HeartSaVioR commented on code in PR #45376:
URL: https://github.com/apache/spark/pull/45376#discussion_r1591752313
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/IncrementalExecution.scala:
##########
@@ -347,6 +347,28 @@ class IncrementalExecution(
eventTimeWatermarkForEviction =
inputWatermarkForEviction(m.stateInfo.get)
)
+ // UpdateEventTimeColumnExec is used to tag the eventTime column, and
validate
+ // emitted rows adhere to watermark in the output of transformWithState.
+ // Hence, this node shares the same watermark value as
TransformWithStateExec.
+ // However, given that UpdateEventTimeColumnExec does not store any
state, it
+ // does not have any StateInfo. We simply use the StateInfo of
transformWithStateExec
+ // to propagate watermark to both UpdateEventTimeColumnExec and
transformWithStateExec.
+ case UpdateEventTimeColumnExec(eventTime, delay, None,
+ SerializeFromObjectExec(serializer,
+ t: TransformWithStateExec)) if t.stateInfo.isDefined =>
+
+ val stateInfo = t.stateInfo.get
+ val eventTimeWatermarkForLateEvents =
inputWatermarkForLateEvents(stateInfo)
+ val eventTimeWatermarkForEviction =
inputWatermarkForLateEvents(stateInfo)
+
+ UpdateEventTimeColumnExec(eventTime, delay,
eventTimeWatermarkForEviction,
Review Comment:
eventTimeWatermarkForLateEvents?
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala:
##########
@@ -442,6 +442,16 @@ abstract class SparkStrategies extends
QueryPlanner[SparkPlan] {
case EventTimeWatermark(columnName, delay, child) =>
EventTimeWatermarkExec(columnName, delay, planLater(child)) :: Nil
+ case UpdateEventTimeWatermarkColumn(columnName, delay, child) =>
+ // we expect watermarkDelay to be resolved before physical planning.
+ if (delay.isEmpty) {
+ // This is a sanity check. We should not reach here as delay is
updated during
+ // query plan resolution in
[[ResolveUpdateEventTimeWatermarkColumn]] Analyzer rule.
+ throw SparkException.internalError(
+ "You hit a query analyzer bug. Please report your query to Spark
user mailing list.")
Review Comment:
This error message is basically provided for all internal errors. Could we
please add one-liner high-level context here if we have? I guess it'd be just
OK with stack trace, so 2 cents.
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/EventTimeWatermarkExec.scala:
##########
@@ -107,25 +109,70 @@ case class EventTimeWatermarkExec(
}
// Update the metadata on the eventTime column to include the desired delay.
- override val output: Seq[Attribute] = child.output.map { a =>
- if (a semanticEquals eventTime) {
- val updatedMetadata = new MetadataBuilder()
- .withMetadata(a.metadata)
- .putLong(EventTimeWatermark.delayKey, delayMs)
- .build()
- a.withMetadata(updatedMetadata)
- } else if (a.metadata.contains(EventTimeWatermark.delayKey)) {
- // Remove existing watermark
- val updatedMetadata = new MetadataBuilder()
- .withMetadata(a.metadata)
- .remove(EventTimeWatermark.delayKey)
- .build()
- a.withMetadata(updatedMetadata)
- } else {
- a
- }
+ override val output: Seq[Attribute] = {
+ val delayMs = EventTimeWatermark.getDelayMs(delay)
+ updateEventTimeColumn(child.output, delayMs, eventTime)
}
override protected def withNewChildInternal(newChild: SparkPlan):
EventTimeWatermarkExec =
copy(child = newChild)
}
+
+/**
+ * Updates the event time column to [[eventTime]] in the child output.
+ * Any watermark calculations performed after this node will use the
+ * updated eventTimeColumn.
+ *
+ * This node also ensures that output emitted by the child node adheres
+ * to watermark. If the child node emits rows which are older than global
+ * watermark, the node will throw an query execution error and fail the user
+ * query.
+ */
+case class UpdateEventTimeColumnExec(
+ eventTime: Attribute,
+ delay: CalendarInterval,
+ eventTimeWatermarkForEviction: Option[Long],
+ child: SparkPlan) extends UnaryExecNode {
+
+ override protected def doExecute(): RDD[InternalRow] = {
+ child.execute().mapPartitions[InternalRow] { dataIterator =>
+ val watermarkExpression = WatermarkSupport.watermarkExpression(
+ Some(eventTime), eventTimeWatermarkForEviction)
+
+ if (watermarkExpression.isEmpty) {
+ // watermark should always be defined in this node.
+ throw QueryExecutionErrors.cannotGetEventTimeWatermarkError()
+ }
+
+ val predicate = Predicate.create(watermarkExpression.get, child.output)
+ new Iterator[InternalRow] {
+ override def hasNext: Boolean = dataIterator.hasNext
+
+ override def next(): InternalRow = {
+ val row = dataIterator.next()
+ if (predicate.eval(row)) {
+ // child node emitted a row which is older than current watermark
+ // this is not allowed
+ val boundEventTimeExpression =
bindReference[Expression](eventTime, child.output)
+ val eventTimeProjection =
UnsafeProjection.create(boundEventTimeExpression)
+ val rowEventTime = eventTimeProjection(row)
+ throw QueryExecutionErrors.emittedRowsAreOlderThanWatermark(
+ eventTimeWatermarkForEviction.get, rowEventTime.getLong(0))
+ }
+ row
+ }
+ }
+ }
+ }
+
+ override def outputOrdering: Seq[SortOrder] = child.outputOrdering
Review Comment:
`override def outputPartitioning: Partitioning = child.outputPartitioning`
as well
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/IncrementalExecution.scala:
##########
@@ -347,6 +347,28 @@ class IncrementalExecution(
eventTimeWatermarkForEviction =
inputWatermarkForEviction(m.stateInfo.get)
)
+ // UpdateEventTimeColumnExec is used to tag the eventTime column, and
validate
+ // emitted rows adhere to watermark in the output of transformWithState.
+ // Hence, this node shares the same watermark value as
TransformWithStateExec.
+ // However, given that UpdateEventTimeColumnExec does not store any
state, it
+ // does not have any StateInfo. We simply use the StateInfo of
transformWithStateExec
+ // to propagate watermark to both UpdateEventTimeColumnExec and
transformWithStateExec.
+ case UpdateEventTimeColumnExec(eventTime, delay, None,
+ SerializeFromObjectExec(serializer,
+ t: TransformWithStateExec)) if t.stateInfo.isDefined =>
+
+ val stateInfo = t.stateInfo.get
+ val eventTimeWatermarkForLateEvents =
inputWatermarkForLateEvents(stateInfo)
+ val eventTimeWatermarkForEviction =
inputWatermarkForLateEvents(stateInfo)
Review Comment:
inputWatermarkForEviction?
##########
sql/core/src/test/scala/org/apache/spark/sql/streaming/TransformWithStateChainingSuite.scala:
##########
@@ -0,0 +1,365 @@
+/*
+ * 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.streaming
+
+import java.sql.Timestamp
+import java.time.{Instant, LocalDateTime, ZoneId}
+
+import org.apache.spark.{SparkRuntimeException, SparkThrowable}
+import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql.catalyst.ExtendedAnalysisException
+import org.apache.spark.sql.execution.streaming.{MemoryStream, StreamExecution}
+import org.apache.spark.sql.execution.streaming.state.RocksDBStateStoreProvider
+import org.apache.spark.sql.functions.window
+import org.apache.spark.sql.internal.SQLConf
+
+case class InputEventRow(
+ key: String,
+ eventTime: Timestamp,
+ event: String)
+
+case class OutputRow(
+ key: String,
+ outputEventTime: Timestamp,
+ count: Int)
+
+class TestStatefulProcessor
+ extends StatefulProcessor[String, InputEventRow, OutputRow] {
+ override def init(outputMode: OutputMode, timeMode: TimeMode): Unit = {}
+
+ override def handleInputRows(
+ key: String,
+ inputRows: Iterator[InputEventRow],
+ timerValues: TimerValues,
+ expiredTimerInfo: ExpiredTimerInfo): Iterator[OutputRow] = {
+ if (inputRows.isEmpty) {
+ Iterator.empty
+ } else {
+ var minEventTime = inputRows.next().eventTime
+ var count = 1
+ inputRows.foreach { row =>
+ if (row.eventTime.before(minEventTime)) {
+ minEventTime = row.eventTime
+ }
+ count += 1
+ }
+ Iterator.single(OutputRow(key, minEventTime, count))
+ }
+ }
+}
+
+class InputCountStatefulProcessor[T]
+ extends StatefulProcessor[String, T, Int] {
+ override def init(outputMode: OutputMode, timeMode: TimeMode): Unit = {}
+
+ override def handleInputRows(
+ key: String,
+ inputRows: Iterator[T],
+ timerValues: TimerValues,
+ expiredTimerInfo: ExpiredTimerInfo): Iterator[Int] = {
+ Iterator.single(inputRows.size)
+ }
+}
+
+/**
+ * Emits output row with timestamp older than current watermark for batchId >
0.
+ */
+class StatefulProcessorEmittingRowsOlderThanWatermark
+ extends StatefulProcessor[String, InputEventRow, OutputRow] {
+ override def init(outputMode: OutputMode, timeMode: TimeMode): Unit = {}
+
+ override def handleInputRows(
+ key: String,
+ inputRows: Iterator[InputEventRow],
+ timerValues: TimerValues,
+ expiredTimerInfo: ExpiredTimerInfo): Iterator[OutputRow] = {
+ if (timerValues.getCurrentWatermarkInMs() > 0) {
+ Iterator.single(
+ OutputRow(
+ key,
+
Timestamp.from(Instant.ofEpochMilli(timerValues.getCurrentWatermarkInMs() - 1)),
Review Comment:
Hmm... this is coupled with UpdateEventTimeWatermarkColumn. If we change the
node to compare against watermark for late event, this wouldn't break as
expected.
##########
sql/core/src/test/scala/org/apache/spark/sql/streaming/TransformWithStateChainingSuite.scala:
##########
@@ -0,0 +1,365 @@
+/*
+ * 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.streaming
+
+import java.sql.Timestamp
+import java.time.{Instant, LocalDateTime, ZoneId}
+
+import org.apache.spark.{SparkRuntimeException, SparkThrowable}
+import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql.catalyst.ExtendedAnalysisException
+import org.apache.spark.sql.execution.streaming.{MemoryStream, StreamExecution}
+import org.apache.spark.sql.execution.streaming.state.RocksDBStateStoreProvider
+import org.apache.spark.sql.functions.window
+import org.apache.spark.sql.internal.SQLConf
+
+case class InputEventRow(
+ key: String,
+ eventTime: Timestamp,
+ event: String)
+
+case class OutputRow(
+ key: String,
+ outputEventTime: Timestamp,
+ count: Int)
+
+class TestStatefulProcessor
+ extends StatefulProcessor[String, InputEventRow, OutputRow] {
+ override def init(outputMode: OutputMode, timeMode: TimeMode): Unit = {}
+
+ override def handleInputRows(
+ key: String,
+ inputRows: Iterator[InputEventRow],
+ timerValues: TimerValues,
+ expiredTimerInfo: ExpiredTimerInfo): Iterator[OutputRow] = {
+ if (inputRows.isEmpty) {
+ Iterator.empty
+ } else {
+ var minEventTime = inputRows.next().eventTime
+ var count = 1
+ inputRows.foreach { row =>
+ if (row.eventTime.before(minEventTime)) {
+ minEventTime = row.eventTime
+ }
+ count += 1
+ }
+ Iterator.single(OutputRow(key, minEventTime, count))
+ }
+ }
+}
+
+class InputCountStatefulProcessor[T]
+ extends StatefulProcessor[String, T, Int] {
+ override def init(outputMode: OutputMode, timeMode: TimeMode): Unit = {}
+
+ override def handleInputRows(
+ key: String,
+ inputRows: Iterator[T],
+ timerValues: TimerValues,
+ expiredTimerInfo: ExpiredTimerInfo): Iterator[Int] = {
+ Iterator.single(inputRows.size)
+ }
+}
+
+/**
+ * Emits output row with timestamp older than current watermark for batchId >
0.
+ */
+class StatefulProcessorEmittingRowsOlderThanWatermark
+ extends StatefulProcessor[String, InputEventRow, OutputRow] {
+ override def init(outputMode: OutputMode, timeMode: TimeMode): Unit = {}
+
+ override def handleInputRows(
+ key: String,
+ inputRows: Iterator[InputEventRow],
+ timerValues: TimerValues,
+ expiredTimerInfo: ExpiredTimerInfo): Iterator[OutputRow] = {
+ if (timerValues.getCurrentWatermarkInMs() > 0) {
+ Iterator.single(
+ OutputRow(
+ key,
+
Timestamp.from(Instant.ofEpochMilli(timerValues.getCurrentWatermarkInMs() - 1)),
+ inputRows.size))
+ } else {
+ var minEventTime = inputRows.next().eventTime
+ var count = 1
+ inputRows.foreach { row =>
+ if (row.eventTime.before(minEventTime)) {
+ minEventTime = row.eventTime
+ }
+ count += 1
+ }
+ Iterator.single(OutputRow(key, minEventTime, count))
+ }
+ }
+}
+
+case class Window(
+ start: Timestamp,
+ end: Timestamp)
+
+case class AggEventRow(
+ window: Window,
+ count: Long)
+
+class TransformWithStateChainingSuite extends StreamTest {
+ import testImplicits._
+
+ test("watermark is propagated correctly for next stateful operator" +
+ " after transformWithState") {
+ withSQLConf(SQLConf.STATE_STORE_PROVIDER_CLASS.key ->
+ classOf[RocksDBStateStoreProvider].getName) {
+ val inputData = MemoryStream[InputEventRow]
+
+ val result = inputData.toDS()
+ .withWatermark("eventTime", "1 minute")
+ .groupByKey(x => x.key)
+ .transformWithState[OutputRow](
+ new TestStatefulProcessor(),
+ "outputEventTime",
+ OutputMode.Append())
+ .groupBy(window($"outputEventTime", "1 minute"))
+ .count()
+ .as[AggEventRow]
+
+ testStream(result, OutputMode.Append())(
+ AddData(inputData, InputEventRow("k1", timestamp("2024-01-01
00:00:00"), "e1")),
+ // watermark should be 1 minute behind `2024-01-01 00:00:00`, nothing
is
+ // emitted as all records have timestamp > epoch
+ CheckNewAnswer(),
+ Execute("assertWatermarkEquals") { q =>
+ assertWatermarkEquals(q, timestamp("2023-12-31 23:59:00"))
+ },
+ AddData(inputData, InputEventRow("k1", timestamp("2024-02-01
00:00:00"), "e1")),
+ // global watermark should now be 1 minute behind `2024-02-01
00:00:00`.
+ CheckNewAnswer(AggEventRow(
+ Window(timestamp("2024-01-01 00:00:00"), timestamp("2024-01-01
00:01:00")), 1)
+ ),
+ Execute("assertWatermarkEquals") { q =>
+ assertWatermarkEquals(q, timestamp("2024-01-31 23:59:00"))
+ },
+ AddData(inputData, InputEventRow("k1", timestamp("2024-02-02
00:00:00"), "e1")),
+ CheckNewAnswer(AggEventRow(
+ Window(timestamp("2024-02-01 00:00:00"), timestamp("2024-02-01
00:01:00")), 1)
+ )
+ )
+ }
+ }
+
+ test("passing eventTime column to transformWithState fails if" +
+ " no watermark is defined") {
+ withSQLConf(SQLConf.STATE_STORE_PROVIDER_CLASS.key ->
+ classOf[RocksDBStateStoreProvider].getName) {
+ val inputData = MemoryStream[InputEventRow]
+
+ val ex = intercept[AnalysisException] {
+ inputData.toDS()
+ .groupByKey(x => x.key)
+ .transformWithState[OutputRow](
+ new TestStatefulProcessor(),
+ "outputEventTime",
+ OutputMode.Append())
+ }
+
+ checkError(ex, "CANNOT_ASSIGN_EVENT_TIME_COLUMN_WITHOUT_WATERMARK")
+ }
+ }
+
+ test("missing eventTime column to transformWithState fails the query if" +
+ " another stateful operator is added") {
+ withSQLConf(SQLConf.STATE_STORE_PROVIDER_CLASS.key ->
+ classOf[RocksDBStateStoreProvider].getName) {
+ val inputData = MemoryStream[InputEventRow]
+
+ val result = inputData.toDS()
+ .withWatermark("eventTime", "1 minute")
+ .groupByKey(x => x.key)
+ .transformWithState[OutputRow](
+ new TestStatefulProcessor(),
+ TimeMode.None(),
+ OutputMode.Append())
+ .groupBy(window($"outputEventTime", "1 minute"))
+ .count()
+
+ val ex = intercept[ExtendedAnalysisException] {
+ testStream(result, OutputMode.Append())(
+ StartStream()
+ )
+ }
+ assert(ex.getMessage.contains("there are streaming aggregations on" +
+ " streaming DataFrames/DataSets without watermark"))
+ }
+ }
+
+ test("chaining multiple transformWithState operators") {
+ withSQLConf(SQLConf.STATE_STORE_PROVIDER_CLASS.key ->
+ classOf[RocksDBStateStoreProvider].getName) {
+ val inputData = MemoryStream[InputEventRow]
+
+ val result = inputData.toDS()
+ .withWatermark("eventTime", "1 minute")
+ .groupByKey(x => x.key)
+ .transformWithState[OutputRow](
+ new TestStatefulProcessor(),
+ "outputEventTime",
+ OutputMode.Append())
+ .groupByKey(x => x.key)
+ .transformWithState(
+ new InputCountStatefulProcessor[OutputRow](),
+ TimeMode.None(),
+ OutputMode.Append()
+ )
+
+ testStream(result, OutputMode.Append())(
+ AddData(inputData, InputEventRow("k1", timestamp("2024-01-01
00:00:00"), "e1")),
+ CheckNewAnswer(1),
+ Execute("assertWatermarkEquals") { q =>
+ assertWatermarkEquals(q, timestamp("2023-12-31 23:59:00"))
+ },
+ AddData(inputData, InputEventRow("k1", timestamp("2024-02-01
00:00:00"), "e1")),
+ CheckNewAnswer(1),
+ Execute("assertWatermarkEquals") { q =>
+ assertWatermarkEquals(q, timestamp("2024-01-31 23:59:00"))
+ },
+ AddData(inputData, InputEventRow("k1", timestamp("2024-02-02
00:00:00"), "e1")),
+ CheckNewAnswer(1)
+ )
+ }
+ }
+
+ test("dropDuplicateWithWatermark after transformWithState operator" +
+ " fails if watermark column is not provided") {
+ withSQLConf(SQLConf.STATE_STORE_PROVIDER_CLASS.key ->
+ classOf[RocksDBStateStoreProvider].getName) {
+ val inputData = MemoryStream[InputEventRow]
+ val result = inputData.toDS()
+ .withWatermark("eventTime", "1 minute")
+ .groupByKey(x => x.key)
+ .transformWithState[OutputRow](
+ new TestStatefulProcessor(),
+ TimeMode.None(),
+ OutputMode.Append())
+ .dropDuplicatesWithinWatermark()
+
+ val ex = intercept[ExtendedAnalysisException] {
+ testStream(result, OutputMode.Append())(
+ StartStream()
+ )
+ }
+ assert(ex.getMessage.contains("dropDuplicatesWithinWatermark is not
supported on" +
+ " streaming DataFrames/DataSets without watermark"))
+ }
+ }
+
+ test("dropDuplicateWithWatermark after transformWithState operator") {
+ withSQLConf(SQLConf.STATE_STORE_PROVIDER_CLASS.key ->
+ classOf[RocksDBStateStoreProvider].getName) {
+ val inputData = MemoryStream[InputEventRow]
+ val result = inputData.toDS()
+ .withWatermark("eventTime", "1 minute")
+ .groupByKey(x => x.key)
+ .transformWithState[OutputRow](
+ new TestStatefulProcessor(),
+ "outputEventTime",
+ OutputMode.Append())
+ .dropDuplicatesWithinWatermark()
+
+ testStream(result, OutputMode.Append())(
+ AddData(inputData, InputEventRow("k1", timestamp("2024-02-01
00:00:00"), "e1"),
+ InputEventRow("k1", timestamp("2024-02-01 00:00:00"), "e1")),
+ CheckNewAnswer(OutputRow("k1", timestamp("2024-02-01 00:00:00"), 2)),
+ Execute("assertWatermarkEquals") { q =>
+ assertWatermarkEquals(q, timestamp("2024-01-31 23:59:00"))
+ }
+ )
+ }
+ }
+
+ test("query fails if the output dataset does not contain specified
eventTimeColumn") {
+ withSQLConf(SQLConf.STATE_STORE_PROVIDER_CLASS.key ->
+ classOf[RocksDBStateStoreProvider].getName) {
+ val inputData = MemoryStream[InputEventRow]
+
+ val ex = intercept[ExtendedAnalysisException] {
+ val result = inputData.toDS()
+ .withWatermark("eventTime", "1 minute")
+ .groupByKey(x => x.key)
+ .transformWithState[OutputRow](
+ new TestStatefulProcessor(),
+ "missingEventTimeColumn",
+ OutputMode.Append())
+
+ testStream(result, OutputMode.Append())(
+ StartStream()
+ )
+ }
+
+ checkError(ex, "UNRESOLVED_COLUMN.WITH_SUGGESTION",
+ parameters = Map(
+ "objectName" -> "`missingEventTimeColumn`",
+ "proposal" -> "`outputEventTime`, `count`, `key`"))
+ }
+ }
+
+ test("query fails if the output dataset contains rows older than current
watermark") {
+ withSQLConf(SQLConf.STATE_STORE_PROVIDER_CLASS.key ->
+ classOf[RocksDBStateStoreProvider].getName) {
+ val inputData = MemoryStream[InputEventRow]
+ val result = inputData.toDS()
+ .withWatermark("eventTime", "1 minute")
+ .groupByKey(x => x.key)
+ .transformWithState[OutputRow](
+ new StatefulProcessorEmittingRowsOlderThanWatermark(),
+ "outputEventTime",
+ OutputMode.Append())
+
+ testStream(result, OutputMode.Append())(
+ AddData(inputData, InputEventRow("k1", timestamp("2024-02-01
00:00:00"), "e1")),
+ CheckNewAnswer(OutputRow("k1", timestamp("2024-02-01 00:00:00"), 1)),
+ // this batch would fail now
+ AddData(inputData, InputEventRow("k1", timestamp("2024-02-02
00:00:00"), "e1")),
+ ExpectFailure[SparkRuntimeException] { ex =>
+ checkError(ex.asInstanceOf[SparkThrowable],
+ "EMITTING_ROWS_OLDER_THAN_WATERMARK_NOT_ALLOWED",
+ parameters = Map("currentWatermark" -> "1706774340000",
+ "emittedRowEventTime" -> "1706774339999000"))
+ }
+ )
+ }
+ }
Review Comment:
Could we add a test case where we have source and watermark definition to be
in temporal view and read from temporal view and add transformWithState?
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