I got the same problem with rdd,repartition() in my streaming app, which
generated a few huge partitions and many tiny partitions. The resulting high
data skew makes the processing time of a batch unpredictable and often
exceeding the batch interval. I eventually solved the problem by using
rdd.coalesce() instead, which however is expensive as it yields a lot of
shuffle traffic and also takes a long time.
Du
On Thursday, June 18, 2015 1:00 AM, Al M <alasdair.mcbr...@gmail.com>
wrote:
Thanks for the suggestion. Repartition didn't help us unfortunately. It
still puts everything into the same partition.
We did manage to improve the situation by making a new partitioner that
extends HashPartitioner. It treats certain "exception" keys differently.
These keys that are known to appear very often are assigned random
partitions instead of using the existing partitioning mechanism.
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