diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/HDFSMetadataLog.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/HDFSMetadataLog.scala
index e6a48a06a03f596e4453994f8e629cfb6337676b..6af60d60d56d390fd0f84fa4d41250ba6d71a9a7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/HDFSMetadataLog.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/HDFSMetadataLog.scala
@@ -63,8 +63,34 @@ class HDFSMetadataLog[T <: AnyRef : ClassTag](sparkSession: SparkSession, path:
val metadataPath = new Path(path)
protected val fileManager = createFileManager()
- if (!fileManager.exists(metadataPath)) {
- fileManager.mkdirs(metadataPath)
+ runUninterruptiblyIfLocal {
+ if (!fileManager.exists(metadataPath)) {
+ fileManager.mkdirs(metadataPath)
+ }
+ }
+
+ private def runUninterruptiblyIfLocal[T](body: => T): T = {
+ if (fileManager.isLocalFileSystem && Thread.currentThread.isInstanceOf[UninterruptibleThread]) {
+ // When using a local file system, some file system APIs like "create" or "mkdirs" must be
+ // called in [[org.apache.spark.util.UninterruptibleThread]] so that interrupts can be
+ // disabled.
+ //
+ // This is because there is a potential dead-lock in Hadoop "Shell.runCommand" before
+ // 2.5.0 (HADOOP-10622). If the thread running "Shell.runCommand" is interrupted, then
+ // the thread can get deadlocked. In our case, file system APIs like "create" or "mkdirs"
+ // will call "Shell.runCommand" to set the file permission if using the local file system,
+ // and can get deadlocked if the stream execution thread is stopped by interrupt.
+ //
+ // Hence, we use "runUninterruptibly" here to disable interrupts here. (SPARK-14131)
+ Thread.currentThread.asInstanceOf[UninterruptibleThread].runUninterruptibly {
+ body
+ }
+ } else {
+ // For a distributed file system, such as HDFS or S3, if the network is broken, write
+ // operations may just hang until timeout. We should enable interrupts to allow stopping
+ // the query fast.
+ body
+ }
}
/**
@@ -109,39 +135,14 @@ class HDFSMetadataLog[T <: AnyRef : ClassTag](sparkSession: SparkSession, path:
override def add(batchId: Long, metadata: T): Boolean = {
get(batchId).map(_ => false).getOrElse {
// Only write metadata when the batch has not yet been written
- if (fileManager.isLocalFileSystem) {
- Thread.currentThread match {
- case ut: UninterruptibleThread =>
- // When using a local file system, "writeBatch" must be called on a
- // [[org.apache.spark.util.UninterruptibleThread]] so that interrupts can be disabled
- // while writing the batch file.
- //
- // This is because Hadoop "Shell.runCommand" swallows InterruptException (HADOOP-14084).
- // If the user tries to stop a query, and the thread running "Shell.runCommand" is
- // interrupted, then InterruptException will be dropped and the query will be still
- // running. (Note: `writeBatch` creates a file using HDFS APIs and will call
- // "Shell.runCommand" to set the file permission if using the local file system)
- //
- // Hence, we make sure that "writeBatch" is called on [[UninterruptibleThread]] which
- // allows us to disable interrupts here, in order to propagate the interrupt state
- // correctly. Also see SPARK-19599.
- ut.runUninterruptibly { writeBatch(batchId, metadata) }
- case _ =>
- throw new IllegalStateException(
- "HDFSMetadataLog.add() on a local file system must be executed on " +
- "a o.a.spark.util.UninterruptibleThread")
- }
- } else {
- // For a distributed file system, such as HDFS or S3, if the network is broken, write
- // operations may just hang until timeout. We should enable interrupts to allow stopping
- // the query fast.
+ runUninterruptiblyIfLocal {
writeBatch(batchId, metadata)
}
true
}
}
- def writeTempBatch(metadata: T): Option[Path] = {
+ private def writeTempBatch(metadata: T): Option[Path] = {
while (true) {
val tempPath = new Path(metadataPath, s".${UUID.randomUUID.toString}.tmp")
try {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamExecution.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamExecution.scala
index 9346a6769d4f52588a5214c552df0892ac4afb1d..93face4390acba9eca84b0aaf5e0d0521d8caa4e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamExecution.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamExecution.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.execution.streaming
import java.util.UUID
import java.util.concurrent.{CountDownLatch, TimeUnit}
+import java.util.concurrent.atomic.AtomicReference
import java.util.concurrent.locks.ReentrantLock
import scala.collection.mutable.ArrayBuffer
@@ -157,8 +158,7 @@ class StreamExecution(
}
/** Defines the internal state of execution */
- @volatile
- private var state: State = INITIALIZING
+ private val state = new AtomicReference[State](INITIALIZING)
@volatile
var lastExecution: IncrementalExecution = _
@@ -178,8 +178,9 @@ class StreamExecution(
/**
* The thread that runs the micro-batches of this stream. Note that this thread must be
- * [[org.apache.spark.util.UninterruptibleThread]] to avoid swallowing `InterruptException` when
- * using [[HDFSMetadataLog]]. See SPARK-19599 for more details.
+ * [[org.apache.spark.util.UninterruptibleThread]] to workaround KAFKA-1894: interrupting a
+ * running `KafkaConsumer` may cause endless loop, and HADOOP-10622: interrupting
+ * `Shell.runCommand` causes deadlock. (SPARK-14131)
*/
val microBatchThread =
new StreamExecutionThread(s"stream execution thread for $prettyIdString") {
@@ -200,10 +201,10 @@ class StreamExecution(
val offsetLog = new OffsetSeqLog(sparkSession, checkpointFile("offsets"))
/** Whether all fields of the query have been initialized */
- private def isInitialized: Boolean = state != INITIALIZING
+ private def isInitialized: Boolean = state.get != INITIALIZING
/** Whether the query is currently active or not */
- override def isActive: Boolean = state != TERMINATED
+ override def isActive: Boolean = state.get != TERMINATED
/** Returns the [[StreamingQueryException]] if the query was terminated by an exception. */
override def exception: Option[StreamingQueryException] = Option(streamDeathCause)
@@ -249,53 +250,56 @@ class StreamExecution(
updateStatusMessage("Initializing sources")
// force initialization of the logical plan so that the sources can be created
logicalPlan
- state = ACTIVE
- // Unblock `awaitInitialization`
- initializationLatch.countDown()
-
- triggerExecutor.execute(() => {
- startTrigger()
-
- val isTerminated =
- if (isActive) {
- reportTimeTaken("triggerExecution") {
- if (currentBatchId < 0) {
- // We'll do this initialization only once
- populateStartOffsets()
- logDebug(s"Stream running from $committedOffsets to $availableOffsets")
- } else {
- constructNextBatch()
+ if (state.compareAndSet(INITIALIZING, ACTIVE)) {
+ // Unblock `awaitInitialization`
+ initializationLatch.countDown()
+
+ triggerExecutor.execute(() => {
+ startTrigger()
+
+ val continueToRun =
+ if (isActive) {
+ reportTimeTaken("triggerExecution") {
+ if (currentBatchId < 0) {
+ // We'll do this initialization only once
+ populateStartOffsets()
+ logDebug(s"Stream running from $committedOffsets to $availableOffsets")
+ } else {
+ constructNextBatch()
+ }
+ if (dataAvailable) {
+ currentStatus = currentStatus.copy(isDataAvailable = true)
+ updateStatusMessage("Processing new data")
+ runBatch()
+ }
}
+
+ // Report trigger as finished and construct progress object.
+ finishTrigger(dataAvailable)
if (dataAvailable) {
- currentStatus = currentStatus.copy(isDataAvailable = true)
- updateStatusMessage("Processing new data")
- runBatch()
+ // We'll increase currentBatchId after we complete processing current batch's data
+ currentBatchId += 1
+ } else {
+ currentStatus = currentStatus.copy(isDataAvailable = false)
+ updateStatusMessage("Waiting for data to arrive")
+ Thread.sleep(pollingDelayMs)
}
- }
-
- // Report trigger as finished and construct progress object.
- finishTrigger(dataAvailable)
- if (dataAvailable) {
- // We'll increase currentBatchId after we complete processing current batch's data
- currentBatchId += 1
+ true
} else {
- currentStatus = currentStatus.copy(isDataAvailable = false)
- updateStatusMessage("Waiting for data to arrive")
- Thread.sleep(pollingDelayMs)
+ false
}
- true
- } else {
- false
- }
- // Update committed offsets.
- committedOffsets ++= availableOffsets
- updateStatusMessage("Waiting for next trigger")
- isTerminated
- })
- updateStatusMessage("Stopped")
+ // Update committed offsets.
+ committedOffsets ++= availableOffsets
+ updateStatusMessage("Waiting for next trigger")
+ continueToRun
+ })
+ updateStatusMessage("Stopped")
+ } else {
+ // `stop()` is already called. Let `finally` finish the cleanup.
+ }
} catch {
- case _: InterruptedException if state == TERMINATED => // interrupted by stop()
+ case _: InterruptedException if state.get == TERMINATED => // interrupted by stop()
updateStatusMessage("Stopped")
case e: Throwable =>
streamDeathCause = new StreamingQueryException(
@@ -318,7 +322,7 @@ class StreamExecution(
initializationLatch.countDown()
try {
- state = TERMINATED
+ state.set(TERMINATED)
currentStatus = status.copy(isTriggerActive = false, isDataAvailable = false)
// Update metrics and status
@@ -562,7 +566,7 @@ class StreamExecution(
override def stop(): Unit = {
// Set the state to TERMINATED so that the batching thread knows that it was interrupted
// intentionally
- state = TERMINATED
+ state.set(TERMINATED)
if (microBatchThread.isAlive) {
microBatchThread.interrupt()
microBatchThread.join()