HeartSaVioR commented on code in PR #37893: URL: https://github.com/apache/spark/pull/37893#discussion_r975780795
########## sql/core/src/main/scala/org/apache/spark/sql/execution/python/FlatMapGroupsInPandasWithStateExec.scala: ########## @@ -0,0 +1,214 @@ +/* + * 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 org.apache.spark.TaskContext +import org.apache.spark.api.python.{ChainedPythonFunctions, PythonEvalType} +import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.encoders.{ExpressionEncoder, RowEncoder} +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.plans.logical.{EventTimeTimeout, ProcessingTimeTimeout} +import org.apache.spark.sql.catalyst.plans.physical.Distribution +import org.apache.spark.sql.execution.{GroupedIterator, SparkPlan, UnaryExecNode} +import org.apache.spark.sql.execution.python.PandasGroupUtils.resolveArgOffsets +import org.apache.spark.sql.execution.streaming._ +import org.apache.spark.sql.execution.streaming.GroupStateImpl.NO_TIMESTAMP +import org.apache.spark.sql.execution.streaming.state.FlatMapGroupsWithStateExecHelper.StateData +import org.apache.spark.sql.execution.streaming.state.StateStore +import org.apache.spark.sql.streaming.{GroupStateTimeout, OutputMode} +import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.util.ArrowUtils +import org.apache.spark.util.CompletionIterator + +/** + * Physical operator for executing + * [[org.apache.spark.sql.catalyst.plans.logical.FlatMapGroupsInPandasWithState]] + * + * @param functionExpr function called on each group + * @param groupingAttributes used to group the data + * @param outAttributes used to define the output rows + * @param stateType used to serialize/deserialize state before calling `functionExpr` + * @param stateInfo `StatefulOperatorStateInfo` to identify the state store for a given operator. + * @param stateFormatVersion the version of state format. + * @param outputMode the output mode of `functionExpr` + * @param timeoutConf used to timeout groups that have not received data in a while + * @param batchTimestampMs processing timestamp of the current batch. + * @param eventTimeWatermark event time watermark for the current batch + * @param child logical plan of the underlying data + */ +case class FlatMapGroupsInPandasWithStateExec( + functionExpr: Expression, + groupingAttributes: Seq[Attribute], + outAttributes: Seq[Attribute], + stateType: StructType, + stateInfo: Option[StatefulOperatorStateInfo], + stateFormatVersion: Int, + outputMode: OutputMode, + timeoutConf: GroupStateTimeout, + batchTimestampMs: Option[Long], + eventTimeWatermark: Option[Long], + child: SparkPlan) extends UnaryExecNode with FlatMapGroupsWithStateExecBase { + + // TODO(SPARK-40444): Add the support of initial state. + override protected val initialStateDeserializer: Expression = null + override protected val initialStateGroupAttrs: Seq[Attribute] = null + override protected val initialStateDataAttrs: Seq[Attribute] = null + override protected val initialState: SparkPlan = null + override protected val hasInitialState: Boolean = false + + override protected val stateEncoder: ExpressionEncoder[Any] = + RowEncoder(stateType).resolveAndBind().asInstanceOf[ExpressionEncoder[Any]] + + override def output: Seq[Attribute] = outAttributes + + private val sessionLocalTimeZone = conf.sessionLocalTimeZone + private val pythonRunnerConf = ArrowUtils.getPythonRunnerConfMap(conf) + + private val pythonFunction = functionExpr.asInstanceOf[PythonUDF].func + private val chainedFunc = Seq(ChainedPythonFunctions(Seq(pythonFunction))) + private lazy val (dedupAttributes, argOffsets) = resolveArgOffsets( + groupingAttributes ++ child.output, groupingAttributes) + private lazy val unsafeProj = UnsafeProjection.create(dedupAttributes, child.output) + + override def requiredChildDistribution: Seq[Distribution] = + StatefulOperatorPartitioning.getCompatibleDistribution( + groupingAttributes, getStateInfo, conf) :: Nil + + override def requiredChildOrdering: Seq[Seq[SortOrder]] = Seq( + groupingAttributes.map(SortOrder(_, Ascending))) + + override def shortName: String = "applyInPandasWithState" + + override protected def withNewChildInternal( + newChild: SparkPlan): FlatMapGroupsInPandasWithStateExec = copy(child = newChild) + + override def createInputProcessor( + store: StateStore): InputProcessor = new InputProcessor(store: StateStore) { + + override def processNewData(dataIter: Iterator[InternalRow]): Iterator[InternalRow] = { + val groupedIter = GroupedIterator(dataIter, groupingAttributes, child.output) + val processIter = groupedIter.map { case (keyRow, valueRowIter) => + val keyUnsafeRow = keyRow.asInstanceOf[UnsafeRow] + val stateData = stateManager.getState(store, keyUnsafeRow) + (keyUnsafeRow, stateData, valueRowIter.map(unsafeProj)) + } + + process(processIter, hasTimedOut = false) + } + + override def processNewDataWithInitialState( + childDataIter: Iterator[InternalRow], + initStateIter: Iterator[InternalRow]): Iterator[InternalRow] = { + throw new UnsupportedOperationException("Should not reach here!") + } + + override def processTimedOutState(): Iterator[InternalRow] = { + if (isTimeoutEnabled) { + val timeoutThreshold = timeoutConf match { + case ProcessingTimeTimeout => batchTimestampMs.get + case EventTimeTimeout => eventTimeWatermark.get + case _ => + throw new IllegalStateException( + s"Cannot filter timed out keys for $timeoutConf") + } + val timingOutPairs = stateManager.getAllState(store).filter { state => + state.timeoutTimestamp != NO_TIMESTAMP && state.timeoutTimestamp < timeoutThreshold + } + + val processIter = timingOutPairs.map { stateData => + val joinedKeyRow = unsafeProj( + new JoinedRow( + stateData.keyRow, + new GenericInternalRow(Array.fill(dedupAttributes.length)(null: Any)))) + + (stateData.keyRow, stateData, Iterator.single(joinedKeyRow)) + } + + process(processIter, hasTimedOut = true) + } else Iterator.empty + } + + private def process( + iter: Iterator[(UnsafeRow, StateData, Iterator[InternalRow])], + hasTimedOut: Boolean): Iterator[InternalRow] = { + val runner = new ApplyInPandasWithStatePythonRunner( + chainedFunc, + PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF_WITH_STATE, + Array(argOffsets), + StructType.fromAttributes(dedupAttributes), + sessionLocalTimeZone, + pythonRunnerConf, + stateEncoder.asInstanceOf[ExpressionEncoder[Row]], + groupingAttributes.toStructType, + child.output.toStructType, + stateType) + + val context = TaskContext.get() + + val processIter = iter.map { case (keyRow, stateData, valueIter) => + val groupedState = GroupStateImpl.createForStreaming( + Option(stateData.stateObj).map { r => assert(r.isInstanceOf[Row]); r }, + batchTimestampMs.getOrElse(NO_TIMESTAMP), + eventTimeWatermark.getOrElse(NO_TIMESTAMP), + timeoutConf, + hasTimedOut = hasTimedOut, + watermarkPresent).asInstanceOf[GroupStateImpl[Row]] + (keyRow, groupedState, valueIter) + } + runner.compute(processIter, context.partitionId(), context).flatMap { + case (stateIter, outputIter) => + // When the iterator is consumed, then write changes to state. + // state does not affect each others, hence when to update does not affect to the result. + def onIteratorCompletion: Unit = { + stateIter.foreach { case (keyRow, newGroupState, oldTimeoutTimestamp) => + if (newGroupState.isRemoved && !newGroupState.getTimeoutTimestampMs.isPresent()) { + stateManager.removeState(store, keyRow) + numRemovedStateRows += 1 + } else { + val currentTimeoutTimestamp = newGroupState.getTimeoutTimestampMs + .orElse(NO_TIMESTAMP) + val hasTimeoutChanged = currentTimeoutTimestamp != oldTimeoutTimestamp + val shouldWriteState = newGroupState.isUpdated || newGroupState.isRemoved || + hasTimeoutChanged + + if (shouldWriteState) { Review Comment: > basically if the state was removed but there is still timeout set? Will you keep the user state object around till the timeout fires? I'm not 100% understanding the intention of the original codebase, but it seems so. Here the removal of state is removal of "value object" of the state. We don't allow users to set "null" on value object, hence removal of state is the only way to clear the value object. In the meanwhile, we seem to still allow setting the timeout with state having undefined value object. The status of the state would be the same when you start with new state and only set the timeout without setting the value object. Given we allow this, above case probably has to be allowed as well. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
