Running large join in ALS example through PySpark

["Laird, Benjamin" <Be...@capitalone.com>]

Hello all -

I'm running the ALS/Collaborative Filtering code through pySpark on spark0.9.0. (http://spark.apache.org/docs/0.9.0/mllib-guide.html#using-mllib-in-python)

My data file has about 27M tuples (User, Item, Rating). ALS.train(ratings,1,30) runs on my 3 node cluster (24 cores, 60GB RAM) in about 5 minutes.

However, the following seems to hang:
testdata = ratings.map(lambda p: (int(p[0]), int(p[1])))
predictions = model.predictAll(testdata).map(lambda r: ((r[0], r[1]), r[2]))
ratesAndPreds = ratings.map(lambda r: ((r[0], r[1]), r[2])).join(predictions)
When the join in ratesAndPreds is calculated, 38 tasks are created. 32 are completed with locality level PROCESS_LOCAL in about ~5 minutes. However, 6 tasks are in locality NODE_LOCAL and run for over 45 minutes without completing.

I was receiving a no heartbeat message from the Scheduler, so I changed my java args in spark-env.sh. I don't receive that now, but I have a suspicion that there are still some GC issues.

Does anyone have any suggestions? I read that I can get GC problems or other memory issues if I have too few partitions. Should I investigate that?

Thanks!
Ben


Ben Laird
Data Scientist
(202) 695-6205
Benjamin.Laird@CapitalOne.com<ma...@CapitalOne.com>
[cid:image001.png@01CF5E44.F673E050]<http://www.capitalonelabs.com/>
http://www.capitalonelabs.com<http://www.capitalonelabs.com/>

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