Hi all – I’m running a long running batch-processing job with Spark through
Yarn. I am doing the following
Batch Process
val resultsArr =
sc.accumulableCollection(mutable.ArrayBuffer[ListenableFuture[Result]]())
InMemoryArray.forEach{
1) Using a thread pool, generate callable jobs that operate on an RDD
1a) These callable jobs perform an operation combining that RDD and a
broadcasted array and store the result of that computation as an Array (Result)
2) Store the results of this operation (upon resolution) in the
accumulableCollection
}
sc.parallelize(resultsArr).saveAsObjectFile (about 1gb of data), happens a
total of about 4 times during execution over the course of several hours.
My immediate problem is that during this execution two things happen.
Firstly, on my driver node I eventually run out of memory, and start swapping
to disk (which causes slowdown). However, each Batch can be processed entirely
within the available memory on the driver, so basically this memory is somehow
not being released between runs (even though I leave the context of the
function running the Batch process)
Secondly, during execution, things are being written to HDFS and I am running
out of space on the local partitions on the node. Note, this is NOT the
explicit saveAsObjectFile call that I am making, but appears to be something
going on with Spark internally.
Can anyone speak to what is going on under the hood here and what I can do to
resolve this?
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