# OOM in mappers

In a certain setting, the default input split size is too large for Hivemall. Due to that, OutOfMemoryError cloud happen on mappers in the middle of training.

Then, revise your a Hadoop setting (mapred.child.java.opts/mapred.map.child.java.opts) first to use a larger value as possible.

If an OOM error still caused after that, set smaller mapred.max.split.size value before training.

SET mapred.max.split.size=67108864;


Then, the number of training examples used for each trainer is reduced (as the number of mappers increases) and the trained model would fit in the memory.

# OOM in shuffle/merge

If OOM caused during the merge step, try setting a larger mapred.reduce.tasks value before training and revise shuffle/reduce parameters.

SET mapred.reduce.tasks=64;