rangadi commented on code in PR #39931:
URL: https://github.com/apache/spark/pull/39931#discussion_r1114957687
##########
sql/core/src/test/scala/org/apache/spark/sql/streaming/EventTimeWatermarkSuite.scala:
##########
@@ -548,17 +549,131 @@ class EventTimeWatermarkSuite extends StreamTest with
BeforeAndAfter with Matche
assert(e.getMessage contains "should not be negative.")
}
- test("the new watermark should override the old one") {
- val df = MemoryStream[(Long, Long)].toDF()
+ private def buildTestQueryForOverridingWatermark(): (MemoryStream[(Long,
Long)], DataFrame) = {
+ val input = MemoryStream[(Long, Long)]
+ val df = input.toDF()
.withColumn("first", timestamp_seconds($"_1"))
.withColumn("second", timestamp_seconds($"_2"))
.withWatermark("first", "1 minute")
+ .select("*")
.withWatermark("second", "2 minutes")
+ .groupBy(window($"second", "1 minute"))
+ .count()
- val eventTimeColumns = df.logicalPlan.output
- .filter(_.metadata.contains(EventTimeWatermark.delayKey))
- assert(eventTimeColumns.size === 1)
- assert(eventTimeColumns(0).name === "second")
+ (input, df)
+ }
+
+ test("overriding watermark should not be allowed by default") {
+ val (input, df) = buildTestQueryForOverridingWatermark()
+ testStream(df)(
+ AddData(input, (100L, 200L)),
+ ExpectFailure[AnalysisException](assertFailure = exc => {
+ assert(exc.getMessage.contains("Redefining watermark is disallowed."))
+
assert(exc.getMessage.contains(SQLConf.STATEFUL_OPERATOR_ALLOW_MULTIPLE.key))
+ })
+ )
+ }
+
+ test("overriding watermark should not fail in compatibility mode") {
+ withSQLConf(SQLConf.STATEFUL_OPERATOR_ALLOW_MULTIPLE.key -> "false") {
+ val (input, df) = buildTestQueryForOverridingWatermark()
+ testStream(df)(
+ AddData(input, (100L, 200L)),
+ CheckAnswer(),
+ Execute { query =>
+ val lastExecution = query.lastExecution
+ val aggSaveOperator = lastExecution.executedPlan.collect {
+ case j: StateStoreSaveExec => j
+ }.head
+
+ // - watermark from first definition = 100 - 60 = 40
+ // - watermark from second definition = 200 - 120 = 80
+ // - global watermark = min(40, 60) = 40
+ //
+ // As we see the result, even though we override the watermark
definition, the old
+ // definition of watermark still plays to calculate global watermark.
+ //
+ // This is conceptually the right behavior. For operators after the
first watermark
+ // definition, the column named "first" is considered as event time
column, and for
+ // operators after the second watermark definition, the column named
"second" is
+ // considered as event time column. The correct "single" value of
watermark satisfying
+ // all operators should be lower bound of both columns "first" and
"second".
+ //
+ // That said, this easily leads to incorrect definition - e.g.
re-define watermark
+ // against the output of streaming aggregation for append mode. The
global watermark
+ // cannot advance. This is the reason we don't allow re-define
watermark in new behavior.
+ val expectedWatermarkMs = 40 * 1000
+
+ assert(aggSaveOperator.eventTimeWatermarkForLateEvents ===
Some(expectedWatermarkMs))
+ assert(aggSaveOperator.eventTimeWatermarkForEviction ===
Some(expectedWatermarkMs))
+
+ val eventTimeCols = aggSaveOperator.keyExpressions.filter(
+ _.metadata.contains(EventTimeWatermark.delayKey))
+ assert(eventTimeCols.size === 1)
+ assert(eventTimeCols.head.name === "window")
+ // 2 minutes delay threshold
+
assert(eventTimeCols.head.metadata.getLong(EventTimeWatermark.delayKey) === 120
* 1000)
+ }
+ )
+ }
+ }
+
+ private def buildTestQueryForMultiEventTimeColumns()
+ : (MemoryStream[(String, Long)], MemoryStream[(String, Long)], DataFrame)
= {
+ val input1 = MemoryStream[(String, Long)]
+ val input2 = MemoryStream[(String, Long)]
+ val df1 = input1.toDF()
+ .selectExpr("_1 AS id1", "timestamp_seconds(_2) AS ts1")
+ .withWatermark("ts1", "1 minute")
+
+ val df2 = input2.toDF()
+ .selectExpr("_1 AS id2", "timestamp_seconds(_2) AS ts2")
+ .withWatermark("ts2", "2 minutes")
+
+ val joined = df1.join(df2, expr("id1 = id2 AND ts1 = ts2 + INTERVAL 10
SECONDS"), "inner")
+ .selectExpr("id1", "ts1", "ts2")
+ // the output of join contains both ts1 and ts2
+ val dedup = joined.dropDuplicates()
+ .selectExpr("id1", "CAST(ts1 AS LONG) AS ts1", "CAST(ts2 AS LONG) AS
ts2")
+
+ (input1, input2, dedup)
+ }
+
+ test("multiple event time columns in an input DataFrame for stateful
operator is " +
Review Comment:
How do we make this query work? Looks like correct query.
##########
sql/core/src/test/scala/org/apache/spark/sql/streaming/MultiStatefulOperatorsSuite.scala:
##########
@@ -463,6 +437,442 @@ class MultiStatefulOperatorsSuite
)
}
+ test("stream-stream time interval left outer join -> aggregation, append
mode") {
+ val input1 = MemoryStream[(String, Timestamp)]
+ val input2 = MemoryStream[(String, Timestamp)]
+
+ val s1 = input1.toDF()
+ .selectExpr("_1 AS id1", "_2 AS timestamp1")
Review Comment:
Minor: I think `.toDF("id1", "timestamp1")` is equivalent to this.
##########
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(
Review Comment:
Yes. Just to help the readers.
##########
sql/core/src/test/scala/org/apache/spark/sql/streaming/MultiStatefulOperatorsSuite.scala:
##########
@@ -463,6 +437,442 @@ class MultiStatefulOperatorsSuite
)
}
+ test("stream-stream time interval left outer join -> aggregation, append
mode") {
+ val input1 = MemoryStream[(String, Timestamp)]
+ val input2 = MemoryStream[(String, Timestamp)]
+
+ val s1 = input1.toDF()
+ .selectExpr("_1 AS id1", "_2 AS timestamp1")
+ .withWatermark("timestamp1", "0 seconds")
+ .as("s1")
+
+ val s2 = input2.toDF()
+ .selectExpr("_1 AS id2", "_2 AS timestamp2")
+ .withWatermark("timestamp2", "0 seconds")
+ .as("s2")
+
+ val s3 = s1.join(s2, expr("s1.id1 = s2.id2 AND (s1.timestamp1 BETWEEN " +
+ "s2.timestamp2 - INTERVAL 1 hour AND s2.timestamp2 + INTERVAL 1 hour)"),
"leftOuter")
+
+ val agg = s3.groupBy(window($"timestamp1", "10 minutes"))
Review Comment:
How is this different from `join() followed deDup()` test in
EventTimeWatermarkSuite.scala? The latter was not allowed.
##########
sql/core/src/test/scala/org/apache/spark/sql/streaming/MultiStatefulOperatorsSuite.scala:
##########
@@ -463,6 +437,442 @@ class MultiStatefulOperatorsSuite
)
}
+ test("stream-stream time interval left outer join -> aggregation, append
mode") {
+ val input1 = MemoryStream[(String, Timestamp)]
+ val input2 = MemoryStream[(String, Timestamp)]
+
+ val s1 = input1.toDF()
+ .selectExpr("_1 AS id1", "_2 AS timestamp1")
+ .withWatermark("timestamp1", "0 seconds")
+ .as("s1")
+
+ val s2 = input2.toDF()
+ .selectExpr("_1 AS id2", "_2 AS timestamp2")
+ .withWatermark("timestamp2", "0 seconds")
+ .as("s2")
+
+ val s3 = s1.join(s2, expr("s1.id1 = s2.id2 AND (s1.timestamp1 BETWEEN " +
+ "s2.timestamp2 - INTERVAL 1 hour AND s2.timestamp2 + INTERVAL 1 hour)"),
"leftOuter")
+
+ val agg = s3.groupBy(window($"timestamp1", "10 minutes"))
+ .agg(count("*").as("cnt"))
+ .selectExpr("CAST(window.start AS STRING) AS window_start",
+ "CAST(window.end AS STRING) AS window_end", "cnt")
+
+ // for ease of verification, we change the session timezone to UTC
+ withSQLConf(SQLConf.SESSION_LOCAL_TIMEZONE.key -> "UTC") {
+ testStream(agg)(
+ MultiAddData(
+ (input1, Seq(
+ ("1", Timestamp.valueOf("2023-01-01 01:00:10")),
+ ("2", Timestamp.valueOf("2023-01-01 01:00:30")))
+ ),
+ (input2, Seq(
+ ("1", Timestamp.valueOf("2023-01-01 01:00:20"))))
+ ),
+
+ // < data batch >
+ // global watermark (0, 0)
+ // op1 (join)
+ // -- IW (0, 0)
+ // -- OW 0
+ // -- left state
+ // ("1", "2023-01-01 01:00:10", matched=true)
+ // ("1", "2023-01-01 01:00:30", matched=false)
+ // -- right state
+ // ("1", "2023-01-01 01:00:20")
+ // -- result
+ // ("1", "2023-01-01 01:00:10", "1", "2023-01-01 01:00:20")
+ // op2 (aggregation)
+ // -- IW (0, 0)
+ // -- OW 0
+ // -- state row
+ // ("2023-01-01 01:00:00", "2023-01-01 01:10:00", 1)
+ // -- result
+ // None
+
+ // -- watermark calculation
+ // watermark in left input: 2023-01-01 01:00:30
+ // watermark in right input: 2023-01-01 01:00:20
+ // origin watermark: 2023-01-01 01:00:20
+
+ // < no-data batch >
+ // global watermark (0, 2023-01-01 01:00:20)
+ // op1 (join)
+ // -- IW (0, 2023-01-01 01:00:20)
+ // -- OW 2023-01-01 00:00:19.999999
+ // -- left state
+ // ("1", "2023-01-01 01:00:10", matched=true)
+ // ("1", "2023-01-01 01:00:30", matched=false)
+ // -- right state
+ // ("1", "2023-01-01 01:00:20")
+ // -- result
+ // None
+ // op2 (aggregation)
+ // -- IW (0, 2023-01-01 00:00:19.999999)
+ // -- OW 2023-01-01 00:00:19.999999
+ // -- state row
+ // ("2023-01-01 01:00:00", "2023-01-01 01:10:00", 1)
+ // -- result
+ // None
+ CheckAnswer(),
+
+ Execute { query =>
+ val lastExecution = query.lastExecution
+ val joinOperator = lastExecution.executedPlan.collect {
+ case j: StreamingSymmetricHashJoinExec => j
+ }.head
+ val aggSaveOperator = lastExecution.executedPlan.collect {
+ case j: StateStoreSaveExec => j
+ }.head
+
+ assert(joinOperator.eventTimeWatermarkForLateEvents === Some(0))
+ assert(joinOperator.eventTimeWatermarkForEviction ===
+ Some(Timestamp.valueOf("2023-01-01 01:00:20").getTime))
+
+ assert(aggSaveOperator.eventTimeWatermarkForLateEvents === Some(0))
+ assert(aggSaveOperator.eventTimeWatermarkForEviction ===
+ Some(Timestamp.valueOf("2023-01-01 00:00:20").getTime - 1))
+ },
+
+ MultiAddData(
+ (input1, Seq(("5", Timestamp.valueOf("2023-01-01 01:15:00")))),
+ (input2, Seq(("6", Timestamp.valueOf("2023-01-01 01:15:00"))))
+ ),
+
+ // < data batch >
Review Comment:
Thanks for these detailed comments in the tests.
##########
sql/core/src/test/scala/org/apache/spark/sql/streaming/MultiStatefulOperatorsSuite.scala:
##########
@@ -463,6 +437,442 @@ class MultiStatefulOperatorsSuite
)
}
+ test("stream-stream time interval left outer join -> aggregation, append
mode") {
Review Comment:
This is a detailed test with great comments inline. It will nice to give a
short overview of this test does at the top here.
##########
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:
[For understanding]: Where is called? want to see if we can figure out how
it is only called only once.
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