GitHub user davies opened a pull request:
https://github.com/apache/spark/pull/12114
[SPARK-14334] [SQL] add toLocalIterator for Dataset/DataFrame
## What changes were proposed in this pull request?
RDD.toLocalIterator() could be used to fetch one partition at a time to
reduce the memory usage. Right now, for Dataset/Dataframe we have to use
df.rdd.toLocalIterator, which is super slow also requires lots of memory
(because of the Java serializer or even Kyro serializer).
This PR introduce an optimized toLocalIterator for Dataset/DataFrame, which
is much faster and requires much less memory. For a partition with 5 millions
rows, `df.rdd.toIterator` took about 100 seconds, but df.toIterator took less
than 7 seconds. For 10 millions row, rdd.toIterator will crash (not enough
memory) with 4G heap, but df.toLocalIterator could finished in 12 seconds.
The JDBC server has been updated to use DataFrame.toIterator.
## How was this patch tested?
Existing tests.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/davies/spark local_iterator
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/12114.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 #12114
----
commit 52d752031f8502cc383efe4ed57f2677db1a2905
Author: Davies Liu <[email protected]>
Date: 2016-04-01T20:28:43Z
add toLocalIterator for Dataset
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