GitHub user aarondav opened a pull request:
https://github.com/apache/spark/pull/1138
SPARK-2203: PySpark defaults to use same num reduce partitions as map side
For shuffle-based operators, such as rdd.groupBy() or rdd.sortByKey(),
PySpark will always assume that the default parallelism to use for the reduce
side is ctx.defaultParallelism, which is a constant typically determined by the
number of cores in cluster.
In contrast, Spark's Partitioner#defaultPartitioner will use the same
number of reduce partitions as map partitions unless the defaultParallelism
config is explicitly set. This tends to be a better default in order to avoid
OOMs, and should also be the behavior of PySpark.
JIRA: https://issues.apache.org/jira/browse/SPARK-2203
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/aarondav/spark pyfix
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/1138.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #1138
----
commit 1bd5751fad08b0b2c69f9a0816b6b20fa06621fe
Author: Aaron Davidson <aa...@databricks.com>
Date: 2014-06-19T19:43:50Z
SPARK-2203: PySpark defaults to use same num reduce partitions as map
partitions
For shuffle-based operators, such as rdd.groupBy() or rdd.sortByKey(),
PySpark will always
assume that the default parallelism to use for the reduce side is
ctx.defaultParallelism,
which is a constant typically determined by the number of cores in cluster.
In contrast, Spark's Partitioner#defaultPartitioner will use the same
number of reduce
partitions as map partitions unless the defaultParallelism config is
explicitly set. This
tends to be a better default in order to avoid OOMs, and should also be the
behavior of PySpark.
JIRA: https://issues.apache.org/jira/browse/SPARK-2203
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