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https://issues.apache.org/jira/browse/SPARK-2546?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14099239#comment-14099239
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Patrick Wendell commented on SPARK-2546:
----------------------------------------
Hey Andrew I think due to us cutting SPARK-2585 from this release it will
remain broken in Spark 1.1. We could look into a solution based on clone()'ing
the conf for future patch releases in the 1.1 branch.
> Configuration object thread safety issue
> ----------------------------------------
>
> Key: SPARK-2546
> URL: https://issues.apache.org/jira/browse/SPARK-2546
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 0.9.1
> Reporter: Andrew Ash
> Assignee: Josh Rosen
> Priority: Critical
>
> // observed in 0.9.1 but expected to exist in 1.0.1 as well
> This ticket is copy-pasted from a thread on the dev@ list:
> {quote}
> We discovered a very interesting bug in Spark at work last week in Spark
> 0.9.1 — that the way Spark uses the Hadoop Configuration object is prone to
> thread safety issues. I believe it still applies in Spark 1.0.1 as well.
> Let me explain:
> Observations
> - Was running a relatively simple job (read from Avro files, do a map, do
> another map, write back to Avro files)
> - 412 of 413 tasks completed, but the last task was hung in RUNNING state
> - The 412 successful tasks completed in median time 3.4s
> - The last hung task didn't finish even in 20 hours
> - The executor with the hung task was responsible for 100% of one core of
> CPU usage
> - Jstack of the executor attached (relevant thread pasted below)
> Diagnosis
> After doing some code spelunking, we determined the issue was concurrent use
> of a Configuration object for each task on an executor. In Hadoop each task
> runs in its own JVM, but in Spark multiple tasks can run in the same JVM, so
> the single-threaded access assumptions of the Configuration object no longer
> hold in Spark.
> The specific issue is that the AvroRecordReader actually _modifies_ the
> JobConf it's given when it's instantiated! It adds a key for the RPC
> protocol engine in the process of connecting to the Hadoop FileSystem. When
> many tasks start at the same time (like at the start of a job), many tasks
> are adding this configuration item to the one Configuration object at once.
> Internally Configuration uses a java.lang.HashMap, which isn't threadsafe…
> The below post is an excellent explanation of what happens in the situation
> where multiple threads insert into a HashMap at the same time.
> http://mailinator.blogspot.com/2009/06/beautiful-race-condition.html
> The gist is that you have a thread following a cycle of linked list nodes
> indefinitely. This exactly matches our observations of the 100% CPU core and
> also the final location in the stack trace.
> So it seems the way Spark shares a Configuration object between task threads
> in an executor is incorrect. We need some way to prevent concurrent access
> to a single Configuration object.
> Proposed fix
> We can clone the JobConf object in HadoopRDD.getJobConf() so each task gets
> its own JobConf object (and thus Configuration object). The optimization of
> broadcasting the Configuration object across the cluster can remain, but on
> the other side I think it needs to be cloned for each task to allow for
> concurrent access. I'm not sure the performance implications, but the
> comments suggest that the Configuration object is ~10KB so I would expect a
> clone on the object to be relatively speedy.
> Has this been observed before? Does my suggested fix make sense? I'd be
> happy to file a Jira ticket and continue discussion there for the right way
> to fix.
> Thanks!
> Andrew
> P.S. For others seeing this issue, our temporary workaround is to enable
> spark.speculation, which retries failed (or hung) tasks on other machines.
> {noformat}
> "Executor task launch worker-6" daemon prio=10 tid=0x00007f91f01fe000
> nid=0x54b1 runnable [0x00007f92d74f1000]
> java.lang.Thread.State: RUNNABLE
> at java.util.HashMap.transfer(HashMap.java:601)
> at java.util.HashMap.resize(HashMap.java:581)
> at java.util.HashMap.addEntry(HashMap.java:879)
> at java.util.HashMap.put(HashMap.java:505)
> at org.apache.hadoop.conf.Configuration.set(Configuration.java:803)
> at org.apache.hadoop.conf.Configuration.set(Configuration.java:783)
> at org.apache.hadoop.conf.Configuration.setClass(Configuration.java:1662)
> at org.apache.hadoop.ipc.RPC.setProtocolEngine(RPC.java:193)
> at
> org.apache.hadoop.hdfs.NameNodeProxies.createNNProxyWithClientProtocol(NameNodeProxies.java:343)
> at
> org.apache.hadoop.hdfs.NameNodeProxies.createNonHAProxy(NameNodeProxies.java:168)
> at
> org.apache.hadoop.hdfs.NameNodeProxies.createProxy(NameNodeProxies.java:129)
> at org.apache.hadoop.hdfs.DFSClient.<init>(DFSClient.java:436)
> at org.apache.hadoop.hdfs.DFSClient.<init>(DFSClient.java:403)
> at
> org.apache.hadoop.hdfs.DistributedFileSystem.initialize(DistributedFileSystem.java:125)
> at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2262)
> at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:86)
> at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2296)
> at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2278)
> at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:316)
> at org.apache.hadoop.fs.Path.getFileSystem(Path.java:194)
> at org.apache.avro.mapred.FsInput.<init>(FsInput.java:37)
> at
> org.apache.avro.mapred.AvroRecordReader.<init>(AvroRecordReader.java:43)
> at
> org.apache.avro.mapred.AvroInputFormat.getRecordReader(AvroInputFormat.java:52)
> at org.apache.spark.rdd.HadoopRDD$$anon$1.<init>(HadoopRDD.scala:156)
> at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:149)
> at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:64)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:241)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:232)
> at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:241)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:232)
> at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:241)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:232)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:109)
> at org.apache.spark.scheduler.Task.run(Task.scala:53)
> at
> org.apache.spark.executor.Executor$TaskRunner$$anonfun$run$1.apply$mcV$sp(Executor.scala:211)
> at
> org.apache.spark.deploy.SparkHadoopUtil$$anon$1.run(SparkHadoopUtil.scala:42)
> at
> org.apache.spark.deploy.SparkHadoopUtil$$anon$1.run(SparkHadoopUtil.scala:41)
> at java.security.AccessController.doPrivileged(Native Method)
> at javax.security.auth.Subject.doAs(Subject.java:415)
> at
> org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1408)
> at
> org.apache.spark.deploy.SparkHadoopUtil.runAsUser(SparkHadoopUtil.scala:41)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:176)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:745)
> {noformat}
> {quote}
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