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Posted to commits@spark.apache.org by mr...@apache.org on 2023/09/29 03:54:37 UTC
[spark] branch branch-3.4 updated: [SPARK-45227][CORE] Fix a subtle thread-safety issue with CoarseGrainedExecutorBackend
This is an automated email from the ASF dual-hosted git repository.
mridulm80 pushed a commit to branch branch-3.4
in repository https://gitbox.apache.org/repos/asf/spark.git
The following commit(s) were added to refs/heads/branch-3.4 by this push:
new e706ba1cd4b [SPARK-45227][CORE] Fix a subtle thread-safety issue with CoarseGrainedExecutorBackend
e706ba1cd4b is described below
commit e706ba1cd4b94bcb215355721914002748590d46
Author: Bo Xiong <xi...@amazon.com>
AuthorDate: Thu Sep 28 22:53:37 2023 -0500
[SPARK-45227][CORE] Fix a subtle thread-safety issue with CoarseGrainedExecutorBackend
### What changes were proposed in this pull request?
Fix a subtle thread-safety issue with CoarseGrainedExecutorBackend where an executor process randomly gets stuck
### Why are the changes needed?
For each executor, the single-threaded dispatcher can run into an "infinite loop" (as explained in the SPARK-45227). Once an executor process runs into a state, it'd stop launching tasks from the driver or reporting task status back.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
```
$ build/mvn package -DskipTests -pl core
$ build/mvn -Dtest=none -DwildcardSuites=org.apache.spark.executor.CoarseGrainedExecutorBackendSuite test
```
### Was this patch authored or co-authored using generative AI tooling?
No
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**_Please feel free to skip reading unless you're interested in details_**
******************************************************************************
### Symptom
Our Spark 3 app running on EMR 6.10.0 with Spark 3.3.1 got stuck in the very last step of writing a data frame to S3 by calling `df.write`. Looking at Spark UI, we saw that an executor process hung over 1 hour. After we manually killed the executor process, the app succeeded. Note that the same EMR cluster with two worker nodes was able to run the same app without any issue before and after the incident.
Below is what's observed from relevant container logs and thread dump.
- A regular task that's sent to the executor, which also reported back to the driver upon the task completion.
```
$zgrep 'task 150' container_1694029806204_12865_01_000001/stderr.gz
23/09/12 18:13:55 INFO TaskSetManager: Starting task 150.0 in stage 23.0 (TID 923) (ip-10-0-185-107.ec2.internal, executor 3, partition 150, NODE_LOCAL, 4432 bytes) taskResourceAssignments Map()
23/09/12 18:13:55 INFO TaskSetManager: Finished task 150.0 in stage 23.0 (TID 923) in 126 ms on ip-10-0-185-107.ec2.internal (executor 3) (16/200)
$zgrep ' 923' container_1694029806204_12865_01_000004/stderr.gz
23/09/12 18:13:55 INFO YarnCoarseGrainedExecutorBackend: Got assigned task 923
$zgrep 'task 150' container_1694029806204_12865_01_000004/stderr.gz
23/09/12 18:13:55 INFO Executor: Running task 150.0 in stage 23.0 (TID 923)
23/09/12 18:13:55 INFO Executor: Finished task 150.0 in stage 23.0 (TID 923). 4495 bytes result sent to driver
```
- Another task that's sent to the executor but didn't get launched since the single-threaded dispatcher was stuck (presumably in an "infinite loop" as explained later).
```
$zgrep 'task 153' container_1694029806204_12865_01_000001/stderr.gz
23/09/12 18:13:55 INFO TaskSetManager: Starting task 153.0 in stage 23.0 (TID 924) (ip-10-0-185-107.ec2.internal, executor 3, partition 153, NODE_LOCAL, 4432 bytes) taskResourceAssignments Map()
$zgrep ' 924' container_1694029806204_12865_01_000004/stderr.gz
23/09/12 18:13:55 INFO YarnCoarseGrainedExecutorBackend: Got assigned task 924
$zgrep 'task 153' container_1694029806204_12865_01_000004/stderr.gz
>> note that the above command has no matching result, indicating that task 153.0 in stage 23.0 (TID 924) was never launched
```
- Thread dump shows that the dispatcher-Executor thread has the following stack trace.
```
"dispatcher-Executor" #40 daemon prio=5 os_prio=0 tid=0x0000ffff98e37800 nid=0x1aff runnable [0x0000ffff73bba000]
java.lang.Thread.State: RUNNABLE
at scala.runtime.BoxesRunTime.equalsNumObject(BoxesRunTime.java:142)
at scala.runtime.BoxesRunTime.equals2(BoxesRunTime.java:131)
at scala.runtime.BoxesRunTime.equals(BoxesRunTime.java:123)
at scala.collection.mutable.HashTable.elemEquals(HashTable.scala:365)
at scala.collection.mutable.HashTable.elemEquals$(HashTable.scala:365)
at scala.collection.mutable.HashMap.elemEquals(HashMap.scala:44)
at scala.collection.mutable.HashTable.findEntry0(HashTable.scala:140)
at scala.collection.mutable.HashTable.findOrAddEntry(HashTable.scala:169)
at scala.collection.mutable.HashTable.findOrAddEntry$(HashTable.scala:167)
at scala.collection.mutable.HashMap.findOrAddEntry(HashMap.scala:44)
at scala.collection.mutable.HashMap.put(HashMap.scala:126)
at scala.collection.mutable.HashMap.update(HashMap.scala:131)
at org.apache.spark.executor.CoarseGrainedExecutorBackend$$anonfun$receive$1.applyOrElse(CoarseGrainedExecutorBackend.scala:200)
at org.apache.spark.rpc.netty.Inbox.$anonfun$process$1(Inbox.scala:115)
at org.apache.spark.rpc.netty.Inbox$$Lambda$323/1930826709.apply$mcV$sp(Unknown Source)
at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:213)
at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:100)
at org.apache.spark.rpc.netty.MessageLoop.org$apache$spark$rpc$netty$MessageLoop$$receiveLoop(MessageLoop.scala:75)
at org.apache.spark.rpc.netty.MessageLoop$$anon$1.run(MessageLoop.scala:41)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:750)
```
### Relevant code paths
Within an executor process, there's a [dispatcher thread](https://github.com/apache/spark/blob/1fdd46f173f7bc90e0523eb0a2d5e8e27e990102/core/src/main/scala/org/apache/spark/rpc/netty/MessageLoop.scala#L170) dedicated to CoarseGrainedExecutorBackend(a single RPC endpoint) that launches tasks scheduled by the driver. Each task is run on a TaskRunner thread backed by a thread pool created for the executor. The TaskRunner thread and the dispatcher thread are different. However, they read [...]
### What's going on?
Based on the above observations, our hypothesis is that the dispatcher thread runs into an "infinite loop" due to a race condition when two threads access the same hashmap object. For illustration purpose, let's consider the following scenario where two threads (Thread 1 and Thread 2) access a hash table without thread-safety
- Thread 1 sees A.next = B, but then yields execution to Thread 2
<img src="https://issues.apache.org/jira/secure/attachment/13063040/13063040_hashtable1.png" width="400">
- Thread 2 triggers a resize operation resulting in B.next = A (Note that hashmap doesn't care about ordering), and then yields execution to Thread 1.
<img src="https://issues.apache.org/jira/secure/attachment/13063041/13063041_hashtable2.png" width="400">
- After taking over CPU, Thread 1 would run into an "infinite loop" when traversing the list in the last bucket, given A.next = B and B.next = A in its view.
Closes #43021 from xiongbo-sjtu/master.
Authored-by: Bo Xiong <xi...@amazon.com>
Signed-off-by: Mridul Muralidharan <mridul<at>gmail.com>
(cherry picked from commit 8e6b1603a66706ee27a0b16d850f5ee56d633354)
Signed-off-by: Mridul Muralidharan <mridulatgmail.com>
---
.../apache/spark/executor/CoarseGrainedExecutorBackend.scala | 11 +++++++----
.../spark/executor/CoarseGrainedExecutorBackendSuite.scala | 6 +++---
2 files changed, 10 insertions(+), 7 deletions(-)
diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
index ab238626efe..c695a9ec285 100644
--- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
+++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
@@ -20,9 +20,9 @@ package org.apache.spark.executor
import java.net.URL
import java.nio.ByteBuffer
import java.util.Locale
+import java.util.concurrent.ConcurrentHashMap
import java.util.concurrent.atomic.AtomicBoolean
-import scala.collection.mutable
import scala.util.{Failure, Success}
import scala.util.control.NonFatal
@@ -71,9 +71,12 @@ private[spark] class CoarseGrainedExecutorBackend(
/**
* Map each taskId to the information about the resource allocated to it, Please refer to
* [[ResourceInformation]] for specifics.
+ * CHM is used to ensure thread-safety (https://issues.apache.org/jira/browse/SPARK-45227)
* Exposed for testing only.
*/
- private[executor] val taskResources = new mutable.HashMap[Long, Map[String, ResourceInformation]]
+ private[executor] val taskResources = new ConcurrentHashMap[
+ Long, Map[String, ResourceInformation]
+ ]
private var decommissioned = false
@@ -184,7 +187,7 @@ private[spark] class CoarseGrainedExecutorBackend(
} else {
val taskDesc = TaskDescription.decode(data.value)
logInfo("Got assigned task " + taskDesc.taskId)
- taskResources(taskDesc.taskId) = taskDesc.resources
+ taskResources.put(taskDesc.taskId, taskDesc.resources)
executor.launchTask(this, taskDesc)
}
@@ -261,7 +264,7 @@ private[spark] class CoarseGrainedExecutorBackend(
}
override def statusUpdate(taskId: Long, state: TaskState, data: ByteBuffer): Unit = {
- val resources = taskResources.getOrElse(taskId, Map.empty[String, ResourceInformation])
+ val resources = taskResources.getOrDefault(taskId, Map.empty[String, ResourceInformation])
val cpus = executor.runningTasks.get(taskId).taskDescription.cpus
val msg = StatusUpdate(executorId, taskId, state, data, cpus, resources)
if (TaskState.isFinished(state)) {
diff --git a/core/src/test/scala/org/apache/spark/executor/CoarseGrainedExecutorBackendSuite.scala b/core/src/test/scala/org/apache/spark/executor/CoarseGrainedExecutorBackendSuite.scala
index 7b8b7cf4cdd..1a427745ee4 100644
--- a/core/src/test/scala/org/apache/spark/executor/CoarseGrainedExecutorBackendSuite.scala
+++ b/core/src/test/scala/org/apache/spark/executor/CoarseGrainedExecutorBackendSuite.scala
@@ -303,7 +303,7 @@ class CoarseGrainedExecutorBackendSuite extends SparkFunSuite
resourceProfile = ResourceProfile.getOrCreateDefaultProfile(conf))
assert(backend.taskResources.isEmpty)
- val taskId = 1000000
+ val taskId = 1000000L
// We don't really verify the data, just pass it around.
val data = ByteBuffer.wrap(Array[Byte](1, 2, 3, 4))
val taskDescription = new TaskDescription(taskId, 2, "1", "TASK 1000000", 19,
@@ -340,14 +340,14 @@ class CoarseGrainedExecutorBackendSuite extends SparkFunSuite
backend.self.send(LaunchTask(new SerializableBuffer(serializedTaskDescription)))
eventually(timeout(10.seconds)) {
assert(backend.taskResources.size == 1)
- val resources = backend.taskResources(taskId)
+ val resources = backend.taskResources.get(taskId)
assert(resources(GPU).addresses sameElements Array("0", "1"))
}
// Update the status of a running task shall not affect `taskResources` map.
backend.statusUpdate(taskId, TaskState.RUNNING, data)
assert(backend.taskResources.size == 1)
- val resources = backend.taskResources(taskId)
+ val resources = backend.taskResources.get(taskId)
assert(resources(GPU).addresses sameElements Array("0", "1"))
// Update the status of a finished task shall remove the entry from `taskResources` map.
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