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https://issues.apache.org/jira/browse/SPARK-4498?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14230910#comment-14230910
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Mark Hamstra commented on SPARK-4498:
-------------------------------------
I'd argue against reverting 2425 on the grounds that a long-running application
being killed when it is still able to make progress is a worse bug than
Executors repeatedly trying to run an application that no longer exists.
Either way, it seems to me that the existing logic may not be too far from
being right. The flaw simply seems to be that an Executor process starting and
successfully connecting to stderr and stdout are necessary but not sufficient
conditions for that Executor to be transitioned to RUNNING. If the Executor
doesn't become RUNNING until it succeeds in connecting to its Application, then
I think the problem is almost entirely and perhaps completely solved.
(Although I think SPARK-2424 should also be implemented.)
> 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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