SNOW-966360 Support Explode Function

src/main/java/com/snowflake/snowpark_java/TableFunctions.java

@@ -150,4 +150,33 @@ public static Column flatten(
   public static Column flatten(Column input) {
     return new Column(com.snowflake.snowpark.tableFunctions.flatten(input.toScalaColumn()));
   }
+
+  /**
+   * Flattens a given array or map type column into individual rows. The output column(s) in case of
+   * array input column is `VALUE`, and are `KEY` and `VALUE` in case of amp input column.
+   *
+   * <p>Example
+   *
+   * <pre>{@code
+   * DataFrame df =
+   *   getSession()
+   *     .createDataFrame(
+   *       new Row[] {Row.create("{\"a\":1, \"b\":2}")},
+   *       StructType.create(new StructField("col", DataTypes.StringType)));
+   * DataFrame df1 =
+   *   df.select(
+   *     Functions.parse_json(df.col("col"))
+   *       .cast(DataTypes.createMapType(DataTypes.StringType, DataTypes.IntegerType))
+   *       .as("col"));
+   * df1.select(TableFunctions.explode(df1.col("col"))).show()
+   * }</pre>
+   *
+   * @since 1.10.0
+   * @param input The expression that will be unseated into rows. The expression must be either
+   *     MapType or ArrayType data.
+   * @return The result Column reference
+   */
+  public static Column explode(Column input) {
+    return new Column(com.snowflake.snowpark.tableFunctions.explode(input.toScalaColumn()));
+  }
 }

src/main/scala/com/snowflake/snowpark/DataFrame.scala

@@ -2,11 +2,13 @@ package com.snowflake.snowpark
 
 import scala.reflect.ClassTag
 import scala.util.Random
+import com.snowflake.snowpark.internal.analyzer.{TableFunction => TF}
 import com.snowflake.snowpark.internal.ErrorMessage
 import com.snowflake.snowpark.internal.{Logging, Utils}
 import com.snowflake.snowpark.internal.analyzer._
 import com.snowflake.snowpark.types._
 import com.github.vertical_blank.sqlformatter.SqlFormatter
+import com.snowflake.snowpark.functions.lit
 import com.snowflake.snowpark.internal.Utils.{
   TempObjectType,
   getTableFunctionExpression,
@@ -1969,23 +1971,49 @@ class DataFrame private[snowpark] (
   private def joinTableFunction(
       func: TableFunctionExpression,
       partitionByOrderBy: Option[WindowSpecDefinition]): DataFrame = {
-    val originalResult = withPlan {
-      TableFunctionJoin(this.plan, func, partitionByOrderBy)
+    func match {
+      // explode is a client side function
+      case TF(funcName, args) if funcName.toLowerCase().trim.equals("explode") =>
+        // explode has only one argument
+        joinWithExplode(args.head, partitionByOrderBy)
+      case _ =>
+        val originalResult = withPlan {
+          TableFunctionJoin(this.plan, func, partitionByOrderBy)
+        }
+        val resultSchema = originalResult.schema
+        val columnNames = resultSchema.map(_.name)
+        // duplicated names
+        val dup = columnNames.diff(columnNames.distinct).distinct.map(quoteName)
+        // guarantee no duplicated names in the result
+        if (dup.nonEmpty) {
+          val dfPrefix = DataFrame.generatePrefix('o')
+          val renamedDf =
+            this.select(this.output.map(_.name).map(aliasIfNeeded(this, _, dfPrefix, dup.toSet)))
+          withPlan {
+            TableFunctionJoin(renamedDf.plan, func, partitionByOrderBy)
+          }
+        } else {
+          originalResult
+        }
     }
-    val resultSchema = originalResult.schema
-    val columnNames = resultSchema.map(_.name)
-    // duplicated names
-    val dup = columnNames.diff(columnNames.distinct).distinct.map(quoteName)
-    // guarantee no duplicated names in the result
-    if (dup.nonEmpty) {
-      val dfPrefix = DataFrame.generatePrefix('o')
-      val renamedDf =
-        this.select(this.output.map(_.name).map(aliasIfNeeded(this, _, dfPrefix, dup.toSet)))
-      withPlan {
-        TableFunctionJoin(renamedDf.plan, func, partitionByOrderBy)
-      }
-    } else {
-      originalResult
+  }
+
+  private def joinWithExplode(
+      expr: Expression,
+      partitionByOrderBy: Option[WindowSpecDefinition]): DataFrame = {
+    val columns: Seq[Column] = this.output.map(attr => col(attr.name))
+    // check the column type of input column
+    this.select(Column(expr)).schema.head.dataType match {
+      case _: ArrayType =>
+        joinTableFunction(
+          tableFunctions.flatten.call(Map("input" -> Column(expr), "mode" -> lit("array"))),
+          partitionByOrderBy).select(columns :+ Column("VALUE"))
+      case _: MapType =>
+        joinTableFunction(
+          tableFunctions.flatten.call(Map("input" -> Column(expr), "mode" -> lit("object"))),
+          partitionByOrderBy).select(columns ++ Seq(Column("KEY"), Column("VALUE")))
+      case otherType =>
+        throw ErrorMessage.MISC_INVALID_EXPLODE_ARGUMENT_TYPE(otherType.typeName)
     }
   }
 

src/main/scala/com/snowflake/snowpark/Session.scala

@@ -529,7 +529,10 @@ class Session private (private[snowpark] val conn: ServerConnection) extends Log
     // Use df.join to apply function result if args contains a DF column
     val sourceDFs = args.flatMap(_.expr.sourceDFs)
     if (sourceDFs.isEmpty) {
-      DataFrame(this, TableFunctionRelation(func.call(args: _*)))
+      // explode function requires a special handling since it is a client side function.
+      if (func.funcName.trim.toLowerCase() == "explode") {
+        callExplode(args.head)
+      } else DataFrame(this, TableFunctionRelation(func.call(args: _*)))
     } else if (sourceDFs.toSet.size > 1) {
       throw UDF_CANNOT_ACCEPT_MANY_DF_COLS()
     } else {
@@ -580,6 +583,18 @@ class Session private (private[snowpark] val conn: ServerConnection) extends Log
     }
   }
 
+  // process explode function with literal values
+  private def callExplode(input: Column): DataFrame = {
+    import this.implicits._
+    // to reuse the DataFrame.join function, the input column has to be converted to
+    // a DataFrame column. The best the solution is to create an empty dataframe and
+    // then append this column via withColumn function. However, Snowpark doesn't support
+    // empty DataFrame, therefore creating a dummy dataframe instead.
+    val dummyDF = Seq(1).toDF("a")
+    val sourceDF = dummyDF.withColumn("b", input)
+    sourceDF.select(tableFunctions.explode(sourceDF("b")))
+  }
+
   /**
    * Creates a new DataFrame from the given table function.
    *

src/main/scala/com/snowflake/snowpark/internal/ErrorMessage.scala

@@ -153,7 +153,11 @@ private[snowpark] object ErrorMessage {
     "0420" -> "Invalid RSA private key. The error is: %s",
     "0421" -> "Invalid stage location: %s. Reason: %s.",
     "0422" -> "Internal error: Server fetching is disabled for the parameter %s and there is no default value for it.",
-    "0423" -> "Invalid input argument, Session.tableFunction only supports table function arguments")
+    "0423" -> "Invalid input argument, Session.tableFunction only supports table function arguments",
+    "0424" ->
+      """Invalid input argument type, the input argument type of Explode function should be either Map or Array types.
+        |The input argument type: %s
+        |""".stripMargin)
   // scalastyle:on
 
   /*
@@ -393,6 +397,9 @@ private[snowpark] object ErrorMessage {
   def MISC_INVALID_TABLE_FUNCTION_INPUT(): SnowparkClientException =
     createException("0423")
 
+  def MISC_INVALID_EXPLODE_ARGUMENT_TYPE(argumentType: String): SnowparkClientException =
+    createException("0424", argumentType)
+
   /**
    * Create Snowpark client Exception.
    *

src/main/scala/com/snowflake/snowpark/tableFunctions.scala

@@ -197,4 +197,29 @@ object tableFunctions {
         "outer" -> lit(outer),
         "recursive" -> lit(recursive),
         "mode" -> lit(mode)))
+
+  /**
+   * Flattens a given array or map type column into individual rows.
+   * The output column(s) in case of array input column is `VALUE`,
+   * and are `KEY` and `VALUE` in case of amp input column.
+   *
+   * Example
+   * {{{
+   * import com.snowflake.snowpark.functions._
+   *
+   * val df = Seq("""{"a":1, "b": 2}""").toDF("a")
+   * val df1 = df.select(
+   *   parse_json(df("a"))
+   *   .cast(types.MapType(types.StringType, types.IntegerType))
+   *   .as("a"))
+   * df1.select(lit(1), tableFunctions.explode(df1("a")), df1("a")("a")).show()
+   * }}}
+   *
+   * @since 1.10.0
+   * @param input The expression that will be unseated into rows.
+   *              The expression must be either MapType or ArrayType data.
+   * @return The result Column reference
+   */
+  def explode(input: Column): Column = TableFunction("explode").apply(input)
+
 }

src/test/java/com/snowflake/snowpark_test/JavaSessionSuite.java

@@ -82,11 +82,10 @@ public void flatten() {
   }
 
   @Test
-  public void getOrCreate()
-  {
+  public void getOrCreate() {
     String expectedSessionInfo = getSession().getSessionInfo();
     String actualSessionInfo = Session.builder().getOrCreate().getSessionInfo();
-    assert(actualSessionInfo.equals(expectedSessionInfo));
+    assert (actualSessionInfo.equals(expectedSessionInfo));
   }
 
   @Test

src/test/java/com/snowflake/snowpark_test/JavaTableFunctionSuite.java

@@ -121,4 +121,63 @@ public void argumentInFlatten() {
             .select("value"),
         new Row[] {Row.create("1"), Row.create("2")});
   }
+
+  @Test
+  public void explodeWithDataFrame() {
+    // select
+    DataFrame df =
+        getSession()
+            .createDataFrame(
+                new Row[] {Row.create("{\"a\":1, \"b\":2}")},
+                StructType.create(new StructField("col", DataTypes.StringType)));
+    DataFrame df1 =
+        df.select(
+            Functions.parse_json(df.col("col"))
+                .cast(DataTypes.createMapType(DataTypes.StringType, DataTypes.IntegerType))
+                .as("col"));
+    checkAnswer(
+        df1.select(TableFunctions.explode(df1.col("col"))),
+        new Row[] {Row.create("a", "1"), Row.create("b", "2")});
+    // join
+    df =
+        getSession()
+            .createDataFrame(
+                new Row[] {Row.create("[1, 2]")},
+                StructType.create(new StructField("col", DataTypes.StringType)));
+    df1 =
+        df.select(
+            Functions.parse_json(df.col("col"))
+                .cast(DataTypes.createArrayType(DataTypes.IntegerType))
+                .as("col"));
+    checkAnswer(
+        df1.join(TableFunctions.explode(df1.col("col"))).select("VALUE"),
+        new Row[] {Row.create("1"), Row.create("2")});
+  }
+
+  @Test
+  public void explodeWithSession() {
+    DataFrame df =
+        getSession()
+            .createDataFrame(
+                new Row[] {Row.create("{\"a\":1, \"b\":2}")},
+                StructType.create(new StructField("col", DataTypes.StringType)));
+    DataFrame df1 =
+        df.select(
+            Functions.parse_json(df.col("col"))
+                .cast(DataTypes.createMapType(DataTypes.StringType, DataTypes.IntegerType))
+                .as("col"));
+    checkAnswer(
+        getSession().tableFunction(TableFunctions.explode(df1.col("col"))).select("KEY", "VALUE"),
+        new Row[] {Row.create("a", "1"), Row.create("b", "2")});
+
+    checkAnswer(
+        getSession()
+            .tableFunction(
+                TableFunctions.explode(
+                    Functions.parse_json(Functions.lit("{\"a\":1, \"b\":2}"))
+                        .cast(
+                            DataTypes.createMapType(DataTypes.StringType, DataTypes.IntegerType))))
+            .select("KEY", "VALUE"),
+        new Row[] {Row.create("a", "1"), Row.create("b", "2")});
+  }
 }

src/test/scala/com/snowflake/snowpark/ErrorMessageSuite.scala

@@ -830,4 +830,15 @@ class ErrorMessageSuite extends FunSuite {
       ex.message.startsWith("Error Code: 0423, Error message: Invalid input argument, " +
         "Session.tableFunction only supports table function arguments"))
   }
+
+  test("MISC_INVALID_EXPLODE_ARGUMENT_TYPE") {
+    val ex = ErrorMessage.MISC_INVALID_EXPLODE_ARGUMENT_TYPE(types.IntegerType.typeName)
+    assert(ex.telemetryMessage.equals(ErrorMessage.getMessage("0424")))
+    assert(
+      ex.message.startsWith(
+        "Error Code: 0424, Error message: " +
+          "Invalid input argument type, the input argument type of " +
+          "Explode function should be either Map or Array types.\n" +
+          "The input argument type: Integer"))
+  }
 }

src/test/scala/com/snowflake/snowpark_test/TableFunctionSuite.scala

@@ -3,9 +3,6 @@ package com.snowflake.snowpark_test
 import com.snowflake.snowpark.functions._
 import com.snowflake.snowpark.{Row, _}
 
-import scala.collection.Seq
-import scala.collection.immutable.Map
-
 class TableFunctionSuite extends TestData {
   import session.implicits._
 
@@ -386,4 +383,64 @@ class TableFunctionSuite extends TestData {
     checkAnswer(result.select(df("value")), Seq(Row("1,2"), Row("1,2"), Row("3,4"), Row("3,4")))
   }
 
+  test("explode with array column") {
+    val df = Seq("[1, 2]").toDF("a")
+    val df1 = df.select(parse_json(df("a")).cast(types.ArrayType(types.IntegerType)).as("a"))
+    checkAnswer(
+      df1.select(lit(1), tableFunctions.explode(df1("a")), df1("a")(1)),
+      Seq(Row(1, "1", "2"), Row(1, "2", "2")))
+  }
+
+  test("explode with map column") {
+    val df = Seq("""{"a":1, "b": 2}""").toDF("a")
+    val df1 = df.select(
+      parse_json(df("a"))
+        .cast(types.MapType(types.StringType, types.IntegerType))
+        .as("a"))
+    checkAnswer(
+      df1.select(lit(1), tableFunctions.explode(df1("a")), df1("a")("a")),
+      Seq(Row(1, "a", "1", "1"), Row(1, "b", "2", "1")))
+  }
+
+  test("explode with other column") {
+    val df = Seq("""{"a":1, "b": 2}""").toDF("a")
+    val df1 = df.select(
+      parse_json(df("a"))
+        .as("a"))
+    val error = intercept[SnowparkClientException] {
+      df1.select(tableFunctions.explode(df1("a"))).show()
+    }
+    assert(
+      error.message.contains(
+        "the input argument type of Explode function should be either Map or Array types"))
+    assert(error.message.contains("The input argument type: Variant"))
+  }
+
+  test("explode with DataFrame.join") {
+    val df = Seq("[1, 2]").toDF("a")
+    val df1 = df.select(parse_json(df("a")).cast(types.ArrayType(types.IntegerType)).as("a"))
+    checkAnswer(
+      df1.join(tableFunctions.explode(df1("a"))).select("VALUE"),
+      Seq(Row("1"), Row("2")))
+  }
+
+  test("explode with session.tableFunction") {
+    // with dataframe column
+    val df = Seq("""{"a":1, "b": 2}""").toDF("a")
+    val df1 = df.select(
+      parse_json(df("a"))
+        .cast(types.MapType(types.StringType, types.IntegerType))
+        .as("a"))
+    checkAnswer(
+      session.tableFunction(tableFunctions.explode(df1("a"))),
+      Seq(Row("a", "1"), Row("b", "2")))
+
+    // with literal value
+    checkAnswer(
+      session.tableFunction(
+        tableFunctions
+          .explode(parse_json(lit("[1, 2]")).cast(types.ArrayType(types.IntegerType)))),
+      Seq(Row("1"), Row("2")))
+  }
+
 }