You can write the data to local hdfs (or local disk) and just load it from
there.
On Mon, Oct 5, 2015 at 4:37 PM, Jegan <jega...@gmail.com> wrote:
> Thanks for your suggestion Ted.
>
> Unfortunately at this point of time I cannot go beyond 1000 partitions. I
> am writing this data to BigQuery and it has a limit of 1000 jobs per day
> for a table(they have some limits on this) I currently create 1 load job
> per partition. Is there any other work-around?
>
> Thanks again.
>
> Regards,
> Jegan
>
> On Mon, Oct 5, 2015 at 3:53 PM, Ted Yu <yuzhih...@gmail.com> wrote:
>
>> As a workaround, can you set the number of partitions higher in the
>> sc.textFile method ?
>>
>> Cheers
>>
>> On Mon, Oct 5, 2015 at 3:31 PM, Jegan <jega...@gmail.com> wrote:
>>
>>> Hi All,
>>>
>>> I am facing the below exception when the size of the file being read in
>>> a partition is above 2GB. This is apparently because Java's limitation on
>>> memory mapped files. It supports mapping only 2GB files.
>>>
>>> Caused by: 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:1207)
>>> 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.DiskStore.putIterator(DiskStore.scala:102)
>>> at
>>> org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:791)
>>> at
>>> org.apache.spark.storage.BlockManager.putIterator(BlockManager.scala:638)
>>> at
>>> org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:153)
>>> at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78)
>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:262)
>>> at
>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>>> at
>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>>> at org.apache.spark.scheduler.Task.run(Task.scala:88)
>>> at
>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
>>> at
>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>>> at
>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>>> at java.lang.Thread.run(Thread.java:745)
>>>
>>> My use case is to read the files from S3 and do some processing. I am
>>> caching the data like below in order to avoid SocketTimeoutExceptions from
>>> another library I am using for the processing.
>>>
>>> val rdd1 = sc.textFile("*******").coalesce(1000)
>>> rdd1.persist(DISK_ONLY_2) // replication factor 2
>>> rdd1.foreachPartition { iter => } // one pass over the data to download
>>>
>>> The 3rd line fails with the above error when a partition contains a file
>>> of size more than 2GB file.
>>>
>>> Do you think this needs to be fixed in Spark? One idea may be is to use
>>> a wrapper class (something called BigByteBuffer) which keeps an array of
>>> ByteBuffers and keeps the index of the current buffer being read etc. Below
>>> is the modified DiskStore.scala.
>>>
>>> private def getBytes(file: File, offset: Long, length: Long):
>>> Option[ByteBuffer] = {
>>> val channel = new RandomAccessFile(file, "r").getChannel
>>> Utils.tryWithSafeFinally {
>>> // For small files, directly read rather than memory map
>>> if (length < minMemoryMapBytes) {
>>> // Map small file in Memory
>>> } else {
>>> // TODO Create a BigByteBuffer
>>>
>>> }
>>> } {
>>> channel.close()
>>> }
>>> }
>>>
>>> class BigByteBuffer extends ByteBuffer {
>>> val buffers: Array[ByteBuffer]
>>> var currentIndex = 0
>>>
>>> ... // Other methods
>>> }
>>>
>>> Please let me know if there is any other work-around for the same. Thanks
>>> for your time.
>>>
>>> Regards,
>>> Jegan
>>>
>>
>>
>
