Github user zsxwing commented on a diff in the pull request:
https://github.com/apache/spark/pull/19984#discussion_r158399908
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousExecution.scala
---
@@ -0,0 +1,343 @@
+/*
+ * 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.continuous
+
+import java.util.concurrent.TimeUnit
+
+import scala.collection.JavaConverters._
+import scala.collection.mutable.{ArrayBuffer, Map => MutableMap}
+
+import org.apache.spark.SparkEnv
+import org.apache.spark.sql.{AnalysisException, SparkSession}
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap,
CurrentBatchTimestamp, CurrentDate, CurrentTimestamp}
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.execution.SQLExecution
+import
org.apache.spark.sql.execution.datasources.v2.{DataSourceV2Relation,
WriteToDataSourceV2}
+import
org.apache.spark.sql.execution.streaming.{ContinuousExecutionRelation,
StreamingRelationV2, _}
+import org.apache.spark.sql.sources.v2.{ContinuousReadSupport,
ContinuousWriteSupport, DataSourceV2Options}
+import org.apache.spark.sql.sources.v2.reader.{ContinuousReader, Offset,
PartitionOffset}
+import org.apache.spark.sql.sources.v2.writer.ContinuousWriter
+import org.apache.spark.sql.streaming.{OutputMode, ProcessingTime, Trigger}
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.util.{Clock, Utils}
+
+class ContinuousExecution(
+ sparkSession: SparkSession,
+ name: String,
+ checkpointRoot: String,
+ analyzedPlan: LogicalPlan,
+ sink: ContinuousWriteSupport,
+ trigger: Trigger,
+ triggerClock: Clock,
+ outputMode: OutputMode,
+ extraOptions: Map[String, String],
+ deleteCheckpointOnStop: Boolean)
+ extends StreamExecution(
+ sparkSession, name, checkpointRoot, analyzedPlan, sink,
+ trigger, triggerClock, outputMode, deleteCheckpointOnStop) {
+
+ @volatile protected var continuousSources: Seq[ContinuousReader] =
Seq.empty
+ override protected def sources: Seq[BaseStreamingSource] =
continuousSources
+
+ override lazy val logicalPlan: LogicalPlan = {
+ assert(queryExecutionThread eq Thread.currentThread,
+ "logicalPlan must be initialized in StreamExecutionThread " +
+ s"but the current thread was ${Thread.currentThread}")
+ var nextSourceId = 0L
+ val toExecutionRelationMap = MutableMap[StreamingRelationV2,
ContinuousExecutionRelation]()
+ analyzedPlan.transform {
+ case r @ StreamingRelationV2(
+ source: ContinuousReadSupport, _, extraReaderOptions, output, _)
=>
+ toExecutionRelationMap.getOrElseUpdate(r, {
+ ContinuousExecutionRelation(source, extraReaderOptions,
output)(sparkSession)
+ })
+ case StreamingRelationV2(_, sourceName, _, _, _) =>
+ throw new AnalysisException(
+ s"Data source $sourceName does not support continuous
processing.")
+ }
+ }
+
+ private val triggerExecutor = trigger match {
+ case ContinuousTrigger(t) => ProcessingTimeExecutor(ProcessingTime(t),
triggerClock)
+ case _ => throw new IllegalStateException(s"Unsupported type of
trigger: $trigger")
+ }
+
+ override protected def runActivatedStream(sparkSessionForStream:
SparkSession): Unit = {
+ do {
+ try {
+ runContinuous(sparkSessionForStream)
+ } catch {
+ case _: InterruptedException if state.get().equals(RECONFIGURING)
=>
+ // swallow exception and run again
+ state.set(ACTIVE)
+ }
+ } while (state.get() == ACTIVE)
+ }
+
+ /**
+ * Populate the start offsets to start the execution at the current
offsets stored in the sink
+ * (i.e. avoid reprocessing data that we have already processed). This
function must be called
+ * before any processing occurs and will populate the following fields:
+ * - currentBatchId
+ * - committedOffsets
+ * The basic structure of this method is as follows:
+ *
+ * Identify (from the commit log) the latest epoch that has committed
+ * IF last epoch exists THEN
+ * Get end offsets for the epoch
+ * Set those offsets as the current commit progress
+ * Set the next epoch ID as the last + 1
+ * Return the end offsets of the last epoch as start for the next one
+ * DONE
+ * ELSE
+ * Start a new query log
+ * DONE
+ */
+ private def getStartOffsets(sparkSessionToRunBatches: SparkSession):
OffsetSeq = {
+ // Note that this will need a slight modification for exactly once. If
ending offsets were
+ // reported but not committed for any epochs, we must replay exactly
to those offsets.
+ // For at least once, we can just ignore those reports and risk
duplicates.
+ commitLog.getLatest() match {
+ case Some((latestEpochId, _)) =>
+ val nextOffsets = offsetLog.get(latestEpochId).getOrElse {
+ throw new IllegalStateException(
+ s"Batch $latestEpochId was committed without end epoch
offsets!")
+ }
+ committedOffsets = nextOffsets.toStreamProgress(sources)
+
+ // Forcibly align commit and offset logs by slicing off any
spurious offset logs from
+ // a previous run. We can't allow commits to an epoch that a
previous run reached but
+ // this run has not.
+ offsetLog.purgeAfter(latestEpochId)
+
+ currentBatchId = latestEpochId + 1
+ logDebug(s"Resuming at epoch $currentBatchId with committed
offsets $committedOffsets")
+ nextOffsets
+ case None =>
+ // We are starting this stream for the first time. Offsets are all
None.
+ logInfo(s"Starting new streaming query.")
+ currentBatchId = 0
+ OffsetSeq.fill(continuousSources.map(_ => null): _*)
+ }
+ }
+
+ /**
+ * Do a continuous run.
+ * @param sparkSessionForQuery Isolated [[SparkSession]] to run the
continuous query with.
+ */
+ private def runContinuous(sparkSessionForQuery: SparkSession): Unit = {
+ // A list of attributes that will need to be updated.
+ val replacements = new ArrayBuffer[(Attribute, Attribute)]
+ // Translate from continuous relation to the underlying data source.
+ var nextSourceId = 0
+ continuousSources = logicalPlan.collect {
+ case ContinuousExecutionRelation(dataSource, extraReaderOptions,
output) =>
+ val metadataPath = s"$resolvedCheckpointRoot/sources/$nextSourceId"
+ nextSourceId += 1
+
+ dataSource.createContinuousReader(
+ java.util.Optional.empty[StructType](),
+ metadataPath,
+ new DataSourceV2Options(extraReaderOptions.asJava))
+ }
+ uniqueSources = continuousSources.distinct
+
+ val offsets = getStartOffsets(sparkSessionForQuery)
+
+ var insertedSourceId = 0
+ val withNewSources = logicalPlan transform {
+ case ContinuousExecutionRelation(_, _, output) =>
+ val reader = continuousSources(insertedSourceId)
+ insertedSourceId += 1
+ val newOutput = reader.readSchema().toAttributes
+
+ assert(output.size == newOutput.size,
+ s"Invalid reader: ${Utils.truncatedString(output, ",")} != " +
+ s"${Utils.truncatedString(newOutput, ",")}")
+ replacements ++= output.zip(newOutput)
+
+ val loggedOffset = offsets.offsets(0)
+ val realOffset = loggedOffset.map(off =>
reader.deserializeOffset(off.json))
+ reader.setOffset(java.util.Optional.ofNullable(realOffset.orNull))
+ DataSourceV2Relation(newOutput, reader)
+ }
+
+ // Rewire the plan to use the new attributes that were returned by the
source.
+ val replacementMap = AttributeMap(replacements)
+ val triggerLogicalPlan = withNewSources transformAllExpressions {
+ case a: Attribute if replacementMap.contains(a) =>
+ replacementMap(a).withMetadata(a.metadata)
+ case (_: CurrentTimestamp | _: CurrentDate) =>
+ throw new IllegalStateException(
+ "CurrentTimestamp and CurrentDate not yet supported for
continuous processing")
+ }
+
+ val writer = sink.createContinuousWriter(
+ s"$runId",
+ triggerLogicalPlan.schema,
+ outputMode,
+ new DataSourceV2Options(extraOptions.asJava))
+ val withSink = WriteToDataSourceV2(writer.get(), triggerLogicalPlan)
+
+ val reader = withSink.collect {
+ case DataSourceV2Relation(_, r: ContinuousReader) => r
+ }.head
+
+ reportTimeTaken("queryPlanning") {
+ lastExecution = new IncrementalExecution(
+ sparkSessionForQuery,
+ withSink,
+ outputMode,
+ checkpointFile("state"),
+ runId,
+ currentBatchId,
+ offsetSeqMetadata)
+ lastExecution.executedPlan // Force the lazy generation of execution
plan
+ }
+
+ sparkSession.sparkContext.setLocalProperty(
+ ContinuousExecution.START_EPOCH_KEY, currentBatchId.toString)
+ sparkSession.sparkContext.setLocalProperty(
+ ContinuousExecution.RUN_ID_KEY, runId.toString)
+
+ // Use the parent Spark session for the endpoint since it's where this
query ID is registered.
+ val epochEndpoint =
+ EpochCoordinatorRef.create(
+ writer.get(), reader, currentBatchId,
+ id.toString, runId.toString, sparkSession, SparkEnv.get)
+ val epochUpdateThread = new Thread(new Runnable {
+ override def run: Unit = {
+ try {
+ triggerExecutor.execute(() => {
+ startTrigger()
+
+ if (reader.needsReconfiguration()) {
+ stopSources()
+ state.set(RECONFIGURING)
--- End diff --
Stopping a source may cause an exception, e.g., it closes a socket while
queryExecutionThread is reading from it.
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