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[SPARK-23939][SQL] Add transform_keys function #22013

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0a19cc4
Added Support for transform_keys function
codeatri Aug 6, 2018
12e0259
Merge branch 'master' into SPARK-23939
codeatri Aug 7, 2018
5806ac4
Addressed Review Commenst
codeatri Aug 8, 2018
150a6a5
Additional Tests containing nulls
codeatri Aug 8, 2018
9f6a8ab
nit: style
codeatri Aug 8, 2018
6526630
added additional unit test
codeatri Aug 8, 2018
f7fd231
nit:space
codeatri Aug 8, 2018
1cbaf0c
addressed review comments
codeatri Aug 9, 2018
bb52630
Merge branch 'master' into SPARK-23939
codeatri Aug 14, 2018
621213d
fix style
codeatri Aug 14, 2018
5db526b
Merge master Refactoring changes
codeatri Aug 14, 2018
e5d9b05
review comments
codeatri Aug 15, 2018
fb885f4
review comments
codeatri Aug 15, 2018
58b60b2
fix unit test
codeatri Aug 15, 2018
2f4943f
review
codeatri Aug 15, 2018
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addressed review comments
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@codeatri
codeatri committed Aug 9, 2018
commit 1cbaf0c6adc508299d42a82628f4f0954bed7a95
4 changes: 2 additions & 2 deletions ...alyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/higherOrderFunctions.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -451,9 +451,9 @@ case class ArrayAggregate(
usage = "_FUNC_(expr, func) - Transforms elements in a map using the function.",
examples = """
Examples:
> SELECT _FUNC_(map(array(1, 2, 3), array(1, 2, 3), (k, v) -> k + 1);
> SELECT _FUNC_(map(array(1, 2, 3), array(1, 2, 3)), (k, v) -> k + 1);
map(array(2, 3, 4), array(1, 2, 3))
> SELECT _FUNC_(map(array(1, 2, 3), array(1, 2, 3), (k, v) -> k + v);
> SELECT _FUNC_(map(array(1, 2, 3), array(1, 2, 3)), (k, v) -> k + v);
map(array(2, 4, 6), array(1, 2, 3))
""",
since = "2.4.0")
Expand Down
43 changes: 30 additions & 13 deletions .../src/test/scala/org/apache/spark/sql/catalyst/expressions/HigherOrderFunctionsSuite.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@
package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.SparkFunSuite
import org.apache.spark.sql.catalyst.util.ArrayBasedMapData
import org.apache.spark.sql.types._

class HigherOrderFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
Expand Down Expand Up @@ -60,9 +61,8 @@ class HigherOrderFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper
}

def transformKeys(expr: Expression, f: (Expression, Expression) => Expression): Expression = {
val valueType = expr.dataType.asInstanceOf[MapType].valueType
val keyType = expr.dataType.asInstanceOf[MapType].keyType
TransformKeys(expr, createLambda(keyType, false, valueType, true, f))
val map = expr.dataType.asInstanceOf[MapType]
TransformKeys(expr, createLambda(map.keyType, false, map.valueType, map.valueContainsNull, f))
}

def aggregate(
Expand Down Expand Up @@ -239,35 +239,45 @@ class HigherOrderFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper

test("TransformKeys") {
val ai0 = Literal.create(
Map(1 -> 1, 2 -> 2, 3 -> 3),
MapType(IntegerType, IntegerType))
Map(1 -> 1, 2 -> 2, 3 -> 3, 4 -> 4),
MapType(IntegerType, IntegerType, valueContainsNull = false))
val ai1 = Literal.create(
Map.empty[Int, Int],
MapType(IntegerType, IntegerType))
MapType(IntegerType, IntegerType, valueContainsNull = true))
val ai2 = Literal.create(
Map(1 -> 1, 2 -> null, 3 -> 3),
MapType(IntegerType, IntegerType))
MapType(IntegerType, IntegerType, valueContainsNull = true))
val ai3 = Literal.create(null, MapType(IntegerType, IntegerType, valueContainsNull = false))

val plusOne: (Expression, Expression) => Expression = (k, v) => k + 1
val plusValue: (Expression, Expression) => Expression = (k, v) => k + v
val modKey: (Expression, Expression) => Expression = (k, v) => k % 3

checkEvaluation(transformKeys(ai0, plusOne), Map(2 -> 1, 3 -> 2, 4 -> 3))
checkEvaluation(transformKeys(ai0, plusValue), Map(2 -> 1, 4 -> 2, 6 -> 3))
checkEvaluation(transformKeys(ai0, plusOne), Map(2 -> 1, 3 -> 2, 4 -> 3, 5 -> 4))
checkEvaluation(transformKeys(ai0, plusValue), Map(2 -> 1, 4 -> 2, 6 -> 3, 8 -> 4))
checkEvaluation(
transformKeys(transformKeys(ai0, plusOne), plusValue), Map(3 -> 1, 5 -> 2, 7 -> 3))
transformKeys(transformKeys(ai0, plusOne), plusValue), Map(3 -> 1, 5 -> 2, 7 -> 3, 9 -> 4))
checkEvaluation(transformKeys(ai0, modKey),
ArrayBasedMapData(Array(1, 2, 0, 1), Array(1, 2, 3, 4)))
checkEvaluation(transformKeys(ai1, plusOne), Map.empty[Int, Int])
checkEvaluation(transformKeys(ai1, plusOne), Map.empty[Int, Int])
checkEvaluation(
transformKeys(transformKeys(ai1, plusOne), plusValue), Map.empty[Int, Int])
checkEvaluation(transformKeys(ai2, plusOne), Map(2 -> 1, 3 -> null, 4 -> 3))
checkEvaluation(
transformKeys(transformKeys(ai2, plusOne), plusOne), Map(3 -> 1, 4 -> null, 5 -> 3))
checkEvaluation(transformKeys(ai3, plusOne), null)

val as0 = Literal.create(
Map("a" -> "xy", "bb" -> "yz", "ccc" -> "zx"), MapType(StringType, StringType))
Map("a" -> "xy", "bb" -> "yz", "ccc" -> "zx"),
MapType(StringType, StringType, valueContainsNull = false))
val as1 = Literal.create(
Map("a" -> "xy", "bb" -> "yz", "ccc" -> null), MapType(StringType, StringType))
val asn = Literal.create(Map.empty[StringType, StringType], MapType(StringType, StringType))
Map("a" -> "xy", "bb" -> "yz", "ccc" -> null),
MapType(StringType, StringType, valueContainsNull = true))
val as2 = Literal.create(null,
MapType(StringType, StringType, valueContainsNull = false))
val asn = Literal.create(Map.empty[StringType, StringType],
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@ueshin ueshin Aug 15, 2018

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as3?

MapType(StringType, StringType, valueContainsNull = true))

val concatValue: (Expression, Expression) => Expression = (k, v) => Concat(Seq(k, v))
val convertKeyToKeyLength: (Expression, Expression) => Expression =
Expand All @@ -286,6 +296,13 @@ class HigherOrderFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper
Map(2 -> "xy", 3 -> "yz", 4 -> "zx"))
checkEvaluation(transformKeys(as1, convertKeyToKeyLength),
Map(2 -> "xy", 3 -> "yz", 4 -> null))
checkEvaluation(transformKeys(as2, convertKeyToKeyLength), null)
checkEvaluation(transformKeys(asn, convertKeyToKeyLength), Map.empty[Int, String])

val ax0 = Literal.create(
Map(1 -> "x", 2 -> "y", 3 -> "z"),
MapType(IntegerType, StringType, valueContainsNull = false))

checkEvaluation(transformKeys(ax0, plusOne), Map(2 -> "x", 3 -> "y", 4 -> "z"))
}
}
125 changes: 12 additions & 113 deletions sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
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Expand Up @@ -2123,71 +2123,42 @@ class DataFrameFunctionsSuite extends QueryTest with SharedSQLContext {
).toDF("i")

val dfExample2 = Seq(
Map[Int, String](1 -> "a", 2 -> "b", 3 -> "c")
).toDF("x")

val dfExample3 = Seq(
Map[String, Int]("a" -> 1, "b" -> 2, "c" -> 3)
).toDF("y")

val dfExample4 = Seq(
Map[Int, Double](1 -> 1.0E0, 2 -> 1.4E0, 3 -> 1.7E0)
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@ueshin ueshin Aug 15, 2018

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Do we need E0?

).toDF("z")
).toDF("j")

val dfExample5 = Seq(
val dfExample3 = Seq(
Map[Int, Boolean](25 -> true, 26 -> false)
).toDF("a")

val dfExample6 = Seq(
Map[Int, String](25 -> "ab", 26 -> "cd")
).toDF("b")
).toDF("x")

val dfExample7 = Seq(
val dfExample4 = Seq(
Map[Array[Int], Boolean](Array(1, 2) -> false)
).toDF("c")
).toDF("y")


def testMapOfPrimitiveTypesCombination(): Unit = {
checkAnswer(dfExample1.selectExpr("transform_keys(i, (k, v) -> k + v)"),
Seq(Row(Map(2 -> 1, 18 -> 9, 16 -> 8, 14 -> 7))))

checkAnswer(dfExample2.selectExpr("transform_keys(x, (k, v) -> k + 1)"),
Seq(Row(Map(2 -> "a", 3 -> "b", 4 -> "c"))))

checkAnswer(dfExample3.selectExpr("transform_keys(y, (k, v) -> v * v)"),
Seq(Row(Map(1 -> 1, 4 -> 2, 9 -> 3))))

checkAnswer(dfExample3.selectExpr("transform_keys(y, (k, v) -> length(k) + v)"),
Seq(Row(Map(2 -> 1, 3 -> 2, 4 -> 3))))

checkAnswer(
dfExample3.selectExpr("transform_keys(y, (k, v) -> concat(k, cast(v as String)))"),
Seq(Row(Map("a1" -> 1, "b2" -> 2, "c3" -> 3))))

checkAnswer(dfExample4.selectExpr("transform_keys(z, " +
checkAnswer(dfExample2.selectExpr("transform_keys(j, " +
"(k, v) -> map_from_arrays(ARRAY(1, 2, 3), ARRAY('one', 'two', 'three'))[k])"),
Seq(Row(Map("one" -> 1.0, "two" -> 1.4, "three" -> 1.7))))

checkAnswer(dfExample4.selectExpr("transform_keys(z, (k, v) -> CAST(v * 2 AS BIGINT) + k)"),
checkAnswer(dfExample2.selectExpr("transform_keys(j, (k, v) -> CAST(v * 2 AS BIGINT) + k)"),
Seq(Row(Map(3 -> 1.0, 4 -> 1.4, 6 -> 1.7))))

checkAnswer(dfExample4.selectExpr("transform_keys(z, (k, v) -> k + v)"),
checkAnswer(dfExample2.selectExpr("transform_keys(j, (k, v) -> k + v)"),
Seq(Row(Map(2.0 -> 1.0, 3.4 -> 1.4, 4.7 -> 1.7))))

checkAnswer(dfExample5.selectExpr("transform_keys(a, (k, v) -> k % 2 = 0 OR v)"),
checkAnswer(dfExample3.selectExpr("transform_keys(x, (k, v) -> k % 2 = 0 OR v)"),
Seq(Row(Map(true -> true, true -> false))))

checkAnswer(dfExample5.selectExpr("transform_keys(a, (k, v) -> if(v, 2 * k, 3 * k))"),
checkAnswer(dfExample3.selectExpr("transform_keys(x, (k, v) -> if(v, 2 * k, 3 * k))"),
Seq(Row(Map(50 -> true, 78 -> false))))

checkAnswer(dfExample5.selectExpr("transform_keys(a, (k, v) -> if(v, 2 * k, 3 * k))"),
checkAnswer(dfExample3.selectExpr("transform_keys(x, (k, v) -> if(v, 2 * k, 3 * k))"),
Seq(Row(Map(50 -> true, 78 -> false))))

checkAnswer(dfExample6.selectExpr(
"transform_keys(b, (k, v) -> concat(conv(k, 10, 16) , substr(v, 1, 1)))"),
Seq(Row(Map("19a" -> "ab", "1Ac" -> "cd"))))

checkAnswer(dfExample7.selectExpr("transform_keys(c, (k, v) -> array_contains(k, 3) AND v)"),
checkAnswer(dfExample4.selectExpr("transform_keys(y, (k, v) -> array_contains(k, 3) AND v)"),
Seq(Row(Map(false -> false))))
}
// Test with local relation, the Project will be evaluated without codegen
Expand All @@ -2196,52 +2167,10 @@ class DataFrameFunctionsSuite extends QueryTest with SharedSQLContext {
dfExample2.cache()
dfExample3.cache()
dfExample4.cache()
dfExample5.cache()
dfExample6.cache()
// Test with cached relation, the Project will be evaluated with codegen
testMapOfPrimitiveTypesCombination()
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@mn-mikke mn-mikke Aug 7, 2018

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Do we have do that if the expression implements CodegenFallback?

}

test("transform keys function - test empty") {
val dfExample1 = Seq(
Map.empty[Int, Int]
).toDF("i")

val dfExample2 = Seq(
Map.empty[BigInt, String]
).toDF("j")

def testEmpty(): Unit = {
checkAnswer(dfExample1.selectExpr("transform_keys(i, (k, v) -> NULL)"),
Seq(Row(Map.empty[Null, Null])))

checkAnswer(dfExample1.selectExpr("transform_keys(i, (k, v) -> k)"),
Seq(Row(Map.empty[Null, Null])))

checkAnswer(dfExample1.selectExpr("transform_keys(i, (k, v) -> v)"),
Seq(Row(Map.empty[Null, Null])))

checkAnswer(dfExample1.selectExpr("transform_keys(i, (k, v) -> 0)"),
Seq(Row(Map.empty[Int, Null])))

checkAnswer(dfExample1.selectExpr("transform_keys(i, (k, v) -> 'key')"),
Seq(Row(Map.empty[String, Null])))

checkAnswer(dfExample1.selectExpr("transform_keys(i, (k, v) -> true)"),
Seq(Row(Map.empty[Boolean, Null])))

checkAnswer(dfExample2.selectExpr("transform_keys(j, (k, v) -> k + cast(v as BIGINT))"),
Seq(Row(Map.empty[BigInt, Null])))

checkAnswer(dfExample2.selectExpr("transform_keys(j, (k, v) -> v)"),
Seq(Row(Map())))
}
testEmpty()
dfExample1.cache()
dfExample2.cache()
testEmpty()
}

test("transform keys function - Invalid lambda functions and exceptions") {
val dfExample1 = Seq(
Map[Int, Int](1 -> 1, 9 -> 9, 8 -> 8, 7 -> 7)
Expand Down Expand Up @@ -2279,36 +2208,6 @@ class DataFrameFunctionsSuite extends QueryTest with SharedSQLContext {
testInvalidLambdaFunctions()
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@ueshin ueshin Aug 15, 2018

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We need dfExample3.cache() as well?

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@mn-mikke mn-mikke Aug 15, 2018

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@ueshin I would like to ask you a generic question regarding higher-order functions. Is it necessary to perform checks with codegen paths if all the newly added functions extends from CodegenFallback? Eventually, is there a plan to add coden for these functions in future?

}

test("transform keys function - test null") {
val dfExample1 = Seq(
Map[Boolean, Integer](true -> 1, false -> null)
).toDF("a")

def testNullValues(): Unit = {
checkAnswer(dfExample1.selectExpr("transform_keys(a, (k, v) -> if(k, NOT k, v IS NULL))"),
Seq(Row(Map(false -> 1, true -> null))))
}

testNullValues()
dfExample1.cache()
testNullValues()
}

test("transform keys function - test duplicate keys") {
val dfExample1 = Seq(
Map[Int, String](1 -> "a", 2 -> "b", 3 -> "c", 4 -> "d")
).toDF("a")

def testNullValues(): Unit = {
checkAnswer(dfExample1.selectExpr("transform_keys(a, (k, v) -> k%3)"),
Seq(Row(Map(1 -> "a", 2 -> "b", 0 -> "c", 1 -> "d"))))
}

testNullValues()
dfExample1.cache()
testNullValues()
}

private def assertValuesDoNotChangeAfterCoalesceOrUnion(v: Column): Unit = {
import DataFrameFunctionsSuite.CodegenFallbackExpr
for ((codegenFallback, wholeStage) <- Seq((true, false), (false, false), (false, true))) {
Expand Down