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) > >
