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 <pwend...@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 <and...@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 <yaosheng...@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 <and...@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) > >> > > >> > > >> >
