I am sorry, I didn't understand it completely. Are you suggesting to copy
the files from S3 to HDFS? Actually, that is what I am doing. I am reading
the files using Spark and persisting it locally.
Or did you actually mean to ask the producer to write the files directly to
HDFS instead of S3? I am not sure I can do this now either.
Please clarify me if I misunderstood what you meant.
Thanks,
Jegan
On Mon, Oct 5, 2015 at 4:42 PM, Reynold Xin <r...@databricks.com> wrote:
> 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
>>>>
>>>
>>>
>>
>
