Merge pull request #43 from VirtusLab/repl-utils

Some utils for REPL

Author: prolativ

src/main/DataFrame.scala

@@ -2,6 +2,7 @@ package org.virtuslab.iskra
 
 import org.apache.spark.sql
 import org.apache.spark.sql.SparkSession
+import org.virtuslab.iskra.types.Encoder
 
 
 trait DataFrame:
@@ -16,6 +17,11 @@ object DataFrame:
   export Where.dataFrameWhereOps
   export WithColumns.dataFrameWithColumnsOps
 
+
+  // Has to be inline - otherwise for an unknown reason the runtime and compile-time schemas don't match 
+  inline def apply[A](elements: A*)(using encoder: Encoder[A])(using spark: SparkSession): ClassDataFrame[A] =
+    DataFrameBuilders.toDF(elements)
+
   given dataFrameOps: {} with
     extension (df: DataFrame)
       inline def show(): Unit = df.untyped.show()

src/main/api/api.scala

@@ -1,51 +1,52 @@
 package org.virtuslab.iskra
-package api
 
-export DataFrameBuilders.toDF
-export types.{
-  boolean,
-  boolean_?,
-  BooleanNotNull,
-  BooleanOrNull,
-  string,
-  string_?,
-  StringNotNull,
-  StringOrNull,
-  byte,
-  byte_?,
-  ByteNotNull,
-  ByteOrNull,
-  short,
-  short_?,
-  ShortNotNull,
-  ShortOrNull,
-  int,
-  int_?,
-  IntNotNull,
-  IntOrNull,
-  long,
-  long_?,
-  LongNotNull,
-  LongOrNull,
-  float,
-  float_?,
-  FloatNotNull,
-  FloatOrNull,
-  double,
-  double_?,
-  DoubleNotNull,
-  DoubleOrNull,
-  struct,
-  struct_?,
-  StructNotNull,
-  StructOrNull
-}
-export UntypedOps.typed
-export org.virtuslab.iskra.$
-export org.virtuslab.iskra.{Column, Columns, Col, DataFrame, ClassDataFrame, NamedColumns, StructDataFrame, UntypedColumn, UntypedDataFrame, :=, /}
+object api:
 
-object functions:
-  export org.virtuslab.iskra.functions.{lit, when}
-  export org.virtuslab.iskra.functions.Aggregates.*
+  export DataFrameBuilders.toDF
+  export types.{
+    boolean,
+    boolean_?,
+    BooleanNotNull,
+    BooleanOrNull,
+    string,
+    string_?,
+    StringNotNull,
+    StringOrNull,
+    byte,
+    byte_?,
+    ByteNotNull,
+    ByteOrNull,
+    short,
+    short_?,
+    ShortNotNull,
+    ShortOrNull,
+    int,
+    int_?,
+    IntNotNull,
+    IntOrNull,
+    long,
+    long_?,
+    LongNotNull,
+    LongOrNull,
+    float,
+    float_?,
+    FloatNotNull,
+    FloatOrNull,
+    double,
+    double_?,
+    DoubleNotNull,
+    DoubleOrNull,
+    struct,
+    struct_?,
+    StructNotNull,
+    StructOrNull
+  }
+  export UntypedOps.typed
+  export org.virtuslab.iskra.$
+  export org.virtuslab.iskra.{Column, Columns, Col, DataFrame, ClassDataFrame, NamedColumns, StructDataFrame, UntypedColumn, UntypedDataFrame, :=, /}
 
-export org.apache.spark.sql.SparkSession
+  object functions:
+    export org.virtuslab.iskra.functions.{lit, when}
+    export org.virtuslab.iskra.functions.Aggregates.*
+
+  export org.apache.spark.sql.SparkSession

src/main/repl/repl.scala

@@ -0,0 +1,22 @@
+/*
+Suggested usage in your REPL:
+
+    import org.virtuslab.iskra.repl.*
+
+Then you can simply create a data frame like
+
+    val df = DF(1, 2, 3)
+
+*/
+
+package org.virtuslab.iskra.repl
+
+import org.apache.spark.sql.SparkSession
+
+// Not using `given` to make it accessible with a * wildcard import
+implicit lazy val spark: SparkSession =
+    SparkSession.builder().master("local").getOrCreate()
+
+export org.virtuslab.iskra.api.*
+
+export org.virtuslab.iskra.api.DataFrame as DF