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Posted to reviews@spark.apache.org by "Hisoka-X (via GitHub)" <gi...@apache.org> on 2023/07/17 06:03:31 UTC
[GitHub] [spark] Hisoka-X commented on a diff in pull request #41939: [SPARK-44341][SQL][PYTHON] Define the computing logic through PartitionEvaluator API and use it in WindowExec and WindowInPandasExec
Hisoka-X commented on code in PR #41939:
URL: https://github.com/apache/spark/pull/41939#discussion_r1264906463
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/python/WindowInPandasEvaluatorFactory.scala:
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@@ -0,0 +1,369 @@
+/*
+ * 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.execution.python
+
+import java.io.File
+
+import scala.collection.JavaConverters._
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.spark.{JobArtifactSet, PartitionEvaluator, PartitionEvaluatorFactory, SparkEnv, TaskContext}
+import org.apache.spark.api.python.{ChainedPythonFunctions, PythonEvalType}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, BoundReference, EmptyRow, Expression, JoinedRow, NamedExpression, PythonFuncExpression, PythonUDAF, SortOrder, SpecificInternalRow, UnsafeProjection, UnsafeRow, WindowExpression}
+import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression
+import org.apache.spark.sql.errors.QueryExecutionErrors
+import org.apache.spark.sql.execution.ExternalAppendOnlyUnsafeRowArray
+import org.apache.spark.sql.execution.metric.SQLMetric
+import org.apache.spark.sql.execution.window.{SlidingWindowFunctionFrame, UnboundedFollowingWindowFunctionFrame, UnboundedPrecedingWindowFunctionFrame, UnboundedWindowFunctionFrame, WindowEvaluatorFactoryBase, WindowFunctionFrame}
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types.{DataType, IntegerType, StructField, StructType}
+import org.apache.spark.sql.util.ArrowUtils
+import org.apache.spark.util.Utils
+
+class WindowInPandasEvaluatorFactory(
+ val windowExpression: Seq[NamedExpression],
+ val partitionSpec: Seq[Expression],
+ val orderSpec: Seq[SortOrder],
+ val childOutput: Seq[Attribute],
+ val spillSize: SQLMetric,
+ pythonMetrics: Map[String, SQLMetric])
+ extends PartitionEvaluatorFactory[InternalRow, InternalRow] with WindowEvaluatorFactoryBase {
+ val conf: SQLConf = SQLConf.get
+
+ /**
+ * Helper functions and data structures for window bounds
+ *
+ * It contains:
+ * (1) Total number of window bound indices in the python input row
+ * (2) Function from frame index to its lower bound column index in the python input row
+ * (3) Function from frame index to its upper bound column index in the python input row
+ * (4) Seq from frame index to its window bound type
+ */
+ private type WindowBoundHelpers = (Int, Int => Int, Int => Int, Seq[WindowBoundType])
+
+ /**
+ * Enum for window bound types. Used only inside this class.
+ */
+ private sealed case class WindowBoundType(value: String)
+
+ private object UnboundedWindow extends WindowBoundType("unbounded")
+
+ private object BoundedWindow extends WindowBoundType("bounded")
+
+ private val windowBoundTypeConf = "pandas_window_bound_types"
+
+ private def collectFunctions(
+ udf: PythonFuncExpression): (ChainedPythonFunctions, Seq[Expression]) = {
+ udf.children match {
+ case Seq(u: PythonFuncExpression) =>
+ val (chained, children) = collectFunctions(u)
+ (ChainedPythonFunctions(chained.funcs ++ Seq(udf.func)), children)
+ case children =>
+ // There should not be any other UDFs, or the children can't be evaluated directly.
+ assert(children.forall(!_.exists(_.isInstanceOf[PythonFuncExpression])))
+ (ChainedPythonFunctions(Seq(udf.func)), udf.children)
+ }
+ }
+
+ // Helper functions
+ /**
+ * See [[WindowBoundHelpers]] for details.
+ */
+ private def computeWindowBoundHelpers(
+ factories: Seq[InternalRow => WindowFunctionFrame]): WindowBoundHelpers = {
+ val functionFrames = factories.map(_ (EmptyRow))
+
+ val windowBoundTypes = functionFrames.map {
+ case _: UnboundedWindowFunctionFrame => UnboundedWindow
+ case _: UnboundedFollowingWindowFunctionFrame |
+ _: SlidingWindowFunctionFrame |
+ _: UnboundedPrecedingWindowFunctionFrame => BoundedWindow
+ // It should be impossible to get other types of window function frame here
+ case frame => throw QueryExecutionErrors.unexpectedWindowFunctionFrameError(frame.toString)
+ }
+
+ val requiredIndices = functionFrames.map {
+ case _: UnboundedWindowFunctionFrame => 0
+ case _ => 2
+ }
+
+ val upperBoundIndices = requiredIndices.scan(0)(_ + _).tail
+
+ val boundIndices = requiredIndices.zip(upperBoundIndices).map { case (num, upperBoundIndex) =>
+ if (num == 0) {
+ // Sentinel values for unbounded window
+ (-1, -1)
+ } else {
+ (upperBoundIndex - 2, upperBoundIndex - 1)
+ }
+ }
+
+ def lowerBoundIndex(frameIndex: Int) = boundIndices(frameIndex)._1
+
+ def upperBoundIndex(frameIndex: Int) = boundIndices(frameIndex)._2
+
+ (requiredIndices.sum, lowerBoundIndex, upperBoundIndex, windowBoundTypes)
+ }
+
+ // Unwrap the expressions and factories from the map.
+ private val expressionsWithFrameIndex =
Review Comment:
I haven't come across a factory that needs to define member variables (except for parameters)😂
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/python/WindowInPandasEvaluatorFactory.scala:
##########
@@ -0,0 +1,369 @@
+/*
+ * 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.execution.python
+
+import java.io.File
+
+import scala.collection.JavaConverters._
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.spark.{JobArtifactSet, PartitionEvaluator, PartitionEvaluatorFactory, SparkEnv, TaskContext}
+import org.apache.spark.api.python.{ChainedPythonFunctions, PythonEvalType}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, BoundReference, EmptyRow, Expression, JoinedRow, NamedExpression, PythonFuncExpression, PythonUDAF, SortOrder, SpecificInternalRow, UnsafeProjection, UnsafeRow, WindowExpression}
+import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression
+import org.apache.spark.sql.errors.QueryExecutionErrors
+import org.apache.spark.sql.execution.ExternalAppendOnlyUnsafeRowArray
+import org.apache.spark.sql.execution.metric.SQLMetric
+import org.apache.spark.sql.execution.window.{SlidingWindowFunctionFrame, UnboundedFollowingWindowFunctionFrame, UnboundedPrecedingWindowFunctionFrame, UnboundedWindowFunctionFrame, WindowEvaluatorFactoryBase, WindowFunctionFrame}
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types.{DataType, IntegerType, StructField, StructType}
+import org.apache.spark.sql.util.ArrowUtils
+import org.apache.spark.util.Utils
+
+class WindowInPandasEvaluatorFactory(
+ val windowExpression: Seq[NamedExpression],
+ val partitionSpec: Seq[Expression],
+ val orderSpec: Seq[SortOrder],
+ val childOutput: Seq[Attribute],
+ val spillSize: SQLMetric,
+ pythonMetrics: Map[String, SQLMetric])
+ extends PartitionEvaluatorFactory[InternalRow, InternalRow] with WindowEvaluatorFactoryBase {
+ val conf: SQLConf = SQLConf.get
+
+ /**
+ * Helper functions and data structures for window bounds
+ *
+ * It contains:
+ * (1) Total number of window bound indices in the python input row
+ * (2) Function from frame index to its lower bound column index in the python input row
+ * (3) Function from frame index to its upper bound column index in the python input row
+ * (4) Seq from frame index to its window bound type
+ */
+ private type WindowBoundHelpers = (Int, Int => Int, Int => Int, Seq[WindowBoundType])
+
+ /**
+ * Enum for window bound types. Used only inside this class.
+ */
+ private sealed case class WindowBoundType(value: String)
+
+ private object UnboundedWindow extends WindowBoundType("unbounded")
+
+ private object BoundedWindow extends WindowBoundType("bounded")
+
+ private val windowBoundTypeConf = "pandas_window_bound_types"
+
+ private def collectFunctions(
+ udf: PythonFuncExpression): (ChainedPythonFunctions, Seq[Expression]) = {
+ udf.children match {
+ case Seq(u: PythonFuncExpression) =>
+ val (chained, children) = collectFunctions(u)
+ (ChainedPythonFunctions(chained.funcs ++ Seq(udf.func)), children)
+ case children =>
+ // There should not be any other UDFs, or the children can't be evaluated directly.
+ assert(children.forall(!_.exists(_.isInstanceOf[PythonFuncExpression])))
+ (ChainedPythonFunctions(Seq(udf.func)), udf.children)
+ }
+ }
+
+ // Helper functions
+ /**
+ * See [[WindowBoundHelpers]] for details.
+ */
+ private def computeWindowBoundHelpers(
+ factories: Seq[InternalRow => WindowFunctionFrame]): WindowBoundHelpers = {
+ val functionFrames = factories.map(_ (EmptyRow))
+
+ val windowBoundTypes = functionFrames.map {
+ case _: UnboundedWindowFunctionFrame => UnboundedWindow
+ case _: UnboundedFollowingWindowFunctionFrame |
+ _: SlidingWindowFunctionFrame |
+ _: UnboundedPrecedingWindowFunctionFrame => BoundedWindow
+ // It should be impossible to get other types of window function frame here
+ case frame => throw QueryExecutionErrors.unexpectedWindowFunctionFrameError(frame.toString)
+ }
+
+ val requiredIndices = functionFrames.map {
+ case _: UnboundedWindowFunctionFrame => 0
+ case _ => 2
+ }
+
+ val upperBoundIndices = requiredIndices.scan(0)(_ + _).tail
+
+ val boundIndices = requiredIndices.zip(upperBoundIndices).map { case (num, upperBoundIndex) =>
+ if (num == 0) {
+ // Sentinel values for unbounded window
+ (-1, -1)
+ } else {
+ (upperBoundIndex - 2, upperBoundIndex - 1)
+ }
+ }
+
+ def lowerBoundIndex(frameIndex: Int) = boundIndices(frameIndex)._1
+
+ def upperBoundIndex(frameIndex: Int) = boundIndices(frameIndex)._2
+
+ (requiredIndices.sum, lowerBoundIndex, upperBoundIndex, windowBoundTypes)
+ }
+
+ // Unwrap the expressions and factories from the map.
+ private val expressionsWithFrameIndex =
Review Comment:
I haven't come across a factory that needs to define member variables (except for parameters)😂
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