Try allocating some more resources to your application.
You seem to be using 512Mb for you worker node - (you can verify that from
the master UI)
Try putting the following settings into your code and see if it helps -
System.setProperty("spark.executor.memory","15g") // Will allocate more
memory
System.setProperty("spark.akka.frameSize","2000")
System.setProperty("spark.akka.threads","16") // Dependent upon
number of cores with your worker machine
On Fri, Dec 6, 2013 at 1:06 AM, learner1014 all <learner1...@gmail.com>wrote:
> Hi,
>
> Trying to do a join operation on an RDD, my input is pipe delimited data
> and there are 2 files.
> One file is 24MB and the other file is 285MB.
> Setup being used is the single node (server) setup: SPARK_MEM set to 512m
>
> Master
> /pkg/java/jdk1.7.0_11/bin/java -cp
> :/spark-0.8.0-incubating-bin-cdh4/conf:/spark-0.8.0-incubating-bin-cdh4/assembly/target/scala-2.9.3/spark-assembly-0.8.0-incubating-hadoop1.2.1.jar
> -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps
> -Dspark.boundedMemoryCache.memoryFraction=0.4
> -Dspark.cache.class=spark.DiskSpillingCache -XX:+UseConcMarkSweepGC
> -Djava.library.path= -Xms512m -Xmx512m
> org.apache.spark.deploy.master.Master --ip localhost --port 7077
> --webui-port 8080
>
> Worker
> /pkg/java/jdk1.7.0_11/bin/java -cp
> :/spark-0.8.0-incubating-bin-cdh4/conf:/spark-0.8.0-incubating-bin-cdh4/assembly/target/scala-2.9.3/spark-assembly-0.8.0-incubating-hadoop1.2.1.jar
> -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps
> -Dspark.boundedMemoryCache.memoryFraction=0.4
> -Dspark.cache.class=spark.DiskSpillingCache -XX:+UseConcMarkSweepGC
> -Djava.library.path= -Xms512m -Xmx512m
> org.apache.spark.deploy.worker.Worker spark://localhost:7077
>
>
> App
> /pkg/java/jdk1.7.0_11/bin/java -cp
> :/spark-0.8.0-incubating-bin-cdh4/conf:/spark-0.8.0-incubating-bin-cdh4/assembly/target/scala-2.9.3/spark-assembly-0.8.0-incubating-hadoop1.2.1.jar:/spark-0.8.0-incubating-bin-cdh4/core/target/scala-2.9.3/test-classes:/spark-0.8.0-incubating-bin-cdh4/repl/target/scala-2.9.3/test-classes:/spark-0.8.0-incubating-bin-cdh4/mllib/target/scala-2.9.3/test-classes:/spark-0.8.0-incubating-bin-cdh4/bagel/target/scala-2.9.3/test-classes:/spark-0.8.0-incubating-bin-cdh4/streaming/target/scala-2.9.3/test-classes
> -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps
> -Dspark.boundedMemoryCache.memoryFraction=0.4
> -Dspark.cache.class=spark.DiskSpillingCache -XX:+UseConcMarkSweepGC
> -Xms512M -Xmx512M org.apache.spark.executor.StandaloneExecutorBackend
> akka://spark@localhost:33024/user/StandaloneScheduler 1 localhost 4
>
>
> Here is the code
> import org.apache.spark.SparkContext
> import org.apache.spark.SparkContext._
> import org.apache.spark.storage.StorageLevel
>
> object SimpleApp {
>
> def main (args: Array[String]) {
>
>
> System.setProperty("spark.local.dir","/spark-0.8.0-incubating-bin-cdh4/tmp");
> System.setProperty("spark.serializer",
> "org.apache.spark.serializer.KryoSerializer")
> System.setProperty("spark.akka.timeout", "30") //in seconds
>
> val dataFile2 = "/tmp_data/data1.txt"
> val dataFile1 = "/tmp_data/data2.txt"
> val sc = new SparkContext("spark://localhost:7077", "Simple App",
> "/spark-0.8.0-incubating-bin-cdh4",
> List("target/scala-2.9.3/simple-project_2.9.3-1.0.jar"))
>
> val data10 = sc.textFile(dataFile1, 128)
> val data11 = data10.map(x => x.split("|"))
> val data12 = data11.map( x => (x(1).toInt -> x) )
>
>
> val data20 = sc.textFile(dataFile2, 128)
> val data21 = data20.map(x => x.split("|"))
> val data22 = data21.map(x => (x(1).toInt -> x))
>
>
> val data3 = data12.join(data22, 128)
> val data4 = data3.distinct(4)
> val numAs = data10.count()
> val numBs = data20.count()
> val numCs = data3.count()
> val numDs = data4.count()
> println("Lines in 1: %s, Lines in 2: %s Lines in 3: %s Lines in 4:
> %s".format(numAs, numBs, numCs, numDs))
> data4.foreach(println)
> }
>
> I see the following errors
> 13/12/04 10:53:55 WARN storage.BlockManagerMaster: Error sending message
> to BlockManagerMaster in 1 attempts
> java.util.concurrent.TimeoutException: Futures timed out after [10000]
> milliseconds
> at akka.dispatch.DefaultPromise.ready(Future.scala:870)
> at akka.dispatch.DefaultPromise.result(Future.scala:874)
> at akka.dispatch.Await$.result(Future.scala:74)
>
> and
> 13/12/04 10:53:55 ERROR executor.Executor: Exception in task ID 517
> java.lang.OutOfMemoryError: Java heap space
> at com.esotericsoftware.kryo.io.Input.readString(Input.java:448)
> at
> com.esotericsoftware.kryo.serializers.DefaultArraySerializers$StringArraySerializer.read(DefaultArraySerializers.java:282)
> at
> com.esotericsoftware.kryo.serializers.DefaultArraySerializers$StringArraySerializer.read(DefaultArraySerializers.java:262)
> at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:729)
> at
> com.twitter.chill.Tuple2Serializer.read(TupleSerializers.scala:43)
> at
> com.twitter.chill.Tuple2Serializer.read(TupleSerializers.scala:34)
> at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:729)
> at
> org.apache.spark.serializer.KryoDeserializationStream.readObject(KryoSerializer.scala:106)
> at
> org.apache.spark.serializer.DeserializationStream$$anon$1.getNext(Serializer.scala:101)
> at
> org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:71)
>
> Lots of hem actually...
>
>
> To give some additional information, i just added single columns in both
> files and passed them through this program and encountered the same issue.
> Out of memory and other errors.
>
> What did work was removal of the following lines:
>
> val data21 = data20.map(x => x.split("|"))
> val data22 = data21.map(x => (x(1).toInt -> x))
>
> which were replaced by:
> val data22 = data20.map(x => (x.toInt -> x))
>
> However as soon as i add additional columns this is of-course not going to
> work.
> So can someone explain this and any suggestions are most welcome.
> Any help is helpful.
> Thanks
>
