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[SPARK-48370][CONNECT][FOLLOW-UP] Use JDK's Cleaner instead #46726

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Original file line number Diff line number Diff line change
Expand Up @@ -3492,7 +3492,7 @@ class Dataset[T] private[sql] (
.getOrElse(throw new RuntimeException("CheckpointCommandResult must be present"))

val cachedRemoteRelation = response.getCheckpointCommandResult.getRelation
sparkSession.cleaner.registerCachedRemoteRelationForCleanup(cachedRemoteRelation)
sparkSession.cleaner.register(cachedRemoteRelation)

// Update the builder with the values from the result.
builder.setCachedRemoteRelation(cachedRemoteRelation)
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Original file line number Diff line number Diff line change
Expand Up @@ -73,11 +73,7 @@ class SparkSession private[sql] (
with Logging {

private[this] val allocator = new RootAllocator()
private var shouldStopCleaner = false
private[sql] lazy val cleaner = {
shouldStopCleaner = true
new SessionCleaner(this)
}
private[sql] lazy val cleaner = new SessionCleaner(this)

// a unique session ID for this session from client.
private[sql] def sessionId: String = client.sessionId
Expand Down Expand Up @@ -719,9 +715,6 @@ class SparkSession private[sql] (
if (releaseSessionOnClose) {
client.releaseSession()
}
if (shouldStopCleaner) {
cleaner.stop()
}
client.shutdown()
allocator.close()
SparkSession.onSessionClose(this)
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Original file line number Diff line number Diff line change
Expand Up @@ -17,130 +17,33 @@

package org.apache.spark.sql.internal

import java.lang.ref.{ReferenceQueue, WeakReference}
import java.util.Collections
import java.util.concurrent.ConcurrentHashMap
import java.lang.ref.Cleaner

import org.apache.spark.connect.proto
import org.apache.spark.internal.Logging
import org.apache.spark.sql.SparkSession

/**
* Classes that represent cleaning tasks.
*/
private sealed trait CleanupTask
private case class CleanupCachedRemoteRelation(dfID: String) extends CleanupTask

/**
* A WeakReference associated with a CleanupTask.
*
* When the referent object becomes only weakly reachable, the corresponding
* CleanupTaskWeakReference is automatically added to the given reference queue.
*/
private class CleanupTaskWeakReference(
val task: CleanupTask,
referent: AnyRef,
referenceQueue: ReferenceQueue[AnyRef])
extends WeakReference(referent, referenceQueue)

/**
* An asynchronous cleaner for objects.
*
* This maintains a weak reference for each CashRemoteRelation, etc. of interest, to be processed
* when the associated object goes out of scope of the application. Actual cleanup is performed in
* a separate daemon thread.
*/
private[sql] class SessionCleaner(session: SparkSession) extends Logging {

/**
* How often (seconds) to trigger a garbage collection in this JVM. This context cleaner
* triggers cleanups only when weak references are garbage collected. In long-running
* applications with large driver JVMs, where there is little memory pressure on the driver,
* this may happen very occasionally or not at all. Not cleaning at all may lead to executors
* running out of disk space after a while.
*/
private val refQueuePollTimeout: Long = 100

/**
* A buffer to ensure that `CleanupTaskWeakReference`s are not garbage collected as long as they
* have not been handled by the reference queue.
*/
private val referenceBuffer =
Collections.newSetFromMap[CleanupTaskWeakReference](new ConcurrentHashMap)

private val referenceQueue = new ReferenceQueue[AnyRef]

private val cleaningThread = new Thread() { override def run(): Unit = keepCleaning() }

@volatile private var started = false
@volatile private var stopped = false

/** Start the cleaner. */
def start(): Unit = {
cleaningThread.setDaemon(true)
cleaningThread.setName("Spark Connect Context Cleaner")
cleaningThread.start()
}

/**
* Stop the cleaning thread and wait until the thread has finished running its current task.
*/
def stop(): Unit = {
stopped = true
// Interrupt the cleaning thread, but wait until the current task has finished before
// doing so. This guards against the race condition where a cleaning thread may
// potentially clean similarly named variables created by a different SparkSession.
synchronized {
cleaningThread.interrupt()
}
cleaningThread.join()
}
private val cleaner = Cleaner.create()
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It seems that the lifecycle of SessionCleaner is the same as SparkSession, so when the client holds multiple SparkSessions, multiple instances of java.lang.ref.Cleaner will be created. If cleaner is defined in the companion object of SessionCleaner, it can allow multiple SessionCleaner to share one java.lang.ref.Cleaner instance . Can this meet the requirements?

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We can have one and share between other sessions but wanted to scope the cleaning specific to a session so it doesn't affect other sessions. I am fine either way though.

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We can do this in a follow-up? @LuciferYang is there any concrete concern here? Or are you just being tidy?

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It's fine, we can make follow-up when this really becomes an issue :)


/** Register a CachedRemoteRelation for cleanup when it is garbage collected. */
def registerCachedRemoteRelationForCleanup(relation: proto.CachedRemoteRelation): Unit = {
registerForCleanup(relation, CleanupCachedRemoteRelation(relation.getRelationId))
}

/** Register an object for cleanup. */
private def registerForCleanup(objectForCleanup: AnyRef, task: CleanupTask): Unit = {
if (!started) {
// Lazily starts when the first cleanup is registered.
start()
started = true
}
referenceBuffer.add(new CleanupTaskWeakReference(task, objectForCleanup, referenceQueue))
def register(relation: proto.CachedRemoteRelation): Unit = {
val dfID = relation.getRelationId
cleaner.register(relation, () => doCleanupCachedRemoteRelation(dfID))
}

/** Keep cleaning objects. */
private def keepCleaning(): Unit = {
while (!stopped && !session.client.channel.isShutdown) {
try {
val reference = Option(referenceQueue.remove(refQueuePollTimeout))
.map(_.asInstanceOf[CleanupTaskWeakReference])
// Synchronize here to avoid being interrupted on stop()
synchronized {
reference.foreach { ref =>
logDebug("Got cleaning task " + ref.task)
referenceBuffer.remove(ref)
ref.task match {
case CleanupCachedRemoteRelation(dfID) =>
doCleanupCachedRemoteRelation(dfID)
}
private[sql] def doCleanupCachedRemoteRelation(dfID: String): Unit = {
try {
if (!session.client.channel.isShutdown) {
session.execute {
session.newCommand { builder =>
builder.getRemoveCachedRemoteRelationCommandBuilder
.setRelation(proto.CachedRemoteRelation.newBuilder().setRelationId(dfID).build())
}
}
} catch {
case e: Throwable => logError("Error in cleaning thread", e)
}
}
}

/** Perform CleanupCachedRemoteRelation cleanup. */
private[spark] def doCleanupCachedRemoteRelation(dfID: String): Unit = {
session.execute {
session.newCommand { builder =>
builder.getRemoveCachedRemoteRelationCommandBuilder
.setRelation(proto.CachedRemoteRelation.newBuilder().setRelationId(dfID).build())
}
} catch {
case e: Throwable => logError("Error in cleaning thread", e)
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The error is swollen but I think it's better to explicitly log

}
}
}