Github user joseph-torres commented on a diff in the pull request:
https://github.com/apache/spark/pull/19926#discussion_r157047178
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MicroBatchExecution.scala
---
@@ -0,0 +1,407 @@
+/*
+ * 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 scala.collection.mutable.{ArrayBuffer, Map => MutableMap}
+
+import org.apache.spark.sql.{Dataset, SparkSession}
+import org.apache.spark.sql.catalyst.encoders.RowEncoder
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap,
CurrentBatchTimestamp, CurrentDate, CurrentTimestamp}
+import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation,
LogicalPlan}
+import org.apache.spark.sql.execution.SQLExecution
+import org.apache.spark.sql.sources.v2.reader.Offset
+import org.apache.spark.sql.streaming.{OutputMode, ProcessingTime, Trigger}
+import org.apache.spark.util.{Clock, Utils}
+
+class MicroBatchExecution(
+ sparkSession: SparkSession,
+ name: String,
+ checkpointRoot: String,
+ analyzedPlan: LogicalPlan,
+ sink: Sink,
+ trigger: Trigger,
+ triggerClock: Clock,
+ outputMode: OutputMode,
+ deleteCheckpointOnStop: Boolean)
+ extends StreamExecution(
+ sparkSession, name, checkpointRoot, analyzedPlan, sink,
+ trigger, triggerClock, outputMode, deleteCheckpointOnStop) {
+
+ private val triggerExecutor = trigger match {
+ case t: ProcessingTime => ProcessingTimeExecutor(t, triggerClock)
+ case OneTimeTrigger => OneTimeExecutor()
+ case _ => throw new IllegalStateException(s"Unknown type of trigger:
$trigger")
+ }
+
+ override lazy val logicalPlan: LogicalPlan = {
+ assert(queryExecutionThread eq Thread.currentThread,
+ "logicalPlan must be initialized in QueryExecutionThread " +
+ s"but the current thread was ${Thread.currentThread}")
+ var nextSourceId = 0L
+ val toExecutionRelationMap = MutableMap[StreamingRelation,
StreamingExecutionRelation]()
+ val _logicalPlan = analyzedPlan.transform {
+ case streamingRelation@StreamingRelation(dataSource, _, output) =>
+ toExecutionRelationMap.getOrElseUpdate(streamingRelation, {
+ // Materialize source to avoid creating it in every batch
+ val metadataPath =
s"$resolvedCheckpointRoot/sources/$nextSourceId"
+ val source = dataSource.createSource(metadataPath)
+ nextSourceId += 1
+ // We still need to use the previous `output` instead of
`source.schema` as attributes in
+ // "df.logicalPlan" has already used attributes of the previous
`output`.
+ StreamingExecutionRelation(source, output)(sparkSession)
+ })
+ }
+ sources = _logicalPlan.collect { case s: StreamingExecutionRelation =>
s.source }
+ uniqueSources = sources.distinct
+ _logicalPlan
+ }
+
+ /**
+ * Repeatedly attempts to run batches as data arrives.
+ */
+ protected def runActivatedStream(sparkSessionForStream: SparkSession):
Unit = {
+ triggerExecutor.execute(() => {
+ startTrigger()
+
+ if (isActive) {
+ reportTimeTaken("triggerExecution") {
+ if (currentBatchId < 0) {
+ // We'll do this initialization only once
+ populateStartOffsets(sparkSessionForStream)
+
sparkSession.sparkContext.setJobDescription(getBatchDescriptionString)
+ logDebug(s"Stream running from $committedOffsets to
$availableOffsets")
+ } else {
+ constructNextBatch()
+ }
+ if (dataAvailable) {
+ currentStatus = currentStatus.copy(isDataAvailable = true)
+ updateStatusMessage("Processing new data")
+ runBatch(sparkSessionForStream)
+ }
+ }
+ // Report trigger as finished and construct progress object.
+ finishTrigger(dataAvailable)
+ if (dataAvailable) {
+ // Update committed offsets.
+ commitLog.add(currentBatchId)
+ committedOffsets ++= availableOffsets
+ logDebug(s"batch ${currentBatchId} committed")
+ // We'll increase currentBatchId after we complete processing
current batch's data
+ currentBatchId += 1
+
sparkSession.sparkContext.setJobDescription(getBatchDescriptionString)
+ } else {
+ currentStatus = currentStatus.copy(isDataAvailable = false)
+ updateStatusMessage("Waiting for data to arrive")
+ Thread.sleep(pollingDelayMs)
+ }
+ }
+ updateStatusMessage("Waiting for next trigger")
+ isActive
+ })
+ }
+
+ /**
+ * 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
+ * - availableOffsets
+ * The basic structure of this method is as follows:
+ *
+ * Identify (from the offset log) the offsets used to run the last batch
+ * IF last batch exists THEN
+ * Set the next batch to be executed as the last recovered batch
+ * Check the commit log to see which batch was committed last
+ * IF the last batch was committed THEN
+ * Call getBatch using the last batch start and end offsets
+ * // ^^^^ above line is needed since some sources assume last
batch always re-executes
+ * Setup for a new batch i.e., start = last batch end, and identify
new end
+ * DONE
+ * ELSE
+ * Identify a brand new batch
+ * DONE
+ */
+ private def populateStartOffsets(sparkSessionToRunBatches:
SparkSession): Unit = {
+ offsetLog.getLatest() match {
+ case Some((latestBatchId, nextOffsets)) =>
+ /* First assume that we are re-executing the latest known batch
+ * in the offset log */
+ currentBatchId = latestBatchId
+ availableOffsets = nextOffsets.toStreamProgress(sources)
+ /* Initialize committed offsets to a committed batch, which at this
+ * is the second latest batch id in the offset log. */
+ if (latestBatchId != 0) {
+ val secondLatestBatchId = offsetLog.get(latestBatchId -
1).getOrElse {
+ throw new IllegalStateException(s"batch ${latestBatchId - 1}
doesn't exist")
+ }
+ committedOffsets = secondLatestBatchId.toStreamProgress(sources)
+ }
+
+ // update offset metadata
+ nextOffsets.metadata.foreach { metadata =>
+ OffsetSeqMetadata.setSessionConf(metadata,
sparkSessionToRunBatches.conf)
+ offsetSeqMetadata = OffsetSeqMetadata(
+ metadata.batchWatermarkMs, metadata.batchTimestampMs,
sparkSessionToRunBatches.conf)
+ }
+
+ /* identify the current batch id: if commit log indicates we
successfully processed the
+ * latest batch id in the offset log, then we can safely move to
the next batch
+ * i.e., committedBatchId + 1 */
+ commitLog.getLatest() match {
+ case Some((latestCommittedBatchId, _)) =>
+ if (latestBatchId == latestCommittedBatchId) {
+ /* The last batch was successfully committed, so we can
safely process a
+ * new next batch but first:
+ * Make a call to getBatch using the offsets from previous
batch.
+ * because certain sources (e.g., KafkaSource) assume on
restart the last
+ * batch will be executed before getOffset is called again.
*/
+ availableOffsets.foreach { ao: (Source, Offset) =>
+ val (source, end) = ao
+ if (committedOffsets.get(source).map(_ !=
end).getOrElse(true)) {
+ val start = committedOffsets.get(source)
+ source.getBatch(start, end)
+ }
+ }
+ currentBatchId = latestCommittedBatchId + 1
+ committedOffsets ++= availableOffsets
+ // Construct a new batch be recomputing availableOffsets
+ constructNextBatch()
+ } else if (latestCommittedBatchId < latestBatchId - 1) {
+ logWarning(s"Batch completion log latest batch id is " +
+ s"${latestCommittedBatchId}, which is not trailing " +
+ s"batchid $latestBatchId by one")
+ }
+ case None => logInfo("no commit log present")
+ }
+ logDebug(s"Resuming at batch $currentBatchId with committed
offsets " +
+ s"$committedOffsets and available offsets $availableOffsets")
+ case None => // We are starting this stream for the first time.
+ logInfo(s"Starting new streaming query.")
+ currentBatchId = 0
+ constructNextBatch()
+ }
+ }
+
+ /**
+ * Returns true if there is any new data available to be processed.
+ */
+ private def dataAvailable: Boolean = {
+ availableOffsets.exists {
+ case (source, available) =>
+ committedOffsets
+ .get(source)
+ .map(committed => committed != available)
+ .getOrElse(true)
+ }
+ }
+
+ /**
+ * Queries all of the sources to see if any new data is available. When
there is new data the
+ * batchId counter is incremented and a new log entry is written with
the newest offsets.
+ */
+ private def constructNextBatch(): Unit = {
+ // Check to see what new data is available.
+ val hasNewData = {
+ awaitProgressLock.lock()
+ try {
+ val latestOffsets: Map[Source, Option[Offset]] = uniqueSources.map
{ s =>
+ updateStatusMessage(s"Getting offsets from $s")
+ reportTimeTaken("getOffset") {
+ (s, s.getOffset)
+ }
+ }.toMap
+ availableOffsets ++= latestOffsets.filter { case (s, o) =>
o.nonEmpty }.mapValues(_.get)
+
+ if (dataAvailable) {
+ true
+ } else {
+ noNewData = true
+ false
+ }
+ } finally {
+ awaitProgressLock.unlock()
+ }
+ }
+ if (hasNewData) {
+ var batchWatermarkMs = offsetSeqMetadata.batchWatermarkMs
+ // Update the eventTime watermarks if we find any in the plan.
+ if (lastExecution != null) {
+ lastExecution.executedPlan.collect {
+ case e: EventTimeWatermarkExec => e
+ }.zipWithIndex.foreach {
+ case (e, index) if e.eventTimeStats.value.count > 0 =>
+ logDebug(s"Observed event time stats $index:
${e.eventTimeStats.value}")
+ val newWatermarkMs = e.eventTimeStats.value.max - e.delayMs
+ val prevWatermarkMs = watermarkMsMap.get(index)
+ if (prevWatermarkMs.isEmpty || newWatermarkMs >
prevWatermarkMs.get) {
+ watermarkMsMap.put(index, newWatermarkMs)
+ }
+
+ // Populate 0 if we haven't seen any data yet for this watermark
node.
+ case (_, index) =>
+ if (!watermarkMsMap.isDefinedAt(index)) {
+ watermarkMsMap.put(index, 0)
+ }
+ }
+
+ // Update the global watermark to the minimum of all watermark
nodes.
+ // This is the safest option, because only the global watermark is
fault-tolerant. Making
+ // it the minimum of all individual watermarks guarantees it will
never advance past where
+ // any individual watermark operator would be if it were in a plan
by itself.
+ if(!watermarkMsMap.isEmpty) {
+ val newWatermarkMs = watermarkMsMap.minBy(_._2)._2
+ if (newWatermarkMs > batchWatermarkMs) {
+ logInfo(s"Updating eventTime watermark to: $newWatermarkMs ms")
+ batchWatermarkMs = newWatermarkMs
+ } else {
+ logDebug(
+ s"Event time didn't move: $newWatermarkMs < " +
+ s"$batchWatermarkMs")
+ }
+ }
+ }
+ offsetSeqMetadata = offsetSeqMetadata.copy(
+ batchWatermarkMs = batchWatermarkMs,
+ batchTimestampMs = triggerClock.getTimeMillis()) // Current batch
timestamp in milliseconds
+
+ updateStatusMessage("Writing offsets to log")
+ reportTimeTaken("walCommit") {
+ assert(offsetLog.add(
+ currentBatchId,
+ availableOffsets.toOffsetSeq(sources, offsetSeqMetadata)),
+ s"Concurrent update to the log. Multiple streaming jobs detected
for $currentBatchId")
+ logInfo(s"Committed offsets for batch $currentBatchId. " +
+ s"Metadata ${offsetSeqMetadata.toString}")
+
+ // NOTE: The following code is correct because runStream()
processes exactly one
+ // batch at a time. If we add pipeline parallelism (multiple
batches in flight at
+ // the same time), this cleanup logic will need to change.
+
+ // Now that we've updated the scheduler's persistent checkpoint,
it is safe for the
+ // sources to discard data from the previous batch.
+ if (currentBatchId != 0) {
+ val prevBatchOff = offsetLog.get(currentBatchId - 1)
+ if (prevBatchOff.isDefined) {
+ prevBatchOff.get.toStreamProgress(sources).foreach {
+ case (src, off) => src.commit(off)
+ }
+ } else {
+ throw new IllegalStateException(s"batch $currentBatchId
doesn't exist")
+ }
+ }
+
+ // It is now safe to discard the metadata beyond the minimum
number to retain.
+ // Note that purge is exclusive, i.e. it purges everything before
the target ID.
+ if (minLogEntriesToMaintain < currentBatchId) {
+ offsetLog.purge(currentBatchId - minLogEntriesToMaintain)
+ commitLog.purge(currentBatchId - minLogEntriesToMaintain)
+ }
+ }
+ } else {
+ awaitProgressLock.lock()
+ try {
+ // Wake up any threads that are waiting for the stream to progress.
+ awaitProgressLockCondition.signalAll()
+ } finally {
+ awaitProgressLock.unlock()
+ }
+ }
+ }
+
+ /**
+ * Processes any data available between `availableOffsets` and
`committedOffsets`.
+ * @param sparkSessionToRunBatch Isolated [[SparkSession]] to run this
batch with.
+ */
+ private def runBatch(sparkSessionToRunBatch: SparkSession): Unit = {
+ // Request unprocessed data from all sources.
+ newData = reportTimeTaken("getBatch") {
+ availableOffsets.flatMap {
+ case (source, available)
+ if committedOffsets.get(source).map(_ !=
available).getOrElse(true) =>
+ val current = committedOffsets.get(source)
+ val batch = source.getBatch(current, available)
+ assert(batch.isStreaming,
+ s"DataFrame returned by getBatch from $source did not have
isStreaming=true\n" +
+ s"${batch.queryExecution.logical}")
+ logDebug(s"Retrieving data from $source: $current -> $available")
+ Some(source -> batch)
+ case _ => None
+ }
+ }
+
+ // A list of attributes that will need to be updated.
+ val replacements = new ArrayBuffer[(Attribute, Attribute)]
+ // Replace sources in the logical plan with data that has arrived
since the last batch.
+ val withNewSources = logicalPlan transform {
+ case StreamingExecutionRelation(source, output) =>
+ newData.get(source).map { data =>
+ val newPlan = data.logicalPlan
+ assert(output.size == newPlan.output.size,
+ s"Invalid batch: ${Utils.truncatedString(output, ",")} != " +
+ s"${Utils.truncatedString(newPlan.output, ",")}")
+ replacements ++= output.zip(newPlan.output)
+ newPlan
+ }.getOrElse {
+ LocalRelation(output, isStreaming = true)
+ }
+ }
+
+ // 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 ct: CurrentTimestamp =>
--- End diff --
That's a major candidate solution, but we're planning to just not support
CurrentTimestamp for the initial implementation. It would require significant
changes, since control flow won't return here between epochs.
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