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[SPARK-34819][SQL] MapType supports comparable semantics #32552
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192 changes: 192 additions & 0 deletions
192
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/NormalizeMaps.scala
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,192 @@ | ||
| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.spark.sql.catalyst.optimizer | ||
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| import scala.math.Ordering | ||
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| import org.apache.spark.sql.catalyst.InternalRow | ||
| import org.apache.spark.sql.catalyst.expressions._ | ||
| import org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext | ||
| import org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator._ | ||
| import org.apache.spark.sql.catalyst.expressions.codegen.ExprCode | ||
| 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.{ArrayBasedMapData, ArrayData, GenericArrayData, MapData, TypeUtils} | ||
| import org.apache.spark.sql.types._ | ||
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| /** | ||
| * When comparing two maps, we have to make sure two maps having the same key value pairs but | ||
| * with different key ordering are equal (e.g., Map('a' -> 1, 'b' -> 2) should equal to | ||
| * Map('b' -> 2, 'a' -> 1). To make sure the assumption holds, | ||
| * this rule inserts a [[SortMapKeys]] expression to sort map entries by keys. | ||
| * | ||
| * NOTE: this rule must be executed at the end of the optimizer because it may create | ||
| * new joins (the subquery rewrite) and new join conditions (the join reorder). | ||
| */ | ||
| object NormalizeMaps extends Rule[LogicalPlan] { | ||
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| def apply(plan: LogicalPlan): LogicalPlan = plan.transformAllExpressions { | ||
| // The analyzer guarantees left and right types are the same, so | ||
| // we only need to check a type in one side. | ||
| case cmp @ BinaryComparison(left, right) if needNormalize(left) => | ||
| cmp.withNewChildren(SortMapKeys(left) :: SortMapKeys(right) :: Nil) | ||
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| case In(value, list) if needNormalize(value) => | ||
| In(SortMapKeys(value), list.map(SortMapKeys)) | ||
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| case in @ InSet(value, list) if needNormalize(value) => | ||
| val newHset = list.map(c => SortMapKeys(Literal(c, in.child.dataType)).eval()) | ||
| InSet(SortMapKeys(value), newHset) | ||
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| case sort: SortOrder if needNormalize(sort.child) => | ||
| sort.copy(child = SortMapKeys(sort.child)) | ||
| }.transform { | ||
| case w: Window if w.partitionSpec.exists(p => needNormalize(p)) => | ||
| w.copy(partitionSpec = w.partitionSpec.map(normalize)) | ||
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| // TODO: `NormalizeMaps` has the same restriction with `NormalizeFloatingNumbers`; | ||
| // ideally Aggregate should also be handled here, but its grouping expressions are | ||
| // mixed in its aggregate expressions. It's unreliable to change the grouping expressions | ||
| // here. For now we normalize grouping expressions in `AggUtils` during planning. | ||
| } | ||
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| private def needNormalize(expr: Expression): Boolean = expr match { | ||
| case SortMapKeys(_) => false | ||
| case _ => needNormalize(expr.dataType) | ||
| } | ||
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| private def needNormalize(dt: DataType): Boolean = dt match { | ||
| case StructType(fields) => fields.exists(f => needNormalize(f.dataType)) | ||
| case ArrayType(et, _) => needNormalize(et) | ||
| case _: MapType => true | ||
| case _ => false | ||
| } | ||
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| private[sql] def normalize(expr: Expression): Expression = expr match { | ||
| case _ if !needNormalize(expr) => expr | ||
| case _ => SortMapKeys(expr) | ||
| } | ||
| } | ||
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| /** | ||
| * This expression sorts all maps in an expression's result. This expression enables the use of | ||
| * maps in comparisons and equality operations. | ||
| */ | ||
| case class SortMapKeys(child: Expression) extends UnaryExpression with ExpectsInputTypes { | ||
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| override def inputTypes: Seq[AbstractDataType] = | ||
| Seq(TypeCollection(ArrayType, MapType, StructType)) | ||
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| override def dataType: DataType = child.dataType | ||
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| override protected def withNewChildInternal(newChild: Expression): Expression = { | ||
| copy(child = newChild) | ||
| } | ||
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| private def createFuncToSortRecursively(dt: DataType): Any => Any = dt match { | ||
| case m @ MapType(keyType, valueType, _) => | ||
| val sf = createFuncToSortRecursively(valueType) | ||
| val keyOrdering = new Ordering[(Any, Any)] { | ||
| val ordering = TypeUtils.getInterpretedOrdering(keyType) | ||
| override def compare(x: (Any, Any), y: (Any, Any)): Int = ordering.compare(x._1, y._1) | ||
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| } | ||
| (data: Any) => { | ||
| val input = data.asInstanceOf[MapData] | ||
| val length = input.numElements() | ||
| val keys = input.keyArray() | ||
| val values = input.valueArray() | ||
| val buffer = Array.ofDim[(Any, Any)](length) | ||
| var i = 0 | ||
| while (i < length) { | ||
| // Map keys cannot contain map types (See `TypeUtils.checkForMapKeyType`), | ||
| // so we recursively sort values only. | ||
| val k = keys.get(i, m.keyType) | ||
| val v = if (!values.isNullAt(i)) { | ||
| sf(values.get(i, m.valueType)) | ||
| } else { | ||
| null | ||
| } | ||
| buffer(i) = k -> v | ||
| i += 1 | ||
| } | ||
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| java.util.Arrays.sort(buffer, keyOrdering) | ||
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| ArrayBasedMapData(buffer.toIterator, length, identity, identity) | ||
| } | ||
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| case ArrayType(dt, _) => | ||
| val sf = createFuncToSortRecursively(dt) | ||
| (data: Any) => { | ||
| val input = data.asInstanceOf[ArrayData] | ||
| val length = input.numElements() | ||
| val output = Array.ofDim[Any](length) | ||
| var i = 0 | ||
| while (i < length) { | ||
| if (!input.isNullAt(i)) { | ||
| output(i) = sf(input.get(i, dt)) | ||
| } else { | ||
| output(i) = null | ||
| } | ||
| i += 1 | ||
| } | ||
| new GenericArrayData(output) | ||
| } | ||
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| case StructType(fields) => | ||
| val fs = fields.map { field => | ||
| val sf = createFuncToSortRecursively(field.dataType) | ||
| (input: InternalRow, i: Int) => { | ||
| sf(input.get(i, field.dataType)) | ||
| } | ||
| } | ||
| val length = fields.length | ||
| (data: Any) => { | ||
| val input = data.asInstanceOf[InternalRow] | ||
| val output = Array.ofDim[Any](length) | ||
| var i = 0 | ||
| while (i < length) { | ||
| if (!input.isNullAt(i)) { | ||
| output(i) = fs(i)(input, i) | ||
| } else { | ||
| output(i) = null | ||
| } | ||
| i += 1 | ||
| } | ||
| new GenericInternalRow(output) | ||
| } | ||
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| case _ => | ||
| identity | ||
| } | ||
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| @transient private[this] lazy val sortFunc = { | ||
| createFuncToSortRecursively(dataType) | ||
| } | ||
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| override def nullSafeEval(input: Any): Any = sortFunc(input) | ||
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| override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = { | ||
| // TODO: we should code generate this | ||
| val tf = ctx.addReferenceObj("sortFunc", sortFunc, classOf[Any => Any].getCanonicalName) | ||
| nullSafeCodeGen(ctx, ev, eval => { | ||
| s"${ev.value} = (${javaType(dataType)})$tf.apply($eval);" | ||
| }) | ||
| } | ||
| } |
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This part is the same with the
UnsafeArrayDataone.