quesma_top_hits_group_table AS "group_table" LEFT OUTER JOIN
-				__quesma_table_name AS "hit_table" ON ("group_table"."aggr__1__2__key_0"=
-				toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-				"timestamp", 'Europe/Warsaw'))*1000) / 10800000))
-			  WHERE (("timestamp">=fromUnixTimestamp64Milli(1740178800000) AND "timestamp"<=
-				fromUnixTimestamp64Milli(1740831278103)) AND ("tags" __quesma_match 'error' AND
-				"tags" __quesma_match 'security')))
-			SELECT "aggr__1__count", "aggr__1__2__key_0", "aggr__1__2__count",
-			  "top_metrics__1__2__4_col_0", "top_metrics__1__2__4_col_1", "top_hits_rank"
-			FROM "quesma_top_hits_join"
-			WHERE "top_hits_rank"<=10
-			ORDER BY "aggr__1__2__key_0" ASC, "top_hits_rank" ASC`,
-			`
-			WITH quesma_top_hits_group_table AS (
-			  SELECT sum(count(*)) OVER () AS "aggr__1__count",
-				toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-				"timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__1__2__key_0",
-				count(*) AS "aggr__1__2__count"
-			  FROM __quesma_table_name
-			  WHERE (("timestamp">=fromUnixTimestamp64Milli(1740178800000) AND "timestamp"<=
-				fromUnixTimestamp64Milli(1740831278103)) AND ("tags" __quesma_match 'error' AND
-				"tags" __quesma_match 'security'))
-			  GROUP BY toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(
-				toTimezone("timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS
-				"aggr__1__2__key_0"
-			  ORDER BY "aggr__1__2__key_0" ASC) ,
-			quesma_top_hits_join AS (
-			  SELECT "group_table"."aggr__1__count" AS "aggr__1__count",
-				"group_table"."aggr__1__2__key_0" AS "aggr__1__2__key_0",
-				"group_table"."aggr__1__2__count" AS "aggr__1__2__count",
-				"hit_table"."geo.src" AS "top_metrics__1__2__5_col_0",
-				"hit_table"."timestamp" AS "top_metrics__1__2__5_col_1",
-				ROW_NUMBER() OVER (PARTITION BY "group_table"."aggr__1__2__key_0" ORDER BY
-				"timestamp" ASC) AS "top_hits_rank"
-			  FROM quesma_top_hits_group_table AS "group_table" LEFT OUTER JOIN
-				__quesma_table_name AS "hit_table" ON ("group_table"."aggr__1__2__key_0"=
-				toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-				"timestamp", 'Europe/Warsaw'))*1000) / 10800000))
-			  WHERE (("timestamp">=fromUnixTimestamp64Milli(1740178800000) AND "timestamp"<=
-				fromUnixTimestamp64Milli(1740831278103)) AND ("tags" __quesma_match 'error' AND
-				"tags" __quesma_match 'security')))
-			SELECT "aggr__1__count", "aggr__1__2__key_0", "aggr__1__2__count",
-			  "top_metrics__1__2__5_col_0", "top_metrics__1__2__5_col_1", "top_hits_rank"
-			FROM "quesma_top_hits_join"
-			WHERE "top_hits_rank"<=10
-			ORDER BY "aggr__1__2__key_0" ASC, "top_hits_rank" ASC`,
+			" WITH quesma_top_hits_group_table AS (\n" +
+				"  SELECT sum(count(*)) OVER () AS `aggr__1__count`,\n" +
+				"    toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+				"    `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__1__2__key_0`,\n" +
+				"    count(*) AS `aggr__1__2__count`\n" +
+				"  FROM `__quesma_table_name`\n" +
+				"  WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1740178800000) AND `timestamp`<= \n" +
+				"    fromUnixTimestamp64Milli(1740831278103)) AND (`tags` __quesma_match 'error'\n" +
+				"    AND `tags` __quesma_match 'security'))\n" +
+				"  GROUP BY toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(\n" +
+				"    toTimezone(`timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS\n" +
+				"    `aggr__1__2__key_0`\n" +
+				"  ORDER BY `aggr__1__2__key_0` ASC) ,\n" +
+				"quesma_top_hits_join AS (\n" +
+				"  SELECT `group_table`.`aggr__1__count` AS `aggr__1__count`,\n" +
+				"    `group_table`.`aggr__1__2__key_0` AS `aggr__1__2__key_0`,\n" +
+				"    `group_table`.`aggr__1__2__count` AS `aggr__1__2__count`,\n" +
+				"    `hit_table`.`timestamp` AS `top_metrics__1__2__4_col_0`,\n" +
+				"    `hit_table`.`timestamp` AS `top_metrics__1__2__4_col_1`,\n" +
+				"    ROW_NUMBER() OVER (PARTITION BY `group_table`.`aggr__1__2__key_0` ORDER BY\n" +
+				"    `timestamp` ASC) AS `top_hits_rank`\n" +
+				"  FROM quesma_top_hits_group_table AS `group_table` LEFT OUTER JOIN\n" +
+				"    `__quesma_table_name` AS `hit_table` ON (`group_table`.`aggr__1__2__key_0`=\n" +
+				"    toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+				"    `timestamp`, 'Europe/Warsaw'))*1000) / 10800000))\n" +
+				"  WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1740178800000) AND `timestamp`<= \n" +
+				"    fromUnixTimestamp64Milli(1740831278103)) AND (`tags` __quesma_match 'error'\n" +
+				"    AND `tags` __quesma_match 'security')))\n" +
+				"SELECT `aggr__1__count`, `aggr__1__2__key_0`, `aggr__1__2__count`,\n" +
+				"  `top_metrics__1__2__4_col_0`, `top_metrics__1__2__4_col_1`, `top_hits_rank`\n" +
+				"FROM `quesma_top_hits_join`\n" +
+				"WHERE `top_hits_rank`<=10\n" +
+				"ORDER BY `aggr__1__2__key_0` ASC, `top_hits_rank` ASC",
+			" WITH quesma_top_hits_group_table AS (\n" +
+				"  SELECT sum(count(*)) OVER () AS `aggr__1__count`,\n" +
+				"    toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+				"    `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__1__2__key_0`,\n" +
+				"    count(*) AS `aggr__1__2__count`\n" +
+				"  FROM `__quesma_table_name`\n" +
+				"  WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1740178800000) AND `timestamp`<= \n" +
+				"    fromUnixTimestamp64Milli(1740831278103)) AND (`tags` __quesma_match 'error'\n" +
+				"    AND `tags` __quesma_match 'security'))\n" +
+				"  GROUP BY toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(\n" +
+				"    toTimezone(`timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS\n" +
+				"    `aggr__1__2__key_0`\n" +
+				"  ORDER BY `aggr__1__2__key_0` ASC) ,\n" +
+				"quesma_top_hits_join AS (\n" +
+				"  SELECT `group_table`.`aggr__1__count` AS `aggr__1__count`,\n" +
+				"    `group_table`.`aggr__1__2__key_0` AS `aggr__1__2__key_0`,\n" +
+				"    `group_table`.`aggr__1__2__count` AS `aggr__1__2__count`,\n" +
+				"    `hit_table`.`geo.src` AS `top_metrics__1__2__5_col_0`,\n" +
+				"    `hit_table`.`timestamp` AS `top_metrics__1__2__5_col_1`,\n" +
+				"    ROW_NUMBER() OVER (PARTITION BY `group_table`.`aggr__1__2__key_0` ORDER BY\n" +
+				"    `timestamp` ASC) AS `top_hits_rank`\n" +
+				"  FROM quesma_top_hits_group_table AS `group_table` LEFT OUTER JOIN\n" +
+				"    `__quesma_table_name` AS `hit_table` ON (`group_table`.`aggr__1__2__key_0`=\n" +
+				"    toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+				"    `timestamp`, 'Europe/Warsaw'))*1000) / 10800000))\n" +
+				"  WHERE ((`timestamp`>=fromUnixTimestamp64Milli(1740178800000) AND `timestamp`<= \n" +
+				"    fromUnixTimestamp64Milli(1740831278103)) AND (`tags` __quesma_match 'error'\n" +
+				"    AND `tags` __quesma_match 'security')))\n" +
+				"SELECT `aggr__1__count`, `aggr__1__2__key_0`, `aggr__1__2__count`,\n" +
+				"  `top_metrics__1__2__5_col_0`, `top_metrics__1__2__5_col_1`, `top_hits_rank`\n" +
+				"FROM `quesma_top_hits_join`\n" +
+				"WHERE `top_hits_rank`<=10\n" +
+				"ORDER BY `aggr__1__2__key_0` ASC, `top_hits_rank` ASC",
 		},
 		ExpectedAdditionalPancakeResults: [][]model.QueryResultRow{
 			{
@@ -560,11 +556,10 @@ var KibanaSampleDataLogs = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0_col_0", int64(833)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT uniq("clientip") AS "metric__0_col_0"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1740178800000) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740831278103))`,
+		ExpectedPancakeSQL: "SELECT uniq(`clientip`) AS `metric__0_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740178800000) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1740831278103))",
 	},
 	{ // [3]
 		TestName: "Response Codes Over Time + Annotations (2/2 request)",
@@ -853,20 +848,18 @@ var KibanaSampleDataLogs = []AggregationTestCase{
 				model.NewQueryResultCol("filter_2__aggr__0__1__count", int64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-			  "timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__0__key_0",
-			  count(*) AS "aggr__0__count",
-			  countIf(("response">=200 AND "response"<400)) AS "filter_0__aggr__0__1__count"
-			  ,
-			  countIf(("response">=400 AND "response"<500)) AS "filter_1__aggr__0__1__count"
-			  , countIf("response">=500) AS "filter_2__aggr__0__1__count"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1740178800000) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740831278103))
-			GROUP BY toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-			  "timestamp", 'Europe/Warsaw'))*1000) / 10800000) AS "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__0__key_0`,\n" +
+			"  count(*) AS `aggr__0__count`,\n" +
+			"  countIf((`response`>=200 AND `response`<400)) AS `filter_0__aggr__0__1__count`\n" +
+			"  ,\n" +
+			"  countIf((`response`>=400 AND `response`<500)) AS `filter_1__aggr__0__1__count`\n" +
+			"  , countIf(`response`>=500) AS `filter_2__aggr__0__1__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740178800000) AND `timestamp`<=fromUnixTimestamp64Milli(1740831278103))\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 10800000) AS `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__key_0` ASC",
 	},
 	{ // [4]
 		TestName: "HTTP 5xx",
@@ -986,11 +979,10 @@ var KibanaSampleDataLogs = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0-bucket_col_0", int64(63)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT countIf("response">=500) AS "metric__0-bucket_col_0"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1740178800000) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740831278103))`,
+		ExpectedPancakeSQL: "SELECT countIf(`response`>=500) AS `metric__0-bucket_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740178800000) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1740831278103))",
 	},
 	{ // [5]
 		TestName: "HTTP 4xx",
@@ -1130,11 +1122,10 @@ var KibanaSampleDataLogs = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0-bucket_col_0", int64(72)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT countIf(("response">=400 AND "response"<500)) AS "metric__0-bucket_col_0"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1740178800000) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740831278103))`,
+		ExpectedPancakeSQL: "SELECT countIf((`response`>=400 AND `response`<500)) AS `metric__0-bucket_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740178800000) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1740831278103))",
 	},
 	{ // [6]
 		TestName: "Table gz, css, zip, etc.",
@@ -1363,38 +1354,33 @@ var KibanaSampleDataLogs = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0__4-bucket__4-metric_col_0", 0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-			  "extension" AS "aggr__0__key_0", count(*) AS "aggr__0__count",
-			  sumOrNull("bytes") AS "metric__0__1_col_0",
-			  uniq("clientip") AS "metric__0__3_col_0",
-			  countIf(("timestamp">=fromUnixTimestamp64Milli(1740749972445) AND "timestamp"
-			  <=fromUnixTimestamp64Milli(1740753572445))) AS "aggr__0__2-bucket__count",
-			  sumOrNullIf("bytes", ("timestamp">=fromUnixTimestamp64Milli(1740749972445) AND
-			  "timestamp"<=fromUnixTimestamp64Milli(1740753572445))) AS
-			  "metric__0__2-bucket__2-metric_col_0"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1740092400000) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740753572445))
-			GROUP BY "extension" AS "aggr__0__key_0"
-			ORDER BY "metric__0__1_col_0" DESC, "aggr__0__key_0" ASC
-			LIMIT 11`,
-		ExpectedAdditionalPancakeSQLs: []string{`
-			SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-			  "extension" AS "aggr__0__key_0", count(*) AS "aggr__0__count",
-			  sumOrNull("bytes") AS "metric__0__1_col_0",
-			  uniq("clientip") AS "metric__0__3_col_0",
-			  countIf(("timestamp">=fromUnixTimestamp64Milli(1740749972445) AND "timestamp"
-			  <=fromUnixTimestamp64Milli(1740753572445))) AS "aggr__0__4-bucket__count",
-			  uniqIf("clientip", ("timestamp">=fromUnixTimestamp64Milli(1740749972445) AND
-			  "timestamp"<=fromUnixTimestamp64Milli(1740753572445))) AS
-			  "metric__0__4-bucket__4-metric_col_0"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1740092400000) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740753572445))
-			GROUP BY "extension" AS "aggr__0__key_0"
-			ORDER BY "metric__0__1_col_0" DESC, "aggr__0__key_0" ASC
-			LIMIT 11`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"  `extension` AS `aggr__0__key_0`, count(*) AS `aggr__0__count`,\n" +
+			"  sumOrNull(`bytes`) AS `metric__0__1_col_0`,\n" +
+			"  uniq(`clientip`) AS `metric__0__3_col_0`,\n" +
+			"  countIf((`timestamp`>=fromUnixTimestamp64Milli(1740749972445) AND `timestamp`<=fromUnixTimestamp64Milli(1740753572445))) AS `aggr__0__2-bucket__count`,\n" +
+			"  sumOrNullIf(`bytes`, (`timestamp`>=fromUnixTimestamp64Milli(1740749972445) AND\n" +
+			"  `timestamp`<=fromUnixTimestamp64Milli(1740753572445))) AS\n" +
+			"  `metric__0__2-bucket__2-metric_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740092400000) AND `timestamp`<=fromUnixTimestamp64Milli(1740753572445))\n" +
+			"GROUP BY `extension` AS `aggr__0__key_0`\n" +
+			"ORDER BY `metric__0__1_col_0` DESC, `aggr__0__key_0` ASC\n" +
+			"LIMIT 11",
+		ExpectedAdditionalPancakeSQLs: []string{
+			"SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+				"  `extension` AS `aggr__0__key_0`, count(*) AS `aggr__0__count`,\n" +
+				"  sumOrNull(`bytes`) AS `metric__0__1_col_0`,\n" +
+				"  uniq(`clientip`) AS `metric__0__3_col_0`,\n" +
+				"  countIf((`timestamp`>=fromUnixTimestamp64Milli(1740749972445) AND `timestamp`<=fromUnixTimestamp64Milli(1740753572445))) AS `aggr__0__4-bucket__count`,\n" +
+				"  uniqIf(`clientip`, (`timestamp`>=fromUnixTimestamp64Milli(1740749972445) AND\n" +
+				"  `timestamp`<=fromUnixTimestamp64Milli(1740753572445))) AS\n" +
+				"  `metric__0__4-bucket__4-metric_col_0`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740092400000) AND `timestamp`<=fromUnixTimestamp64Milli(1740753572445))\n" +
+				"GROUP BY `extension` AS `aggr__0__key_0`\n" +
+				"ORDER BY `metric__0__1_col_0` DESC, `aggr__0__key_0` ASC\n" +
+				"LIMIT 11",
 		},
 		ExpectedAdditionalPancakeResults: [][]model.QueryResultRow{
 			{
@@ -1680,20 +1666,19 @@ var KibanaSampleDataLogs = []AggregationTestCase{
 				model.NewQueryResultCol("metric__0__8_col_0", []float64{5929.5}),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__0__parent_count",
-			  "url" AS "aggr__0__key_0", count(*) AS "aggr__0__count",
-			  uniq("clientip") AS "metric__0__2_col_0",
-			  countIf("response">=500) AS "metric__0__3-bucket_col_0",
-			  countIf(("response">=400 AND "response"<500)) AS "metric__0__5-bucket_col_0",
-			  quantiles(0.950000)("bytes") AS "metric__0__7_col_0",
-			  quantiles(0.500000)("bytes") AS "metric__0__8_col_0"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1740178800000) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740831278103))
-			GROUP BY "url" AS "aggr__0__key_0"
-			ORDER BY "aggr__0__count" DESC, "aggr__0__key_0" ASC
-			LIMIT 1001`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__0__parent_count`,\n" +
+			"  `url` AS `aggr__0__key_0`, count(*) AS `aggr__0__count`,\n" +
+			"  uniq(`clientip`) AS `metric__0__2_col_0`,\n" +
+			"  countIf(`response`>=500) AS `metric__0__3-bucket_col_0`,\n" +
+			"  countIf((`response`>=400 AND `response`<500)) AS `metric__0__5-bucket_col_0`,\n" +
+			"  quantiles(0.950000)(`bytes`) AS `metric__0__7_col_0`,\n" +
+			"  quantiles(0.500000)(`bytes`) AS `metric__0__8_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740178800000) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1740831278103))\n" +
+			"GROUP BY `url` AS `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__count` DESC, `aggr__0__key_0` ASC\n" +
+			"LIMIT 1001",
 	},
 	{ // [8]
 		TestName: "Total Requests and Bytes (1/2 request)",
@@ -1883,28 +1868,26 @@ var KibanaSampleDataLogs = []AggregationTestCase{
 				model.NewQueryResultCol("metric__gridSplit__sum_of_bytes_col_0", float64(450382.0)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT FLOOR(((__quesma_geo_lon("geo.coordinates")+180)/360)*POWER(2, 6))
-			  AS "aggr__gridSplit__key_0",
-			  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat("geo.coordinates")))+(1/COS(RADIANS(
-			  __quesma_geo_lat("geo.coordinates")))))/PI())/2*POWER(2, 6))
-			  AS "aggr__gridSplit__key_1", count(*) AS "aggr__gridSplit__count",
-			  avgOrNull(__quesma_geo_lat("geo_coordinates")) AS
-			  "metric__gridSplit__gridCentroid_col_0",
-			  avgOrNull(__quesma_geo_lon("geo_coordinates")) AS
-			  "metric__gridSplit__gridCentroid_col_1",
-			  count(*) AS "metric__gridSplit__gridCentroid_col_2",
-			  sumOrNull("bytes") AS "metric__gridSplit__sum_of_bytes_col_0"
-			FROM __quesma_table_name
-			WHERE ("geo.coordinates" IS NOT NULL AND ("timestamp">=fromUnixTimestamp64Milli(
-			  1740178800000) AND "timestamp"<=fromUnixTimestamp64Milli(1740831278103)))
-			GROUP BY FLOOR(((__quesma_geo_lon("geo.coordinates")+180)/360)*POWER(2, 6))
-			  AS "aggr__gridSplit__key_0",
-			  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat("geo.coordinates")))+(1/COS(
-			  RADIANS(__quesma_geo_lat("geo.coordinates")))))/PI())/2*POWER(2, 6)) AS "aggr__gridSplit__key_1"
-			ORDER BY "aggr__gridSplit__count" DESC, "aggr__gridSplit__key_0" ASC,
-  			  "aggr__gridSplit__key_1" ASC
-			LIMIT 65535`,
+		ExpectedPancakeSQL: "SELECT FLOOR(((__quesma_geo_lon(`geo.coordinates`)+180)/360)*POWER(2, 6))\n" +
+			"  AS `aggr__gridSplit__key_0`,\n" +
+			"  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat(`geo.coordinates`)))+(1/COS(RADIANS(\n" +
+			"  __quesma_geo_lat(`geo.coordinates`)))))/PI())/2*POWER(2, 6)) AS `aggr__gridSplit__key_1`, count(*) AS `aggr__gridSplit__count`,\n" +
+			"  avgOrNull(__quesma_geo_lat(`geo_coordinates`)) AS\n" +
+			"  `metric__gridSplit__gridCentroid_col_0`,\n" +
+			"  avgOrNull(__quesma_geo_lon(`geo_coordinates`)) AS\n" +
+			"  `metric__gridSplit__gridCentroid_col_1`,\n" +
+			"  count(*) AS `metric__gridSplit__gridCentroid_col_2`,\n" +
+			"  sumOrNull(`bytes`) AS `metric__gridSplit__sum_of_bytes_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`geo.coordinates` IS NOT NULL AND (`timestamp`>=fromUnixTimestamp64Milli(\n" +
+			"  1740178800000) AND `timestamp`<=fromUnixTimestamp64Milli(1740831278103)))\n" +
+			"GROUP BY FLOOR(((__quesma_geo_lon(`geo.coordinates`)+180)/360)*POWER(2, 6))\n" +
+			"  AS `aggr__gridSplit__key_0`,\n" +
+			"  FLOOR((1-LOG(TAN(RADIANS(__quesma_geo_lat(`geo.coordinates`)))+(1/COS(\n" +
+			"  RADIANS(__quesma_geo_lat(`geo.coordinates`)))))/PI())/2*POWER(2, 6)) AS `aggr__gridSplit__key_1`\n" +
+			"ORDER BY `aggr__gridSplit__count` DESC, `aggr__gridSplit__key_0` ASC,\n" +
+			"  `aggr__gridSplit__key_1` ASC\n" +
+			"LIMIT 65535",
 	},
 	{ // [9]
 		TestName: "Total Requests and Bytes (2/2 request)",
@@ -2020,15 +2003,14 @@ var KibanaSampleDataLogs = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__join__count", int64(260)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__join__parent_count",
-			  "geo.dest" AS "aggr__join__key_0", count(*) AS "aggr__join__count"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1740178800000) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740831278103))
-			GROUP BY "geo.dest" AS "aggr__join__key_0"
-			ORDER BY "aggr__join__count" DESC, "aggr__join__key_0" ASC
-			LIMIT 65536`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__join__parent_count`,\n" +
+			"  `geo.dest` AS `aggr__join__key_0`, count(*) AS `aggr__join__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740178800000) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1740831278103))\n" +
+			"GROUP BY `geo.dest` AS `aggr__join__key_0`\n" +
+			"ORDER BY `aggr__join__count` DESC, `aggr__join__key_0` ASC\n" +
+			"LIMIT 65536",
 	},
 	{ // [10]
 		TestName: "Unique Destination Heatmap",
@@ -2191,35 +2173,34 @@ var KibanaSampleDataLogs = []AggregationTestCase{
 				model.NewQueryResultCol("metric__countries__hours__unique_col_0", int64(2)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__countries__parent_count", "aggr__countries__key_0",
-			  "aggr__countries__count", "aggr__countries__hours__key_0",
-			  "aggr__countries__hours__count", "metric__countries__hours__unique_col_0"
-			FROM (
-			  SELECT "aggr__countries__parent_count", "aggr__countries__key_0",
-				"aggr__countries__count", "aggr__countries__hours__key_0",
-				"aggr__countries__hours__count", "metric__countries__hours__unique_col_0",
-				dense_rank() OVER (ORDER BY "aggr__countries__count" DESC,
-				"aggr__countries__key_0" ASC) AS "aggr__countries__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__countries__key_0" ORDER BY
-				"aggr__countries__hours__key_0" ASC) AS
-				"aggr__countries__hours__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__countries__parent_count",
-				  "geo.dest" AS "aggr__countries__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__countries__key_0") AS
-				  "aggr__countries__count",
-				  "hour_of_day" AS "aggr__countries__hours__key_0",
-				  count(*) AS "aggr__countries__hours__count",
-				  uniq("clientip") AS "metric__countries__hours__unique_col_0"
-				FROM __quesma_table_name
-				WHERE ("@timestamp">=fromUnixTimestamp64Milli(1740178800000) AND
-				  "@timestamp"<=fromUnixTimestamp64Milli(1740831278103))
-				GROUP BY "geo.dest" AS "aggr__countries__key_0",
-				  "hour_of_day" AS "aggr__countries__hours__key_0"))
-			WHERE "aggr__countries__order_1_rank"<=26
-			ORDER BY "aggr__countries__order_1_rank" ASC,
-			  "aggr__countries__hours__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__countries__parent_count`, `aggr__countries__key_0`,\n" +
+			"  `aggr__countries__count`, `aggr__countries__hours__key_0`,\n" +
+			"  `aggr__countries__hours__count`, `metric__countries__hours__unique_col_0`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__countries__parent_count`, `aggr__countries__key_0`,\n" +
+			"    `aggr__countries__count`, `aggr__countries__hours__key_0`,\n" +
+			"    `aggr__countries__hours__count`, `metric__countries__hours__unique_col_0`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__countries__count` DESC,\n" +
+			"    `aggr__countries__key_0` ASC) AS `aggr__countries__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__countries__key_0` ORDER BY\n" +
+			"    `aggr__countries__hours__key_0` ASC) AS\n" +
+			"    `aggr__countries__hours__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(count(*)) OVER () AS `aggr__countries__parent_count`,\n" +
+			"      `geo.dest` AS `aggr__countries__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__countries__key_0`) AS\n" +
+			"      `aggr__countries__count`,\n" +
+			"      `hour_of_day` AS `aggr__countries__hours__key_0`,\n" +
+			"      count(*) AS `aggr__countries__hours__count`,\n" +
+			"      uniq(`clientip`) AS `metric__countries__hours__unique_col_0`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    WHERE (`@timestamp`>=fromUnixTimestamp64Milli(1740178800000) AND\n" +
+			"      `@timestamp`<=fromUnixTimestamp64Milli(1740831278103))\n" +
+			"    GROUP BY `geo.dest` AS `aggr__countries__key_0`,\n" +
+			"      `hour_of_day` AS `aggr__countries__hours__key_0`))\n" +
+			"WHERE `aggr__countries__order_1_rank`<=26\n" +
+			"ORDER BY `aggr__countries__order_1_rank` ASC,\n" +
+			"  `aggr__countries__hours__order_1_rank` ASC",
 	},
 	{ // [11]
 		TestName: "TODO Machine OS and Destination Sankey Chart",
@@ -2345,17 +2326,16 @@ var KibanaSampleDataLogs = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__table__count", int64(11)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "machine.os" AS "aggr__table__key_0", "geo.dest" AS "aggr__table__key_1",
-			  count(*) AS "aggr__table__count"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1740178800000) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740831278103))
-			GROUP BY "machine.os" AS "aggr__table__key_0",
-			  "geo.dest" AS "aggr__table__key_1"
-			ORDER BY "aggr__table__count" DESC, "aggr__table__key_0" ASC,
-			  "aggr__table__key_1" ASC
-			LIMIT 3`,
+		ExpectedPancakeSQL: "SELECT `machine.os` AS `aggr__table__key_0`, `geo.dest` AS `aggr__table__key_1`,\n" +
+			"  count(*) AS `aggr__table__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740178800000) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1740831278103))\n" +
+			"GROUP BY `machine.os` AS `aggr__table__key_0`,\n" +
+			"  `geo.dest` AS `aggr__table__key_1`\n" +
+			"ORDER BY `aggr__table__count` DESC, `aggr__table__key_0` ASC,\n" +
+			"  `aggr__table__key_1` ASC\n" +
+			"LIMIT 3",
 	},
 	{ // [12]
 		TestName: "Bytes distribution",
@@ -2479,12 +2459,11 @@ var KibanaSampleDataLogs = []AggregationTestCase{
 				model.NewQueryResultCol("aggr__0__count", int64(2)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT floor("bytes"/50)*50 AS "aggr__0__key_0", count(*) AS "aggr__0__count"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1740178800000) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1740831278103))
-			GROUP BY floor("bytes"/50)*50 AS "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT floor(`bytes`/50)*50 AS `aggr__0__key_0`, count(*) AS `aggr__0__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1740178800000) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1740831278103))\n" +
+			"GROUP BY floor(`bytes`/50)*50 AS `aggr__0__key_0`\n" +
+			"ORDER BY `aggr__0__key_0` ASC",
 	},
 }
diff --git a/platform/testdata/opensearch-visualize/aggregation_requests.go b/platform/testdata/opensearch-visualize/aggregation_requests.go
index efb3dc528..8b71bec0c 100644
--- a/platform/testdata/opensearch-visualize/aggregation_requests.go
+++ b/platform/testdata/opensearch-visualize/aggregation_requests.go
@@ -129,16 +129,15 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("range_1__metric__2__1_col_0", 1),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT countIf("ftd_session_time"<1000) AS "range_0__aggr__2__count",
-			  uniqIf("ftd_session_time", "ftd_session_time"<1000) AS
-			  "range_0__metric__2__1_col_0",
-			  countIf("ftd_session_time">=-100) AS "range_1__aggr__2__count",
-			  uniqIf("ftd_session_time", "ftd_session_time">=-100) AS
-			  "range_1__metric__2__1_col_0"
-			FROM ` + TableName + `
-			WHERE ("epoch_time">='2024-04-27T14:25:59.383Z' AND "epoch_time"<=
-			  '2024-04-27T14:40:59.383Z')`,
+		ExpectedPancakeSQL: "SELECT countIf(`ftd_session_time`<1000) AS `range_0__aggr__2__count`,\n" +
+			"  uniqIf(`ftd_session_time`, `ftd_session_time`<1000) AS\n" +
+			"  `range_0__metric__2__1_col_0`,\n" +
+			"  countIf(`ftd_session_time`>=-100) AS `range_1__aggr__2__count`,\n" +
+			"  uniqIf(`ftd_session_time`, `ftd_session_time`>=-100) AS\n" +
+			"  `range_1__metric__2__1_col_0`\n" +
+			"FROM `" + TableName + "`\n" +
+			"WHERE (`epoch_time`>='2024-04-27T14:25:59.383Z' AND `epoch_time`<=\n" +
+			"  '2024-04-27T14:40:59.383Z')",
 	},
 	{ // [1]
 		TestName: "Range with subaggregations. Reproduce: Visualize -> Pie chart -> Aggregation: Top Hit, Buckets: Aggregation: Range",
@@ -319,12 +318,11 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("range_1__aggr__2__count", uint64(1880)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT countIf("properties.entry_time"<1000) AS "range_0__aggr__2__count",
-			  countIf("properties.entry_time">=-100) AS "range_1__aggr__2__count"
-			FROM __quesma_table_name
-			WHERE ("epoch_time">='2024-04-27T14:38:33.527Z' AND "epoch_time"<=
-			  '2024-04-27T14:53:33.527Z')`,
+		ExpectedPancakeSQL: "SELECT countIf(`properties.entry_time`<1000) AS `range_0__aggr__2__count`,\n" +
+			"  countIf(`properties.entry_time`>=-100) AS `range_1__aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`epoch_time`>='2024-04-27T14:38:33.527Z' AND `epoch_time`<=\n" +
+			"  '2024-04-27T14:53:33.527Z')",
 		ExpectedAdditionalPancakeResults: [][]model.QueryResultRow{
 			{{}}, // 0 results
 			{
@@ -332,17 +330,17 @@ var AggregationTests = []testdata.AggregationTestCase{
 				{Cols: []model.QueryResultCol{model.NewQueryResultCol("top_hits__2__1_col_0", uint64(1704129696028))}},
 			},
 		},
-		ExpectedAdditionalPancakeSQLs: []string{`
-			SELECT "properties.entry_time" AS "top_hits__2__1_col_0"
-			FROM __quesma_table_name
-			WHERE ("properties.entry_time"<1000 AND ("epoch_time">=
-			  '2024-04-27T14:38:33.527Z' AND "epoch_time"<='2024-04-27T14:53:33.527Z'))
-			LIMIT 2`, `
-			SELECT "properties.entry_time" AS "top_hits__2__1_col_0"
-			FROM __quesma_table_name
-			WHERE ("properties.entry_time">=-100 AND ("epoch_time">=
-			  '2024-04-27T14:38:33.527Z' AND "epoch_time"<='2024-04-27T14:53:33.527Z'))
-			LIMIT 2`,
+		ExpectedAdditionalPancakeSQLs: []string{
+			"SELECT `properties.entry_time` AS `top_hits__2__1_col_0`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"WHERE (`properties.entry_time`<1000 AND (`epoch_time`>=\n" +
+				"  '2024-04-27T14:38:33.527Z' AND `epoch_time`<='2024-04-27T14:53:33.527Z'))\n" +
+				"LIMIT 2",
+			"SELECT `properties.entry_time` AS `top_hits__2__1_col_0`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"WHERE (`properties.entry_time`>=-100 AND (`epoch_time`>=\n" +
+				"  '2024-04-27T14:38:33.527Z' AND `epoch_time`<='2024-04-27T14:53:33.527Z'))\n" +
+				"LIMIT 2",
 		},
 	},
 	{ // [2]
@@ -475,17 +473,16 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("range_1__metric__2__1_col_0", 7460679809210584.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT countIf(("epoch_time_original">=0 AND "epoch_time_original"<1000)) AS
-			  "range_0__aggr__2__count",
-			  sumOrNullIf("properties.entry_time", ("epoch_time_original">=0 AND
-			  "epoch_time_original"<1000)) AS "range_0__metric__2__1_col_0",
-			  countIf("epoch_time_original">=1000) AS "range_1__aggr__2__count",
-			  sumOrNullIf("properties.entry_time", "epoch_time_original">=1000) AS
-			  "range_1__metric__2__1_col_0"
-			FROM ` + TableName + `
-			WHERE ("epoch_time">='2024-04-28T14:34:22.674Z' AND "epoch_time"<=
-			  '2024-04-28T14:49:22.674Z')`,
+		ExpectedPancakeSQL: "SELECT countIf((`epoch_time_original`>=0 AND `epoch_time_original`<1000)) AS\n" +
+			"  `range_0__aggr__2__count`,\n" +
+			"  sumOrNullIf(`properties.entry_time`, (`epoch_time_original`>=0 AND\n" +
+			"  `epoch_time_original`<1000)) AS `range_0__metric__2__1_col_0`,\n" +
+			"  countIf(`epoch_time_original`>=1000) AS `range_1__aggr__2__count`,\n" +
+			"  sumOrNullIf(`properties.entry_time`, `epoch_time_original`>=1000) AS\n" +
+			"  `range_1__metric__2__1_col_0`\n" +
+			"FROM `" + TableName + "`\n" +
+			"WHERE (`epoch_time`>='2024-04-28T14:34:22.674Z' AND `epoch_time`<=\n" +
+			"  '2024-04-28T14:49:22.674Z')",
 	},
 	{ // [3]
 		TestName: "Range with subaggregations. Reproduce: Visualize -> Heat Map -> Metrics: Median, Buckets: X-Asis Range",
@@ -614,24 +611,23 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("range_1__metric__2__1_col_0", []float64{math.NaN()}),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT countIf(("properties::exoestimation_connection_speedinkbps">=0 AND
-			  "properties::exoestimation_connection_speedinkbps"<1000)) AS
-			  "range_0__aggr__2__count",
-			  quantilesIf(0.500000)("properties::entry_time", (
-			  "properties::exoestimation_connection_speedinkbps">=0 AND
-			  "properties::exoestimation_connection_speedinkbps"<1000)) AS
-			  "range_0__metric__2__1_col_0",
-			  countIf(("properties::exoestimation_connection_speedinkbps">=1000 AND
-			  "properties::exoestimation_connection_speedinkbps"<2000)) AS
-			  "range_1__aggr__2__count",
-			  quantilesIf(0.500000)("properties::entry_time", (
-			  "properties::exoestimation_connection_speedinkbps">=1000 AND
-			  "properties::exoestimation_connection_speedinkbps"<2000)) AS
-			  "range_1__metric__2__1_col_0"
-			FROM ` + TableName + `
-			WHERE ("epoch_time">='2024-04-18T04:40:12.252Z' AND "epoch_time"<=
-			  '2024-05-03T04:40:12.252Z')`,
+		ExpectedPancakeSQL: "SELECT countIf((`properties::exoestimation_connection_speedinkbps`>=0 AND\n" +
+			"  `properties::exoestimation_connection_speedinkbps`<1000)) AS\n" +
+			"  `range_0__aggr__2__count`,\n" +
+			"  quantilesIf(0.500000)(`properties::entry_time`, (\n" +
+			"  `properties::exoestimation_connection_speedinkbps`>=0 AND\n" +
+			"  `properties::exoestimation_connection_speedinkbps`<1000)) AS\n" +
+			"  `range_0__metric__2__1_col_0`,\n" +
+			"  countIf((`properties::exoestimation_connection_speedinkbps`>=1000 AND\n" +
+			"  `properties::exoestimation_connection_speedinkbps`<2000)) AS\n" +
+			"  `range_1__aggr__2__count`,\n" +
+			"  quantilesIf(0.500000)(`properties::entry_time`, (\n" +
+			"  `properties::exoestimation_connection_speedinkbps`>=1000 AND\n" +
+			"  `properties::exoestimation_connection_speedinkbps`<2000)) AS\n" +
+			"  `range_1__metric__2__1_col_0`\n" +
+			"FROM `" + TableName + "`\n" +
+			"WHERE (`epoch_time`>='2024-04-18T04:40:12.252Z' AND `epoch_time`<=\n" +
+			"  '2024-05-03T04:40:12.252Z')",
 	},
 	{ // [4]
 		TestName: "Max on DateTime field. Reproduce: Visualize -> Line: Metrics -> Max @timestamp, Buckets: Add X-Asis, Aggregation: Significant Terms",
@@ -766,16 +762,15 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__1_col_0", util.ParseTime("2024-05-02T15:59:12.949Z")),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-			  "response" AS "aggr__2__key_0", count(*) AS "aggr__2__count",
-			  maxOrNull("timestamp") AS "metric__2__1_col_0"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1713401399517) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1714697399517))
-			GROUP BY "response" AS "aggr__2__key_0"
-			ORDER BY "aggr__2__count" DESC, "aggr__2__key_0" ASC
-			LIMIT 4`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n" +
+			"  `response` AS `aggr__2__key_0`, count(*) AS `aggr__2__count`,\n" +
+			"  maxOrNull(`timestamp`) AS `metric__2__1_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1713401399517) AND `timestamp`<=\n" +
+			"  fromUnixTimestamp64Milli(1714697399517))\n" +
+			"GROUP BY `response` AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__count` DESC, `aggr__2__key_0` ASC\n" +
+			"LIMIT 4",
 	},
 	{ // [5]
 		TestName: "Min on DateTime field. Reproduce: Visualize -> Line: Metrics -> Min @timestamp, Buckets: Add X-Asis, Aggregation: Significant Terms",
@@ -910,16 +905,15 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__1_col_0", util.ParseTime("2024-04-21T03:30:25.131Z")),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-			  "response" AS "aggr__2__key_0", count(*) AS "aggr__2__count",
-			  minOrNull("timestamp") AS "metric__2__1_col_0"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1713401460471) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1714697460471))
-			GROUP BY "response" AS "aggr__2__key_0"
-			ORDER BY "aggr__2__count" DESC, "aggr__2__key_0" ASC
-			LIMIT 4`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n" +
+			"  `response` AS `aggr__2__key_0`, count(*) AS `aggr__2__count`,\n" +
+			"  minOrNull(`timestamp`) AS `metric__2__1_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1713401460471) AND `timestamp`<=\n" +
+			"  fromUnixTimestamp64Milli(1714697460471))\n" +
+			"GROUP BY `response` AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__count` DESC, `aggr__2__key_0` ASC\n" +
+			"LIMIT 4",
 	},
 	{ // [6]
 		TestName: "Percentiles on DateTime field. Reproduce: Visualize -> Line: Metrics -> Percentiles (or Median, it's the same aggregation) @timestamp, Buckets: Add X-Asis, Aggregation: Significant Terms",
@@ -1081,22 +1075,21 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__1_col_6", []time.Time{util.ParseTime("2024-05-02T16:09:28.003Z")}),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__2__parent_count",
-			  "response" AS "aggr__2__key_0", count(*) AS "aggr__2__count",
-			  quantiles(0.010000)("timestamp") AS "metric__2__1_col_0",
-			  quantiles(0.020000)("timestamp") AS "metric__2__1_col_1",
-			  quantiles(0.250000)("timestamp") AS "metric__2__1_col_2",
-			  quantiles(0.500000)("timestamp") AS "metric__2__1_col_3",
-			  quantiles(0.750000)("timestamp") AS "metric__2__1_col_4",
-			  quantiles(0.950000)("timestamp") AS "metric__2__1_col_5",
-			  quantiles(0.990000)("timestamp") AS "metric__2__1_col_6"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1713401475845) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1714697475845))
-			GROUP BY "response" AS "aggr__2__key_0"
-			ORDER BY "aggr__2__count" DESC, "aggr__2__key_0" ASC
-			LIMIT 4`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__2__parent_count`,\n" +
+			"  `response` AS `aggr__2__key_0`, count(*) AS `aggr__2__count`,\n" +
+			"  quantiles(0.010000)(`timestamp`) AS `metric__2__1_col_0`,\n" +
+			"  quantiles(0.020000)(`timestamp`) AS `metric__2__1_col_1`,\n" +
+			"  quantiles(0.250000)(`timestamp`) AS `metric__2__1_col_2`,\n" +
+			"  quantiles(0.500000)(`timestamp`) AS `metric__2__1_col_3`,\n" +
+			"  quantiles(0.750000)(`timestamp`) AS `metric__2__1_col_4`,\n" +
+			"  quantiles(0.950000)(`timestamp`) AS `metric__2__1_col_5`,\n" +
+			"  quantiles(0.990000)(`timestamp`) AS `metric__2__1_col_6`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1713401475845) AND `timestamp`<=\n" +
+			"  fromUnixTimestamp64Milli(1714697475845))\n" +
+			"GROUP BY `response` AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__count` DESC, `aggr__2__key_0` ASC\n" +
+			"LIMIT 4",
 	},
 	{ // [7]
 		TestName: "Percentile_ranks keyed=false. Reproduce: Visualize -> Line -> Metrics: Percentile Ranks, Buckets: X-Asis Date Histogram",
@@ -1232,16 +1225,15 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__1_col_1", 50.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-  			  "timestamp", 'Europe/Warsaw'))*1000) / 3600000) AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count",
-			  countIf("AvgTicketPrice"<=0)/count(*)*100 AS "metric__2__1_col_0",
-			  countIf("AvgTicketPrice"<=50000)/count(*)*100 AS "metric__2__1_col_1"
-			FROM ` + TableName + `
-			GROUP BY toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-              "timestamp", 'Europe/Warsaw'))*1000) / 3600000) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 3600000) AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`,\n" +
+			"  countIf(`AvgTicketPrice`<=0)/count(*)*100 AS `metric__2__1_col_0`,\n" +
+			"  countIf(`AvgTicketPrice`<=50000)/count(*)*100 AS `metric__2__1_col_1`\n" +
+			"FROM `" + TableName + "`\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 3600000) AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [8]
 		TestName: "Min/max with simple script. Reproduce: Visualize -> Line -> Metrics: Count, Buckets: X-Asis Histogram",
@@ -1341,10 +1333,9 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__minAgg_col_0", 0.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT maxOrNull(toHour("timestamp")) AS "metric__maxAgg_col_0",
-			  minOrNull(toHour("timestamp")) AS "metric__minAgg_col_0"
-			FROM ` + TableName + ``,
+		ExpectedPancakeSQL: "SELECT maxOrNull(toHour(`timestamp`)) AS `metric__maxAgg_col_0`,\n" +
+			"  minOrNull(toHour(`timestamp`)) AS `metric__minAgg_col_0`\n" +
+			"FROM `" + TableName + "`",
 	},
 	{ // [9]
 		TestName: "Histogram with simple script. Reproduce: Visualize -> Line -> Metrics: Count, Buckets: X-Asis Histogram",
@@ -1456,10 +1447,9 @@ var AggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", 34),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toHour("timestamp") AS "aggr__2__key_0", count(*) AS "aggr__2__count"
-			FROM ` + TableName + `
-			GROUP BY toHour("timestamp") AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toHour(`timestamp`) AS `aggr__2__key_0`, count(*) AS `aggr__2__count`\n" +
+			"FROM `" + TableName + "`\n" +
+			"GROUP BY toHour(`timestamp`) AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 }
diff --git a/platform/testdata/opensearch-visualize/pipeline_aggregation_requests.go b/platform/testdata/opensearch-visualize/pipeline_aggregation_requests.go
index 6c2a5bec7..961e3455a 100644
--- a/platform/testdata/opensearch-visualize/pipeline_aggregation_requests.go
+++ b/platform/testdata/opensearch-visualize/pipeline_aggregation_requests.go
@@ -125,13 +125,12 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__order_1", 1.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "day_of_week_i" AS "aggr__2__key_0", count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			WHERE ("order_date">=fromUnixTimestamp64Milli(1706095390802) AND "order_date"<=
-			  fromUnixTimestamp64Milli(1715163790802))
-			GROUP BY "day_of_week_i" AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT `day_of_week_i` AS `aggr__2__key_0`, count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`order_date`>=fromUnixTimestamp64Milli(1706095390802) AND `order_date`<= \n" +
+			"  fromUnixTimestamp64Milli(1715163790802))\n" +
+			"GROUP BY `day_of_week_i` AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [1]
 		TestName: "Cumulative sum with other aggregation. Reproduce: Visualize -> Vertical Bar: Metrics: Cumulative Sum (Aggregation: Average), Buckets: Histogram",
@@ -253,12 +252,11 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__1-metric_col_0", 1.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "day_of_week_i" AS "aggr__2__key_0", count(*) AS "aggr__2__count",
-			  avgOrNull("day_of_week_i") AS "metric__2__1-metric_col_0"
-			FROM __quesma_table_name
-			GROUP BY "day_of_week_i" AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT `day_of_week_i` AS `aggr__2__key_0`, count(*) AS `aggr__2__count`,\n" +
+			"  avgOrNull(`day_of_week_i`) AS `metric__2__1-metric_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY `day_of_week_i` AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [2]
 		TestName: "Cumulative sum to other cumulative sum. Reproduce: Visualize -> Vertical Bar: Metrics: Cumulative Sum (Aggregation: Cumulative Sum (Aggregation: Count)), Buckets: Histogram",
@@ -376,11 +374,10 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", uint64(300)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "day_of_week_i" AS "aggr__2__key_0", count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY "day_of_week_i" AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT `day_of_week_i` AS `aggr__2__key_0`, count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY `day_of_week_i` AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [3]
 		TestName: "Cumulative sum - quite complex, a graph of pipelines. Reproduce: Visualize -> Vertical Bar: Metrics: Cumulative Sum (Aggregation: Cumulative Sum (Aggregation: Max)), Buckets: Histogram",
@@ -511,12 +508,11 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__1-metric-metric_col_0", 200.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "day_of_week_i" AS "aggr__2__key_0", count(*) AS "aggr__2__count",
-			  maxOrNull("products.base_price") AS "metric__2__1-metric-metric_col_0"
-			FROM __quesma_table_name
-			GROUP BY "day_of_week_i" AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT `day_of_week_i` AS `aggr__2__key_0`, count(*) AS `aggr__2__count`,\n" +
+			"  maxOrNull(`products.base_price`) AS `metric__2__1-metric-metric_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY `day_of_week_i` AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [4]
 		TestName: "Simplest Derivative (count). Reproduce: Visualize -> Vertical Bar: Metrics: Derivative (Aggregation: Count), Buckets: Histogram",
@@ -641,12 +637,11 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", uint64(21)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT floor("bytes"/200)*200 AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY floor("bytes"/200)*200 AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT floor(`bytes`/200)*200 AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY floor(`bytes`/200)*200 AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [5]
 		TestName: "Derivative with other aggregation. Reproduce: Visualize -> Vertical Bar: Metrics: Derivative (Aggregation: Sum), Buckets: Date Histogram",
@@ -847,13 +842,12 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__1-metric_col_0", 27.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp64Milli("timestamp") / 600000) AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count",
-			  sumOrNull(toHour("timestamp")) AS "metric__2__1-metric_col_0"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp64Milli("timestamp") / 600000) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp64Milli(`timestamp`) / 600000) AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`,\n" +
+			"  sumOrNull(toHour(`timestamp`)) AS `metric__2__1-metric_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY toInt64(toUnixTimestamp64Milli(`timestamp`) / 600000) AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [6]
 		TestName: "Derivative to cumulative sum. Reproduce: Visualize -> Vertical Bar: Metrics: Derivative (Aggregation: Cumulative Sum (Aggregation: Count)), Buckets: Date Histogram",
@@ -1226,13 +1220,12 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", uint64(2)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp64Milli("timestamp") / 600000) AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp64Milli("timestamp") / 600000) AS
-			  "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp64Milli(`timestamp`) / 600000) AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY toInt64(toUnixTimestamp64Milli(`timestamp`) / 600000) AS\n" +
+			"  `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [7]
 		TestName: "Simplest Serial Diff (count), lag=default (1). Reproduce: Visualize -> Vertical Bar: Metrics: Serial Diff (Aggregation: Count), Buckets: Histogram",
@@ -1370,12 +1363,11 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", uint64(21)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT floor("bytes"/200)*200 AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY floor("bytes"/200)*200 AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT floor(`bytes`/200)*200 AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY floor(`bytes`/200)*200 AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [8]
 		TestName: "Simplest Serial Diff (count), lag=2. Don't know how to reproduce in OpenSearch, but you can click out:" +
@@ -1517,12 +1509,11 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", uint64(21)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT floor("bytes"/200)*200 AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY floor("bytes"/200)*200 AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT floor(`bytes`/200)*200 AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY floor(`bytes`/200)*200 AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [9]
 		TestName: "Serial diff with other aggregation. Reproduce: Visualize -> Vertical Bar: Metrics: Serial Diff (Aggregation: Sum), Buckets: Date Histogram",
@@ -1723,13 +1714,12 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__1-metric_col_0", 27.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp64Milli("timestamp") / 600000) AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count",
-			  sumOrNull(toHour("timestamp")) AS "metric__2__1-metric_col_0"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp64Milli("timestamp") / 600000) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp64Milli(`timestamp`) / 600000) AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`,\n" +
+			"  sumOrNull(toHour(`timestamp`)) AS `metric__2__1-metric_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY toInt64(toUnixTimestamp64Milli(`timestamp`) / 600000) AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [10]
 		TestName: "Serial Diff to cumulative sum. Reproduce: Visualize -> Vertical Bar: Metrics: Serial Diff (Aggregation: Cumulative Sum (Aggregation: Count)), Buckets: Date Histogram",
@@ -1949,14 +1939,13 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__count", uint64(2)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-  			  "timestamp", 'Europe/Warsaw'))*1000) / 600000) AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			GROUP BY toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-  			  "timestamp", 'Europe/Warsaw'))*1000) / 600000) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 600000) AS `aggr__2__key_0`,\n" +
+			"  count(*) AS `aggr__2__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"  `timestamp`, 'Europe/Warsaw'))*1000) / 600000) AS `aggr__2__key_0`\n" +
+			"ORDER BY `aggr__2__key_0` ASC",
 	},
 	{ // [11]
 		TestName: "Simplest avg_bucket. Reproduce: Visualize -> Vertical Bar: Metrics: Average Bucket (Bucket: Date Histogram, Metric: Count)",
@@ -2072,13 +2061,12 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__1-bucket__count", uint64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp64Milli("timestamp") / 600000) AS
-			  "aggr__1-bucket__key_0", count(*) AS "aggr__1-bucket__count"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp64Milli("timestamp") / 600000) AS
-			  "aggr__1-bucket__key_0"
-			ORDER BY "aggr__1-bucket__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp64Milli(`timestamp`) / 600000) AS\n" +
+			"  `aggr__1-bucket__key_0`, count(*) AS `aggr__1-bucket__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY toInt64(toUnixTimestamp64Milli(`timestamp`) / 600000) AS\n" +
+			"  `aggr__1-bucket__key_0`\n" +
+			"ORDER BY `aggr__1-bucket__key_0` ASC",
 	},
 	{ // [12]
 		TestName: "avg_bucket. Reproduce: Visualize -> Vertical Bar: Metrics: Average Bucket (Bucket: Date Histogram, Metric: Max)",
@@ -2213,14 +2201,13 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__1-bucket__1-metric_col_0", 9199.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp64Milli("timestamp") / 600000) AS
-			  "aggr__1-bucket__key_0", count(*) AS "aggr__1-bucket__count",
-			  maxOrNull("bytes") AS "metric__1-bucket__1-metric_col_0"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp64Milli("timestamp") / 600000) AS
-			  "aggr__1-bucket__key_0"
-			ORDER BY "aggr__1-bucket__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp64Milli(`timestamp`) / 600000) AS\n" +
+			"  `aggr__1-bucket__key_0`, count(*) AS `aggr__1-bucket__count`,\n" +
+			"  maxOrNull(`bytes`) AS `metric__1-bucket__1-metric_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY toInt64(toUnixTimestamp64Milli(`timestamp`) / 600000) AS\n" +
+			"  `aggr__1-bucket__key_0`\n" +
+			"ORDER BY `aggr__1-bucket__key_0` ASC",
 	},
 	/* TODO need fix for date_range and subaggregations. Same one, as already merged ~1-2 weeks ago for range. It's WIP.
 	{ // [13]
@@ -2673,25 +2660,24 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__2__1-bucket__count", uint64(3)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__key_0", "aggr__2__count", "aggr__2__1-bucket__key_0",
-			  "aggr__2__1-bucket__count"
-			FROM (
-			  SELECT "aggr__2__key_0", "aggr__2__count", "aggr__2__1-bucket__key_0",
-				"aggr__2__1-bucket__count",
-				dense_rank() OVER (ORDER BY "aggr__2__key_0" ASC) AS "aggr__2__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"aggr__2__1-bucket__key_0" ASC) AS "aggr__2__1-bucket__order_1_rank"
-			  FROM (
-				SELECT toInt64(toUnixTimestamp64Milli("timestamp") / 600000) AS
-				  "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  "bytes" AS "aggr__2__1-bucket__key_0",
-				  count(*) AS "aggr__2__1-bucket__count"
-				FROM __quesma_table_name
-				GROUP BY toInt64(toUnixTimestamp64Milli("timestamp") / 600000) AS
-				  "aggr__2__key_0", "bytes" AS "aggr__2__1-bucket__key_0"))
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__1-bucket__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__key_0`, `aggr__2__count`, `aggr__2__1-bucket__key_0`,\n" +
+			"  `aggr__2__1-bucket__count`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__key_0`, `aggr__2__count`, `aggr__2__1-bucket__key_0`,\n" +
+			"    `aggr__2__1-bucket__count`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__key_0` ASC) AS `aggr__2__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+			"    `aggr__2__1-bucket__key_0` ASC) AS `aggr__2__1-bucket__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT toInt64(toUnixTimestamp64Milli(`timestamp`) / 600000) AS\n" +
+			"      `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      `bytes` AS `aggr__2__1-bucket__key_0`,\n" +
+			"      count(*) AS `aggr__2__1-bucket__count`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY toInt64(toUnixTimestamp64Milli(`timestamp`) / 600000) AS\n" +
+			"      `aggr__2__key_0`, `bytes` AS `aggr__2__1-bucket__key_0`))\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__1-bucket__order_1_rank` ASC",
 	},
 	{ // [15]
 		TestName: "Simplest min_bucket. Reproduce: Visualize -> Vertical Bar: Metrics: Min Bucket (Bucket: Terms, Metric: Count)",
@@ -2849,15 +2835,14 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__1-bucket__count", uint64(1)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__1-bucket__parent_count",
-			  "clientip" AS "aggr__1-bucket__key_0", count(*) AS "aggr__1-bucket__count"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1715413213606) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1715467213606))
-			GROUP BY "clientip" AS "aggr__1-bucket__key_0"
-			ORDER BY "aggr__1-bucket__key_0" DESC
-			LIMIT 6`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__1-bucket__parent_count`,\n" +
+			"  `clientip` AS `aggr__1-bucket__key_0`, count(*) AS `aggr__1-bucket__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1715413213606) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1715467213606))\n" +
+			"GROUP BY `clientip` AS `aggr__1-bucket__key_0`\n" +
+			"ORDER BY `aggr__1-bucket__key_0` DESC\n" +
+			"LIMIT 6",
 	},
 	{ // [16]
 		TestName: "min_bucket. Reproduce: Visualize -> Vertical Bar: Metrics: Min Bucket (Bucket: Terms, Metric: Unique Count)",
@@ -3029,14 +3014,13 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__1-bucket__1-metric_col_0", 1),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__1-bucket__parent_count",
-			  "clientip" AS "aggr__1-bucket__key_0", count(*) AS "aggr__1-bucket__count",
-			  uniq("geo.coordinates") AS "metric__1-bucket__1-metric_col_0"
-			FROM __quesma_table_name
-			GROUP BY "clientip" AS "aggr__1-bucket__key_0"
-			ORDER BY "aggr__1-bucket__key_0" DESC
-			LIMIT 6`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__1-bucket__parent_count`,\n" +
+			"  `clientip` AS `aggr__1-bucket__key_0`, count(*) AS `aggr__1-bucket__count`,\n" +
+			"  uniq(`geo.coordinates`) AS `metric__1-bucket__1-metric_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY `clientip` AS `aggr__1-bucket__key_0`\n" +
+			"ORDER BY `aggr__1-bucket__key_0` DESC\n" +
+			"LIMIT 6",
 	},
 	{ // [17]
 		TestName: "complex min_bucket. Reproduce: Visualize -> Vertical Bar: Metrics: Min Bucket (Bucket: Terms, Metric: Sum), Buckets: Split Series: Histogram",
@@ -3236,30 +3220,29 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__1-bucket__1-metric_col_0", 18.0),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__key_0", "aggr__2__count", "aggr__2__1-bucket__parent_count",
-			  "aggr__2__1-bucket__key_0", "aggr__2__1-bucket__count",
-			  "metric__2__1-bucket__1-metric_col_0"
-			FROM (
-			  SELECT "aggr__2__key_0", "aggr__2__count", "aggr__2__1-bucket__parent_count",
-				"aggr__2__1-bucket__key_0", "aggr__2__1-bucket__count",
-				"metric__2__1-bucket__1-metric_col_0",
-				dense_rank() OVER (ORDER BY "aggr__2__key_0" ASC) AS "aggr__2__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__key_0" ORDER BY
-				"aggr__2__1-bucket__key_0" DESC) AS "aggr__2__1-bucket__order_1_rank"
-			  FROM (
-				SELECT floor("bytes"/200)*200 AS "aggr__2__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS "aggr__2__count",
-				  sum(count(*)) OVER (PARTITION BY "aggr__2__key_0") AS
-				  "aggr__2__1-bucket__parent_count",
-				  "clientip" AS "aggr__2__1-bucket__key_0",
-				  count(*) AS "aggr__2__1-bucket__count",
-				  sumOrNull("bytes") AS "metric__2__1-bucket__1-metric_col_0"
-				FROM __quesma_table_name
-				GROUP BY floor("bytes"/200)*200 AS "aggr__2__key_0",
-				  "clientip" AS "aggr__2__1-bucket__key_0"))
-			WHERE "aggr__2__1-bucket__order_1_rank"<=3
-			ORDER BY "aggr__2__order_1_rank" ASC, "aggr__2__1-bucket__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__key_0`, `aggr__2__count`, `aggr__2__1-bucket__parent_count`,\n" +
+			"  `aggr__2__1-bucket__key_0`, `aggr__2__1-bucket__count`,\n" +
+			"  `metric__2__1-bucket__1-metric_col_0`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__key_0`, `aggr__2__count`, `aggr__2__1-bucket__parent_count`,\n" +
+			"    `aggr__2__1-bucket__key_0`, `aggr__2__1-bucket__count`,\n" +
+			"    `metric__2__1-bucket__1-metric_col_0`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__key_0` ASC) AS `aggr__2__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__key_0` ORDER BY\n" +
+			"    `aggr__2__1-bucket__key_0` DESC) AS `aggr__2__1-bucket__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT floor(`bytes`/200)*200 AS `aggr__2__key_0`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS `aggr__2__count`,\n" +
+			"      sum(count(*)) OVER (PARTITION BY `aggr__2__key_0`) AS\n" +
+			"      `aggr__2__1-bucket__parent_count`,\n" +
+			"      `clientip` AS `aggr__2__1-bucket__key_0`,\n" +
+			"      count(*) AS `aggr__2__1-bucket__count`,\n" +
+			"      sumOrNull(`bytes`) AS `metric__2__1-bucket__1-metric_col_0`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    GROUP BY floor(`bytes`/200)*200 AS `aggr__2__key_0`,\n" +
+			"      `clientip` AS `aggr__2__1-bucket__key_0`))\n" +
+			"WHERE `aggr__2__1-bucket__order_1_rank`<=3\n" +
+			"ORDER BY `aggr__2__order_1_rank` ASC, `aggr__2__1-bucket__order_1_rank` ASC",
 	},
 	{ // [18]
 		TestName: "Simplest max_bucket. Reproduce: Visualize -> Line: Metrics: Max Bucket (Bucket: Terms, Metric: Count)",
@@ -3381,15 +3364,14 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__1-bucket__count", uint64(1923)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__1-bucket__parent_count",
-			  "Cancelled" AS "aggr__1-bucket__key_0", count(*) AS "aggr__1-bucket__count"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1714255011264) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1715551011264))
-			GROUP BY "Cancelled" AS "aggr__1-bucket__key_0"
-			ORDER BY "aggr__1-bucket__key_0" DESC
-			LIMIT 6`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__1-bucket__parent_count`,\n" +
+			"  `Cancelled` AS `aggr__1-bucket__key_0`, count(*) AS `aggr__1-bucket__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1714255011264) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1715551011264))\n" +
+			"GROUP BY `Cancelled` AS `aggr__1-bucket__key_0`\n" +
+			"ORDER BY `aggr__1-bucket__key_0` DESC\n" +
+			"LIMIT 6",
 	},
 	{ // [19]
 		TestName: "Max/Sum bucket with some null buckets. Reproduce: Visualize -> Vertical Bar: Metrics: Max (Sum) Bucket (Aggregation: Date Histogram, Metric: Min)",
@@ -3538,14 +3520,13 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__1-bucket__1-metric_col_0", nil),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT toInt64(toUnixTimestamp64Milli("timestamp") / 600000) AS
-			  "aggr__1-bucket__key_0", count(*) AS "aggr__1-bucket__count",
-			  minOrNull("memory") AS "metric__1-bucket__1-metric_col_0"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp64Milli("timestamp") / 600000) AS
-			  "aggr__1-bucket__key_0"
-			ORDER BY "aggr__1-bucket__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT toInt64(toUnixTimestamp64Milli(`timestamp`) / 600000) AS\n" +
+			"  `aggr__1-bucket__key_0`, count(*) AS `aggr__1-bucket__count`,\n" +
+			"  minOrNull(`memory`) AS `metric__1-bucket__1-metric_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY toInt64(toUnixTimestamp64Milli(`timestamp`) / 600000) AS\n" +
+			"  `aggr__1-bucket__key_0`\n" +
+			"ORDER BY `aggr__1-bucket__key_0` ASC",
 	},
 	{ // [20]
 		TestName: "Different pipeline aggrs with some null buckets. Reproduce: Visualize -> Vertical Bar: Metrics: Max/Sum Bucket/etc. (Aggregation: Histogram, Metric: Max)",
@@ -3771,12 +3752,11 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__1-bucket__1-metric_col_0", float64(452)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT "bytes" AS "aggr__1-bucket__key_0", count(*) AS "aggr__1-bucket__count",
-			  maxOrNull("memory") AS "metric__1-bucket__1-metric_col_0"
-			FROM __quesma_table_name
-			GROUP BY "bytes" AS "aggr__1-bucket__key_0"
-			ORDER BY "aggr__1-bucket__key_0" ASC`,
+		ExpectedPancakeSQL: "SELECT `bytes` AS `aggr__1-bucket__key_0`, count(*) AS `aggr__1-bucket__count`,\n" +
+			"  maxOrNull(`memory`) AS `metric__1-bucket__1-metric_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY `bytes` AS `aggr__1-bucket__key_0`\n" +
+			"ORDER BY `aggr__1-bucket__key_0` ASC",
 	},
 	/* waits for probably a simple filters fix */
 	{ // [21]
@@ -3916,14 +3896,13 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("filter_1__metric__1-bucket__1-metric_col_0", 4968221.14887619),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT countIf("FlightDelayMin" > '-100') AS "filter_0__aggr__1-bucket__count",
-			  sumOrNullIf("DistanceKilometers", "FlightDelayMin" > '-100') AS
-			  "filter_0__metric__1-bucket__1-metric_col_0",
-			  countIf(false) AS "filter_1__aggr__1-bucket__count",
-			  sumOrNullIf("DistanceKilometers", false) AS
-			  "filter_1__metric__1-bucket__1-metric_col_0"
-			FROM __quesma_table_name`,
+		ExpectedPancakeSQL: "SELECT countIf(`FlightDelayMin` > '-100') AS `filter_0__aggr__1-bucket__count`,\n" +
+			"  sumOrNullIf(`DistanceKilometers`, `FlightDelayMin` > '-100') AS\n" +
+			"  `filter_0__metric__1-bucket__1-metric_col_0`,\n" +
+			"  countIf(false) AS `filter_1__aggr__1-bucket__count`,\n" +
+			"  sumOrNullIf(`DistanceKilometers`, false) AS\n" +
+			"  `filter_1__metric__1-bucket__1-metric_col_0`\n" +
+			"FROM `__quesma_table_name`",
 	},
 	/* waits for probably a simple filters fix */
 	{ // [22] TODO check this test with other pipeline aggregations
@@ -4320,15 +4299,14 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("aggr__1-bucket__count", uint64(298)),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__1-bucket__parent_count",
-			  "extension" AS "aggr__1-bucket__key_0", count(*) AS "aggr__1-bucket__count"
-			FROM __quesma_table_name
-			WHERE ("timestamp">=fromUnixTimestamp64Milli(1714256186906) AND "timestamp"<=
-			  fromUnixTimestamp64Milli(1715552186906))
-			GROUP BY "extension" AS "aggr__1-bucket__key_0"
-			ORDER BY "aggr__1-bucket__key_0" DESC
-			LIMIT 6`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__1-bucket__parent_count`,\n" +
+			"  `extension` AS `aggr__1-bucket__key_0`, count(*) AS `aggr__1-bucket__count`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"WHERE (`timestamp`>=fromUnixTimestamp64Milli(1714256186906) AND `timestamp`<= \n" +
+			"  fromUnixTimestamp64Milli(1715552186906))\n" +
+			"GROUP BY `extension` AS `aggr__1-bucket__key_0`\n" +
+			"ORDER BY `aggr__1-bucket__key_0` DESC\n" +
+			"LIMIT 6",
 	},
 	{ // [24]
 		TestName: "sum_bucket. Reproduce: Visualize -> Horizontal Bar: Metrics: Sum Bucket (Bucket: Significant Terms, Metric: Average)",
@@ -4476,14 +4454,13 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__1-bucket__1-metric_col_0", 12786004614.736841),
 			}},
 		},
-		ExpectedPancakeSQL: `
-			SELECT sum(count(*)) OVER () AS "aggr__1-bucket__parent_count",
-			  "extension" AS "aggr__1-bucket__key_0", count(*) AS "aggr__1-bucket__count",
-			  avgOrNull("machine.ram") AS "metric__1-bucket__1-metric_col_0"
-			FROM __quesma_table_name
-			GROUP BY "extension" AS "aggr__1-bucket__key_0"
-			ORDER BY "aggr__1-bucket__count" DESC, "aggr__1-bucket__key_0" ASC
-			LIMIT 6`,
+		ExpectedPancakeSQL: "SELECT sum(count(*)) OVER () AS `aggr__1-bucket__parent_count`,\n" +
+			"  `extension` AS `aggr__1-bucket__key_0`, count(*) AS `aggr__1-bucket__count`,\n" +
+			"  avgOrNull(`machine.ram`) AS `metric__1-bucket__1-metric_col_0`\n" +
+			"FROM `__quesma_table_name`\n" +
+			"GROUP BY `extension` AS `aggr__1-bucket__key_0`\n" +
+			"ORDER BY `aggr__1-bucket__count` DESC, `aggr__1-bucket__key_0` ASC\n" +
+			"LIMIT 6",
 	},
 	{ // [25]
 		TestName: "complex sum_bucket. Reproduce: Visualize -> Vertical Bar: Metrics: Sum Bucket (Bucket: Date Histogram, Metric: Average), Buckets: X-Asis: Histogram",
@@ -4846,37 +4823,36 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 			"timed_out": false,
 			"took": 40
 		}`,
-		ExpectedPancakeSQL: `
-			SELECT "aggr__2__count", "aggr__2__3__key_0", "aggr__2__3__count",
-			  "aggr__2__3__1-bucket__key_0", "aggr__2__3__1-bucket__count",
-			  "metric__2__3__1-bucket__1-metric_col_0"
-			FROM (
-			  SELECT "aggr__2__count", "aggr__2__3__key_0", "aggr__2__3__count",
-				"aggr__2__3__1-bucket__key_0", "aggr__2__3__1-bucket__count",
-				"metric__2__3__1-bucket__1-metric_col_0",
-				dense_rank() OVER (ORDER BY "aggr__2__3__key_0" ASC) AS
-				"aggr__2__3__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__2__3__key_0" ORDER BY
-				"aggr__2__3__1-bucket__key_0" ASC) AS "aggr__2__3__1-bucket__order_1_rank"
-			  FROM (
-				SELECT sum(countIf(("bytes">=0 AND "bytes"<1000))) OVER () AS
-				  "aggr__2__count", floor("bytes"/200)*200 AS "aggr__2__3__key_0",
-				  sum(countIf(("bytes">=0 AND "bytes"<1000))) OVER (PARTITION BY
-				  "aggr__2__3__key_0") AS "aggr__2__3__count",
-				  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-                  "timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS
-				  "aggr__2__3__1-bucket__key_0",
-				  countIf(("bytes">=0 AND "bytes"<1000)) AS "aggr__2__3__1-bucket__count",
-				  avgOrNullIf("memory", ("bytes">=0 AND "bytes"<1000)) AS
-				  "metric__2__3__1-bucket__1-metric_col_0"
-				FROM __quesma_table_name
-				WHERE ("bytes">=0 AND "bytes"<1000)
-				GROUP BY floor("bytes"/200)*200 AS "aggr__2__3__key_0",
-				  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-                  "timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS
-				  "aggr__2__3__1-bucket__key_0"))
-			ORDER BY "aggr__2__3__order_1_rank" ASC,
-			  "aggr__2__3__1-bucket__order_1_rank" ASC`,
+		ExpectedPancakeSQL: "SELECT `aggr__2__count`, `aggr__2__3__key_0`, `aggr__2__3__count`,\n" +
+			"  `aggr__2__3__1-bucket__key_0`, `aggr__2__3__1-bucket__count`,\n" +
+			"  `metric__2__3__1-bucket__1-metric_col_0`\n" +
+			"FROM (\n" +
+			"  SELECT `aggr__2__count`, `aggr__2__3__key_0`, `aggr__2__3__count`,\n" +
+			"    `aggr__2__3__1-bucket__key_0`, `aggr__2__3__1-bucket__count`,\n" +
+			"    `metric__2__3__1-bucket__1-metric_col_0`,\n" +
+			"    dense_rank() OVER (ORDER BY `aggr__2__3__key_0` ASC) AS\n" +
+			"    `aggr__2__3__order_1_rank`,\n" +
+			"    dense_rank() OVER (PARTITION BY `aggr__2__3__key_0` ORDER BY\n" +
+			"    `aggr__2__3__1-bucket__key_0` ASC) AS `aggr__2__3__1-bucket__order_1_rank`\n" +
+			"  FROM (\n" +
+			"    SELECT sum(countIf((`bytes`>=0 AND `bytes`<1000))) OVER () AS\n" +
+			"      `aggr__2__count`, floor(`bytes`/200)*200 AS `aggr__2__3__key_0`,\n" +
+			"      sum(countIf((`bytes`>=0 AND `bytes`<1000))) OVER (PARTITION BY\n" +
+			"      `aggr__2__3__key_0`) AS `aggr__2__3__count`,\n" +
+			"      toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"      `timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS\n" +
+			"      `aggr__2__3__1-bucket__key_0`,\n" +
+			"      countIf((`bytes`>=0 AND `bytes`<1000)) AS `aggr__2__3__1-bucket__count`,\n" +
+			"      avgOrNullIf(`memory`, (`bytes`>=0 AND `bytes`<1000)) AS\n" +
+			"      `metric__2__3__1-bucket__1-metric_col_0`\n" +
+			"    FROM `__quesma_table_name`\n" +
+			"    WHERE (`bytes`>=0 AND `bytes`<1000)\n" +
+			"    GROUP BY floor(`bytes`/200)*200 AS `aggr__2__3__key_0`,\n" +
+			"      toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+			"      `timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS\n" +
+			"      `aggr__2__3__1-bucket__key_0`))\n" +
+			"ORDER BY `aggr__2__3__order_1_rank` ASC,\n" +
+			"  `aggr__2__3__1-bucket__order_1_rank` ASC",
 		ExpectedPancakeResults: []model.QueryResultRow{
 			{Cols: []model.QueryResultCol{
 				model.NewQueryResultCol("aggr__2__count", uint64(168)),
@@ -4927,37 +4903,37 @@ var PipelineAggregationTests = []testdata.AggregationTestCase{
 				model.NewQueryResultCol("metric__2__3__1-bucket__1-metric_col_0", 27400),
 			}},
 		},
-		ExpectedAdditionalPancakeSQLs: []string{`
-		 SELECT "aggr__2__count", "aggr__2__3__key_0", "aggr__2__3__count",
-		  "aggr__2__3__1-bucket__key_0", "aggr__2__3__1-bucket__count",
-		  "metric__2__3__1-bucket__1-metric_col_0"
-		FROM (
-		  SELECT "aggr__2__count", "aggr__2__3__key_0", "aggr__2__3__count",
-			"aggr__2__3__1-bucket__key_0", "aggr__2__3__1-bucket__count",
-			"metric__2__3__1-bucket__1-metric_col_0",
-			dense_rank() OVER (ORDER BY "aggr__2__3__key_0" ASC) AS
-			"aggr__2__3__order_1_rank",
-			dense_rank() OVER (PARTITION BY "aggr__2__3__key_0" ORDER BY
-			"aggr__2__3__1-bucket__key_0" ASC) AS "aggr__2__3__1-bucket__order_1_rank"
-		  FROM (
-			SELECT sum(countIf(("bytes">=1000 AND "bytes"<2000))) OVER () AS
-			  "aggr__2__count", floor("bytes"/200)*200 AS "aggr__2__3__key_0",
-			  sum(countIf(("bytes">=1000 AND "bytes"<2000))) OVER (PARTITION BY
-			  "aggr__2__3__key_0") AS "aggr__2__3__count",
-			  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-                "timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS
-			  "aggr__2__3__1-bucket__key_0",
-			  countIf(("bytes">=1000 AND "bytes"<2000)) AS "aggr__2__3__1-bucket__count",
-			  avgOrNullIf("memory", ("bytes">=1000 AND "bytes"<2000)) AS
-			  "metric__2__3__1-bucket__1-metric_col_0"
-			FROM __quesma_table_name
-			WHERE ("bytes">=1000 AND "bytes"<2000)
-			GROUP BY floor("bytes"/200)*200 AS "aggr__2__3__key_0",
-			  toInt64((toUnixTimestamp64Milli("timestamp")+timeZoneOffset(toTimezone(
-                "timestamp", 'Europe/Warsaw'))*1000) / 43200000) AS
-			  "aggr__2__3__1-bucket__key_0"))
-		ORDER BY "aggr__2__3__order_1_rank" ASC,
-		  "aggr__2__3__1-bucket__order_1_rank" ASC`,
+		ExpectedAdditionalPancakeSQLs: []string{
+			"SELECT `aggr__2__count`, `aggr__2__3__key_0`, `aggr__2__3__count`,\n" +
+				"  `aggr__2__3__1-bucket__key_0`, `aggr__2__3__1-bucket__count`,\n" +
+				"  `metric__2__3__1-bucket__1-metric_col_0`\n" +
+				"FROM (\n" +
+				"  SELECT `aggr__2__count`, `aggr__2__3__key_0`, `aggr__2__3__count`,\n" +
+				"    `aggr__2__3__1-bucket__key_0`, `aggr__2__3__1-bucket__count`,\n" +
+				"    `metric__2__3__1-bucket__1-metric_col_0`,\n" +
+				"    dense_rank() OVER (ORDER BY `aggr__2__3__key_0` ASC) AS\n" +
+				"    `aggr__2__3__order_1_rank`,\n" +
+				"    dense_rank() OVER (PARTITION BY `aggr__2__3__key_0` ORDER BY\n" +
+				"    `aggr__2__3__1-bucket__key_0` ASC) AS `aggr__2__3__1-bucket__order_1_rank`\n" +
+				"  FROM (\n" +
+				"    SELECT sum(countIf((`bytes`>=1000 AND `bytes`<2000))) OVER () AS\n" +
+				"      `aggr__2__count`, floor(`bytes`/200)*200 AS `aggr__2__3__key_0`,\n" +
+				"      sum(countIf((`bytes`>=1000 AND `bytes`<2000))) OVER (PARTITION BY\n" +
+				"      `aggr__2__3__key_0`) AS `aggr__2__3__count`,\n" +
+				"      toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+				"      `timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS\n" +
+				"      `aggr__2__3__1-bucket__key_0`,\n" +
+				"      countIf((`bytes`>=1000 AND `bytes`<2000)) AS `aggr__2__3__1-bucket__count`,\n" +
+				"      avgOrNullIf(`memory`, (`bytes`>=1000 AND `bytes`<2000)) AS\n" +
+				"      `metric__2__3__1-bucket__1-metric_col_0`\n" +
+				"    FROM `__quesma_table_name`\n" +
+				"    WHERE (`bytes`>=1000 AND `bytes`<2000)\n" +
+				"    GROUP BY floor(`bytes`/200)*200 AS `aggr__2__3__key_0`,\n" +
+				"      toInt64((toUnixTimestamp64Milli(`timestamp`)+timeZoneOffset(toTimezone(\n" +
+				"      `timestamp`, 'Europe/Warsaw'))*1000) / 43200000) AS\n" +
+				"      `aggr__2__3__1-bucket__key_0`))\n" +
+				"ORDER BY `aggr__2__3__order_1_rank` ASC,\n" +
+				"  `aggr__2__3__1-bucket__order_1_rank` ASC",
 		},
 		ExpectedAdditionalPancakeResults: [][]model.QueryResultRow{
 			{
diff --git a/platform/testdata/opensearch_requests.go b/platform/testdata/opensearch_requests.go
index fa0ae2d18..895b15de3 100644
--- a/platform/testdata/opensearch_requests.go
+++ b/platform/testdata/opensearch_requests.go
@@ -80,23 +80,24 @@ var OpensearchSearchTests = []SearchTestCase{
 			"track_total_hits": true
 		}`,
 		WantedSql: []string{
-			`("__timestamp">=fromUnixTimestamp64Milli(1712236698149) AND "__timestamp"<=fromUnixTimestamp64Milli(1712237598149))`,
+			"(`__timestamp`>=fromUnixTimestamp64Milli(1712236698149) AND `__timestamp`<=fromUnixTimestamp64Milli(1712237598149))",
 		},
 		WantedQueryType: model.ListAllFields,
 		WantedQueries: []string{
-			`SELECT "__bytes", "__timestamp", "message_____"
-			FROM __quesma_table_name
-			WHERE ("__timestamp">=fromUnixTimestamp64Milli(1712236698149) AND "__timestamp"<=fromUnixTimestamp64Milli(1712237598149))
-			ORDER BY "__timestamp" DESC LIMIT 500`,
-			`SELECT sum(count(*)) OVER () AS "metric____quesma_total_count_col_0",
-			  toInt64((toUnixTimestamp64Milli("__timestamp")+timeZoneOffset(toTimezone(
-			  "__timestamp", 'Europe/Warsaw'))*1000) / 30000) AS "aggr__2__key_0",
-			  count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			WHERE ("__timestamp">=fromUnixTimestamp64Milli(1712236698149) AND "__timestamp"<=fromUnixTimestamp64Milli(1712237598149))
-			GROUP BY toInt64((toUnixTimestamp64Milli("__timestamp")+timeZoneOffset(
-			  toTimezone("__timestamp", 'Europe/Warsaw'))*1000) / 30000) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+			"SELECT `__bytes`, `__timestamp`, `message_____`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"WHERE (`__timestamp`>=fromUnixTimestamp64Milli(1712236698149) AND `__timestamp`<=fromUnixTimestamp64Milli(1712237598149))\n" +
+				"ORDER BY `__timestamp` DESC LIMIT 500",
+
+			"SELECT sum(count(*)) OVER () AS `metric____quesma_total_count_col_0`,\n" +
+				"  toInt64((toUnixTimestamp64Milli(`__timestamp`)+timeZoneOffset(toTimezone(\n" +
+				"  `__timestamp`, 'Europe/Warsaw'))*1000) / 30000) AS `aggr__2__key_0`,\n" +
+				"  count(*) AS `aggr__2__count`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"WHERE (`__timestamp`>=fromUnixTimestamp64Milli(1712236698149) AND `__timestamp`<=fromUnixTimestamp64Milli(1712237598149))\n" +
+				"GROUP BY toInt64((toUnixTimestamp64Milli(`__timestamp`)+timeZoneOffset(\n" +
+				"  toTimezone(`__timestamp`, 'Europe/Warsaw'))*1000) / 30000) AS `aggr__2__key_0`\n" +
+				"ORDER BY `aggr__2__key_0` ASC",
 		},
 	},
 	{
@@ -171,17 +172,17 @@ var OpensearchSearchTests = []SearchTestCase{
 			"track_total_hits": true
 		}`,
 		WantedSql: []string{
-			`("__timestamp">=fromUnixTimestamp64Milli(1712236698149) AND "__timestamp"<=fromUnixTimestamp64Milli(1712237598149))`,
+			"(`__timestamp`>=fromUnixTimestamp64Milli(1712236698149) AND `__timestamp`<=fromUnixTimestamp64Milli(1712237598149))",
 		},
 		WantedQueryType: model.Normal,
 		WantedQueries: []string{
-			`SELECT sum(count(*)) OVER () AS "metric____quesma_total_count_col_0",
-			  toInt64(toUnixTimestamp64Milli("__timestamp") / 30000) AS "aggr__2__key_0",
-       		  count(*) AS "aggr__2__count"
-			FROM __quesma_table_name
-			WHERE ("__timestamp">=fromUnixTimestamp64Milli(1712236698149) AND "__timestamp"<=fromUnixTimestamp64Milli(1712237598149))
-			GROUP BY toInt64(toUnixTimestamp64Milli("__timestamp") / 30000) AS "aggr__2__key_0"
-			ORDER BY "aggr__2__key_0" ASC`,
+			"SELECT sum(count(*)) OVER () AS `metric____quesma_total_count_col_0`,\n" +
+				"  toInt64(toUnixTimestamp64Milli(`__timestamp`) / 30000) AS `aggr__2__key_0`,\n" +
+				"       count(*) AS `aggr__2__count`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"WHERE (`__timestamp`>=fromUnixTimestamp64Milli(1712236698149) AND `__timestamp`<=fromUnixTimestamp64Milli(1712237598149))\n" +
+				"GROUP BY toInt64(toUnixTimestamp64Milli(`__timestamp`) / 30000) AS `aggr__2__key_0`\n" +
+				"ORDER BY `aggr__2__key_0` ASC",
 		},
 	},
 }
diff --git a/platform/testdata/requests.go b/platform/testdata/requests.go
index 74de55573..7a3ef002a 100644
--- a/platform/testdata/requests.go
+++ b/platform/testdata/requests.go
@@ -148,21 +148,21 @@ var TestsAsyncSearch = []AsyncSearchTestCase{
 		"no comment yet",
 		model.HitsCountInfo{Type: model.Normal},
 		[]string{
-			`SELECT sum(count(*)) OVER () AS "aggr__sample__count",
-			  sum(count("host_name")) OVER () AS "metric__sample__sample_count_col_0",
-			  sum(count(*)) OVER () AS "aggr__sample__top_values__parent_count",
-			  "host_name" AS "aggr__sample__top_values__key_0",
-			  count(*) AS "aggr__sample__top_values__count"
-			FROM (
-			  SELECT "host_name"
-			  FROM __quesma_table_name
-			  WHERE (("@timestamp">=fromUnixTimestamp64Milli(1706009236820) AND "@timestamp"
-				<=fromUnixTimestamp64Milli(1706010136820)) AND "message" iLIKE '%user%')
-			  LIMIT 20000)
-			GROUP BY "host_name" AS "aggr__sample__top_values__key_0"
-			ORDER BY "aggr__sample__top_values__count" DESC,
-			  "aggr__sample__top_values__key_0" ASC
-			LIMIT 11`,
+			"SELECT sum(count(*)) OVER () AS `aggr__sample__count`,\n" +
+				"  sum(count(`host_name`)) OVER () AS `metric__sample__sample_count_col_0`,\n" +
+				"  sum(count(*)) OVER () AS `aggr__sample__top_values__parent_count`,\n" +
+				"  `host_name` AS `aggr__sample__top_values__key_0`,\n" +
+				"  count(*) AS `aggr__sample__top_values__count`\n" +
+				"FROM (\n" +
+				"  SELECT `host_name`\n" +
+				"  FROM `__quesma_table_name`\n" +
+				"  WHERE ((`@timestamp`>=fromUnixTimestamp64Milli(1706009236820) AND `@timestamp\n" +
+				"    `<=fromUnixTimestamp64Milli(1706010136820)) AND `message` iLIKE '%user%')\n" +
+				"  LIMIT 20000)\n" +
+				"GROUP BY `host_name` AS `aggr__sample__top_values__key_0`\n" +
+				"ORDER BY `aggr__sample__top_values__count` DESC,\n" +
+				"  `aggr__sample__top_values__key_0` ASC\n" +
+				"LIMIT 11",
 		},
 		true,
 	},
@@ -304,16 +304,16 @@ var TestsAsyncSearch = []AsyncSearchTestCase{
 `, "there should be 97 results, I truncated most of them",
 		model.HitsCountInfo{Type: model.ListByField, RequestedFields: []string{"message"}, Size: 100},
 		[]string{
-			`SELECT "message"
-			FROM __quesma_table_name
-			WHERE ((("@timestamp">=fromUnixTimestamp64Milli(1706020999481) AND "@timestamp"<=fromUnixTimestamp64Milli(1706021899481)) 
-			  AND "message" iLIKE '%user%') AND "message" IS NOT NULL)
-			ORDER BY "@timestamp" DESC
-			LIMIT 100`,
-			`SELECT count(*) AS "column_0"
-			FROM __quesma_table_name
-			WHERE ((("@timestamp">=fromUnixTimestamp64Milli(1706020999481) AND "@timestamp"<=fromUnixTimestamp64Milli(1706021899481)) 
-			  AND "message" iLIKE '%user%') AND "message" IS NOT NULL)`,
+			"SELECT `message`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"WHERE (((`@timestamp`>=fromUnixTimestamp64Milli(1706020999481) AND `@timestamp`<=fromUnixTimestamp64Milli(1706021899481)) \n" +
+				"  AND `message` iLIKE '%user%') AND `message` IS NOT NULL)\n" +
+				"ORDER BY `@timestamp` DESC\n" +
+				"LIMIT 100",
+			"SELECT count(*) AS `column_0`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"WHERE (((`@timestamp`>=fromUnixTimestamp64Milli(1706020999481) AND `@timestamp`<=fromUnixTimestamp64Milli(1706021899481)) \n" +
+				"  AND `message` iLIKE '%user%') AND `message` IS NOT NULL)",
 		},
 		false,
 	},
@@ -554,12 +554,12 @@ var TestsAsyncSearch = []AsyncSearchTestCase{
 	}`,
 		"Truncated most results. TODO Check what's at the end of response, probably count?",
 		model.HitsCountInfo{Type: model.ListAllFields, RequestedFields: []string{"*"}, Size: 500},
-		[]string{`
-			SELECT "@timestamp", "event_dataset", "host_name", "message", "properties_isreg"
-			FROM __quesma_table_name
-			WHERE ("message" iLIKE '%user%' AND ("@timestamp">=fromUnixTimestamp64Milli(1706020999481) AND "@timestamp"<=fromUnixTimestamp64Milli(1706021899481)))
-			ORDER BY "@timestamp" DESC
-			LIMIT 500`,
+		[]string{
+			"SELECT `@timestamp`, `event_dataset`, `host_name`, `message`, `properties_isreg`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"WHERE (`message` iLIKE '%user%' AND (`@timestamp`>=fromUnixTimestamp64Milli(1706020999481) AND `@timestamp`<=fromUnixTimestamp64Milli(1706021899481)))\n" +
+				"ORDER BY `@timestamp` DESC\n" +
+				"LIMIT 500",
 		},
 		false,
 	},
@@ -693,17 +693,18 @@ var TestsAsyncSearch = []AsyncSearchTestCase{
 		"no comment yet",
 		model.HitsCountInfo{Type: model.ListByField, RequestedFields: []string{"@timestamp"}, Size: 100},
 		[]string{
-			`SELECT sum(count(*)) OVER () AS "metric____quesma_total_count_col_0",
-			  toInt64(toUnixTimestamp64Milli("@timestamp") / 30000) AS "aggr__0__key_0",
-			  count(*) AS "aggr__0__count"
-			FROM __quesma_table_name
-			WHERE ("message" iLIKE '%user%' AND ("@timestamp">=fromUnixTimestamp64Milli(1706020999481) AND "@timestamp"<=fromUnixTimestamp64Milli(1706021899481)))
-			GROUP BY toInt64(toUnixTimestamp64Milli("@timestamp") / 30000) AS "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
-			`SELECT "@timestamp"
-			FROM __quesma_table_name
-			WHERE ("message" iLIKE '%user%' AND ("@timestamp">=fromUnixTimestamp64Milli(1706020999481) AND "@timestamp"<=fromUnixTimestamp64Milli(1706021899481)))
-			LIMIT 100`,
+			"SELECT sum(count(*)) OVER () AS `metric____quesma_total_count_col_0`,\n" +
+				"  toInt64(toUnixTimestamp64Milli(`@timestamp`) / 30000) AS `aggr__0__key_0`,\n" +
+				"  count(*) AS `aggr__0__count`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"WHERE (`message` iLIKE '%user%' AND (`@timestamp`>=fromUnixTimestamp64Milli(1706020999481) AND `@timestamp`<=fromUnixTimestamp64Milli(1706021899481)))\n" +
+				"GROUP BY toInt64(toUnixTimestamp64Milli(`@timestamp`) / 30000) AS `aggr__0__key_0`\n" +
+				"ORDER BY `aggr__0__key_0` ASC",
+
+			"SELECT `@timestamp`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"WHERE (`message` iLIKE '%user%' AND (`@timestamp`>=fromUnixTimestamp64Milli(1706020999481) AND `@timestamp`<=fromUnixTimestamp64Milli(1706021899481)))\n" +
+				"LIMIT 100",
 		},
 		true,
 	},
@@ -743,31 +744,31 @@ var TestsAsyncSearch = []AsyncSearchTestCase{
 		"no comment yet",
 		model.HitsCountInfo{Type: model.Normal},
 		[]string{
-			`SELECT "aggr__stats__parent_count", "aggr__stats__key_0", "aggr__stats__count",
-			  "aggr__stats__series__key_0", "aggr__stats__series__count"
-			FROM (
-			  SELECT "aggr__stats__parent_count", "aggr__stats__key_0",
-				"aggr__stats__count", "aggr__stats__series__key_0",
-				"aggr__stats__series__count",
-				dense_rank() OVER (ORDER BY "aggr__stats__count" DESC, "aggr__stats__key_0"
-				ASC) AS "aggr__stats__order_1_rank",
-				dense_rank() OVER (PARTITION BY "aggr__stats__key_0" ORDER BY
-				"aggr__stats__series__key_0" ASC) AS "aggr__stats__series__order_1_rank"
-			  FROM (
-				SELECT sum(count(*)) OVER () AS "aggr__stats__parent_count",
-				  COALESCE("event_dataset", 'unknown') AS "aggr__stats__key_0",
-				  sum(count(*)) OVER (PARTITION BY "aggr__stats__key_0") AS
-				  "aggr__stats__count",
-				  toInt64(toUnixTimestamp64Milli("@timestamp") / 60000) AS
-				  "aggr__stats__series__key_0", count(*) AS "aggr__stats__series__count"
-				FROM __quesma_table_name
-				WHERE ("@timestamp">fromUnixTimestamp64Milli(1706194439033) AND "@timestamp"<=fromUnixTimestamp64Milli(1706195339033))
-				GROUP BY COALESCE("event_dataset", 'unknown') AS "aggr__stats__key_0",
-				  toInt64(toUnixTimestamp64Milli("@timestamp") / 60000) AS
-				  "aggr__stats__series__key_0"))
-			WHERE "aggr__stats__order_1_rank"<=4
-			ORDER BY "aggr__stats__order_1_rank" ASC,
-			  "aggr__stats__series__order_1_rank" ASC`,
+			"SELECT `aggr__stats__parent_count`, `aggr__stats__key_0`, `aggr__stats__count`,\n" +
+				"  `aggr__stats__series__key_0`, `aggr__stats__series__count`\n" +
+				"FROM (\n" +
+				"  SELECT `aggr__stats__parent_count`, `aggr__stats__key_0`,\n" +
+				"    `aggr__stats__count`, `aggr__stats__series__key_0`,\n" +
+				"    `aggr__stats__series__count`,\n" +
+				"    dense_rank() OVER (ORDER BY `aggr__stats__count` DESC, `aggr__stats__key_0`\n" +
+				"    ASC) AS `aggr__stats__order_1_rank`,\n" +
+				"    dense_rank() OVER (PARTITION BY `aggr__stats__key_0` ORDER BY\n" +
+				"    `aggr__stats__series__key_0` ASC) AS `aggr__stats__series__order_1_rank`\n" +
+				"  FROM (\n" +
+				"    SELECT sum(count(*)) OVER () AS `aggr__stats__parent_count`,\n" +
+				"      COALESCE(`event_dataset`, 'unknown') AS `aggr__stats__key_0`,\n" +
+				"      sum(count(*)) OVER (PARTITION BY `aggr__stats__key_0`) AS\n" +
+				"      `aggr__stats__count`,\n" +
+				"      toInt64(toUnixTimestamp64Milli(`@timestamp`) / 60000) AS\n" +
+				"      `aggr__stats__series__key_0`, count(*) AS `aggr__stats__series__count`\n" +
+				"    FROM `__quesma_table_name`\n" +
+				"    WHERE (`@timestamp`>fromUnixTimestamp64Milli(1706194439033) AND `@timestamp`<=fromUnixTimestamp64Milli(1706195339033))\n" +
+				"    GROUP BY COALESCE(`event_dataset`, 'unknown') AS `aggr__stats__key_0`,\n" +
+				"      toInt64(toUnixTimestamp64Milli(`@timestamp`) / 60000) AS\n" +
+				"      `aggr__stats__series__key_0`))\n" +
+				"WHERE `aggr__stats__order_1_rank`<=4\n" +
+				"ORDER BY `aggr__stats__order_1_rank` ASC,\n" +
+				"  `aggr__stats__series__order_1_rank` ASC",
 		},
 		true,
 	},
@@ -851,12 +852,12 @@ var TestsAsyncSearch = []AsyncSearchTestCase{
 		"no comment yet",
 		model.HitsCountInfo{Type: model.Normal},
 		[]string{
-			`SELECT minOrNull("@timestamp") AS "metric__earliest_timestamp_col_0",
-			  maxOrNull("@timestamp") AS "metric__latest_timestamp_col_0",
-			  count(*) AS "metric____quesma_total_count_col_0"
-			FROM __quesma_table_name
-			WHERE (("message" iLIKE '%posei%' AND "message" ILIKE '%User logged out%') AND
-			  "host_name" ILIKE '%poseidon%')`,
+			"SELECT minOrNull(`@timestamp`) AS `metric__earliest_timestamp_col_0`,\n" +
+				"  maxOrNull(`@timestamp`) AS `metric__latest_timestamp_col_0`,\n" +
+				"  count(*) AS `metric____quesma_total_count_col_0`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"WHERE ((`message` iLIKE '%posei%' AND `message` ILIKE '%User logged out%') AND\n" +
+				"  `host_name` ILIKE '%poseidon%')",
 		},
 		true,
 	},
@@ -873,9 +874,9 @@ var TestsAsyncSearch = []AsyncSearchTestCase{
 		"no comment yet",
 		model.HitsCountInfo{Type: model.ListAllFields, RequestedFields: []string{"*"}, Size: 50},
 		[]string{
-			`SELECT "@timestamp", "event_dataset", "host_name", "message", "properties_isreg"
-			FROM __quesma_table_name
-			LIMIT 50`,
+			"SELECT `@timestamp`, `event_dataset`, `host_name`, `message`, `properties_isreg`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"LIMIT 50",
 		},
 		false,
 	},
@@ -938,14 +939,8 @@ var TestsAsyncSearch = []AsyncSearchTestCase{
 		``,
 		"happens e.g. in Explorer > Field Statistics view",
 		model.HitsCountInfo{Type: model.ListByField, RequestedFields: []string{"properties::isreg"}, Size: 100},
-		[]string{`
-			SELECT "properties_isreg"
-			FROM __quesma_table_name
-			WHERE ((("@timestamp">=fromUnixTimestamp64Milli(1710171234276) AND "@timestamp"
-			  <=fromUnixTimestamp64Milli(1710172134276)) AND ("@timestamp">=
-			  fromUnixTimestamp64Milli(1710171234276) AND "@timestamp"<=
-			  fromUnixTimestamp64Milli(1710172134276))) AND "properties_isreg" IS NOT NULL)
-			LIMIT 100`,
+		[]string{
+			"SELECT `properties_isreg`\nFROM `__quesma_table_name`\nWHERE (((`@timestamp`>=fromUnixTimestamp64Milli(1710171234276) AND `@timestamp\n  `<=fromUnixTimestamp64Milli(1710172134276)) AND (`@timestamp`>=\n  fromUnixTimestamp64Milli(1710171234276) AND `@timestamp`<=\n  fromUnixTimestamp64Milli(1710172134276))) AND `properties_isreg` IS NOT NULL)\nLIMIT 100",
 		},
 		false,
 	},
@@ -964,7 +959,7 @@ var TestsSearch = []SearchTestCase{
 		[]string{""},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" FROM ` + TableName + ` LIMIT 10`,
+			"SELECT `message` FROM `__quesma_table_name` LIMIT 10",
 		},
 		[]string{},
 	},
@@ -984,11 +979,11 @@ var TestsSearch = []SearchTestCase{
 			},
 			"track_total_hits": true
 		}`,
-		[]string{`"type"='task'`},
+		[]string{"`type`='task'"},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" FROM ` + TableName + ` WHERE "type"='task' LIMIT 10`,
-			`SELECT count(*) AS "column_0" FROM ` + TableName,
+			"SELECT `message` FROM " + TableName + " WHERE `type`='task' LIMIT 10",
+			"SELECT count(*) AS `column_0` FROM " + TableName,
 		},
 		[]string{},
 	},
@@ -1014,11 +1009,11 @@ var TestsSearch = []SearchTestCase{
 			},
 			"track_total_hits": true
 		}`,
-		[]string{`("type"='task' AND "task.enabled" IN tuple(true, 54, 'abc', 'abc\'s'))`},
+		[]string{`(` + "`type`" + `='task' AND ` + "`task.enabled`" + ` IN tuple(true, 54, 'abc', 'abc\'s'))`},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" FROM ` + TableName + ` WHERE ("type"='task' AND "task.enabled" IN tuple(true, 54, 'abc', 'abc\\'s')) LIMIT 10`,
-			`SELECT count(*) AS "column_0" FROM ` + TableName,
+			"SELECT `message` FROM " + TableName + " WHERE (`type`='task' AND `task.enabled` IN tuple(true, 54, 'abc', 'abc\\'s')) LIMIT 10",
+			"SELECT count(*) AS `column_0` FROM " + TableName,
 		},
 		[]string{},
 	},
@@ -1053,14 +1048,14 @@ var TestsSearch = []SearchTestCase{
 			"track_total_hits": true
 		}`,
 		[]string{
-			`(` + fullTextFieldName + ` iLIKE '%user%' AND ("@timestamp">=fromUnixTimestamp64Milli(1705487298815) AND "@timestamp"<=fromUnixTimestamp64Milli(1705488198815)))`,
+			`(` + fullTextFieldName + ` iLIKE '%user%' AND (` + "`@timestamp`" + `>=fromUnixTimestamp64Milli(1705487298815) AND ` + "`@timestamp`" + `<=fromUnixTimestamp64Milli(1705488198815)))`,
 		},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" FROM ` + TableName + ` WHERE ("message" iLIKE '%user%' ` +
-				`AND ("@timestamp">=fromUnixTimestamp64Milli(1705487298815) AND "@timestamp"<=fromUnixTimestamp64Milli(1705488198815))) ` +
-				`LIMIT 10`,
-			`SELECT count(*) AS "column_0" FROM ` + TableName,
+			"SELECT `message` FROM " + TableName + " WHERE (`message` iLIKE '%user%' " +
+				"AND (`@timestamp`>=fromUnixTimestamp64Milli(1705487298815) AND `@timestamp`<=fromUnixTimestamp64Milli(1705488198815))) " +
+				"LIMIT 10",
+			"SELECT count(*) AS `column_0` FROM " + TableName,
 		},
 		[]string{},
 	},
@@ -1092,16 +1087,16 @@ var TestsSearch = []SearchTestCase{
 			"track_total_hits": true
 		}`,
 		[]string{
-			`((("user.id"='kimchy' AND "tags"='production') AND ("tags"='env1' OR "tags"='deployed')) AND NOT (("age">=10 AND "age"<=20)))`,
+			`(((` + "`user.id`" + `='kimchy' AND ` + "`tags`" + `='production') AND (` + "`tags`" + `='env1' OR ` + "`tags`" + `='deployed')) AND NOT ((` + "`age`" + `>=10 AND ` + "`age`" + `<=20)))`,
 		},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" FROM ` + TableName + ` WHERE ((("user.id"='kimchy' AND "tags"='production') ` +
-				`AND ("tags"='env1' OR "tags"='deployed')) AND NOT (("age".=.0 AND "age".=.0))) ` +
-				`LIMIT 10`,
-			`SELECT count(*) AS "column_0" FROM ` + TableName + ` ` +
-				`WHERE ((("user.id"='kimchy' AND "tags"='production') ` +
-				`AND ("tags"='env1' OR "tags"='deployed')) AND NOT (("age".=.0 AND "age".=.0)))`,
+			"SELECT `message` FROM " + TableName + " WHERE (((`user.id`='kimchy' AND `tags`='production') " +
+				"AND (`tags`='env1' OR `tags`='deployed')) AND NOT ((`age`=.0 AND `age`=.0))) " +
+				"LIMIT 10",
+			"SELECT count(*) AS `column_0` FROM " + TableName + " " +
+				"WHERE (((`user.id`='kimchy' AND `tags`='production') " +
+				"AND (`tags`='env1' OR `tags`='deployed')) AND NOT ((`age`=.0 AND `age`=.0)))",
 		},
 		[]string{},
 	},
@@ -1131,9 +1126,11 @@ var TestsSearch = []SearchTestCase{
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"host_name" __quesma_match '%prometheus%'`},
+		[]string{"`host_name` __quesma_match '%prometheus%'"},
 		model.ListAllFields,
-		[]string{`SELECT "message" FROM ` + TableName + ` WHERE "host_name"='prometheus' LIMIT 10`},
+		[]string{
+			"SELECT `message` FROM `" + TableName + "` WHERE `host_name`='prometheus' LIMIT 10",
+		},
 		[]string{},
 	},
 	{ // [6]
@@ -1148,12 +1145,12 @@ var TestsSearch = []SearchTestCase{
 			"size": 100,
 			"track_total_hits": false
 		}`,
-		[]string{`((("message" __quesma_match '%this%' OR "message" __quesma_match '%is%') OR "message" __quesma_match '%a%') OR "message" __quesma_match '%test%')`},
+		[]string{"(((`message` __quesma_match '%this%' OR `message` __quesma_match '%is%') OR `message` __quesma_match '%a%') OR `message` __quesma_match '%test%')"},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" FROM ` + TableName + ` WHERE ((("message" ILIKE '%this%' OR "message" ILIKE '%is%') ` +
-				`OR "message" ILIKE '%a%') OR "message" ILIKE '%test%') ` +
-				`LIMIT 100`,
+			"SELECT `message` FROM " + TableName + " WHERE (((`message` ILIKE '%this%' OR `message` ILIKE '%is%') " +
+				"OR `message` ILIKE '%a%') OR `message` ILIKE '%test%') " +
+				"LIMIT 100",
 		},
 		[]string{},
 	},
@@ -1174,9 +1171,11 @@ var TestsSearch = []SearchTestCase{
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"status"='pending'`},
+		[]string{"`status`='pending'"},
 		model.ListAllFields,
-		[]string{`SELECT "message" FROM ` + TableName + ` WHERE "status"='pending'`},
+		[]string{
+			"SELECT `message` FROM `" + TableName + "` WHERE `status`='pending'",
+		},
 		[]string{},
 	},
 	{ // [8]
@@ -1222,12 +1221,12 @@ var TestsSearch = []SearchTestCase{
 			"track_total_hits": false
 		}`,
 		[]string{
-			`("type"='upgrade-assistant-reindex-operation' AND NOT (("namespace" IS NOT NULL OR "namespaces" IS NOT NULL)))`},
+			"(`type`='upgrade-assistant-reindex-operation' AND NOT ((`namespace` IS NOT NULL OR `namespaces` IS NOT NULL)))"},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE ("type"='upgrade-assistant-reindex-operation' AND NOT (("namespace" IS NOT NULL OR "namespaces" IS NOT NULL)))`,
+			"SELECT `message` " +
+				"FROM " + TableName + " " +
+				"WHERE (`type`='upgrade-assistant-reindex-operation' AND NOT ((`namespace` IS NOT NULL OR `namespaces` IS NOT NULL)))",
 		},
 		[]string{},
 	},
@@ -1252,9 +1251,11 @@ var TestsSearch = []SearchTestCase{
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"exception-list-agnostic.list_id" __quesma_match 'endpoint\_event\_filters'`},
+		[]string{"`exception-list-agnostic.list_id` __quesma_match 'endpoint\\_event\\_filters'"},
 		model.ListAllFields,
-		[]string{`SELECT "message" FROM ` + TableName + ` WHERE "exception-list-agnostic.list_id"='endpoint\\_event\\_filters'`},
+		[]string{
+			"SELECT `message` FROM `" + TableName + "` WHERE `exception-list-agnostic.list_id`='endpoint\\_event\\_filters'",
+		},
 		[]string{},
 	},
 	{ // [10]
@@ -1281,7 +1282,9 @@ var TestsSearch = []SearchTestCase{
 		}`,
 		[]string{fullTextFieldName + ` __quesma_match 'ingest-agent-policies'`},
 		model.ListAllFields,
-		[]string{`SELECT "message" FROM ` + TableName + ` WHERE ` + fullTextFieldName + ` ILIKE 'ingest-agent-policies'`},
+		[]string{
+			"SELECT `message` FROM `" + TableName + "` WHERE `" + fullTextFieldName + "` ILIKE 'ingest-agent-policies'",
+		},
 		[]string{},
 	},
 	{ // [11]
@@ -1303,9 +1306,11 @@ var TestsSearch = []SearchTestCase{
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"task.taskType" iLIKE 'alerting:%'`},
+		[]string{"`task.taskType` iLIKE 'alerting:%'"},
 		model.ListAllFields,
-		[]string{`SELECT "message" FROM ` + TableName + ` WHERE "task.taskType" iLIKE 'alerting:%'`},
+		[]string{
+			"SELECT `message` FROM `" + TableName + "` WHERE `task.taskType` iLIKE 'alerting:%'",
+		},
 		[]string{},
 	},
 	{ // [12]
@@ -1327,9 +1332,11 @@ var TestsSearch = []SearchTestCase{
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"alert.actions.actionRef" iLIKE 'preconfigured:%'`},
+		[]string{"`alert.actions.actionRef` iLIKE 'preconfigured:%'"},
 		model.ListAllFields,
-		[]string{`SELECT "message" FROM ` + TableName + ` WHERE "alert.actions.actionRef" iLIKE 'preconfigured:%'`},
+		[]string{
+			"SELECT `message` FROM `" + TableName + "` WHERE `alert.actions.actionRef` iLIKE 'preconfigured:%'",
+		},
 		[]string{},
 	},
 	{ // [13]
@@ -1342,9 +1349,11 @@ var TestsSearch = []SearchTestCase{
 			"track_total_hits": false,
 			"size": 10
 		}`,
-		[]string{`"user" iLIKE 'ki%'`},
+		[]string{"`user` iLIKE 'ki%'"},
 		model.ListAllFields,
-		[]string{`SELECT "message" FROM ` + TableName + ` WHERE "user" iLIKE 'ki%'`},
+		[]string{
+			"SELECT `message` FROM `" + TableName + "` WHERE `user` iLIKE 'ki%'",
+		},
 		[]string{},
 	},
 	{ // [14]
@@ -1357,9 +1366,11 @@ var TestsSearch = []SearchTestCase{
 			"track_total_hits": false,
 			"size": 10
 		}`,
-		[]string{`"user" iLIKE 'ki\%\_\\ \\\%%'`},
+		[]string{"`user` iLIKE 'ki\\%\\_\\\\ \\\\\\%%'"},
 		model.ListAllFields,
-		[]string{`SELECT "message" FROM ` + TableName + ` WHERE "user" iLIKE 'ki\\%\\_\\\\ \\\\\\%%'`},
+		[]string{
+			"SELECT `message` FROM `" + TableName + "` WHERE `user` iLIKE 'ki\\%\\_\\\\ \\\\\\%%'",
+		},
 		[]string{},
 	},
 	{ // [15]
@@ -1377,9 +1388,11 @@ var TestsSearch = []SearchTestCase{
 			"track_total_hits": false,
 			"size": 1
 		}`,
-		[]string{`"message" __quesma_match '% logged'`},
+		[]string{"`message` __quesma_match '% logged'"},
 		model.ListAllFields,
-		[]string{`SELECT "message" FROM ` + TableName + ` WHERE "message" ILIKE '% logged'`},
+		[]string{
+			"SELECT `message` FROM `" + TableName + "` WHERE `message` ILIKE '% logged'",
+		},
 		[]string{},
 	},
 	{ // [16]
@@ -1399,8 +1412,8 @@ var TestsSearch = []SearchTestCase{
 		[]string{""},
 		model.ListAllFields,
 		[]string{
-			`SELECT count(*) AS "column_0" FROM ` + TableName,
-			`SELECT "message" FROM ` + TableName,
+			"SELECT count(*) AS `column_0` FROM `" + TableName + "`",
+			"SELECT `message` FROM `" + TableName + "`",
 		},
 		[]string{},
 	},
@@ -1414,9 +1427,11 @@ var TestsSearch = []SearchTestCase{
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"message" __quesma_match '%this is a test%'`},
+		[]string{"`message` __quesma_match '%this is a test%'"},
 		model.ListAllFields,
-		[]string{`SELECT "message" FROM ` + TableName + ` WHERE "message" ILIKE '%this is a test%'`},
+		[]string{
+			"SELECT `message` FROM `" + TableName + "` WHERE `message` ILIKE '%this is a test%'",
+		},
 		[]string{},
 	},
 	{ // [18]
@@ -1432,9 +1447,11 @@ var TestsSearch = []SearchTestCase{
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"message" __quesma_match '%this is a test%'`},
+		[]string{"`message` __quesma_match '%this is a test%'"},
 		model.ListAllFields,
-		[]string{`SELECT "message" FROM ` + TableName + ` WHERE "message" ILIKE '%this is a test%'`},
+		[]string{
+			"SELECT `message` FROM `" + TableName + "` WHERE `message` ILIKE '%this is a test%'",
+		},
 		[]string{},
 	},
 	{ // [19]
@@ -1465,9 +1482,11 @@ var TestsSearch = []SearchTestCase{
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"references.type"='tag'`},
+		[]string{"`references.type`='tag'"},
 		model.ListAllFields,
-		[]string{`SELECT "message" FROM ` + TableName + ` WHERE "references.type"='tag'`},
+		[]string{
+			"SELECT `message` FROM `" + TableName + "` WHERE `references.type`='tag'",
+		},
 		[]string{},
 	},
 	{ // [20]
@@ -1530,24 +1549,23 @@ var TestsSearch = []SearchTestCase{
 		  }
 		`,
 		[]string{
-			`(` + fullTextFieldName + ` iLIKE '%user%' AND ("@timestamp">=fromUnixTimestamp64Milli(1705915570299) AND "@timestamp"<=fromUnixTimestamp64Milli(1705916470299)))`,
-			`((` + fullTextFieldName + ` iLIKE '%user%' AND ("@timestamp">=fromUnixTimestamp64Milli(1705915570299) AND "@timestamp"<=fromUnixTimestamp64Milli(1705916470299))) ` +
-				`AND "stream.namespace" IS NOT NULL)`,
+			"(" + fullTextFieldName + " iLIKE '%user%' AND (`@timestamp`>=fromUnixTimestamp64Milli(1705915570299) AND `@timestamp`<=fromUnixTimestamp64Milli(1705916470299)))",
+			"((" + fullTextFieldName + " iLIKE '%user%' AND (`@timestamp`>=fromUnixTimestamp64Milli(1705915570299) AND `@timestamp`<=fromUnixTimestamp64Milli(1705916470299))) AND `stream.namespace` IS NOT NULL)",
 		},
 		model.Normal,
 		[]string{},
 		[]string{
-			`SELECT uniqMerge(uniqState("stream_namespace")) OVER () AS
-			  "metric__unique_terms_col_0",
-			  sum(count(*)) OVER () AS "metric____quesma_total_count_col_0",
-			  sum(count(*)) OVER () AS "aggr__suggestions__parent_count",
-			  "stream_namespace" AS "aggr__suggestions__key_0",
-			  count(*) AS "aggr__suggestions__count"
-			FROM __quesma_table_name
-			WHERE ("message" iLIKE '%user%' AND ("@timestamp">=fromUnixTimestamp64Milli(1705915570299) AND "@timestamp"<=fromUnixTimestamp64Milli(1705916470299)))
-			GROUP BY "stream_namespace" AS "aggr__suggestions__key_0"
-			ORDER BY "aggr__suggestions__count" DESC, "aggr__suggestions__key_0" ASC
-			LIMIT 11`,
+			"SELECT uniqMerge(uniqState(`stream_namespace`)) OVER () AS" +
+				"  `metric__unique_terms_col_0`," +
+				"  sum(count(*)) OVER () AS `metric____quesma_total_count_col_0`," +
+				"  sum(count(*)) OVER () AS `aggr__suggestions__parent_count`," +
+				"  `stream_namespace` AS `aggr__suggestions__key_0`," +
+				"  count(*) AS `aggr__suggestions__count`" +
+				"FROM __quesma_table_name" +
+				"WHERE (`message` iLIKE '%user%' AND (`@timestamp`>=fromUnixTimestamp64Milli(1705915570299) AND `@timestamp`<=fromUnixTimestamp64Milli(1705916470299)))" +
+				"GROUP BY `stream_namespace` AS `aggr__suggestions__key_0`" +
+				"ORDER BY `aggr__suggestions__count` DESC, `aggr__suggestions__key_0` ASC" +
+				"LIMIT 11",
 		},
 	},
 	{ // [21]
@@ -1617,22 +1635,22 @@ var TestsSearch = []SearchTestCase{
 		  }
 		`,
 		[]string{
-			`(("service.name"='admin' AND ("@timestamp">=fromUnixTimestamp64Milli(1705934075873) AND "@timestamp"<=fromUnixTimestamp64Milli(1705934975873))) ` +
-				`AND "namespace" IS NOT NULL)`,
-			`("service.name"='admin' AND ("@timestamp">=fromUnixTimestamp64Milli(1705934075873) AND "@timestamp"<=fromUnixTimestamp64Milli(1705934975873)))`,
+			"((`service.name`='admin' AND (`@timestamp`>=fromUnixTimestamp64Milli(1705934075873) AND `@timestamp`<=fromUnixTimestamp64Milli(1705934975873))) " +
+				"AND `namespace` IS NOT NULL)",
+			"(`service.name`='admin' AND (`@timestamp`>=fromUnixTimestamp64Milli(1705934075873) AND `@timestamp`<=fromUnixTimestamp64Milli(1705934975873)))",
 		},
 		model.Normal,
 		[]string{},
 		[]string{
-			`SELECT uniqMerge(uniqState("namespace")) OVER () AS "metric__unique_terms_col_0"
-			  , sum(count(*)) OVER () AS "aggr__suggestions__parent_count",
-			  "namespace" AS "aggr__suggestions__key_0",
-			  count(*) AS "aggr__suggestions__count"
-			FROM __quesma_table_name
-			WHERE ("service_name"='admin' AND ("@timestamp">=fromUnixTimestamp64Milli(1705934075873) AND "@timestamp"<=fromUnixTimestamp64Milli(1705934975873)))
-			GROUP BY "namespace" AS "aggr__suggestions__key_0"
-			ORDER BY "aggr__suggestions__count" DESC, "aggr__suggestions__key_0" ASC
-			LIMIT 11`,
+			"SELECT uniqMerge(uniqState(`namespace`)) OVER () AS `metric__unique_terms_col_0`" +
+				"  , sum(count(*)) OVER () AS `aggr__suggestions__parent_count`," +
+				"  `namespace` AS `aggr__suggestions__key_0`," +
+				"  count(*) AS `aggr__suggestions__count`" +
+				"FROM __quesma_table_name" +
+				"WHERE (`service_name`='admin' AND (`@timestamp`>=fromUnixTimestamp64Milli(1705934075873) AND `@timestamp`<=fromUnixTimestamp64Milli(1705934975873)))" +
+				"GROUP BY `namespace` AS `aggr__suggestions__key_0`" +
+				"ORDER BY `aggr__suggestions__count` DESC, `aggr__suggestions__key_0` ASC" +
+				"LIMIT 11",
 		},
 	},
 	{ // [22]
@@ -1696,27 +1714,27 @@ var TestsSearch = []SearchTestCase{
 		"track_total_hits": true
 	}`,
 		[]string{
-			`(("message" __quesma_match '%User logged out%' AND "host.name" __quesma_match '%poseidon%') ` +
-				`AND ("@timestamp">=fromUnixTimestamp64Milli(1706542596491) AND "@timestamp"<=fromUnixTimestamp64Milli(1706551896491)))`,
-			`((("message" __quesma_match '%User logged out%' AND "host.name" __quesma_match '%poseidon%') ` +
-				`AND ("@timestamp">=fromUnixTimestamp64Milli(1706542596491) AND "@timestamp"<=fromUnixTimestamp64Milli(1706551896491))) ` +
-				`AND "stream.namespace" IS NOT NULL)`,
+			"((`message` __quesma_match '%User logged out%' AND `host.name` __quesma_match '%poseidon%') " +
+				"AND (`@timestamp`>=fromUnixTimestamp64Milli(1706542596491) AND `@timestamp`<=fromUnixTimestamp64Milli(1706551896491)))",
+			"(((`message` __quesma_match '%User logged out%' AND `host.name` __quesma_match '%poseidon%') " +
+				"AND (`@timestamp`>=fromUnixTimestamp64Milli(1706542596491) AND `@timestamp`<=fromUnixTimestamp64Milli(1706551896491))) " +
+				"AND `stream.namespace` IS NOT NULL)",
 		},
 		model.Normal,
 		[]string{},
 		[]string{
-			`SELECT uniqMerge(uniqState("stream_namespace")) OVER () AS
-			  "metric__unique_terms_col_0",
-			  sum(count(*)) OVER () AS "metric____quesma_total_count_col_0",
-			  sum(count(*)) OVER () AS "aggr__suggestions__parent_count",
-			  "stream_namespace" AS "aggr__suggestions__key_0",
-			  count(*) AS "aggr__suggestions__count"
-			FROM __quesma_table_name
-			WHERE (("message" ILIKE '%User logged out%' AND "host_name"='poseidon')
-			  AND ("@timestamp">=fromUnixTimestamp64Milli(1706542596491) AND "@timestamp"<=fromUnixTimestamp64Milli(1706551896491)))
-			GROUP BY "stream_namespace" AS "aggr__suggestions__key_0"
-			ORDER BY "aggr__suggestions__count" DESC, "aggr__suggestions__key_0" ASC
-			LIMIT 11`,
+			"SELECT uniqMerge(uniqState(`stream_namespace`)) OVER () AS" +
+				"  `metric__unique_terms_col_0`," +
+				"  sum(count(*)) OVER () AS `metric____quesma_total_count_col_0`," +
+				"  sum(count(*)) OVER () AS `aggr__suggestions__parent_count`," +
+				"  `stream_namespace` AS `aggr__suggestions__key_0`," +
+				"  count(*) AS `aggr__suggestions__count`" +
+				"FROM __quesma_table_name" +
+				"WHERE ((`message` ILIKE '%User logged out%' AND `host_name`='poseidon')" +
+				"  AND (`@timestamp`>=fromUnixTimestamp64Milli(1706542596491) AND `@timestamp`<=fromUnixTimestamp64Milli(1706551896491)))" +
+				"GROUP BY `stream_namespace` AS `aggr__suggestions__key_0`" +
+				"ORDER BY `aggr__suggestions__count` DESC, `aggr__suggestions__key_0` ASC" +
+				"LIMIT 11",
 		},
 	},
 	{ // [23]
@@ -1777,22 +1795,22 @@ var TestsSearch = []SearchTestCase{
 			"timeout": "1000ms"
 		}`,
 		[]string{
-			`((` + fullTextFieldName + ` iLIKE '%user%' AND ("@timestamp">=fromUnixTimestamp64Milli(1705915570299) AND "@timestamp"<=fromUnixTimestamp64Milli(1705916470299))) ` +
-				`AND "namespace" IS NOT NULL)`,
-			`(` + fullTextFieldName + ` iLIKE '%user%' AND ("@timestamp">=fromUnixTimestamp64Milli(1705915570299) AND "@timestamp"<=fromUnixTimestamp64Milli(1705916470299)))`,
+			"((" + fullTextFieldName + " iLIKE '%user%' AND (`@timestamp`>=fromUnixTimestamp64Milli(1705915570299) AND `@timestamp`<=fromUnixTimestamp64Milli(1705916470299))) " +
+				"AND `namespace` IS NOT NULL)",
+			"(" + fullTextFieldName + " iLIKE '%user%' AND (`@timestamp`>=fromUnixTimestamp64Milli(1705915570299) AND `@timestamp`<=fromUnixTimestamp64Milli(1705916470299)))",
 		},
 		model.Normal,
 		[]string{},
 		[]string{
-			`SELECT uniqMerge(uniqState("namespace")) OVER () AS "metric__unique_terms_col_0"
-			  , sum(count(*)) OVER () AS "aggr__suggestions__parent_count",
-			  "namespace" AS "aggr__suggestions__key_0",
-			  count(*) AS "aggr__suggestions__count"
-			FROM __quesma_table_name
-			WHERE ("message" iLIKE '%user%' AND ("@timestamp">=fromUnixTimestamp64Milli(1705915570299) AND "@timestamp"<=fromUnixTimestamp64Milli(1705916470299)))
-			GROUP BY "namespace" AS "aggr__suggestions__key_0"
-			ORDER BY "aggr__suggestions__count" DESC, "aggr__suggestions__key_0" ASC
-			LIMIT 11`,
+			"SELECT uniqMerge(uniqState(`namespace`)) OVER () AS `metric__unique_terms_col_0`" +
+				"  , sum(count(*)) OVER () AS `aggr__suggestions__parent_count`," +
+				"  `namespace` AS `aggr__suggestions__key_0`," +
+				"  count(*) AS `aggr__suggestions__count`" +
+				"FROM __quesma_table_name" +
+				"WHERE (`message` iLIKE '%user%' AND (`@timestamp`>=fromUnixTimestamp64Milli(1705915570299) AND `@timestamp`<=fromUnixTimestamp64Milli(1705916470299)))" +
+				"GROUP BY `namespace` AS `aggr__suggestions__key_0`" +
+				"ORDER BY `aggr__suggestions__count` DESC, `aggr__suggestions__key_0` ASC" +
+				"LIMIT 11",
 		},
 	},
 	{ // [24]
@@ -1856,25 +1874,25 @@ var TestsSearch = []SearchTestCase{
 		"timeout": "1000ms"
 	}`,
 		[]string{
-			`(("message" __quesma_match '%User logged out%' AND "host.name" __quesma_match '%poseidon%') ` +
-				`AND ("@timestamp">=fromUnixTimestamp64Milli(1706542596491) AND "@timestamp"<=fromUnixTimestamp64Milli(1706551896491))) ` +
-				`AND "namespace" IS NOT NULL)`,
-			`(("message" __quesma_match '%User logged out%' AND "host.name" __quesma_match '%poseidon%') ` +
-				`AND ("@timestamp">=fromUnixTimestamp64Milli(1706542596491) AND "@timestamp"<=fromUnixTimestamp64Milli(1706551896491)))`,
+			`((` + "`message`" + ` __quesma_match '%User logged out%' AND ` + "`host.name`" + ` __quesma_match '%poseidon%') ` +
+				`AND (` + "`@timestamp`" + `>=fromUnixTimestamp64Milli(1706542596491) AND ` + "`@timestamp`" + `<=fromUnixTimestamp64Milli(1706551896491))) ` +
+				`AND ` + "`namespace`" + ` IS NOT NULL)`,
+			`((` + "`message`" + ` __quesma_match '%User logged out%' AND ` + "`host.name`" + ` __quesma_match '%poseidon%') ` +
+				`AND (` + "`@timestamp`" + `>=fromUnixTimestamp64Milli(1706542596491) AND ` + "`@timestamp`" + `<=fromUnixTimestamp64Milli(1706551896491)))`,
 		},
 		model.Normal,
 		[]string{},
 		[]string{
-			`SELECT uniqMerge(uniqState("namespace")) OVER () AS "metric__unique_terms_col_0"
-			  , sum(count(*)) OVER () AS "aggr__suggestions__parent_count",
-			  "namespace" AS "aggr__suggestions__key_0",
-			  count(*) AS "aggr__suggestions__count"
-			FROM __quesma_table_name
-			WHERE (("message" ILIKE '%User logged out%' AND "host_name"='poseidon')
-			  AND ("@timestamp">=fromUnixTimestamp64Milli(1706542596491) AND "@timestamp"<=fromUnixTimestamp64Milli(1706551896491)))
-			GROUP BY "namespace" AS "aggr__suggestions__key_0"
-			ORDER BY "aggr__suggestions__count" DESC, "aggr__suggestions__key_0" ASC
-			LIMIT 11`,
+			"SELECT uniqMerge(uniqState(`namespace`)) OVER () AS `metric__unique_terms_col_0`" +
+				"  , sum(count(*)) OVER () AS `aggr__suggestions__parent_count`," +
+				"  `namespace` AS `aggr__suggestions__key_0`," +
+				"  count(*) AS `aggr__suggestions__count`" +
+				"FROM __quesma_table_name" +
+				"WHERE ((`message` ILIKE '%User logged out%' AND `host_name`='poseidon')" +
+				"  AND (`@timestamp`>=fromUnixTimestamp64Milli(1706542596491) AND `@timestamp`<=fromUnixTimestamp64Milli(1706551896491)))" +
+				"GROUP BY `namespace` AS `aggr__suggestions__key_0`" +
+				"ORDER BY `aggr__suggestions__count` DESC, `aggr__suggestions__key_0` ASC" +
+				"LIMIT 11",
 		},
 	},
 	{ // [25]
@@ -1935,21 +1953,21 @@ var TestsSearch = []SearchTestCase{
 			"timeout": "1000ms"
 		}`,
 		[]string{
-			`((` + fullTextFieldName + ` iLIKE '%user%' AND ("@timestamp">=fromUnixTimestamp64Milli(1705915570299) AND "@timestamp"<=fromUnixTimestamp64Milli(1705916470299))) AND "namespace" IS NOT NULL)`,
-			`(` + fullTextFieldName + ` iLIKE '%user%' AND ("@timestamp">=fromUnixTimestamp64Milli(1705915570299) AND "@timestamp"<=fromUnixTimestamp64Milli(1705916470299)))`,
+			`((` + fullTextFieldName + ` iLIKE '%user%' AND (` + "`@timestamp`" + `>=fromUnixTimestamp64Milli(1705915570299) AND ` + "`@timestamp`" + `<=fromUnixTimestamp64Milli(1705916470299))) AND ` + "`namespace`" + ` IS NOT NULL)`,
+			`(` + fullTextFieldName + ` iLIKE '%user%' AND (` + "`@timestamp`" + `>=fromUnixTimestamp64Milli(1705915570299) AND ` + "`@timestamp`" + `<=fromUnixTimestamp64Milli(1705916470299)))`,
 		},
 		model.Normal,
 		[]string{},
 		[]string{
-			`SELECT uniqMerge(uniqState("namespace")) OVER () AS "metric__unique_terms_col_0"
-			  , sum(count(*)) OVER () AS "aggr__suggestions__parent_count",
-			  "namespace" AS "aggr__suggestions__key_0",
-			  count(*) AS "aggr__suggestions__count"
-			FROM __quesma_table_name
-			WHERE ("message" iLIKE '%user%' AND ("@timestamp">=fromUnixTimestamp64Milli(1705915570299) AND "@timestamp"<=fromUnixTimestamp64Milli(1705916470299)))
-			GROUP BY "namespace" AS "aggr__suggestions__key_0"
-			ORDER BY "aggr__suggestions__count" DESC, "aggr__suggestions__key_0" ASC
-			LIMIT 11`,
+			"SELECT uniqMerge(uniqState(`namespace`)) OVER () AS `metric__unique_terms_col_0`" +
+				"  , sum(count(*)) OVER () AS `aggr__suggestions__parent_count`," +
+				"  `namespace` AS `aggr__suggestions__key_0`," +
+				"  count(*) AS `aggr__suggestions__count`" +
+				"FROM __quesma_table_name" +
+				"WHERE (`message` iLIKE '%user%' AND (`@timestamp`>=fromUnixTimestamp64Milli(1705915570299) AND `@timestamp`<=fromUnixTimestamp64Milli(1705916470299)))" +
+				"GROUP BY `namespace` AS `aggr__suggestions__key_0`" +
+				"ORDER BY `aggr__suggestions__count` DESC, `aggr__suggestions__key_0` ASC" +
+				"LIMIT 11",
 		},
 	},
 	{ // [26]
@@ -1989,8 +2007,8 @@ var TestsSearch = []SearchTestCase{
 		[]string{""},
 		model.ListByField,
 		[]string{
-			`SELECT count(*) AS "column_0" FROM ` + TableName,
-			`SELECT "message" FROM ` + TableName + ` LIMIT 500`,
+			"SELECT count(*) AS `column_0` FROM `" + TableName + "`",
+			"SELECT `message` FROM `" + TableName + "` LIMIT 500",
 		},
 		[]string{},
 	},
@@ -2008,8 +2026,8 @@ var TestsSearch = []SearchTestCase{
 		[]string{``},
 		model.ListAllFields,
 		[]string{
-			`SELECT count(*) AS "column_0" FROM ` + TableName,
-			`SELECT "message" FROM ` + TableName + ` LIMIT 10`,
+			"SELECT count(*) AS `column_0` FROM `" + TableName + "`",
+			"SELECT `message` FROM `" + TableName + "` LIMIT 10",
 		},
 		[]string{},
 	},
@@ -2027,7 +2045,7 @@ var TestsSearch = []SearchTestCase{
 		[]string{``},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" FROM ` + TableName + ` LIMIT 10`,
+			"SELECT `message` FROM `" + TableName + "` LIMIT 10",
 		},
 		[]string{},
 	},
@@ -2044,7 +2062,9 @@ var TestsSearch = []SearchTestCase{
 		}`,
 		[]string{``},
 		model.ListAllFields,
-		[]string{`SELECT "message" FROM ` + TableName},
+		[]string{
+			"SELECT `message` FROM `" + TableName + "`",
+		},
 		[]string{},
 	},
 	{ // [30]
@@ -2064,8 +2084,8 @@ var TestsSearch = []SearchTestCase{
 		[]string{``},
 		model.ListAllFields,
 		[]string{
-			`SELECT count(*) AS "column_0" FROM ` + TableName,
-			`SELECT "message" FROM ` + TableName,
+			"SELECT count(*) AS `column_0` FROM `" + TableName + "`",
+			"SELECT `message` FROM `" + TableName + "`",
 		},
 		[]string{},
 	},
@@ -2094,12 +2114,14 @@ var TestsSearch = []SearchTestCase{
 			"track_total_hits": false,
 			"size": 12
 		}`,
-		[]string{`("message" __quesma_match '%User logged out%' AND "message" __quesma_match '%User logged out%')`},
+		[]string{
+			`(` + "`message`" + ` __quesma_match '%User logged out%' AND ` + "`message`" + ` __quesma_match '%User logged out%')`,
+		},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE ("message" ILIKE '%User logged out%' AND "message" ILIKE '%User logged out%')`,
+			"SELECT `message` " +
+				"FROM `" + TableName + "` " +
+				"WHERE (`message` ILIKE '%User logged out%' AND `message` ILIKE '%User logged out%')",
 		},
 		[]string{},
 	},
@@ -2109,8 +2131,8 @@ var TestsSearch = []SearchTestCase{
 		[]string{""},
 		model.ListAllFields,
 		[]string{
-			`SELECT count(*) AS "column_0" FROM (SELECT 1 FROM ` + TableName + ` LIMIT 10000)`,
-			`SELECT "message" FROM __quesma_table_name LIMIT 10`,
+			"SELECT count(*) AS `column_0` FROM (SELECT 1 FROM `" + TableName + "` LIMIT 10000)",
+			"SELECT `message` FROM __quesma_table_name LIMIT 10",
 		},
 		[]string{},
 	},
@@ -2127,9 +2149,11 @@ var TestsSearch = []SearchTestCase{
  			},
 			"track_total_hits": false
 		}`,
-		[]string{`"user.id"='kimchy'`},
+		[]string{"`user.id`='kimchy'"},
 		model.ListAllFields,
-		[]string{`SELECT "message" FROM ` + TableName + ` WHERE "user.id"='kimchy'`},
+		[]string{
+			"SELECT `message` FROM `" + TableName + "` WHERE `user.id`='kimchy'",
+		},
 		[]string{},
 	},
 	{ // [34] this is a snowflake case as `_id` is a special field in ES and in clickhouse we compute
@@ -2157,12 +2181,14 @@ var TestsSearch = []SearchTestCase{
 			  "track_total_hits": false
 			}`,
 		[]string{
-			`("@timestamp">=fromUnixTimestamp64Milli(1705915570299) AND "@timestamp" = toDateTime64('2024-05-24 13:32:47.307',3))`,
+			"(`@timestamp`>=fromUnixTimestamp64Milli(1705915570299) AND `@timestamp` = toDateTime64('2024-05-24 13:32:47.307',3))",
 		},
 		model.ListAllFields,
 		// TestSearchHandler is pretty blunt with config loading so the test below can't be used.
 		// We will probably refactor it as we move forwards with schema which will get even more side-effecting
-		[]string{`SELECT "message" FROM ` + TableName + ` WHERE ("@timestamp">=fromUnixTimestamp64Milli(1705915570299) AND "@timestamp" = toDateTime64('2024-05-24 13:32:47.307',3)) LIMIT 10`},
+		[]string{
+			"SELECT `message` FROM " + TableName + " WHERE (`@timestamp`>=fromUnixTimestamp64Milli(1705915570299) AND `@timestamp` = toDateTime64('2024-05-24 13:32:47.307',3)) LIMIT 10",
+		},
 		[]string{},
 	},
 	{ // [35] Comments in queries
@@ -2177,9 +2203,9 @@ var TestsSearch = []SearchTestCase{
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"user.id"='kimchy'`},
+		[]string{"`user.id`='kimchy'"},
 		model.ListAllFields,
-		[]string{`SELECT "message" FROM ` + TableName + ` WHERE "user.id"='kimchy'`},
+		[]string{"SELECT `message` FROM `" + TableName + "` WHERE `user.id`='kimchy'"},
 		[]string{},
 	},
 	{ // [36] terms with range
@@ -2211,15 +2237,17 @@ var TestsSearch = []SearchTestCase{
 		  },
 		  "track_total_hits": false
 		}`,
-		[]string{`("cliIP" IN tuple('2601:204:c503:c240:9c41:5531:ad94:4d90', '50.116.43.98', '75.246.0.64') AND ("@timestamp">=fromUnixTimestamp64Milli(1715817600000) AND "@timestamp"<=fromUnixTimestamp64Milli(1715990399000)))`},
+		[]string{
+			"(`cliIP` IN tuple('2601:204:c503:c240:9c41:5531:ad94:4d90', '50.116.43.98', '75.246.0.64') AND (`@timestamp`>=fromUnixTimestamp64Milli(1715817600000) AND `@timestamp`<=fromUnixTimestamp64Milli(1715990399000)))",
+		},
 		model.ListAllFields,
 		//[]model.Query{withLimit(justSimplestWhere(`("cliIP" IN ('2601:204:c503:c240:9c41:5531:ad94:4d90','50.116.43.98','75.246.0.64') AND ("@timestamp">=parseDateTime64BestEffort('2024-05-16T00:00:00') AND "@timestamp"<=parseDateTime64BestEffort('2024-05-17T23:59:59')))`), 1)},
 		[]string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE ("cliIP" IN tuple('2601:204:c503:c240:9c41:5531:ad94:4d90', '50.116.43.98', '75.246.0.64') ` +
-				`AND ("@timestamp">=fromUnixTimestamp64Milli(1715817600000) AND "@timestamp"<=fromUnixTimestamp64Milli(1715990399000))) ` +
-				`LIMIT 1`,
+			"SELECT `message` " +
+				"FROM `" + TableName + "` " +
+				"WHERE (`cliIP` IN tuple('2601:204:c503:c240:9c41:5531:ad94:4d90', '50.116.43.98', '75.246.0.64') " +
+				"AND (`@timestamp`>=fromUnixTimestamp64Milli(1715817600000) AND `@timestamp`<=fromUnixTimestamp64Milli(1715990399000))) " +
+				"LIMIT 1",
 		},
 		[]string{},
 	},
@@ -2241,13 +2269,13 @@ var TestsSearch = []SearchTestCase{
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"field" LIKE '%-abb-all-li_mit%s-5'`},
+		[]string{"`field` LIKE '%-abb-all-li_mit%s-5'"},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE "field" LIKE '%-abb-all-li_mit%s-5' ` +
-				`LIMIT 10`,
+			"SELECT `message` " +
+				"FROM `" + TableName + "` " +
+				"WHERE `field` LIKE '%-abb-all-li_mit%s-5' " +
+				"LIMIT 10",
 		},
 		[]string{},
 	},
@@ -2271,13 +2299,13 @@ var TestsSearch = []SearchTestCase{
 		}`,
 		// Escaping _ twice ("\\_") seemed wrong, but it actually works in Clickhouse!
 		// \\\\ means 2 escaped backslashes, actual returned string is "\\"
-		[]string{`"field" LIKE '%\\___'`},
+		[]string{"`field` LIKE '%\\\\___'"},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE "field" LIKE '%\\\\___' ` +
-				`LIMIT 10`,
+			"SELECT `message`" +
+				"FROM `" + TableName + "` " +
+				"WHERE `field` LIKE '%\\\\___' " +
+				"LIMIT 10",
 		},
 		[]string{},
 	},
@@ -2299,13 +2327,13 @@ var TestsSearch = []SearchTestCase{
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"field" REGEXP 'a*-abb-all-li.mit.*s-5'`},
+		[]string{"`field` REGEXP 'a*-abb-all-li.mit.*s-5'"},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE "field" REGEXP 'a*-abb-all-li.mit.*s-5' ` +
-				`LIMIT 10`,
+			"SELECT `message` " +
+				"FROM `" + TableName + "` " +
+				"WHERE `field` REGEXP 'a*-abb-all-li.mit.*s-5' " +
+				"LIMIT 10",
 		},
 		[]string{},
 	},
@@ -2327,13 +2355,13 @@ var TestsSearch = []SearchTestCase{
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"field" REGEXP 'a?'`},
+		[]string{"`field` REGEXP 'a?'"},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE "field" REGEXP 'a\?' ` +
-				`LIMIT 10`,
+			"SELECT `message` " +
+				"FROM `" + TableName + "` " +
+				"WHERE `field` REGEXP 'a\\?' " +
+				"LIMIT 10",
 		},
 		[]string{},
 	},
@@ -2354,11 +2382,11 @@ var TestsSearch = []SearchTestCase{
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"message" __quesma_match '%
-Men\'s Clothing \\ 	%'`},
+		[]string{"`message` __quesma_match '%\nMen\\'s Clothing \\\\ \t%'"},
 		model.ListAllFields,
-		[]string{`SELECT "message" FROM ` + TableName + ` WHERE "message" ILIKE '%
-Men\\'s Clothing \\\\ 	%' LIMIT 10`},
+		[]string{
+			"SELECT `message` FROM `" + TableName + "` WHERE `message` ILIKE '%Men\\'s Clothing \\\\ 	%' LIMIT 10",
+		},
 		[]string{},
 	},
 	{ // [42]
@@ -2374,10 +2402,10 @@ Men\\'s Clothing \\\\ 	%' LIMIT 10`},
 		[]string{`false`},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE false ` +
-				`LIMIT 10`,
+			"SELECT `message` " +
+				"FROM `" + TableName + "` " +
+				"WHERE false " +
+				"LIMIT 10",
 		},
 		[]string{},
 	},
@@ -2391,13 +2419,13 @@ Men\\'s Clothing \\\\ 	%' LIMIT 10`},
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"@timestamp" = toDateTime64('2024-12-21 07:29:03.367',3)`},
+		[]string{"`@timestamp` = toDateTime64('2024-12-21 07:29:03.367',3)"},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE "@timestamp" = toDateTime64('2024-12-21 07:29:03.367',3) ` +
-				`LIMIT 10`,
+			"SELECT `message` " +
+				"FROM `" + TableName + "` " +
+				"WHERE `@timestamp` = toDateTime64('2024-12-21 07:29:03.367',3) " +
+				"LIMIT 10",
 		},
 		[]string{},
 	},
@@ -2412,13 +2440,15 @@ Men\\'s Clothing \\\\ 	%' LIMIT 10`},
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"@timestamp" = toDateTime64('2025-07-06 09:38:03.12',2)`},
+		[]string{
+			"`@timestamp` = toDateTime64('2025-07-06 09:38:03.12',2)",
+		},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE "@timestamp" = toDateTime64('2025-07-06 09:38:03.12',2) ` +
-				`LIMIT 10`,
+			"SELECT `message` " +
+				"FROM `" + TableName + "` " +
+				"WHERE `@timestamp` = toDateTime64('2025-07-06 09:38:03.12',2) " +
+				"LIMIT 10",
 		},
 		[]string{},
 	},
@@ -2433,13 +2463,13 @@ Men\\'s Clothing \\\\ 	%' LIMIT 10`},
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"@timestamp" = toDateTime64('2025-07-04 13:32:43.377',3)`},
+		[]string{"`@timestamp` = toDateTime64('2025-07-04 13:32:43.377',3)"},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE "@timestamp" = toDateTime64('2025-07-04 13:32:43.377',3) ` +
-				`LIMIT 10`,
+			"SELECT `message` " +
+				"FROM `" + TableName + "` " +
+				"WHERE `@timestamp` = toDateTime64('2025-07-04 13:32:43.377',3) " +
+				"LIMIT 10",
 		},
 		[]string{},
 	},
@@ -2454,13 +2484,13 @@ Men\\'s Clothing \\\\ 	%' LIMIT 10`},
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"@timestamp" = toDateTime64('2025-07-06 10:11:03.123456789',9)`},
+		[]string{"`@timestamp` = toDateTime64('2025-07-06 10:11:03.123456789',9)"},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE "@timestamp" = toDateTime64('2025-07-06 10:11:03.123456789',9) ` +
-				`LIMIT 10`,
+			"SELECT `message` " +
+				"FROM `" + TableName + "` " +
+				"WHERE `@timestamp` = toDateTime64('2025-07-06 10:11:03.123456789',9) " +
+				"LIMIT 10",
 		},
 		[]string{},
 	},
@@ -2475,13 +2505,15 @@ Men\\'s Clothing \\\\ 	%' LIMIT 10`},
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"@timestamp" = toDateTime64('2025-07-06 09:36:03.2551236',7)`},
+		[]string{
+			"`@timestamp` = toDateTime64('2025-07-06 09:36:03.2551236',7)",
+		},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE "@timestamp" = toDateTime64('2025-07-06 09:36:03.2551236',7) ` +
-				`LIMIT 10`,
+			"SELECT `message`" +
+				"FROM `" + TableName + "` " +
+				"WHERE `@timestamp` = toDateTime64('2025-07-06 09:36:03.2551236',7) " +
+				"LIMIT 10",
 		},
 		[]string{},
 	},
@@ -2496,13 +2528,15 @@ Men\\'s Clothing \\\\ 	%' LIMIT 10`},
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"@timestamp" = toDateTime64('2025-07-06 09:38:03.1',1)`},
+		[]string{
+			"`@timestamp` = toDateTime64('2025-07-06 09:38:03.1',1)",
+		},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE "@timestamp" = toDateTime64('2025-07-06 09:38:03.1',1) ` +
-				`LIMIT 10`,
+			"SELECT `message`" +
+				"FROM `" + TableName + "` " +
+				"WHERE `@timestamp` = toDateTime64('2025-07-06 09:38:03.1',1) " +
+				"LIMIT 10",
 		},
 		[]string{},
 	},
@@ -2519,13 +2553,15 @@ Men\\'s Clothing \\\\ 	%' LIMIT 10`},
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"@timestamp" IN tuple(toDateTime64('2024-12-21 07:29:03.367',3), toDateTime64('2024-12-21 07:29:02.992',3))`},
+		[]string{
+			"`@timestamp` IN tuple(toDateTime64('2024-12-21 07:29:03.367',3), toDateTime64('2024-12-21 07:29:02.992',3))",
+		},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE "@timestamp" IN tuple(toDateTime64('2024-12-21 07:29:03.367',3), toDateTime64('2024-12-21 07:29:02.992',3)) ` +
-				`LIMIT 10000`,
+			"SELECT `message`" +
+				"FROM `" + TableName + "` " +
+				"WHERE `@timestamp` IN tuple(toDateTime64('2024-12-21 07:29:03.367',3), toDateTime64('2024-12-21 07:29:02.992',3)) " +
+				"LIMIT 10000",
 		},
 		[]string{},
 	},
@@ -2539,13 +2575,13 @@ Men\\'s Clothing \\\\ 	%' LIMIT 10`},
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"@timestamp" = toDateTime64('2024-12-21 07:29:03.367',3)`},
+		[]string{"`@timestamp` = toDateTime64('2024-12-21 07:29:03.367',3)"},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE "@timestamp" = toDateTime64('2024-12-21 07:29:03.367',3) ` +
-				`LIMIT 10000`,
+			"SELECT `message`" +
+				"FROM `" + TableName + "` " +
+				"WHERE `@timestamp` = toDateTime64('2024-12-21 07:29:03.367',3) " +
+				"LIMIT 10000",
 		},
 		[]string{},
 	},
@@ -2559,13 +2595,13 @@ Men\\'s Clothing \\\\ 	%' LIMIT 10`},
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"@timestamp" = toDateTime64('2024-12-21 07:29:03.123456789',9)`},
+		[]string{"`@timestamp` = toDateTime64('2024-12-21 07:29:03.123456789',9)"},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE "@timestamp" = toDateTime64('2024-12-21 07:29:03.123456789',9) ` +
-				`LIMIT 10000`,
+			"SELECT `message`" +
+				"FROM `" + TableName + "` " +
+				"WHERE `@timestamp` = toDateTime64('2024-12-21 07:29:03.123456789',9) " +
+				"LIMIT 10000",
 		},
 		[]string{},
 	},
@@ -2579,14 +2615,14 @@ Men\\'s Clothing \\\\ 	%' LIMIT 10`},
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"@timestamp" = toDateTime64('2024-12-21 07:29:03.',0)`},
+		[]string{"`@timestamp` = toDateTime64('2024-12-21 07:29:03.',0)"},
 		// dot at the end doesn't matter - CH accepts it exactly like it wasn't there
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE "@timestamp" = toDateTime64('2024-12-21 07:29:03.',0) ` +
-				`LIMIT 10000`,
+			"SELECT `message`" +
+				"FROM `" + TableName + "`" +
+				"WHERE `@timestamp` = toDateTime64('2024-12-21 07:29:03.',0)" +
+				"LIMIT 10000",
 		},
 		[]string{},
 	},
@@ -2600,13 +2636,13 @@ Men\\'s Clothing \\\\ 	%' LIMIT 10`},
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`"@timestamp" = toDateTime64('2024-12-21 07:29:03.3',1)`},
+		[]string{"`@timestamp` = toDateTime64('2024-12-21 07:29:03.3',1)"},
 		model.ListAllFields,
 		[]string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE "@timestamp" = toDateTime64('2024-12-21 07:29:03.3',1) ` +
-				`LIMIT 10000`,
+			"SELECT `message`" +
+				"FROM `" + TableName + "` " +
+				"WHERE `@timestamp` = toDateTime64('2024-12-21 07:29:03.3',1) " +
+				"LIMIT 10000",
 		},
 		[]string{},
 	},
@@ -2631,13 +2667,13 @@ Men\\'s Clothing \\\\ 	%' LIMIT 10`},
 			},
 			"track_total_hits": false
 		}`,
-		WantedSql:       []string{`("tsAsUInt64">='2025-03-25T12:32:51.527Z' AND "tsAsUInt64"<='2025-03-25T12:47:51.527Z')`},
+		WantedSql:       []string{"(`tsAsUInt64`>='2025-03-25T12:32:51.527Z' AND `tsAsUInt64`<='2025-03-25T12:47:51.527Z')"},
 		WantedQueryType: model.ListAllFields,
 		WantedRegexes: []string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE ("tsAsUInt64">=1742905971527 AND "tsAsUInt64"<=1742906871527) ` +
-				`LIMIT 10`,
+			"SELECT `message`" +
+				"FROM `" + TableName + "` " +
+				"WHERE (`tsAsUInt64`>=1742905971527 AND `tsAsUInt64`<=1742906871527) " +
+				"LIMIT 10",
 		},
 	},
 	{ // [55]
@@ -2660,13 +2696,13 @@ Men\\'s Clothing \\\\ 	%' LIMIT 10`},
 			},
 			"track_total_hits": false
 		}`,
-		WantedSql:       []string{`("tsAsUInt64">=15 AND "tsAsUInt64"<=2025)`},
+		WantedSql:       []string{"(`tsAsUInt64`>=15 AND `tsAsUInt64`<=2025)"},
 		WantedQueryType: model.ListAllFields,
 		WantedRegexes: []string{
-			`SELECT "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE ("tsAsUInt64">=15 AND "tsAsUInt64"<=1735689600000) ` +
-				`LIMIT 10`,
+			"SELECT `message`" +
+				"FROM `" + TableName + "` " +
+				"WHERE (`tsAsUInt64`>=15 AND `tsAsUInt64`<=1735689600000) " +
+				"LIMIT 10",
 		},
 	},
 	{ // [56]
@@ -2683,7 +2719,9 @@ Men\\'s Clothing \\\\ 	%' LIMIT 10`},
 		}`,
 		[]string{"true"},
 		model.ListAllFields,
-		[]string{`SELECT "message" FROM ` + TableName + " WHERE true"},
+		[]string{
+			"SELECT `message` FROM `" + TableName + "` WHERE true",
+		},
 		[]string{},
 	},
 }
@@ -2714,7 +2752,7 @@ var TestSearchRuntimeMappings = []SearchTestCase{
 		model.ListAllFields,
 		////[]model.Query{newSimplestQuery()},
 		[]string{
-			`SELECT toHour("@timestamp") AS "hour_of_day" FROM ` + TableName + ` LIMIT 10`,
+			"SELECT toHour(`@timestamp`) AS `hour_of_day` FROM `__quesma_table_name` LIMIT 10",
 		},
 		[]string{},
 	},
@@ -2755,10 +2793,10 @@ var TestsSearchNoAttrs = []SearchTestCase{
 			},
 			"track_total_hits": false
 		}`,
-		[]string{`((("@timestamp">=fromUnixTimestamp64Milli(1706188965968) AND "@timestamp"<=fromUnixTimestamp64Milli(1706189865968)) AND "summary" IS NOT NULL) AND NOT ("run_once" IS NOT NULL))`},
+		[]string{"(((`@timestamp`>=fromUnixTimestamp64Milli(1706188965968) AND `@timestamp`<=fromUnixTimestamp64Milli(1706189865968)) AND `summary` IS NOT NULL) AND NOT (`run_once` IS NOT NULL))"},
 		model.ListAllFields,
 		[]string{
-			`SELECT "@timestamp", "message" FROM __quesma_table_name WHERE ((("@timestamp">=fromUnixTimestamp64Milli(1706188965968) AND "@timestamp"<=fromUnixTimestamp64Milli(1706189865968)) AND NULL IS NOT NULL) AND NOT (NULL IS NOT NULL)) LIMIT 10`,
+			"SELECT `@timestamp`, `message` FROM `__quesma_table_name` WHERE (((`@timestamp`>=fromUnixTimestamp64Milli(1706188965968) AND `@timestamp`<=fromUnixTimestamp64Milli(1706189865968)) AND NULL IS NOT NULL) AND NOT (NULL IS NOT NULL)) LIMIT 10",
 		},
 		[]string{},
 	},
@@ -2807,12 +2845,12 @@ var TestSearchFilter = []SearchTestCase{
 		model.Normal,
 		[]string{},
 		[]string{
-			`SELECT toInt64(toUnixTimestamp64Milli("@timestamp") / 30000) AS "aggr__0__key_0"
-			  , count(*) AS "aggr__0__count"
-			FROM __quesma_table_name
-			GROUP BY toInt64(toUnixTimestamp64Milli("@timestamp") / 30000) AS
-			  "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
+			"SELECT toInt64(toUnixTimestamp64Milli(`@timestamp`) / 30000) AS `aggr__0__key_0`\n" +
+				"  , count(*) AS `aggr__0__count`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"GROUP BY toInt64(toUnixTimestamp64Milli(`@timestamp`) / 30000) AS\n" +
+				"  `aggr__0__key_0`\n" +
+				"ORDER BY `aggr__0__key_0` ASC",
 		},
 	},
 	{ // [1]
@@ -2864,14 +2902,14 @@ var TestSearchFilter = []SearchTestCase{
 		model.Normal,
 		[]string{},
 		[]string{
-			`SELECT sum(count(*)) OVER () AS "metric____quesma_total_count_col_0",
-			  toInt64(toUnixTimestamp64Milli("@timestamp") / 30000) AS "aggr__0__key_0",
-			  count(*) AS "aggr__0__count"
-			FROM __quesma_table_name
-			WHERE "@timestamp">subDate(now(), INTERVAL 15 minute)
-			GROUP BY toInt64(toUnixTimestamp64Milli("@timestamp") / 30000) AS
-			  "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
+			"SELECT sum(count(*)) OVER () AS `metric____quesma_total_count_col_0`,\n" +
+				"  toInt64(toUnixTimestamp64Milli(`@timestamp`) / 30000) AS `aggr__0__key_0`,\n" +
+				"  count(*) AS `aggr__0__count`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"WHERE `@timestamp`>subDate(now(), INTERVAL 15 minute)\n" +
+				"GROUP BY toInt64(toUnixTimestamp64Milli(`@timestamp`) / 30000) AS\n" +
+				"  `aggr__0__key_0`\n" +
+				"ORDER BY `aggr__0__key_0` ASC",
 		},
 	},
 	{ // [2]
@@ -2925,14 +2963,14 @@ var TestSearchFilter = []SearchTestCase{
 		model.Normal,
 		[]string{},
 		[]string{
-			`SELECT sum(count(*)) OVER () AS "metric____quesma_total_count_col_0",
-			  toInt64(toUnixTimestamp64Milli("@timestamp") / 30000) AS "aggr__0__key_0",
-			  count(*) AS "aggr__0__count"
-			FROM __quesma_table_name
-			WHERE ("@timestamp">=fromUnixTimestamp64Milli(1727858503270) AND "@timestamp"<=fromUnixTimestamp64Milli(1727859403270))
-			GROUP BY toInt64(toUnixTimestamp64Milli("@timestamp") / 30000) AS
-			  "aggr__0__key_0"
-			ORDER BY "aggr__0__key_0" ASC`,
+			"SELECT sum(count(*)) OVER () AS `metric____quesma_total_count_col_0`,\n" +
+				"  toInt64(toUnixTimestamp64Milli(`@timestamp`) / 30000) AS `aggr__0__key_0`,\n" +
+				"  count(*) AS `aggr__0__count`\n" +
+				"FROM `__quesma_table_name`\n" +
+				"WHERE (`@timestamp`>=fromUnixTimestamp64Milli(1727858503270) AND `@timestamp`<=fromUnixTimestamp64Milli(1727859403270))\n" +
+				"GROUP BY toInt64(toUnixTimestamp64Milli(`@timestamp`) / 30000) AS\n" +
+				"  `aggr__0__key_0`\n" +
+				"ORDER BY `aggr__0__key_0` ASC",
 		},
 	},
 	{ // [3]
@@ -2949,7 +2987,9 @@ var TestSearchFilter = []SearchTestCase{
 		[]string{},
 		model.Normal,
 		[]string{},
-		[]string{`SELECT "@timestamp", "message" FROM __quesma_table_name LIMIT 10`},
+		[]string{
+			"SELECT `@timestamp`, `message` FROM `__quesma_table_name` LIMIT 10",
+		},
 	},
 	{ // [4]
 		"Empty filter with other clauses",
@@ -2977,16 +3017,16 @@ var TestSearchFilter = []SearchTestCase{
 			"track_total_hits": false
 		}`,
 		[]string{
-			`("user.id"='kimchy' AND ("tags"='env1' OR "tags"='deployed')) AND NOT ("age"<=20 AND "age">=10)`,
-			`("user.id"='kimchy' AND ("tags"='env1' OR "tags"='deployed')) AND NOT ("age">=10 AND "age"<=20)`,
+			"(`user.id`='kimchy' AND (`tags`='env1' OR `tags`='deployed')) AND NOT (`age`<=20 AND `age`>=10)",
+			"(`user.id`='kimchy' AND (`tags`='env1' OR `tags`='deployed')) AND NOT (`age`>=10 AND `age`<=20)",
 		},
 		model.Normal,
 		[]string{
-			`SELECT "@timestamp", "message" ` +
-				`FROM ` + TableName + ` ` +
-				`WHERE (("attributes_values"['user.id']='kimchy' AND ("attributes_values"['tags']='env1' OR "attributes_values"['tags']='deployed')) ` +
-				`AND NOT (("attributes_values"['age']>=10 AND "attributes_values"['age']<=20))) ` +
-				`LIMIT 10`,
+			"SELECT `@timestamp`, `message` " +
+				"FROM `__quesma_table_name` " +
+				"WHERE ((`attributes_values`['user.id']='kimchy' AND (`attributes_values`['tags']='env1' OR `attributes_values`['tags']='deployed')) " +
+				"AND NOT ((`attributes_values`['age']>=10 AND `attributes_values`['age']<=20))) " +
+				"LIMIT 10",
 		},
 		[]string{},
 	},
diff --git a/platform/testdata/requests_no_full_text_fields.go b/platform/testdata/requests_no_full_text_fields.go
index 439be3648..4b544f5de 100644
--- a/platform/testdata/requests_no_full_text_fields.go
+++ b/platform/testdata/requests_no_full_text_fields.go
@@ -102,7 +102,7 @@ var TestsSearchNoFullTextFields = []SearchTestCase{
 			}
 		}`,
 		WantedSql: []string{
-			`((((("__quesma_fulltext_field_name" iLIKE '%quick%' AND "__quesma_fulltext_field_name" iLIKE '%fox%') OR ("__quesma_fulltext_field_name" iLIKE '%brown%' AND "__quesma_fulltext_field_name" iLIKE '%fox%')) OR "__quesma_fulltext_field_name" iLIKE '%fox%') AND NOT ("__quesma_fulltext_field_name" iLIKE '%news%')) AND ("timestamp">='2024-03-26T09:56:02.241Z' AND "timestamp"<='2024-04-10T08:56:02.241Z'))`,
+			"(((((`__quesma_fulltext_field_name` iLIKE '%quick%' AND `__quesma_fulltext_field_name` iLIKE '%fox%') OR (`__quesma_fulltext_field_name` iLIKE '%brown%' AND `__quesma_fulltext_field_name` iLIKE '%fox%')) OR `__quesma_fulltext_field_name` iLIKE '%fox%') AND NOT (`__quesma_fulltext_field_name` iLIKE '%news%')) AND (`timestamp`>='2024-03-26T09:56:02.241Z' AND `timestamp`<='2024-04-10T08:56:02.241Z'))",
 		},
 		WantedQueryType: model.ListAllFields,
 		//WantedQuery: []model.Query{
diff --git a/platform/testdata/requests_with_special_characters.go b/platform/testdata/requests_with_special_characters.go
index e5eebeb10..2e7fcd25a 100644
--- a/platform/testdata/requests_with_special_characters.go
+++ b/platform/testdata/requests_with_special_characters.go
@@ -144,32 +144,6 @@ var AggregationTestsWithSpecialCharactersInFieldNames = []AggregationTestCase{
 			}
 		}`,
 		ExpectedPancakeResults: []model.QueryResultRow{}, // checking only the SQLs is enough for now
-		ExpectedPancakeSQL: `WITH quesma_top_hits_group_table AS (
-			  SELECT sum(count(*)) OVER () AS "metric____quesma_total_count_col_0",
-				toInt64(toUnixTimestamp64Milli("__timestamp") / 43200000) AS
-				"aggr__0__key_0", count(*) AS "aggr__0__count"
-			  FROM __quesma_table_name
-			  WHERE "message_____" IS NOT NULL
-			  GROUP BY toInt64(toUnixTimestamp64Milli("__timestamp") / 43200000) AS
-				"aggr__0__key_0"
-			  ORDER BY "aggr__0__key_0" ASC) ,
-			quesma_top_hits_join AS (
-			  SELECT "group_table"."metric____quesma_total_count_col_0" AS
-				"metric____quesma_total_count_col_0",
-				"group_table"."aggr__0__key_0" AS "aggr__0__key_0",
-				"group_table"."aggr__0__count" AS "aggr__0__count",
-				"hit_table"."__bytes" AS "top_metrics__0__1_col_0",
-				"hit_table"."__timestamp" AS "top_metrics__0__1_col_1",
-				ROW_NUMBER() OVER (PARTITION BY "group_table"."aggr__0__key_0" ORDER BY
-				"__timestamp" DESC) AS "top_hits_rank"
-			  FROM quesma_top_hits_group_table AS "group_table" LEFT OUTER JOIN
-				__quesma_table_name AS "hit_table" ON ("group_table"."aggr__0__key_0"=
-				toInt64(toUnixTimestamp64Milli("__timestamp") / 43200000))
-			  WHERE "message_____" IS NOT NULL)
-			SELECT "metric____quesma_total_count_col_0", "aggr__0__key_0", "aggr__0__count",
-			  "top_metrics__0__1_col_0", "top_metrics__0__1_col_1", "top_hits_rank"
-			FROM "quesma_top_hits_join"
-			WHERE "top_hits_rank"<=1
-			ORDER BY "aggr__0__key_0" ASC, "top_hits_rank" ASC`,
+		ExpectedPancakeSQL:     "WITH quesma_top_hits_group_table AS (\n  SELECT sum(count(*)) OVER () AS `metric____quesma_total_count_col_0`,\n    toInt64(toUnixTimestamp64Milli(`__timestamp`) / 43200000) AS `aggr__0__key_0\n    `, count(*) AS `aggr__0__count`\n  FROM `__quesma_table_name`\n  WHERE `message_____` IS NOT NULL\n  GROUP BY toInt64(toUnixTimestamp64Milli(`__timestamp`) / 43200000) AS `\n    aggr__0__key_0`\n  ORDER BY `aggr__0__key_0` ASC) ,\nquesma_top_hits_join AS (\n  SELECT `group_table`.`metric____quesma_total_count_col_0` AS `\n    metric____quesma_total_count_col_0`,\n    `group_table`.`aggr__0__key_0` AS `aggr__0__key_0`,\n    `group_table`.`aggr__0__count` AS `aggr__0__count`,\n    `hit_table`.`__bytes` AS `top_metrics__0__1_col_0`,\n    `hit_table`.`__timestamp` AS `top_metrics__0__1_col_1`,\n    ROW_NUMBER() OVER (PARTITION BY `group_table`.`aggr__0__key_0` ORDER BY `\n    __timestamp` DESC) AS `top_hits_rank`\n  FROM quesma_top_hits_group_table AS `group_table` LEFT OUTER JOIN `\n    __quesma_table_name` AS `hit_table` ON (`group_table`.`aggr__0__key_0`=\n    toInt64(toUnixTimestamp64Milli(`__timestamp`) / 43200000))\n  WHERE `message_____` IS NOT NULL)\nSELECT `metric____quesma_total_count_col_0`, `aggr__0__key_0`, `aggr__0__count`,\n  `top_metrics__0__1_col_0`, `top_metrics__0__1_col_1`, `top_hits_rank`\nFROM `quesma_top_hits_join`\nWHERE `top_hits_rank`<=1\nORDER BY `aggr__0__key_0` ASC, `top_hits_rank` ASC",
 	},
 }
diff --git a/platform/util/utils.go b/platform/util/utils.go
index 464a6feb4..76dc5569c 100644
--- a/platform/util/utils.go
+++ b/platform/util/utils.go
@@ -759,6 +759,14 @@ func SingleQuote(value string) string {
 	return "'" + value + "'"
 }
 
+// EscapeStringNormal is a simple helper function: str -> `str`
+func BackquoteIdentifier(identifier string) string {
+	if len(identifier) >= 2 && identifier[0] == '`' && identifier[len(identifier)-1] == '`' {
+		return identifier
+	}
+	return "`" + identifier + "`"
+}
+
 // IsSingleQuoted checks if a string is single-quoted
 func IsSingleQuoted(s string) bool {
 	return len(s) >= 2 && s[0] == '\'' && s[len(s)-1] == '\''