Thanks Matei! It worked.

On 9 July 2015 at 19:43, Matei Zaharia <matei.zaharia@gmail.com> wrote:
Thus means that one of your cached RDD partitions is bigger than 2 GB of data. You can fix it by having more partitions. If you read data from a file system like HDFS or S3, set the number of partitions higher in the sc.textFile, hadoopFile, etc methods (it's an optional second parameter to those methods). If you create it through parallelize or if this particular RDD comes from a shuffle, use more tasks in the parallelize or shuffle.

Matei

On Jul 9, 2015, at 3:35 PM, Michal Čizmazia <micizma@gmail.com> wrote:

Spark version 1.4.0 in the Standalone mode

2015-07-09 20:12:02 INFO  (sparkDriver-akka.actor.default-dispatcher-3) BlockManagerInfo:59 - Added rdd_0_0 on disk on localhost:51132 (size: 29.8 GB)
2015-07-09 20:12:02 ERROR (Executor task launch worker-0) Executor:96 - Exception in task 0.0 in stage 0.0 (TID 0)
java.lang.IllegalArgumentException: Size exceeds Integer.MAX_VALUE
        at sun.nio.ch.FileChannelImpl.map(FileChannelImpl.java:836)
        at org.apache.spark.storage.DiskStore$$anonfun$getBytes$2.apply(DiskStore.scala:125)
        at org.apache.spark.storage.DiskStore$$anonfun$getBytes$2.apply(DiskStore.scala:113)
        at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1285)
        at org.apache.spark.storage.DiskStore.getBytes(DiskStore.scala:127)
        at org.apache.spark.storage.DiskStore.getBytes(DiskStore.scala:134)
        at org.apache.spark.storage.BlockManager.doGetLocal(BlockManager.scala:509)
        at org.apache.spark.storage.BlockManager.getLocal(BlockManager.scala:427)
        at org.apache.spark.storage.BlockManager.get(BlockManager.scala:615)
        at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:154)
        at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:242)
        at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
        at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:242)
        at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
        at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:242)
        at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:70)
        at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
        at org.apache.spark.scheduler.Task.run(Task.scala:70)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
        at java.lang.Thread.run(Thread.java:745)


On 9 July 2015 at 18:11, Ted Yu <yuzhihong@gmail.com> wrote:
Which release of Spark are you using ?

Can you show the complete stack trace ?

getBytes() could be called from:
    getBytes(file, 0, file.length)
or:
    getBytes(segment.file, segment.offset, segment.length)

Cheers

On Thu, Jul 9, 2015 at 2:50 PM, Michal Čizmazia <micizma@gmail.com> wrote:
Please could anyone give me pointers for appropriate SparkConf to work around "Size exceeds Integer.MAX_VALUE"?

Stacktrace:

2015-07-09 20:12:02 INFO  (sparkDriver-akka.actor.default-dispatcher-3) BlockManagerInfo:59 - Added rdd_0_0 on disk on localhost:51132 (size: 29.8 GB)
2015-07-09 20:12:02 ERROR (Executor task launch worker-0) Executor:96 - Exception in task 0.0 in stage 0.0 (TID 0)
java.lang.IllegalArgumentException: Size exceeds Integer.MAX_VALUE
        at sun.nio.ch.FileChannelImpl.map(FileChannelImpl.java:836)
        at org.apache.spark.storage.DiskStore$$anonfun$getBytes$2.apply(DiskStore.scala:125)
...