GitHub user Forevian opened a pull request:
https://github.com/apache/spark/pull/3346
[SPARK-2418] Custom checkpointing with an external function as parameter
https://issues.apache.org/jira/browse/SPARK-2418
If a job consists of many shuffle heavy transformations the current
resilience model might be unsatisfactory. In our current use-case we need a
persistent checkpoint that we can use to save our RDDs on disk in a custom
location and load it back even if the driver dies. (Possible other use cases:
store the checkpointed data in various formats: SequenceFile, csv, Parquet
file, MySQL etc.)
After talking to Patrick Wendell at the Spark Summit 2014 we concluded that
a checkpoint where one can customize the saving and RDD reloading behavior can
be a good solution. I am open to further suggestions if you have better ideas
about how to make checkpointing more flexible.
***
Note1: I deleted the previous fork as I had messed that version up by some
unsuccessful rebasing attempt.
Note2: The contribution is my original work and I license the work to the
project under the project's open source license.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/Forevian/spark custom-checkpoint-f
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/3346.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 #3346
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commit f3c8efbb64dbff3bd4aa93161eefe75ff6c433a7
Author: Forevian <[email protected]>
Date: 2014-11-18T14:48:54Z
Custom checkpointing by a provided function
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