Configuration object thread safety issue

// 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:

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)