HeartSaVioR commented on code in PR #39931: URL: https://github.com/apache/spark/pull/39931#discussion_r1115349019
########## sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/WatermarkPropagator.scala: ########## @@ -0,0 +1,286 @@ +/* + * 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.streaming + +import java.{util => jutil} + +import scala.collection.mutable + +import org.apache.spark.internal.Logging +import org.apache.spark.sql.AnalysisException +import org.apache.spark.sql.execution.SparkPlan +import org.apache.spark.sql.execution.streaming.WatermarkPropagator.DEFAULT_WATERMARK_MS +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.util.Utils + +/** Interface for propagating watermark. */ +trait WatermarkPropagator { + /** + * Request to propagate watermark among operators based on origin watermark value. The result + * should be input watermark per stateful operator, which Spark will request the value by calling + * getInputWatermarkXXX with operator ID. + * + * It is recommended for implementation to cache the result, as Spark can request the propagation + * multiple times with the same batch ID and origin watermark value. + */ + def propagate(batchId: Long, plan: SparkPlan, originWatermark: Long): Unit + + /** Provide the calculated input watermark for late events for given stateful operator. */ + def getInputWatermarkForLateEvents(batchId: Long, stateOpId: Long): Long + + /** Provide the calculated input watermark for eviction for given stateful operator. */ + def getInputWatermarkForEviction(batchId: Long, stateOpId: Long): Long + + /** + * Request to clean up cached result on propagation. Spark will call this method when the given + * batch ID will be likely to be not re-executed. + */ + def purge(batchId: Long): Unit +} + +/** + * Do nothing. This is dummy implementation to help creating a dummy IncrementalExecution instance. + */ +class NoOpWatermarkPropagator extends WatermarkPropagator { + def propagate(batchId: Long, plan: SparkPlan, originWatermark: Long): Unit = {} + def getInputWatermarkForLateEvents(batchId: Long, stateOpId: Long): Long = Long.MinValue + def getInputWatermarkForEviction(batchId: Long, stateOpId: Long): Long = Long.MinValue + def purge(batchId: Long): Unit = {} +} + +/** + * This implementation uses a single global watermark for late events and eviction. + * + * This implementation provides the behavior before Structured Streaming supports multiple stateful + * operators. (prior to SPARK-40925) This is only used for compatibility mode. + */ +class UseSingleWatermarkPropagator extends WatermarkPropagator { + private val batchIdToWatermark: jutil.TreeMap[Long, Long] = new jutil.TreeMap[Long, Long]() + + private def isInitialized(batchId: Long): Boolean = batchIdToWatermark.containsKey(batchId) + + override def propagate(batchId: Long, plan: SparkPlan, originWatermark: Long): Unit = { + if (batchId < 0) { + // no-op + } else if (isInitialized(batchId)) { + val cached = batchIdToWatermark.get(batchId) + assert(cached == originWatermark, + s"Watermark has been changed for the same batch ID! Batch ID: $batchId, " + + s"Value in cache: $cached, value given: $originWatermark") + } else { + batchIdToWatermark.put(batchId, originWatermark) + } + } + + private def getInputWatermark(batchId: Long, stateOpId: Long): Long = { + if (batchId < 0) { + 0 + } else { + assert(isInitialized(batchId), s"Watermark for batch ID $batchId is not yet set!") + batchIdToWatermark.get(batchId) + } + } + + def getInputWatermarkForLateEvents(batchId: Long, stateOpId: Long): Long = + getInputWatermark(batchId, stateOpId) + + def getInputWatermarkForEviction(batchId: Long, stateOpId: Long): Long = + getInputWatermark(batchId, stateOpId) + + override def purge(batchId: Long): Unit = { + val keyIter = batchIdToWatermark.keySet().iterator() + var stopIter = false + while (keyIter.hasNext && !stopIter) { + val currKey = keyIter.next() + if (currKey <= batchId) { + keyIter.remove() + } else { + stopIter = true + } + } + } +} + +/** + * This implementation simulates propagation of watermark among operators. + * + * The simulation algorithm traverses the physical plan tree via post-order (children first) to + * calculate (input watermark, output watermark) for all nodes. + * + * For each node, below logic is applied: + * + * - Input watermark for specific node is decided by `min(input watermarks from all children)`. + * -- Children providing no input watermark (DEFAULT_WATERMARK_MS) are excluded. + * -- If there is no valid input watermark from children, input watermark = DEFAULT_WATERMARK_MS. + * - Output watermark for specific node is decided as following: + * -- watermark nodes: origin watermark value + * This could be individual origin watermark value, but we decide to retain global watermark + * to keep the watermark model be simple. + * -- stateless nodes: same as input watermark + * -- stateful nodes: the return value of `op.produceWatermark(input watermark)`. + * @see [[StateStoreWriter.produceWatermark]] + * + * Note that this implementation will throw an exception if watermark node sees a valid input + * watermark from children, meaning that we do not support re-definition of watermark. + * + * Once the algorithm traverses the physical plan tree, the association between stateful operator + * and input watermark will be constructed. Spark will request the input watermark for specific + * stateful operator, which this implementation will give the value from the association. + */ +class PropagateWatermarkSimulator extends WatermarkPropagator with Logging { + private val batchIdToWatermark: jutil.TreeMap[Long, Long] = new jutil.TreeMap[Long, Long]() + private val inputWatermarks: mutable.Map[Long, Map[Long, Long]] = + mutable.Map[Long, Map[Long, Long]]() + + private def isInitialized(batchId: Long): Boolean = batchIdToWatermark.containsKey(batchId) + + private def getInputWatermarks( + node: SparkPlan, + nodeToOutputWatermark: mutable.Map[Int, Long]): Seq[Long] = { + node.children.map { child => + nodeToOutputWatermark.getOrElse(child.id, { + throw new IllegalStateException( + s"watermark for the node ${child.id} should be registered") + }) + }.filter { case curr => + // This path is to exclude children from watermark calculation + // which don't have watermark information + curr != DEFAULT_WATERMARK_MS + } + } + + private def doSimulate(batchId: Long, plan: SparkPlan, originWatermark: Long): Unit = { + val statefulOperatorIdToNodeId = mutable.HashMap[Long, Int]() + val nodeToOutputWatermark = mutable.HashMap[Int, Long]() + val nextStatefulOperatorToWatermark = mutable.HashMap[Long, Long]() + + // This calculation relies on post-order traversal of the query plan. + plan.transformUp { + case node: EventTimeWatermarkExec => + val inputWatermarks = getInputWatermarks(node, nodeToOutputWatermark) + if (inputWatermarks.nonEmpty) { + throw new AnalysisException("Redefining watermark is disallowed. You can set the " + + s"config '${SQLConf.STATEFUL_OPERATOR_ALLOW_MULTIPLE.key}' to 'false' to restore " + + "the previous behavior. Note that multiple stateful operators will be disallowed.") + } + + nodeToOutputWatermark.put(node.id, originWatermark) + node + + case node: StateStoreWriter => + val stOpId = node.stateInfo.get.operatorId + statefulOperatorIdToNodeId.put(stOpId, node.id) + + val inputWatermarks = getInputWatermarks(node, nodeToOutputWatermark) + val finalInputWatermarkMs = if (inputWatermarks.nonEmpty) { + inputWatermarks.min + } else { + // We can't throw exception here, as we allow stateful operator to process without + // watermark. E.g. streaming aggregation with update/complete mode. + DEFAULT_WATERMARK_MS + } + + val outputWatermarkMs = node.produceWatermark(finalInputWatermarkMs) + nodeToOutputWatermark.put(node.id, outputWatermarkMs) + nextStatefulOperatorToWatermark.put(stOpId, finalInputWatermarkMs) + node + + case node => + // pass-through, but also consider multiple children like the case of union + val inputWatermarks = getInputWatermarks(node, nodeToOutputWatermark) + val finalInputWatermarkMs = if (inputWatermarks.nonEmpty) { + val minCurrInputWatermarkMs = inputWatermarks.min + minCurrInputWatermarkMs + } else { + DEFAULT_WATERMARK_MS + } + + nodeToOutputWatermark.put(node.id, finalInputWatermarkMs) + node + } + + inputWatermarks.put(batchId, nextStatefulOperatorToWatermark.toMap) + batchIdToWatermark.put(batchId, originWatermark) + + logDebug(s"global watermark for batch ID $batchId is set to $originWatermark") + logDebug(s"input watermarks for batch ID $batchId is set to $nextStatefulOperatorToWatermark") + } + + override def propagate(batchId: Long, plan: SparkPlan, originWatermark: Long): Unit = { + if (batchId < 0) { + // no-op + } else if (isInitialized(batchId)) { + val cached = batchIdToWatermark.get(batchId) + assert(cached == originWatermark, + s"Watermark has been changed for the same batch ID! Batch ID: $batchId, " + + s"Value in cache: $cached, value given: $originWatermark") + } else { + logDebug(s"watermark for batch ID $batchId is received as $originWatermark, " + + s"call site: ${Utils.getCallSite().longForm}") + doSimulate(batchId, plan, originWatermark) Review Comment: Planning of the streaming query (IncrementalExecution), and checking for no-data batch execution (MicrobatchExecution). So it's not only called once - we allow this method to be called multiple times. We just leverage cached result if it had been calculated before. We assume that physical plan of the query won't change across microbatches, and we guarantee we give the same origin watermark value for the same batch ID. (Otherwise assertion will crash the query.) -- 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. 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