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Posted to reviews@spark.apache.org by "zsxwing (via GitHub)" <gi...@apache.org> on 2023/03/03 09:09:14 UTC
[GitHub] [spark] zsxwing commented on a diff in pull request #39931: [SPARK-42376][SS] Introduce watermark propagation among operators
zsxwing commented on code in PR #39931:
URL: https://github.com/apache/spark/pull/39931#discussion_r1124167428
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/IncrementalExecution.scala:
##########
@@ -216,63 +222,140 @@ class IncrementalExecution(
keys,
child,
Some(nextStatefulOperationStateInfo),
- eventTimeWatermarkForLateEvents = Some(eventTimeWatermarkForLateEvents),
- eventTimeWatermarkForEviction = Some(eventTimeWatermarkForEviction))
+ eventTimeWatermarkForLateEvents = None,
+ eventTimeWatermarkForEviction = None)
case m: FlatMapGroupsWithStateExec =>
// We set this to true only for the first batch of the streaming query.
val hasInitialState = (currentBatchId == 0L && m.hasInitialState)
m.copy(
stateInfo = Some(nextStatefulOperationStateInfo),
batchTimestampMs = Some(offsetSeqMetadata.batchTimestampMs),
- eventTimeWatermarkForLateEvents = Some(eventTimeWatermarkForLateEvents),
- eventTimeWatermarkForEviction = Some(eventTimeWatermarkForEviction),
+ eventTimeWatermarkForLateEvents = None,
+ eventTimeWatermarkForEviction = None,
hasInitialState = hasInitialState
)
case m: FlatMapGroupsInPandasWithStateExec =>
m.copy(
stateInfo = Some(nextStatefulOperationStateInfo),
batchTimestampMs = Some(offsetSeqMetadata.batchTimestampMs),
- eventTimeWatermarkForLateEvents = Some(eventTimeWatermarkForLateEvents),
- eventTimeWatermarkForEviction = Some(eventTimeWatermarkForEviction)
+ eventTimeWatermarkForLateEvents = None,
+ eventTimeWatermarkForEviction = None
)
case j: StreamingSymmetricHashJoinExec =>
j.copy(
stateInfo = Some(nextStatefulOperationStateInfo),
- eventTimeWatermarkForLateEvents = Some(eventTimeWatermarkForLateEvents),
- eventTimeWatermarkForEviction = Some(eventTimeWatermarkForEviction),
- stateWatermarkPredicates =
- StreamingSymmetricHashJoinHelper.getStateWatermarkPredicates(
- j.left.output, j.right.output, j.leftKeys, j.rightKeys, j.condition.full,
- Some(eventTimeWatermarkForEviction)))
+ eventTimeWatermarkForLateEvents = None,
+ eventTimeWatermarkForEviction = None
+ )
case l: StreamingGlobalLimitExec =>
l.copy(
stateInfo = Some(nextStatefulOperationStateInfo),
outputMode = Some(outputMode))
+ }
+ }
- case StreamingLocalLimitExec(limit, child) if hasNoStatefulOp(child) =>
- // Optimize limit execution by replacing StreamingLocalLimitExec (consumes the iterator
- // completely) to LocalLimitExec (does not consume the iterator) when the child plan has
- // no stateful operator (i.e., consuming the iterator is not needed).
- LocalLimitExec(limit, child)
+ val watermarkPropagationRule = new Rule[SparkPlan] {
+ private def simulateWatermarkPropagation(plan: SparkPlan): Unit = {
+ val watermarkForPrevBatch = prevOffsetSeqMetadata.map(_.batchWatermarkMs).getOrElse(0L)
+ val watermarkForCurrBatch = offsetSeqMetadata.batchWatermarkMs
+
+ // This is to simulate watermark propagation for late events.
+ watermarkPropagator.propagate(currentBatchId - 1, plan, watermarkForPrevBatch)
+ // This is to simulate watermark propagation for eviction.
+ watermarkPropagator.propagate(currentBatchId, plan, watermarkForCurrBatch)
+ }
+
+ private def inputWatermarkForLateEvents(stateInfo: StatefulOperatorStateInfo): Option[Long] = {
+ Some(watermarkPropagator.getInputWatermarkForLateEvents(currentBatchId,
+ stateInfo.operatorId))
+ }
+
+ private def inputWatermarkForEviction(stateInfo: StatefulOperatorStateInfo): Option[Long] = {
+ Some(watermarkPropagator.getInputWatermarkForEviction(currentBatchId, stateInfo.operatorId))
+ }
+
+ override def apply(plan: SparkPlan): SparkPlan = {
+ simulateWatermarkPropagation(plan)
+ plan transform {
+ case s: StateStoreSaveExec if s.stateInfo.isDefined =>
+ s.copy(
+ eventTimeWatermarkForLateEvents = inputWatermarkForLateEvents(s.stateInfo.get),
+ eventTimeWatermarkForEviction = inputWatermarkForEviction(s.stateInfo.get)
+ )
+
+ case s: SessionWindowStateStoreSaveExec if s.stateInfo.isDefined =>
+ s.copy(
+ eventTimeWatermarkForLateEvents = inputWatermarkForLateEvents(s.stateInfo.get),
+ eventTimeWatermarkForEviction = inputWatermarkForEviction(s.stateInfo.get)
+ )
+
+ case s: SessionWindowStateStoreRestoreExec if s.stateInfo.isDefined =>
+ s.copy(
+ eventTimeWatermarkForLateEvents = inputWatermarkForLateEvents(s.stateInfo.get),
+ eventTimeWatermarkForEviction = inputWatermarkForEviction(s.stateInfo.get)
+ )
+
+ case s: StreamingDeduplicateExec if s.stateInfo.isDefined =>
+ s.copy(
+ eventTimeWatermarkForLateEvents = inputWatermarkForLateEvents(s.stateInfo.get),
+ eventTimeWatermarkForEviction = inputWatermarkForEviction(s.stateInfo.get)
+ )
+
+ case m: FlatMapGroupsWithStateExec if m.stateInfo.isDefined =>
+ m.copy(
+ eventTimeWatermarkForLateEvents = inputWatermarkForLateEvents(m.stateInfo.get),
+ eventTimeWatermarkForEviction = inputWatermarkForEviction(m.stateInfo.get)
+ )
+
+ case m: FlatMapGroupsInPandasWithStateExec if m.stateInfo.isDefined =>
+ m.copy(
+ eventTimeWatermarkForLateEvents = inputWatermarkForLateEvents(m.stateInfo.get),
+ eventTimeWatermarkForEviction = inputWatermarkForEviction(m.stateInfo.get)
+ )
+
+ case j: StreamingSymmetricHashJoinExec =>
+ val iwLateEvents = inputWatermarkForLateEvents(j.stateInfo.get)
+ val iwEviction = inputWatermarkForEviction(j.stateInfo.get)
+ j.copy(
+ eventTimeWatermarkForLateEvents = iwLateEvents,
+ eventTimeWatermarkForEviction = iwEviction,
+ stateWatermarkPredicates =
+ StreamingSymmetricHashJoinHelper.getStateWatermarkPredicates(
+ j.left.output, j.right.output, j.leftKeys, j.rightKeys, j.condition.full,
+ iwEviction, !allowMultipleStatefulOperators)
+ )
+ }
}
}
- override def preparations: Seq[Rule[SparkPlan]] = state +: super.preparations
+ override def preparations: Seq[Rule[SparkPlan]] = Seq(
+ shufflePartitionsRule,
Review Comment:
Is splitting rules just to improve readability? This would traverse the plan tree for 3 more passes. This is probably okay as people usually don't write complicated streaming queries.
Batch queries are using a cheap API [transformUpWithPruning](https://github.com/apache/spark/blob/6874fd1b897680263ffbb3d06f716179aca8e4c566/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala#L557) to traverse tree. Would be great to keep this in mind and consider doing this in future if we need to add more rules.
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/WatermarkPropagator.scala:
##########
@@ -0,0 +1,301 @@
+/*
+ * 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.internal.SQLConf
+import org.apache.spark.util.Utils
+
+/**
+ * Interface for propagating watermark. The implementation is not required to be thread-safe,
+ * as all methods are expected to be called from the query execution thread.
+ * (The guarantee may change on further improvements on Structured Streaming - update
+ * implementations if we change the guarantee.)
+ */
+sealed 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.
+ */
+object 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 {
+ // We use treemap to sort the key (batchID) and evict old batch IDs efficiently.
+ 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.produceOutputWatermark(input watermark)`.
+ *
+ * @see [[StateStoreWriter.produceOutputWatermark]]
+ *
+ * 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 {
+ // We use treemap to sort the key (batchID) and evict old batch IDs efficiently.
+ private val batchIdToWatermark: jutil.TreeMap[Long, Long] = new jutil.TreeMap[Long, Long]()
+
+ // contains the association for batchId -> (stateful operator ID -> input watermark)
+ private val inputWatermarks: mutable.Map[Long, Map[Long, Option[Long]]] =
+ mutable.Map[Long, Map[Long, Option[Long]]]()
+
+ private def isInitialized(batchId: Long): Boolean = batchIdToWatermark.containsKey(batchId)
+
+ /**
+ * Retrieve the available input watermarks for specific node in the plan. Every child will
+ * produce an output watermark, which we capture as input watermark. If the child provides
+ * default watermark value (no watermark info), it is excluded.
+ */
+ private def getInputWatermarks(
+ node: SparkPlan,
+ nodeToOutputWatermark: mutable.Map[Int, Option[Long]]): Seq[Long] = {
+ node.children.flatMap { child =>
+ nodeToOutputWatermark.getOrElse(child.id, {
+ throw new IllegalStateException(
+ s"watermark for the node ${child.id} should be registered")
+ })
+ // Since we use flatMap here, this will exclude children from watermark calculation
+ // which don't have watermark information.
+ }
+ }
+
+ private def doSimulate(batchId: Long, plan: SparkPlan, originWatermark: Long): Unit = {
Review Comment:
Can we skip this heavy work if the query doesn't define a watermark? Most streaming queries are in this case, and don't need to run this in each micro batch.
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MicroBatchExecution.scala:
##########
@@ -212,6 +212,8 @@ class MicroBatchExecution(
logInfo(s"Query $prettyIdString was stopped")
}
+ private val watermarkPropagator = WatermarkPropagator(sparkSession.sessionState.conf)
Review Comment:
How do we recover `WatermarkPropagator` for a query restart?
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/WatermarkPropagator.scala:
##########
@@ -0,0 +1,301 @@
+/*
+ * 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.internal.SQLConf
+import org.apache.spark.util.Utils
+
+/**
+ * Interface for propagating watermark. The implementation is not required to be thread-safe,
+ * as all methods are expected to be called from the query execution thread.
+ * (The guarantee may change on further improvements on Structured Streaming - update
+ * implementations if we change the guarantee.)
+ */
+sealed 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.
+ */
+object 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 {
+ // We use treemap to sort the key (batchID) and evict old batch IDs efficiently.
+ 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.produceOutputWatermark(input watermark)`.
+ *
+ * @see [[StateStoreWriter.produceOutputWatermark]]
+ *
+ * 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 {
+ // We use treemap to sort the key (batchID) and evict old batch IDs efficiently.
+ private val batchIdToWatermark: jutil.TreeMap[Long, Long] = new jutil.TreeMap[Long, Long]()
+
+ // contains the association for batchId -> (stateful operator ID -> input watermark)
+ private val inputWatermarks: mutable.Map[Long, Map[Long, Option[Long]]] =
+ mutable.Map[Long, Map[Long, Option[Long]]]()
+
+ private def isInitialized(batchId: Long): Boolean = batchIdToWatermark.containsKey(batchId)
+
+ /**
+ * Retrieve the available input watermarks for specific node in the plan. Every child will
+ * produce an output watermark, which we capture as input watermark. If the child provides
+ * default watermark value (no watermark info), it is excluded.
+ */
+ private def getInputWatermarks(
+ node: SparkPlan,
+ nodeToOutputWatermark: mutable.Map[Int, Option[Long]]): Seq[Long] = {
+ node.children.flatMap { child =>
+ nodeToOutputWatermark.getOrElse(child.id, {
+ throw new IllegalStateException(
+ s"watermark for the node ${child.id} should be registered")
+ })
+ // Since we use flatMap here, this will exclude children from watermark calculation
+ // which don't have watermark information.
+ }
+ }
+
+ private def doSimulate(batchId: Long, plan: SparkPlan, originWatermark: Long): Unit = {
+ val statefulOperatorIdToNodeId = mutable.HashMap[Long, Int]()
+ val nodeToOutputWatermark = mutable.HashMap[Int, Option[Long]]()
Review Comment:
If we are doing a batch-streaming join, do we still calculate watermarks for the batch side even if they are always empty?
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