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https://issues.apache.org/jira/browse/SPARK-4498?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14228952#comment-14228952
]
Josh Rosen commented on SPARK-4498:
-----------------------------------
In addition to exploring the "missing DisassociatedEvent" theory, it might also
be worthwhile to brainstorm whether problems at other steps in the cleanup
process could cause an application to fail to be removed. I'm not sure that a
single missing DisassociatedEvent could explain the "blip" behavior observed
here, where an entire group of applications fail to be marked as completed /
failed.
In the DisassociatedEvent handler, we index into {{addressToApp}} to determine
which app corresponded to the DisassociatedEvent:
{code}
case DisassociatedEvent(_, address, _) => {
// The disconnected client could've been either a worker or an app;
remove whichever it was
logInfo(s"$address got disassociated, removing it.")
addressToWorker.get(address).foreach(removeWorker)
addressToApp.get(address).foreach(finishApplication)
if (state == RecoveryState.RECOVERING && canCompleteRecovery) {
completeRecovery() }
}
{code}
If the {{addressToApp}} entry was empty / wrong, then we wouldn't properly
clean up the app. However, I don't think that there should be any problems
here because each application actor system should have its own distinct address
and Akka's {{Address}} class properly implements hashCode / equals. Even if
drivers run on the same host, their actor systems should have different port
numbers.
Continuing along:
{code}
def removeApplication(app: ApplicationInfo, state: ApplicationState.Value) {
if (apps.contains(app)) {
logInfo("Removing app " + app.id)
{code}
Is there any way that the {{apps}} HashSet could fail to contain {{app}}? I
don't think so: {{ApplicationInfo}} doesn't override equals/hashCode, but I
don't think that's a problem since we only create one ApplicationInfo per app,
so the default object identity comparison should be fine. We should probably
log an error if we call {{removeApplication}} on an application that has
already been removed, though. (Also, why do we need the {{apps}} HashSet when
we could just use {{idToApp.values}}?)
> Standalone Master can fail to recognize completed/failed applications
> ---------------------------------------------------------------------
>
> Key: SPARK-4498
> URL: https://issues.apache.org/jira/browse/SPARK-4498
> Project: Spark
> Issue Type: Bug
> Components: Deploy, Spark Core
> Affects Versions: 1.1.1, 1.2.0
> Environment: - Linux dn11.chi.shopify.com 3.2.0-57-generic
> #87-Ubuntu SMP 3 x86_64 x86_64 x86_64 GNU/Linux
> - Standalone Spark built from
> apache/spark#c6e0c2ab1c29c184a9302d23ad75e4ccd8060242
> - Python 2.7.3
> java version "1.7.0_71"
> Java(TM) SE Runtime Environment (build 1.7.0_71-b14)
> Java HotSpot(TM) 64-Bit Server VM (build 24.71-b01, mixed mode)
> - 1 Spark master, 40 Spark workers with 32 cores a piece and 60-90 GB of
> memory a piece
> - All client code is PySpark
> Reporter: Harry Brundage
> Priority: Blocker
> Attachments: all-master-logs-around-blip.txt,
> one-applications-master-logs.txt
>
>
> We observe the spark standalone master not detecting that a driver
> application has completed after the driver process has shut down
> indefinitely, leaving that driver's resources consumed indefinitely. The
> master reports applications as Running, but the driver process has long since
> terminated. The master continually spawns one executor for the application.
> It boots, times out trying to connect to the driver application, and then
> dies with the exception below. The master then spawns another executor on a
> different worker, which does the same thing. The application lives until the
> master (and workers) are restarted.
> This happens to many jobs at once, all right around the same time, two or
> three times a day, where they all get suck. Before and after this "blip"
> applications start, get resources, finish, and are marked as finished
> properly. The "blip" is mostly conjecture on my part, I have no hard evidence
> that it exists other than my identification of the pattern in the Running
> Applications table. See
> http://cl.ly/image/2L383s0e2b3t/Screen%20Shot%202014-11-19%20at%203.43.09%20PM.png
> : the applications started before the blip at 1.9 hours ago still have
> active drivers. All the applications started 1.9 hours ago do not, and the
> applications started less than 1.9 hours ago (at the top of the table) do in
> fact have active drivers.
> Deploy mode:
> - PySpark drivers running on one node outside the cluster, scheduled by a
> cron-like application, not master supervised
>
> Other factoids:
> - In most places, we call sc.stop() explicitly before shutting down our
> driver process
> - Here's the sum total of spark configuration options we don't set to the
> default:
> {code}
> "spark.cores.max": 30
> "spark.eventLog.dir": "hdfs://nn.shopify.com:8020/var/spark/event-logs"
> "spark.eventLog.enabled": true
> "spark.executor.memory": "7g"
> "spark.hadoop.fs.defaultFS": "hdfs://nn.shopify.com:8020/"
> "spark.io.compression.codec": "lzf"
> "spark.ui.killEnabled": true
> {code}
> - The exception the executors die with is this:
> {code}
> 14/11/19 19:42:37 INFO CoarseGrainedExecutorBackend: Registered signal
> handlers for [TERM, HUP, INT]
> 14/11/19 19:42:37 WARN NativeCodeLoader: Unable to load native-hadoop library
> for your platform... using builtin-java classes where applicable
> 14/11/19 19:42:37 INFO SecurityManager: Changing view acls to: spark,azkaban
> 14/11/19 19:42:37 INFO SecurityManager: Changing modify acls to: spark,azkaban
> 14/11/19 19:42:37 INFO SecurityManager: SecurityManager: authentication
> disabled; ui acls disabled; users with view permissions: Set(spark, azkaban);
> users with modify permissions: Set(spark, azkaban)
> 14/11/19 19:42:37 INFO Slf4jLogger: Slf4jLogger started
> 14/11/19 19:42:37 INFO Remoting: Starting remoting
> 14/11/19 19:42:38 INFO Remoting: Remoting started; listening on addresses
> :[akka.tcp://driverpropsfetc...@dn13.chi.shopify.com:37682]
> 14/11/19 19:42:38 INFO Utils: Successfully started service
> 'driverPropsFetcher' on port 37682.
> 14/11/19 19:42:38 WARN Remoting: Tried to associate with unreachable remote
> address [akka.tcp://sparkdri...@spark-etl1.chi.shopify.com:58849]. Address is
> now gated for 5000 ms, all messages to this address will be delivered to dead
> letters. Reason: Connection refused:
> spark-etl1.chi.shopify.com/172.16.126.88:58849
> 14/11/19 19:43:08 ERROR UserGroupInformation: PriviledgedActionException
> as:azkaban (auth:SIMPLE) cause:java.util.concurrent.TimeoutException: Futures
> timed out after [30 seconds]
> Exception in thread "main" java.lang.reflect.UndeclaredThrowableException:
> Unknown exception in doAs
> at
> org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1421)
> at
> org.apache.spark.deploy.SparkHadoopUtil.runAsSparkUser(SparkHadoopUtil.scala:59)
> at
> org.apache.spark.executor.CoarseGrainedExecutorBackend$.run(CoarseGrainedExecutorBackend.scala:115)
> at
> org.apache.spark.executor.CoarseGrainedExecutorBackend$.main(CoarseGrainedExecutorBackend.scala:163)
> at
> org.apache.spark.executor.CoarseGrainedExecutorBackend.main(CoarseGrainedExecutorBackend.scala)
> Caused by: java.security.PrivilegedActionException:
> java.util.concurrent.TimeoutException: Futures timed out after [30 seconds]
> at java.security.AccessController.doPrivileged(Native Method)
> at javax.security.auth.Subject.doAs(Subject.java:415)
> at
> org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1408)
> ... 4 more
> Caused by: java.util.concurrent.TimeoutException: Futures timed out after [30
> seconds]
> at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
> at
> scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
> at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107)
> at
> scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
> at scala.concurrent.Await$.result(package.scala:107)
> at
> org.apache.spark.executor.CoarseGrainedExecutorBackend$$anonfun$run$1.apply$mcV$sp(CoarseGrainedExecutorBackend.scala:127)
> at
> org.apache.spark.deploy.SparkHadoopUtil$$anon$1.run(SparkHadoopUtil.scala:60)
> at
> org.apache.spark.deploy.SparkHadoopUtil$$anon$1.run(SparkHadoopUtil.scala:59)
> ... 7 more
> {code}
> Cluster history:
> - We run spark versions built from apache/spark#master snapshots. We did not
> observe this behaviour on {{7eb9cbc273d758522e787fcb2ef68ef65911475f}} (sorry
> its so old), but now observe it on
> {{c6e0c2ab1c29c184a9302d23ad75e4ccd8060242}}. We can try new versions to
> assist debugging.
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