[GitHub] [spark] HyukjinKwon commented on a change in pull request #24232: [SPARK-27297] [SQL] Add higher order functions to scala API
HyukjinKwon commented on a change in pull request #24232: [SPARK-27297] [SQL]
Add higher order functions to scala API
URL: https://github.com/apache/spark/pull/24232#discussion_r315993792
##
File path:
sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
##
@@ -1917,19 +1921,33 @@ class DataFrameFunctionsSuite extends QueryTest with
SharedSparkSession {
null
).toDF("i")
+// transform(i, x -> x + 1)
+val resA = Seq(
+ Row(Seq(2, 10, 9, 8)),
+ Row(Seq(6, 9, 10, 8, 3)),
+ Row(Seq.empty),
+ Row(null))
+
+// transform(i, (x, i) -> x + i)
+val resB = Seq(
+ Row(Seq(1, 10, 10, 10)),
+ Row(Seq(5, 9, 11, 10, 6)),
+ Row(Seq.empty),
+ Row(null))
+
def testArrayOfPrimitiveTypeNotContainsNull(): Unit = {
- checkAnswer(df.selectExpr("transform(i, x -> x + 1)"),
-Seq(
- Row(Seq(2, 10, 9, 8)),
- Row(Seq(6, 9, 10, 8, 3)),
- Row(Seq.empty),
- Row(null)))
- checkAnswer(df.selectExpr("transform(i, (x, i) -> x + i)"),
-Seq(
- Row(Seq(1, 10, 10, 10)),
- Row(Seq(5, 9, 11, 10, 6)),
- Row(Seq.empty),
- Row(null)))
+ checkAnswer(df.selectExpr("transform(i, x -> x + 1)"), resA)
+ checkAnswer(df.selectExpr("transform(i, (x, i) -> x + i)"), resB)
+
+ checkAnswer(df.select(transform(col("i"), x => x + 1)), resA)
+ checkAnswer(df.select(transform(col("i"), (x, i) => x + i)), resB)
+
+ checkAnswer(df.select(transform(col("i"), new JFunc {
+def call(x: Column) = x + 1
+ })), resA)
+ checkAnswer(df.select(transform(col("i"), new JFunc2 {
+def call(x: Column, i: Column) = x + i
+ })), resB)
Review comment:
> As for these, the arguments are scala.Function1 and
org.apache.spark.api.java.function.Function, Java compiler recognizes both as
SAM (Single Abstract Method) type when resolving x -> x, and they are ambiguous.
Sounds like a valid concern to me. Does such `x -> x` syntax work in Java
too?
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[GitHub] [spark] HyukjinKwon commented on a change in pull request #24232: [SPARK-27297] [SQL] Add higher order functions to scala API
HyukjinKwon commented on a change in pull request #24232: [SPARK-27297] [SQL]
Add higher order functions to scala API
URL: https://github.com/apache/spark/pull/24232#discussion_r315993279
##
File path:
sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
##
@@ -1917,19 +1921,33 @@ class DataFrameFunctionsSuite extends QueryTest with
SharedSparkSession {
null
).toDF("i")
+// transform(i, x -> x + 1)
+val resA = Seq(
+ Row(Seq(2, 10, 9, 8)),
+ Row(Seq(6, 9, 10, 8, 3)),
+ Row(Seq.empty),
+ Row(null))
+
+// transform(i, (x, i) -> x + i)
+val resB = Seq(
+ Row(Seq(1, 10, 10, 10)),
+ Row(Seq(5, 9, 11, 10, 6)),
+ Row(Seq.empty),
+ Row(null))
+
def testArrayOfPrimitiveTypeNotContainsNull(): Unit = {
- checkAnswer(df.selectExpr("transform(i, x -> x + 1)"),
-Seq(
- Row(Seq(2, 10, 9, 8)),
- Row(Seq(6, 9, 10, 8, 3)),
- Row(Seq.empty),
- Row(null)))
- checkAnswer(df.selectExpr("transform(i, (x, i) -> x + i)"),
-Seq(
- Row(Seq(1, 10, 10, 10)),
- Row(Seq(5, 9, 11, 10, 6)),
- Row(Seq.empty),
- Row(null)))
+ checkAnswer(df.selectExpr("transform(i, x -> x + 1)"), resA)
+ checkAnswer(df.selectExpr("transform(i, (x, i) -> x + i)"), resB)
+
+ checkAnswer(df.select(transform(col("i"), x => x + 1)), resA)
+ checkAnswer(df.select(transform(col("i"), (x, i) => x + i)), resB)
+
+ checkAnswer(df.select(transform(col("i"), new JFunc {
+def call(x: Column) = x + 1
+ })), resA)
+ checkAnswer(df.select(transform(col("i"), new JFunc2 {
+def call(x: Column, i: Column) = x + i
+ })), resB)
Review comment:
@nvander1, let's move Java specific tests to Java's. Ideally it should be
moved to there and it's better to clarify such doubts.
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[GitHub] [spark] HyukjinKwon commented on a change in pull request #24232: [SPARK-27297] [SQL] Add higher order functions to scala API
HyukjinKwon commented on a change in pull request #24232: [SPARK-27297] [SQL]
Add higher order functions to scala API
URL: https://github.com/apache/spark/pull/24232#discussion_r311453310
##
File path: sql/core/src/main/scala/org/apache/spark/sql/functions.scala
##
@@ -3343,6 +3344,296 @@ object functions {
ArrayExcept(col1.expr, col2.expr)
}
+ private def createLambda(f: Column => Column) = {
+val x = UnresolvedNamedLambdaVariable(Seq("x"))
+val function = f(Column(x)).expr
+LambdaFunction(function, Seq(x))
+ }
+
+ private def createLambda(f: (Column, Column) => Column) = {
+val x = UnresolvedNamedLambdaVariable(Seq("x"))
+val y = UnresolvedNamedLambdaVariable(Seq("y"))
+val function = f(Column(x), Column(y)).expr
+LambdaFunction(function, Seq(x, y))
+ }
+
+ private def createLambda(f: (Column, Column, Column) => Column) = {
+val x = UnresolvedNamedLambdaVariable(Seq("x"))
+val y = UnresolvedNamedLambdaVariable(Seq("y"))
+val z = UnresolvedNamedLambdaVariable(Seq("z"))
+val function = f(Column(x), Column(y), Column(z)).expr
+LambdaFunction(function, Seq(x, y, z))
+ }
+
+ private def createLambda(f: JavaFunction[Column, Column]) = {
+val x = UnresolvedNamedLambdaVariable(Seq("x"))
+val function = f.call(Column(x)).expr
+LambdaFunction(function, Seq(x))
+ }
+
+ private def createLambda(f: JavaFunction2[Column, Column, Column]) = {
+val x = UnresolvedNamedLambdaVariable(Seq("x"))
+val y = UnresolvedNamedLambdaVariable(Seq("y"))
+val function = f.call(Column(x), Column(y)).expr
+LambdaFunction(function, Seq(x, y))
+ }
+
+ private def createLambda(f: JavaFunction3[Column, Column, Column, Column]) =
{
+val x = UnresolvedNamedLambdaVariable(Seq("x"))
+val y = UnresolvedNamedLambdaVariable(Seq("y"))
+val z = UnresolvedNamedLambdaVariable(Seq("z"))
+val function = f.call(Column(x), Column(y), Column(z)).expr
+LambdaFunction(function, Seq(x, y, z))
+ }
+
+ /**
+ * (Scala-specific) Returns an array of elements after applying a
tranformation to each element
+ * in the input array.
+ *
+ * @group collection_funcs
+ */
+ def transform(column: Column, f: Column => Column): Column = withExpr {
+ArrayTransform(column.expr, createLambda(f))
+ }
+
+ /**
+ * (Scala-specific) Returns an array of elements after applying a
tranformation to each element
+ * in the input array.
+ *
+ * @group collection_funcs
+ */
+ def transform(column: Column, f: (Column, Column) => Column): Column =
withExpr {
+ArrayTransform(column.expr, createLambda(f))
+ }
+
+ /**
+ * (Scala-specific) Returns whether a predicate holds for one or more
elements in the array.
+ *
+ * @group collection_funcs
+ */
+ def exists(column: Column, f: Column => Column): Column = withExpr {
+ArrayExists(column.expr, createLambda(f))
+ }
+
+ /**
+ * (Scala-specific) Returns whether a predicate holds for every element in
the array.
+ *
+ * @group collection_funcs
+ */
+ def forall(column: Column, f: Column => Column): Column = withExpr {
+ArrayForAll(column.expr, createLambda(f))
+ }
+
+ /**
+ * (Scala-specific) Returns an array of elements for which a predicate holds
in a given array.
+ *
+ * @group collection_funcs
+ */
+ def filter(column: Column, f: Column => Column): Column = withExpr {
+ArrayFilter(column.expr, createLambda(f))
+ }
+
+ /**
+ * (Scala-specific) Applies a binary operator to an initial state and all
elements in the array,
+ * and reduces this to a single state. The final state is converted into the
final result
+ * by applying a finish function.
+ *
+ * @group collection_funcs
+ */
+ def aggregate(expr: Column, zero: Column, merge: (Column, Column) => Column,
+finish: Column => Column): Column = withExpr {
+ArrayAggregate(
+ expr.expr,
+ zero.expr,
+ createLambda(merge),
+ createLambda(finish)
+)
+ }
+
+ /**
+ * (Scala-specific) Applies a binary operator to an initial state and all
elements in the array,
+ * and reduces this to a single state.
+ *
+ * @group collection_funcs
+ */
+ def aggregate(expr: Column, zero: Column, merge: (Column, Column) =>
Column): Column =
+aggregate(expr, zero, merge, c => c)
+
+ /**
+ * (Scala-specific) Merge two given arrays, element-wise, into a signle
array using a function.
+ * If one array is shorter, nulls are appended at the end to match the
length of the longer
+ * array, before applying the function.
+ *
+ * @group collection_funcs
+ */
+ def zip_with(left: Column, right: Column, f: (Column, Column) => Column):
Column = withExpr {
+ZipWith(left.expr, right.expr, createLambda(f))
+ }
+
+ /**
+ * (Scala-specific) Applies a function to every key-value pair in a map and
returns
+ * a map with the results of those applications as the new keys for the
pairs.
+ *
+ * @group collection
[GitHub] [spark] HyukjinKwon commented on a change in pull request #24232: [SPARK-27297] [SQL] Add higher order functions to scala API
HyukjinKwon commented on a change in pull request #24232: [SPARK-27297] [SQL]
Add higher order functions to scala API
URL: https://github.com/apache/spark/pull/24232#discussion_r269970597
##
File path: sql/core/src/main/scala/org/apache/spark/sql/functions.scala
##
@@ -3316,6 +3316,141 @@ object functions {
ArrayExcept(col1.expr, col2.expr)
}
+ private def expressionFunction(f: Column => Column)
+ : Expression => Expression =
+x => f(Column(x)).expr
+
+ private def expressionFunction(f: (Column, Column) => Column)
+ : (Expression, Expression) => Expression =
+(x, y) => f(Column(x), Column(y)).expr
+
+ private def expressionFunction(f: (Column, Column, Column) => Column)
+ : (Expression, Expression, Expression) => Expression =
+(x, y, z) => f(Column(x), Column(y), Column(z)).expr
+
+ /**
+ * Returns an array of elements after applying a tranformation to each
element
+ * in the input array.
+ *
+ * @group collection_funcs
+ */
+ def transform(column: Column, f: Column => Column): Column = withExpr {
+HigherOrderUtils.transform(column.expr, expressionFunction(f))
+ }
+
+ /**
+ * Returns an array of elements after applying a tranformation to each
element
+ * in the input array.
+ *
+ * @group collection_funcs
+ */
+ def transform(column: Column, f: (Column, Column) => Column): Column =
withExpr {
+HigherOrderUtils.transform(column.expr, expressionFunction(f))
+ }
+
+ /**
+ * Returns whether a predicate holds for one or more elements in the array.
+ *
+ * @group collection_funcs
+ */
+ def exists(column: Column, f: Column => Column): Column = withExpr {
Review comment:
Let's add `(Scala-specific) ` at least for each doc. BTW, please take a look
for style guide at https://github.com/databricks/scala-style-guide
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[GitHub] [spark] HyukjinKwon commented on a change in pull request #24232: [SPARK-27297] [SQL] Add higher order functions to scala API
HyukjinKwon commented on a change in pull request #24232: [SPARK-27297] [SQL]
Add higher order functions to scala API
URL: https://github.com/apache/spark/pull/24232#discussion_r269958689
##
File path: sql/core/src/main/scala/org/apache/spark/sql/functions.scala
##
@@ -3316,6 +3316,141 @@ object functions {
ArrayExcept(col1.expr, col2.expr)
}
+ private def expressionFunction(f: Column => Column)
+ : Expression => Expression =
+x => f(Column(x)).expr
+
+ private def expressionFunction(f: (Column, Column) => Column)
+ : (Expression, Expression) => Expression =
+(x, y) => f(Column(x), Column(y)).expr
+
+ private def expressionFunction(f: (Column, Column, Column) => Column)
+ : (Expression, Expression, Expression) => Expression =
+(x, y, z) => f(Column(x), Column(y), Column(z)).expr
+
+ /**
+ * Returns an array of elements after applying a tranformation to each
element
+ * in the input array.
+ *
+ * @group collection_funcs
+ */
+ def transform(column: Column, f: Column => Column): Column = withExpr {
+HigherOrderUtils.transform(column.expr, expressionFunction(f))
+ }
+
+ /**
+ * Returns an array of elements after applying a tranformation to each
element
+ * in the input array.
+ *
+ * @group collection_funcs
+ */
+ def transform(column: Column, f: (Column, Column) => Column): Column =
withExpr {
+HigherOrderUtils.transform(column.expr, expressionFunction(f))
+ }
+
+ /**
+ * Returns whether a predicate holds for one or more elements in the array.
+ *
+ * @group collection_funcs
+ */
+ def exists(column: Column, f: Column => Column): Column = withExpr {
Review comment:
But how do we support this in Java?
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