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From Patrick Wendell <pwend...@gmail.com>
Subject Re: Hadoop's Configuration object isn't threadsafe
Date Thu, 17 Jul 2014 06:14:21 GMT
Hey Andrew,

I think you are correct and a follow up to SPARK-2521 will end up
fixing this. The desing of SPARK-2521 automatically broadcasts RDD
data in tasks and the approach creates a new copy of the RDD and
associated data for each task. A natural follow-up to that patch is to
stop handling the jobConf separately (since we will now broadcast all
referents of the RDD itself) and just have it broadcasted with the
RDD. I'm not sure if Reynold plans to include this in SPARK-2521 or
afterwards, but it's likely we'd do that soon.

- Patrick

On Wed, Jul 16, 2014 at 10:24 PM, Andrew Ash <andrew@andrewash.com> wrote:
> Hi Patrick, thanks for taking a look.  I filed as
> https://issues.apache.org/jira/browse/SPARK-2546
>
> Would you recommend I pursue the cloned Configuration object approach now
> and send in a PR?
>
> Reynold's recent announcement of the broadcast RDD object patch may also
> have implications of the right path forward here.  I'm not sure I fully
> understand the implications though:
> https://github.com/apache/spark/pull/1452
>
> "Once this is committed, we can also remove the JobConf broadcast in
> HadoopRDD."
>
> Thanks!
> Andrew
>
>
> On Tue, Jul 15, 2014 at 5:20 PM, Patrick Wendell <pwendell@gmail.com> wrote:
>
>> Hey Andrew,
>>
>> Cloning the conf this might be a good/simple fix for this particular
>> problem. It's definitely worth looking into.
>>
>> There are a few things we can probably do in Spark to deal with
>> non-thread-safety inside of the Hadoop FileSystem and Configuration
>> classes. One thing we can do in general is to add barriers around the
>> locations where we knowingly access Hadoop FileSystem and
>> Configuration state from multiple threads (e.g. during our own calls
>> to getRecordReader in this case). But this will only deal with "writer
>> writer" conflicts where we had multiple calls mutating the same object
>> at the same time. It won't deal with "reader writer" conflicts where
>> some of our initialization code touches state that is needed during
>> normal execution of other tasks.
>>
>> - Patrick
>>
>> On Tue, Jul 15, 2014 at 12:56 PM, Andrew Ash <andrew@andrewash.com> wrote:
>> > Hi Shengzhe,
>> >
>> > Even if we did make Configuration threadsafe, it'd take quite some time
>> for
>> > that to trickle down to a Hadoop release that we could actually rely on
>> > Spark users having installed.  I agree we should consider whether making
>> > Configuration threadsafe is something that Hadoop should do, but for the
>> > short term I think Spark needs to be able to handle the common scenario
>> of
>> > Configuration being single-threaded.
>> >
>> > Thanks!
>> > Andrew
>> >
>> >
>> > On Tue, Jul 15, 2014 at 2:43 PM, yao <yaoshengzhe@gmail.com> wrote:
>> >
>> >> Good catch Andrew. In addition to your proposed solution, is that
>> possible
>> >> to fix Configuration class and make it thread-safe ? I think the fix
>> should
>> >> be trivial, just use a ConcurrentHashMap, but I am not sure if we can
>> push
>> >> this change upstream (will hadoop guys accept this change ? for them, it
>> >> seems they never expect Configuration object being accessed by multiple
>> >> threads).
>> >>
>> >> -Shengzhe
>> >>
>> >>
>> >> On Mon, Jul 14, 2014 at 10:22 PM, Andrew Ash <andrew@andrewash.com>
>> wrote:
>> >>
>> >> > Hi Spark devs,
>> >> >
>> >> > 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.
>> >> >
>> >> >
>> >> >
>> >> > "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)
>> >> >
>> >> >
>> >>
>>

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