So I just implemented the logic through a standard join (without collect and broadcast) and it's working great.
The idea behind trying the broadcast was that since the other side of join is a much larger dataset, the process might be faster through collect and broadcast, since it avoids the shuffle of the bigger dataset. I think the join is working much better in this case so I'll probably just use that, still a bit curious as why the error is happening. On Mon, Mar 7, 2016 at 5:55 PM, Tristan Nixon <st...@memeticlabs.org> wrote: > I’m not sure I understand - if it was already distributed over the cluster > in an RDD, why would you want to collect and then re-send it as a broadcast > variable? Why not simply use the RDD that is already distributed on the > worker nodes? > > On Mar 7, 2016, at 7:44 PM, Arash <aras...@gmail.com> wrote: > > Hi Tristan, > > This is not static, I actually collect it from an RDD to the driver. > > On Mon, Mar 7, 2016 at 5:42 PM, Tristan Nixon <st...@memeticlabs.org> > wrote: > >> Hi Arash, >> >> is this static data? Have you considered including it in your jars and >> de-serializing it from jar on each worker node? >> It’s not pretty, but it’s a workaround for serialization troubles. >> >> On Mar 7, 2016, at 5:29 PM, Arash <aras...@gmail.com> wrote: >> >> Hello all, >> >> I'm trying to broadcast a variable of size ~1G to a cluster of 20 nodes >> but haven't been able to make it work so far. >> >> It looks like the executors start to run out of memory during >> deserialization. This behavior only shows itself when the number of >> partitions is above a few 10s, the broadcast does work for 10 or 20 >> partitions. >> >> I'm using the following setup to observe the problem: >> >> val tuples: Array[((String, String), (String, String))] // ~ 10M >> tuples >> val tuplesBc = sc.broadcast(tuples) >> val numsRdd = sc.parallelize(1 to 5000, 100) >> numsRdd.map(n => tuplesBc.value.head).count() >> >> If I set the number of partitions for numsRDD to 20, the count goes >> through successfully, but at 100, I'll start to get errors such as: >> >> 16/03/07 19:35:32 WARN scheduler.TaskSetManager: Lost task 77.0 in stage >> 1.0 (TID 1677, xxx.ec2.internal): java.lang.OutOfMemoryError: Java heap >> space >> at >> java.io.ObjectInputStream$HandleTable.grow(ObjectInputStream.java:3472) >> at >> java.io.ObjectInputStream$HandleTable.assign(ObjectInputStream.java:3278) >> at >> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1789) >> at >> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350) >> at >> java.io.ObjectInputStream.readObject(ObjectInputStream.java:370) >> at >> scala.collection.immutable.HashMap$SerializationProxy.readObject(HashMap.scala:516) >> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) >> at >> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) >> at >> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >> at java.lang.reflect.Method.invoke(Method.java:606) >> at >> java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1058) >> at >> java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1897) >> at >> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798) >> at >> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350) >> at >> java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1997) >> at >> java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1921) >> at >> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798) >> at >> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350) >> at >> java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1997) >> at >> java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1921) >> at >> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798) >> at >> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350) >> at >> java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1997) >> at >> java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1921) >> at >> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798) >> at >> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350) >> at >> java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1997) >> at >> java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1921) >> at >> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798) >> at >> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350) >> at >> java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1997) >> at >> java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1921) >> >> >> I'm using spark 1.5.2. Cluster nodes are amazon r3.2xlarge. The spark >> property maximizeResourceAllocation is set to true (executor.memory = 48G >> according to spark ui environment). We're also using kryo serialization and >> Yarn is the resource manager. >> >> Any ideas as what might be going wrong and how to debug this? >> >> Thanks, >> Arash >> >> >> > >
