Why does Spark RDD partition has 2GB limit for HDFS?
I have get an error when using mllib RandomForest to train data. As my dataset is huge and the default partition is relative small. so an exception thrown indicating that "Size exceeds Integer.MAX_VALUE" ,the orignal stack trace as following,
15/04/16 14:13:03 WARN scheduler.TaskSetManager: Lost task 19.0 in stage 6.0 (TID 120, 10.215.149.47): java.lang.IllegalArgumentException: Size exceeds Integer.MAX_VALUE
at sun.nio.ch.FileChannelImpl.map(FileChannelImpl.java:828) at org.apache.spark.storage.DiskStore.getBytes(DiskStore.scala:123) at org.apache.spark.storage.DiskStore.getBytes(DiskStore.scala:132) at org.apache.spark.storage.BlockManager.doGetLocal(BlockManager.scala:517) at org.apache.spark.storage.BlockManager.getLocal(BlockManager.scala:432) at org.apache.spark.storage.BlockManager.get(BlockManager.scala:618) at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:146) at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:70)
The Integer.MAX_SIZE is 2GB, it seems that some partition out of memory. So i repartiton my rdd partition to 1000, so that each partition could hold far less data as before. Finally, the problem is solved!!!
So, my question is : Why partition size has the 2G limit? It seems that there is no configure set for the limit in the spark
Answer
The basic abstraction for blocks in spark is a ByteBuffer, which unfortunately has a limit of Integer.MAX_VALUE (~2GB).
It is a critical issue which prevents use of spark with very large datasets. Increasing the number of partitions can resolve it (like in OP's case), but is not always feasible, for instance when there is large chain of transformations part of which can increase data (flatMap etc) or in cases where data is skewed.
The solution proposed is to come up with an abstraction like LargeByteBuffer which can support list of bytebuffers for a block. This impacts overall spark architecture, so it has remained unresolved for quite a while.