Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/876#issuecomment-44169636
Yes I had a similar question. This would calculate different partitions for
the same key when called from different places and times, and I imagine that
causes several methods to fail. For example, what about joining two RDDs both
using this partitioner (and with multiple partitions) -- anything that creates
a shuffle dependency among two pair RDDs. Surely the different instances of the
depended-upon RDD's partitioner will return different partitions for keys and
get the answer wrong?
I'm thinking of any time the partitioner instance is copied around -- it
will copy state but then its state, which is essential to its answers, varies.
Maybe someone more knowledgeable than I can confirm an easy way to test this,
or that I really misunderstand and this never happens.
`HashPartitioner` expects to give fairly balanced partitions already,
unless the value's hash function is bad. That is better fixed in the hash
function itself.
I had thought the problem was more often in pair RDDs where one key has a
lot of values, and operations that group by key create imbalanced partitions?
That's not the question here right, that wouldn't be helped by this.
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