maropu commented on a change in pull request #31967:
URL: https://github.com/apache/spark/pull/31967#discussion_r603820580
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
##########
@@ -687,6 +688,118 @@ class CodegenContext extends Logging {
}
"""
s"${addNewFunction(compareFunc, funcCode)}($c1, $c2)"
+ case _ @ MapType(keyType, valueType, valueContainsNull) =>
Review comment:
What's a difference from the @hvanhovell impl.? The @hvanhovell one
looks simpler though.
https://github.com/apache/spark/pull/15970/files#diff-1501206e78d34b65183af1092c8ec392ce18574bb538f905ca93a22983c63ae6R558-R598
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/NormalizeMapType.scala
##########
@@ -0,0 +1,155 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.optimizer
+
+import scala.math.Ordering
+
+import org.apache.spark.sql.catalyst.expressions.{And, EqualTo,
ExpectsInputTypes, Expression, UnaryExpression}
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext
+import
org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator.{getValue,
javaType}
+import org.apache.spark.sql.catalyst.expressions.codegen.ExprCode
+import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
+import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Window}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.catalyst.util.{ArrayBasedMapBuilder, MapData,
TypeUtils}
+import org.apache.spark.sql.types.{AbstractDataType, DataType, MapType}
+
+/**
+ * When comparing two maps, we have to make sure two maps have the same key
value pairs but
+ * with different key ordering are equal.
+ * For example, Map('a' -> 1, 'b' -> 2) equals to Map('b' -> 2, 'a' -> 1).
+ *
+ * We have to specially handle this in grouping/join/window because Spark SQL
turns
+ * grouping/join/window partition keys into binary `UnsafeRow` and compare the
+ * binary data directly instead of using MapType's ordering. So in these
cases, we have
+ * to insert an expression to sort map entries by key.
+ *
+ * Note that, this rule must be executed at the end of optimizer, because the
optimizer may create
+ * new joins(the subquery rewrite) and new join conditions(the join reorder).
+ */
+object NormalizeMapType extends Rule[LogicalPlan] {
+ def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+ case w: Window if w.partitionSpec.exists(p => needNormalize(p)) =>
+ w.copy(partitionSpec = w.partitionSpec.map(normalize))
+
+ case j @ ExtractEquiJoinKeys(_, leftKeys, rightKeys, condition, _, _, _)
+ // The analyzer guarantees left and right joins keys are of the same
data type.
+ if leftKeys.exists(k => needNormalize(k)) =>
+ val newLeftJoinKeys = leftKeys.map(normalize)
+ val newRightJoinKeys = rightKeys.map(normalize)
+ val newConditions = newLeftJoinKeys.zip(newRightJoinKeys).map {
+ case (l, r) => EqualTo(l, r)
+ } ++ condition
+ j.copy(condition = Some(newConditions.reduce(And)))
+ }
+
+ private def needNormalize(expr: Expression): Boolean = expr match {
+ case SortMapKey(_) => false
+ case e if e.dataType.isInstanceOf[MapType] => true
+ case _ => false
+ }
+
+ private[sql] def normalize(expr: Expression): Expression = expr match {
+ case _ if !needNormalize(expr) => expr
+ case e if e.dataType.isInstanceOf[MapType] =>
+ SortMapKey(e)
+ }
+}
+
+case class SortMapKey(child: Expression) extends UnaryExpression with
ExpectsInputTypes {
+ private lazy val MapType(keyType, valueType, valueContainsNull) =
dataType.asInstanceOf[MapType]
+ private lazy val keyOrdering: Ordering[Any] =
TypeUtils.getInterpretedOrdering(keyType)
+ private lazy val mapBuilder = new ArrayBasedMapBuilder(keyType, valueType)
+
+ override def inputTypes: Seq[AbstractDataType] = Seq(MapType)
+
+ override def dataType: DataType = child.dataType
+
+ override def nullSafeEval(input: Any): Any = {
+ val childMap = input.asInstanceOf[MapData]
+ val keys = childMap.keyArray()
Review comment:
We don't need to sort data recursively just for nested case like
`map<map<int,int>,string>` and `map<struct<a: map<int,int>>,string>)`?
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/NormalizeMapType.scala
##########
@@ -0,0 +1,155 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.optimizer
+
+import scala.math.Ordering
+
+import org.apache.spark.sql.catalyst.expressions.{And, EqualTo,
ExpectsInputTypes, Expression, UnaryExpression}
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext
+import
org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator.{getValue,
javaType}
+import org.apache.spark.sql.catalyst.expressions.codegen.ExprCode
+import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
+import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Window}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.catalyst.util.{ArrayBasedMapBuilder, MapData,
TypeUtils}
+import org.apache.spark.sql.types.{AbstractDataType, DataType, MapType}
+
+/**
+ * When comparing two maps, we have to make sure two maps have the same key
value pairs but
+ * with different key ordering are equal.
+ * For example, Map('a' -> 1, 'b' -> 2) equals to Map('b' -> 2, 'a' -> 1).
+ *
+ * We have to specially handle this in grouping/join/window because Spark SQL
turns
+ * grouping/join/window partition keys into binary `UnsafeRow` and compare the
+ * binary data directly instead of using MapType's ordering. So in these
cases, we have
+ * to insert an expression to sort map entries by key.
+ *
+ * Note that, this rule must be executed at the end of optimizer, because the
optimizer may create
+ * new joins(the subquery rewrite) and new join conditions(the join reorder).
+ */
+object NormalizeMapType extends Rule[LogicalPlan] {
+ def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+ case w: Window if w.partitionSpec.exists(p => needNormalize(p)) =>
+ w.copy(partitionSpec = w.partitionSpec.map(normalize))
+
+ case j @ ExtractEquiJoinKeys(_, leftKeys, rightKeys, condition, _, _, _)
+ // The analyzer guarantees left and right joins keys are of the same
data type.
+ if leftKeys.exists(k => needNormalize(k)) =>
+ val newLeftJoinKeys = leftKeys.map(normalize)
+ val newRightJoinKeys = rightKeys.map(normalize)
+ val newConditions = newLeftJoinKeys.zip(newRightJoinKeys).map {
+ case (l, r) => EqualTo(l, r)
+ } ++ condition
+ j.copy(condition = Some(newConditions.reduce(And)))
+ }
+
+ private def needNormalize(expr: Expression): Boolean = expr match {
+ case SortMapKey(_) => false
+ case e if e.dataType.isInstanceOf[MapType] => true
+ case _ => false
+ }
+
+ private[sql] def normalize(expr: Expression): Expression = expr match {
+ case _ if !needNormalize(expr) => expr
+ case e if e.dataType.isInstanceOf[MapType] =>
+ SortMapKey(e)
+ }
+}
+
+case class SortMapKey(child: Expression) extends UnaryExpression with
ExpectsInputTypes {
Review comment:
`SortMapKey` -> `SortMapKeys`?
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/NormalizeMapType.scala
##########
@@ -0,0 +1,155 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.optimizer
+
+import scala.math.Ordering
+
+import org.apache.spark.sql.catalyst.expressions.{And, EqualTo,
ExpectsInputTypes, Expression, UnaryExpression}
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext
+import
org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator.{getValue,
javaType}
+import org.apache.spark.sql.catalyst.expressions.codegen.ExprCode
+import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
+import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Window}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.catalyst.util.{ArrayBasedMapBuilder, MapData,
TypeUtils}
+import org.apache.spark.sql.types.{AbstractDataType, DataType, MapType}
+
+/**
+ * When comparing two maps, we have to make sure two maps have the same key
value pairs but
+ * with different key ordering are equal.
+ * For example, Map('a' -> 1, 'b' -> 2) equals to Map('b' -> 2, 'a' -> 1).
+ *
+ * We have to specially handle this in grouping/join/window because Spark SQL
turns
+ * grouping/join/window partition keys into binary `UnsafeRow` and compare the
+ * binary data directly instead of using MapType's ordering. So in these
cases, we have
+ * to insert an expression to sort map entries by key.
+ *
+ * Note that, this rule must be executed at the end of optimizer, because the
optimizer may create
+ * new joins(the subquery rewrite) and new join conditions(the join reorder).
Review comment:
Could you leave some comments about why this rule does not handle the
Aggregate cases?
https://github.com/apache/spark/pull/31967/files#diff-21f071d73070b8257ad76e6e16ec5ed38a13d1278fe94bd42546c258a69f4410R344
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/NormalizeMapType.scala
##########
@@ -0,0 +1,155 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.optimizer
+
+import scala.math.Ordering
+
+import org.apache.spark.sql.catalyst.expressions.{And, EqualTo,
ExpectsInputTypes, Expression, UnaryExpression}
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext
+import
org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator.{getValue,
javaType}
+import org.apache.spark.sql.catalyst.expressions.codegen.ExprCode
+import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
+import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Window}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.catalyst.util.{ArrayBasedMapBuilder, MapData,
TypeUtils}
+import org.apache.spark.sql.types.{AbstractDataType, DataType, MapType}
+
+/**
+ * When comparing two maps, we have to make sure two maps have the same key
value pairs but
+ * with different key ordering are equal.
+ * For example, Map('a' -> 1, 'b' -> 2) equals to Map('b' -> 2, 'a' -> 1).
+ *
+ * We have to specially handle this in grouping/join/window because Spark SQL
turns
+ * grouping/join/window partition keys into binary `UnsafeRow` and compare the
+ * binary data directly instead of using MapType's ordering. So in these
cases, we have
+ * to insert an expression to sort map entries by key.
+ *
+ * Note that, this rule must be executed at the end of optimizer, because the
optimizer may create
+ * new joins(the subquery rewrite) and new join conditions(the join reorder).
+ */
+object NormalizeMapType extends Rule[LogicalPlan] {
+ def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+ case w: Window if w.partitionSpec.exists(p => needNormalize(p)) =>
Review comment:
You didn't support `BinaryComparison` cases?
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/NormalizeFloatingNumbers.scala
##########
@@ -141,6 +139,27 @@ object NormalizeFloatingNumbers extends Rule[LogicalPlan] {
val function = normalize(lv)
KnownFloatingPointNormalized(ArrayTransform(expr,
LambdaFunction(function, Seq(lv))))
+ case _ if expr.dataType.isInstanceOf[MapType] =>
+ val MapType(kt, vt, containsNull) = expr.dataType
+ var normalized = if (needNormalize(kt)) {
Review comment:
Could you avoid to use `var` here?
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/NormalizeMapType.scala
##########
@@ -0,0 +1,155 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.optimizer
+
+import scala.math.Ordering
+
+import org.apache.spark.sql.catalyst.expressions.{And, EqualTo,
ExpectsInputTypes, Expression, UnaryExpression}
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext
+import
org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator.{getValue,
javaType}
+import org.apache.spark.sql.catalyst.expressions.codegen.ExprCode
+import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
+import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Window}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.catalyst.util.{ArrayBasedMapBuilder, MapData,
TypeUtils}
+import org.apache.spark.sql.types.{AbstractDataType, DataType, MapType}
+
+/**
+ * When comparing two maps, we have to make sure two maps have the same key
value pairs but
+ * with different key ordering are equal.
+ * For example, Map('a' -> 1, 'b' -> 2) equals to Map('b' -> 2, 'a' -> 1).
+ *
+ * We have to specially handle this in grouping/join/window because Spark SQL
turns
+ * grouping/join/window partition keys into binary `UnsafeRow` and compare the
+ * binary data directly instead of using MapType's ordering. So in these
cases, we have
+ * to insert an expression to sort map entries by key.
+ *
+ * Note that, this rule must be executed at the end of optimizer, because the
optimizer may create
+ * new joins(the subquery rewrite) and new join conditions(the join reorder).
+ */
+object NormalizeMapType extends Rule[LogicalPlan] {
+ def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+ case w: Window if w.partitionSpec.exists(p => needNormalize(p)) =>
+ w.copy(partitionSpec = w.partitionSpec.map(normalize))
+
+ case j @ ExtractEquiJoinKeys(_, leftKeys, rightKeys, condition, _, _, _)
+ // The analyzer guarantees left and right joins keys are of the same
data type.
+ if leftKeys.exists(k => needNormalize(k)) =>
+ val newLeftJoinKeys = leftKeys.map(normalize)
+ val newRightJoinKeys = rightKeys.map(normalize)
+ val newConditions = newLeftJoinKeys.zip(newRightJoinKeys).map {
+ case (l, r) => EqualTo(l, r)
+ } ++ condition
+ j.copy(condition = Some(newConditions.reduce(And)))
+ }
+
+ private def needNormalize(expr: Expression): Boolean = expr match {
+ case SortMapKey(_) => false
+ case e if e.dataType.isInstanceOf[MapType] => true
+ case _ => false
+ }
+
+ private[sql] def normalize(expr: Expression): Expression = expr match {
+ case _ if !needNormalize(expr) => expr
+ case e if e.dataType.isInstanceOf[MapType] =>
+ SortMapKey(e)
+ }
+}
+
+case class SortMapKey(child: Expression) extends UnaryExpression with
ExpectsInputTypes {
+ private lazy val MapType(keyType, valueType, valueContainsNull) =
dataType.asInstanceOf[MapType]
+ private lazy val keyOrdering: Ordering[Any] =
TypeUtils.getInterpretedOrdering(keyType)
+ private lazy val mapBuilder = new ArrayBasedMapBuilder(keyType, valueType)
+
+ override def inputTypes: Seq[AbstractDataType] = Seq(MapType)
+
+ override def dataType: DataType = child.dataType
+
+ override def nullSafeEval(input: Any): Any = {
+ val childMap = input.asInstanceOf[MapData]
+ val keys = childMap.keyArray()
+ val values = childMap.valueArray()
+ val sortedKeyIndex = (0 until childMap.numElements()).toArray.sorted(new
Ordering[Int] {
+ override def compare(a: Int, b: Int): Int = {
+ keyOrdering.compare(keys.get(a, keyType), keys.get(b, keyType))
+ }
+ })
+
+ var i = 0
+ while (i < childMap.numElements()) {
+ val index = sortedKeyIndex(i)
+ mapBuilder.put(
+ keys.get(index, keyType),
+ if (values.isNullAt(index)) null else values.get(index, valueType))
+
+ i += 1
+ }
+
+ mapBuilder.build()
+ }
+
+ override protected def doGenCode(ctx: CodegenContext, ev: ExprCode):
ExprCode = {
Review comment:
To make this PR simpler, how about leaving the codegen support into
follow-up PRs just like the original PR?
https://github.com/apache/spark/pull/15970/files#diff-da163d97a5f0fc534aad719c4a39eca97116f25bfc05b7d8941b342a3ed96036R423-R429
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/NormalizeFloatingNumbers.scala
##########
@@ -141,6 +139,27 @@ object NormalizeFloatingNumbers extends Rule[LogicalPlan] {
val function = normalize(lv)
KnownFloatingPointNormalized(ArrayTransform(expr,
LambdaFunction(function, Seq(lv))))
+ case _ if expr.dataType.isInstanceOf[MapType] =>
+ val MapType(kt, vt, containsNull) = expr.dataType
+ var normalized = if (needNormalize(kt)) {
Review comment:
Could you add tests for this new code path in
`NormalizeFloatingPointNumbersSuite`?
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
##########
@@ -230,10 +230,12 @@ abstract class Optimizer(catalogManager: CatalogManager)
ColumnPruning,
CollapseProject,
RemoveNoopOperators) :+
- // This batch must be executed after the `RewriteSubquery` batch, which
creates joins.
+ // Following batches must be executed after the `RewriteSubquery` batch,
which creates joins.
Batch("NormalizeFloatingNumbers", Once, NormalizeFloatingNumbers) :+
+ Batch("NormalizeMapType", Once, NormalizeMapType) :+
Batch("ReplaceUpdateFieldsExpression", Once, ReplaceUpdateFieldsExpression)
+
Review comment:
nit: unnecessary change.
##########
File path: sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
##########
@@ -4140,6 +4142,136 @@ class SQLQuerySuite extends QueryTest with
SharedSparkSession with AdaptiveSpark
}
}
}
+
+ test("SPARK-34819: MapType supports orderable semantics") {
+ Seq(CodegenObjectFactoryMode.CODEGEN_ONLY.toString,
+ CodegenObjectFactoryMode.NO_CODEGEN.toString).foreach {
+ case codegenMode =>
+ withSQLConf(SQLConf.CODEGEN_FACTORY_MODE.key -> codegenMode) {
+ withTable("t", "t2") {
+ val df = Seq(
+ Map("a" -> 1, "b" -> 2, "c" -> 3),
+ Map("c" -> 3, "b" -> 2, "a" -> 1),
+ Map("d" -> 4),
+ Map("a" -> 1, "e" -> 2),
+ Map("d" -> 4),
+ Map("d" -> 5)
+ ).toDF("m")
+ val df2 = Seq(
+ Map("b" -> 2, "a" -> 1, "c" -> 3)
+ ).toDF("m2")
+ df.createOrReplaceTempView("t")
+ df2.createOrReplaceTempView("t2")
+
+ checkAnswer(
+ sql("select m, count(1) from t group by m"),
+ Row(Map("d" -> 4), 2) ::
+ Row(Map("d" -> 5), 1) ::
+ Row(Map("a" -> 1, "e" -> 2), 1) ::
+ Row(Map("a" -> 1, "b" -> 2, "c" -> 3), 2) :: Nil
+ )
+
+ checkAnswer(
+ sql("select distinct m from t"),
+ Row(Map("d" -> 4)) ::
+ Row(Map("d" -> 5)) ::
+ Row(Map("a" -> 1, "e" -> 2)) ::
+ Row(Map("a" -> 1, "b" -> 2, "c" -> 3)) :: Nil
+ )
+
+ checkAnswer(
+ sql("select m from t order by m"),
+ Row(Map("d" -> 4)) ::
+ Row(Map("d" -> 4)) ::
+ Row(Map("d" -> 5)) ::
+ Row(Map("a" -> 1, "e" -> 2)) ::
+ Row(Map("a" -> 1, "b" -> 2, "c" -> 3)) ::
+ Row(Map("c" -> 3, "b" -> 2, "a" -> 1)) :: Nil
+ )
+
+ checkAnswer(
+ sql("select m, count(1) over (partition by m) from t"),
+ Row(Map("d" -> 4), 2) ::
+ Row(Map("d" -> 4), 2) ::
+ Row(Map("d" -> 5), 1) ::
+ Row(Map("a" -> 1, "e" -> 2), 1) ::
+ Row(Map("a" -> 1, "b" -> 2, "c" -> 3), 2) ::
+ Row(Map("c" -> 3, "b" -> 2, "a" -> 1), 2) :: Nil
+ )
+
+ checkAnswer(
+ sql(
+ """select m2, count(1), percentile(id, 0.5) from (
+ | select
+ | case when size(m) == 3 then m else map('b', 2, 'a', 1,
'c', 3)
+ | end as m2,
+ | 1 as id
+ | from t
+ |)
+ |group by m2
+ |""".stripMargin),
+ Row(Map("a" -> 1, "b" -> 2, "c" -> 3), 6, 1.0) :: Nil
+ )
+
+ checkAnswer(
+ sql("select m, m2 from t join t2 on t.m = t2.m2"),
+ Row(Map("a" -> 1, "b" -> 2, "c" -> 3), Map("b" -> 2, "a" -> 1,
"c" -> 3)) ::
+ Row(Map("c" -> 3, "b" -> 2, "a" -> 1), Map("b" -> 2, "a" -> 1,
"c" -> 3)) :: Nil
+ )
+
+ checkAnswer(
+ sql("select distinct m, m2 from t join t2 on t.m = t2.m2"),
+ Row(Map("a" -> 1, "b" -> 2, "c" -> 3), Map("a" -> 1, "b" -> 2,
"c" -> 3)) :: Nil
+ )
+
+ checkAnswer(
+ sql("select m from t where m = map('b', 2, 'a', 1, 'c', 3)"),
+ Row(Map("a" -> 1, "b" -> 2, "c" -> 3)) ::
+ Row(Map("c" -> 3, "b" -> 2, "a" -> 1)) :: Nil
+ )
+ }
+ }
+ }
+ }
+
+ test("SPARK-34819: MapType has nesting complex type supports orderable
semantics") {
+ Seq(CodegenObjectFactoryMode.CODEGEN_ONLY.toString,
+ CodegenObjectFactoryMode.NO_CODEGEN.toString).foreach {
Review comment:
Could you move the two tests into `SQLQueryTestSuite`? You can use the
`CONFIG_DIM` directive there:
https://github.com/apache/spark/blob/master/sql/core/src/test/resources/sql-tests/inputs/postgreSQL/join.sql#L18-L20
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
##########
@@ -687,6 +688,118 @@ class CodegenContext extends Logging {
}
"""
s"${addNewFunction(compareFunc, funcCode)}($c1, $c2)"
+ case _ @ MapType(keyType, valueType, valueContainsNull) =>
Review comment:
Btw, we cannot reuse the Array case?
https://github.com/apache/spark/pull/31967/files#diff-1501206e78d34b65183af1092c8ec392ce18574bb538f905ca93a22983c63ae6R643
##########
File path:
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/NormalizeMapTypeSuite.scala
##########
@@ -0,0 +1,82 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.optimizer
+
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.dsl.plans._
+import org.apache.spark.sql.catalyst.plans.PlanTest
+import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan}
+import org.apache.spark.sql.catalyst.rules.RuleExecutor
+import org.apache.spark.sql.types.{MapType, StringType}
+
+class NormalizeMapTypeSuite extends PlanTest {
+
+ object Optimize extends RuleExecutor[LogicalPlan] {
+ val batches = Batch("NormalizeMapType", Once, NormalizeMapType) :: Nil
+ }
+
+ val testRelation1 = LocalRelation('a.int, 'm.map(MapType(StringType,
StringType, false)))
+ val a1 = testRelation1.output(0)
+ val m1 = testRelation1.output(1)
+
+ val testRelation2 = LocalRelation('a.int, 'm.map(MapType(StringType,
StringType, false)))
+ val a2 = testRelation2.output(0)
+ val m2 = testRelation2.output(1)
+
+ test("normalize map types in window function expressions") {
+ val query = testRelation1.window(Seq(sum(a1).as("sum")), Seq(m1),
Seq(m1.asc))
+ val optimized = Optimize.execute(query)
+ val correctAnswer = testRelation1.window(Seq(sum(a1).as("sum")),
+ Seq(SortMapKey(m1)), Seq(m1.asc))
+
+ comparePlans(optimized, correctAnswer)
+ }
+
+ test("normalize map types in window function expressions - idempotence") {
+ val query = testRelation1.window(Seq(sum(a1).as("sum")), Seq(m1),
Seq(m1.asc))
+ val optimized = Optimize.execute(query)
+ val doubleOptimized = Optimize.execute(optimized)
+ val correctAnswer = testRelation1.window(Seq(sum(a1).as("sum")),
+ Seq(SortMapKey(m1)), Seq(m1.asc))
+
+ comparePlans(doubleOptimized, correctAnswer)
+ }
+
+ test("normalize map types in join keys") {
+ val query = testRelation1.join(testRelation2, condition = Some(m1 === m2))
+
+ val optimized = Optimize.execute(query)
+ val joinCond = Some(SortMapKey(m1) === SortMapKey(m2))
+ val correctAnswer = testRelation1.join(testRelation2, condition = joinCond)
+
+ comparePlans(optimized, correctAnswer)
+ }
+
+ test("normalize map types in join keys - idempotence") {
+ val query = testRelation1.join(testRelation2, condition = Some(m1 === m2))
+
+ val optimized = Optimize.execute(query)
+ val doubleOptimized = Optimize.execute(optimized)
+ val joinCond = Some(SortMapKey(m1) === SortMapKey(m2))
+ val correctAnswer = testRelation1.join(testRelation2, condition = joinCond)
+
+ comparePlans(doubleOptimized, correctAnswer)
+ }
+}
+
+
Review comment:
nit: remove unnecessary blank lines.
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