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akash-shankaranakash-shankaran
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remove unused setting ADVANCED_APPROXIMATE_THRESHOLD (#706)
* remove unused setting Signed-off-by: akash shankaran <akash.shankaran1@gmail.com> * remove unused setting Signed-off-by: akash shankaran <akash.shankaran1@gmail.com> --------- Signed-off-by: akash shankaran <akash.shankaran1@gmail.com>
1 parent 10538d6 commit a060fc6

6 files changed

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‎docs/user_guide.md‎

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@@ -1047,7 +1047,6 @@ For derived source configuration and version-specific behavior, see [docs/derive
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| Setting | Default | Description |
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|---|---|---|
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| `index.knn` | — | Set to `true` to enable the KNN plugin on the index (required) |
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| `index.knn.advanced.approximate_threshold` | `15000` | Minimum document count per segment before an approximate graph is built |
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| `index.knn.disk.vector.shard_level_rescoring_disabled` | `false` | Disable shard-level re-scoring for on-disk vectors |
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| `index.knn.derived_source.enabled` | `true` | Store vectors in derived source fields |
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‎qa/restart-upgrade/src/test/java/org/opensearch/knn/bwc/IndexingIT.java‎

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@@ -49,8 +49,6 @@ public void testKNNIndexDefaultLegacyFieldMapping() throws Exception {
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createKnnIndex(testIndex, getKNNDefaultIndexSettings(), createKnnIndexMapping(TEST_FIELD, DIMENSIONS));
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addKNNDocs(testIndex, TEST_FIELD, DIMENSIONS, DOC_ID, NUM_DOCS);
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} else {
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// update index setting to allow build graph always since we test graph count that are loaded into memory
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updateIndexSettings(testIndex, Settings.builder().put(KNNSettings.INDEX_KNN_ADVANCED_APPROXIMATE_THRESHOLD, 0));
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validateKNNIndexingOnUpgrade(NUM_DOCS);
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}
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}
@@ -246,9 +244,6 @@ public void testKNNIndexCustomLegacyFieldMapping() throws Exception {
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KNN_ALGO_PARAM_M_MIN_VALUE,
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KNN_ALGO_PARAM_EF_CONSTRUCTION_MIN_VALUE
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);
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if (isApproximateThresholdSupported(getBWCVersion())) {
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indexMappingSettings.put(KNNSettings.INDEX_KNN_ADVANCED_APPROXIMATE_THRESHOLD, 0);
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}
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createKnnIndex(testIndex, indexMappingSettings.build(), createKnnIndexMapping(TEST_FIELD, DIMENSIONS));
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addKNNDocs(testIndex, TEST_FIELD, DIMENSIONS, DOC_ID, NUM_DOCS);
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} else {
@@ -368,7 +363,6 @@ public void testNoParametersOnUpgrade() throws Exception {
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// KNN indexing tests when the cluster is upgraded to latest version
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public void validateKNNIndexingOnUpgrade(int numOfDocs) throws Exception {
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updateIndexSettings(testIndex, Settings.builder().put(KNNSettings.INDEX_KNN_ADVANCED_APPROXIMATE_THRESHOLD, 0));
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forceMergeKnnIndex(testIndex);
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QUERY_COUNT = numOfDocs;
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validateKNNSearch(testIndex, TEST_FIELD, DIMENSIONS, QUERY_COUNT, K);

‎src/main/java/org/opensearch/knn/index/KNNSettings.java‎

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@@ -60,7 +60,6 @@ public class KNNSettings {
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* Settings name
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*/
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public static final String KNN_SPACE_TYPE = "index.knn.space_type";
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public static final String INDEX_KNN_ADVANCED_APPROXIMATE_THRESHOLD = "index.knn.advanced.approximate_threshold";
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public static final String KNN_ALGO_PARAM_M = "index.knn.algo_param.m";
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public static final String KNN_ALGO_PARAM_EF_CONSTRUCTION = "index.knn.algo_param.ef_construction";
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public static final String KNN_ALGO_PARAM_EF_SEARCH = "index.knn.algo_param.ef_search";
@@ -94,10 +93,6 @@ public class KNNSettings {
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public static final boolean KNN_DEFAULT_FAISS_AVX512_DISABLED_VALUE = false;
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public static final boolean KNN_DEFAULT_FAISS_AVX512_SPR_DISABLED_VALUE = false;
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public static final String INDEX_KNN_DEFAULT_SPACE_TYPE = "l2";
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public static final Integer INDEX_KNN_ADVANCED_APPROXIMATE_THRESHOLD_DEFAULT_VALUE = 15_000;
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public static final Integer INDEX_KNN_BUILD_VECTOR_DATA_STRUCTURE_THRESHOLD_MIN = -1;
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public static final Integer INDEX_KNN_BUILD_VECTOR_DATA_STRUCTURE_THRESHOLD_MAX = Integer.MAX_VALUE - 2;
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public static final String INDEX_KNN_DEFAULT_SPACE_TYPE_FOR_BINARY = "hamming";
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public static final Integer INDEX_KNN_DEFAULT_ALGO_PARAM_M = 16;
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public static final Integer INDEX_KNN_DEFAULT_ALGO_PARAM_EF_SEARCH = 100;
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public static final Integer INDEX_KNN_DEFAULT_ALGO_PARAM_EF_CONSTRUCTION = 100;
@@ -156,21 +151,6 @@ public class KNNSettings {
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Setting.Property.Deprecated
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);
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/**
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* build_vector_data_structure_threshold - This parameter determines when to build vector data structure for knn fields during indexing
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* and merging. Setting -1 (min) will skip building graph, whereas on any other values, the graph will be built if
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* number of live docs in segment is greater than this threshold. Since max number of documents in a segment can
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* be Integer.MAX_VALUE - 1, this setting will allow threshold to be up to 1 less than max number of documents in a segment
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*/
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public static final Setting<Integer> INDEX_KNN_ADVANCED_APPROXIMATE_THRESHOLD_SETTING = Setting.intSetting(
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INDEX_KNN_ADVANCED_APPROXIMATE_THRESHOLD,
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INDEX_KNN_ADVANCED_APPROXIMATE_THRESHOLD_DEFAULT_VALUE,
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INDEX_KNN_BUILD_VECTOR_DATA_STRUCTURE_THRESHOLD_MIN,
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INDEX_KNN_BUILD_VECTOR_DATA_STRUCTURE_THRESHOLD_MAX,
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IndexScope,
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Dynamic
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);
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/**
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* M - the number of bi-directional links created for every new element during construction.
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* Reasonable range for M is 2-100. Higher M work better on datasets with high intrinsic
@@ -523,7 +503,6 @@ private Setting<?> getSetting(String key) {
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public List<Setting<?>> getSettings() {
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List<Setting<?>> settings = Arrays.asList(
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INDEX_KNN_SPACE_TYPE,
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INDEX_KNN_ADVANCED_APPROXIMATE_THRESHOLD_SETTING,
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INDEX_KNN_ALGO_PARAM_M_SETTING,
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INDEX_KNN_ALGO_PARAM_EF_CONSTRUCTION_SETTING,
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INDEX_KNN_ALGO_PARAM_EF_SEARCH_SETTING,

‎src/main/java/org/opensearch/knn/index/codec/BasePerFieldKnnVectorsFormat.java‎

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@@ -9,9 +9,7 @@
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import lombok.extern.log4j.Log4j2;
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import org.apache.lucene.codecs.KnnVectorsFormat;
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import org.apache.lucene.codecs.perfield.PerFieldKnnVectorsFormat;
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import org.opensearch.index.IndexSettings;
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import org.opensearch.index.mapper.MapperService;
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import org.opensearch.knn.index.KNNSettings;
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import org.opensearch.knn.index.codec.params.KNNScalarQuantizedVectorsFormatParams;
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import org.opensearch.knn.index.codec.params.KNNVectorsFormatParams;
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import org.opensearch.knn.index.engine.KNNEngine;
@@ -181,16 +179,6 @@ public KnnVectorsFormat getKnnVectorsFormatForField(final String field) {
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}
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}
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private int getApproximateThresholdValue() {
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// This is private method and mapperService is already checked for null or valid instance type before this call
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// at caller, hence we don't need additional isPresent check here.
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final IndexSettings indexSettings = mapperService.get().getIndexSettings();
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final Integer approximateThresholdValue = indexSettings.getValue(KNNSettings.INDEX_KNN_ADVANCED_APPROXIMATE_THRESHOLD_SETTING);
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return approximateThresholdValue != null
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? approximateThresholdValue
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: KNNSettings.INDEX_KNN_ADVANCED_APPROXIMATE_THRESHOLD_DEFAULT_VALUE;
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}
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@Override
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public int getMaxDimensions(String fieldName) {
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return getKnnVectorsFormatForField(fieldName).getMaxDimensions(fieldName);

‎src/test/java/org/opensearch/knn/recall/RecallTestsIT.java‎

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@@ -37,7 +37,6 @@
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import static org.opensearch.knn.common.KNNConstants.PARAMETERS;
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import static org.opensearch.knn.common.KNNConstants.TYPE;
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import static org.opensearch.knn.common.KNNConstants.TYPE_KNN_VECTOR;
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import static org.opensearch.knn.index.KNNSettings.INDEX_KNN_ADVANCED_APPROXIMATE_THRESHOLD;
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/**
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* Tests confirm that for the different supported configurations, recall is sound. The recall thresholds are
@@ -213,7 +212,6 @@ private Settings getSettings() {
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.put("number_of_shards", SHARD_COUNT)
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.put("number_of_replicas", REPLICA_COUNT)
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.put("index.knn", true)
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.put(INDEX_KNN_ADVANCED_APPROXIMATE_THRESHOLD, 0)
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.build();
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}
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}

‎src/testFixtures/java/org/opensearch/knn/KNNRestTestCase.java‎

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@@ -102,7 +102,6 @@
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import static org.opensearch.knn.TestUtils.computeGroundTruthValues;
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import static org.opensearch.knn.common.KNNConstants.VECTOR_DATA_TYPE_FIELD;
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import static org.opensearch.knn.index.KNNSettings.INDEX_KNN_ADVANCED_APPROXIMATE_THRESHOLD;
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import static org.opensearch.knn.index.KNNSettings.KNN_INDEX;
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import static org.opensearch.knn.index.SpaceType.L2;
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@@ -1012,7 +1011,6 @@ protected Settings buildKNNIndexSettings(int approximateThreshold) {
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.put("number_of_replicas", 0)
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.put(KNN_INDEX, true)
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// .put(KNNSettings.KNN_DERIVED_SOURCE_ENABLED, true)
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.put(INDEX_KNN_ADVANCED_APPROXIMATE_THRESHOLD, approximateThreshold)
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// .put("use_compound_file", false)
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.build();
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}

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