Yes, https://issues.apache.org/jira/browse/SPARK-2075 is what I met. Thanks!
I think we need to address this issue. At least we need to document this issue. -----Original Message----- From: Sean Owen [mailto:so...@cloudera.com] Sent: Thursday, December 18, 2014 5:47 PM To: Shixiong Zhu Cc: Sun, Rui; shiva...@eecs.berkeley.edu; user@spark.apache.org Subject: Re: weird bytecode incompatability issue between spark-core jar from mvn repo and official spark prebuilt binary Have a look at https://issues.apache.org/jira/browse/SPARK-2075 It's not quite that the API is different, but indeed building different 'flavors' of the same version (hadoop1 vs 2) can strangely lead to this problem, even though the public API is identical and in theory the API is completely separate from the backend bindings. IIRC the idea is that only submitting via spark-submit is really supported, because there you're definitely running exactly what's on your cluster. That should always work. This sort of gotcha turns up in some specific cases but you can always work around it by matching your embedded Spark version as well. On Thu, Dec 18, 2014 at 9:38 AM, Shixiong Zhu <zsxw...@gmail.com> wrote: > @Rui do you mean the spark-core jar in the maven central repo are > incompatible with the same version of the the official pre-built Spark > binary? That's really weird. I thought they should have used the same codes. > > Best Regards, > > Shixiong Zhu > > 2014-12-18 17:22 GMT+08:00 Sean Owen <so...@cloudera.com>: >> >> Well, it's always a good idea to used matched binary versions. Here >> it is more acutely necessary. You can use a pre built binary -- if >> you use it to compile and also run. Why does it not make sense to >> publish artifacts? >> >> Not sure what you mean about core vs assembly, as the assembly >> contains all of the modules. You don't literally need the same jar >> file. >> >> On Thu, Dec 18, 2014 at 3:20 AM, Sun, Rui <rui....@intel.com> wrote: >> > Not using spark-submit. The App directly communicates with the >> > Spark cluster in standalone mode. >> > >> > >> > >> > If mark the Spark dependency as 'provided’, then the spark-core >> > .jar elsewhere must be pointe to in CLASSPATH. However, the >> > pre-built Spark binary only has an assembly jar, not having >> > individual module jars. So you don’t have a chance to point to a >> > module.jar which is the same binary as that in the pre-built Spark >> > binary. >> > >> > >> > >> > Maybe the Spark distribution should contain not only the assembly >> > jar but also individual module jars. Any opinion? >> > >> > >> > >> > From: Shivaram Venkataraman [mailto:shiva...@eecs.berkeley.edu] >> > Sent: Thursday, December 18, 2014 2:20 AM >> > To: Sean Owen >> > Cc: Sun, Rui; user@spark.apache.org >> > Subject: Re: weird bytecode incompatability issue between >> > spark-core jar from mvn repo and official spark prebuilt binary >> > >> > >> > >> > Just to clarify, are you running the application using spark-submit >> > after packaging with sbt package ? One thing that might help is to >> > mark the Spark dependency as 'provided' as then you shouldn't have >> > the Spark classes in your jar. >> > >> > >> > >> > Thanks >> > >> > Shivaram >> > >> > >> > >> > On Wed, Dec 17, 2014 at 4:39 AM, Sean Owen <so...@cloudera.com> wrote: >> > >> > You should use the same binaries everywhere. The problem here is >> > that anonymous functions get compiled to different names when you >> > build different (potentially) so you actually have one function >> > being called when another function is meant. >> > >> > >> > On Wed, Dec 17, 2014 at 12:07 PM, Sun, Rui <rui....@intel.com> wrote: >> >> Hi, >> >> >> >> >> >> >> >> I encountered a weird bytecode incompatability issue between >> >> spark-core jar from mvn repo and official spark prebuilt binary. >> >> >> >> >> >> >> >> Steps to reproduce: >> >> >> >> 1. Download the official pre-built Spark binary 1.1.1 at >> >> http://d3kbcqa49mib13.cloudfront.net/spark-1.1.1-bin-hadoop1.tgz >> >> >> >> 2. Launch the Spark cluster in pseudo cluster mode >> >> >> >> 3. A small scala APP which calls RDD.saveAsObjectFile() >> >> >> >> scalaVersion := "2.10.4" >> >> >> >> >> >> >> >> libraryDependencies ++= Seq( >> >> >> >> "org.apache.spark" %% "spark-core" % "1.1.1" >> >> >> >> ) >> >> >> >> >> >> >> >> val sc = new SparkContext(args(0), "test") //args[0] is the Spark >> >> master URI >> >> >> >> val rdd = sc.parallelize(List(1, 2, 3)) >> >> >> >> rdd.saveAsObjectFile("/tmp/mysaoftmp") >> >> >> >> sc.stop >> >> >> >> >> >> >> >> throws an exception as follows: >> >> >> >> [error] (run-main-0) org.apache.spark.SparkException: Job aborted >> >> due to stage failure: Task 1 in stage 0.0 failed 4 times, most >> >> recent failure: >> >> Lost >> >> task 1.3 in stage 0.0 (TID 6, ray-desktop.sh.intel.com): >> >> java.lang.ClassCastException: scala.Tuple2 cannot be cast to >> >> scala.collection.Iterator >> >> >> >> [error] >> >> org.apache.spark.rdd.RDD$$anonfun$13.apply(RDD.scala:596) >> >> >> >> [error] >> >> org.apache.spark.rdd.RDD$$anonfun$13.apply(RDD.scala:596) >> >> >> >> [error] >> >> >> >> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.sca >> >> la:35) >> >> >> >> [error] >> >> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) >> >> >> >> [error] org.apache.spark.rdd.RDD.iterator(RDD.scala:229) >> >> >> >> [error] >> >> org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31) >> >> >> >> [error] >> >> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) >> >> >> >> [error] org.apache.spark.rdd.RDD.iterator(RDD.scala:229) >> >> >> >> [error] >> >> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:62) >> >> >> >> [error] org.apache.spark.scheduler.Task.run(Task.scala:54) >> >> >> >> [error] >> >> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:1 >> >> 78) >> >> >> >> [error] >> >> >> >> >> >> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecut >> >> or.java:1146) >> >> >> >> [error] >> >> >> >> >> >> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecu >> >> tor.java:615) >> >> >> >> [error] java.lang.Thread.run(Thread.java:701) >> >> >> >> >> >> >> >> After investigation, I found that this is caused by bytecode >> >> incompatibility issue between RDD.class in >> >> spark-core_2.10-1.1.1.jar and the pre-built spark assembly >> >> respectively. >> >> >> >> >> >> >> >> This issue also happens with spark 1.1.0. >> >> >> >> >> >> >> >> Is there anything wrong in my usage of Spark? Or anything wrong in >> >> the process of deploying Spark module jars to maven repo? >> >> >> >> >> > >> > ------------------------------------------------------------------- >> > -- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For >> > additional commands, e-mail: user-h...@spark.apache.org >> >> --------------------------------------------------------------------- >> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For >> additional commands, e-mail: user-h...@spark.apache.org >> >
