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Posted to reviews@spark.apache.org by "jerrypeng (via GitHub)" <gi...@apache.org> on 2023/02/24 06:53:18 UTC

[GitHub] [spark] jerrypeng commented on a diff in pull request #39931: [SPARK-42376][SS] Introduce watermark propagation among operators

jerrypeng commented on code in PR #39931:
URL: https://github.com/apache/spark/pull/39931#discussion_r1116567008


##########
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/WatermarkPropagator.scala:
##########
@@ -0,0 +1,302 @@
+/*
+ * 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. 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.produceWatermark(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, Long]] =
+    mutable.Map[Long, Map[Long, 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, 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.produceOutputWatermark(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)
+    }
+  }
+
+  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!")
+      // In current Spark's logic, event time watermark cannot go down to negative. So even there is
+      // no input watermark for operator, the final input watermark for operator should be 0L.
+      inputWatermarks(batchId).get(stateOpId) match {
+        case Some(wm) => Math.max(0L, wm)
+        case None => throw new IllegalStateException(s"Watermark for batch ID $batchId and " +
+          s"stateOpId $stateOpId is not yet set!")
+      }
+    }
+  }
+
+  override def getInputWatermarkForLateEvents(batchId: Long, stateOpId: Long): Long =
+    getInputWatermark(batchId - 1, stateOpId)
+
+  override 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()
+        inputWatermarks.remove(currKey)
+      } else {
+        stopIter = true
+      }
+    }
+  }
+}
+
+object WatermarkPropagator {
+  val DEFAULT_WATERMARK_MS = -1L

Review Comment:
   Can this be an optional instead of negative?  The logic to filter these operators with no watermarks may be better.  Could there be a situation in the future where a user would like to set -1 as the output watermark for a stateful UDF to signify no advancement of watermarks?



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