GitHub user Forevian opened a pull request:
https://github.com/apache/spark/pull/1345
[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.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/Forevian/spark custom-checkpoint
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/1345.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 #1345
----
commit fe7a234e33baa0c5e9c0b90b1daaa00f8a29293d
Author: Forevian <[email protected]>
Date: 2014-07-09T15:04:50Z
Custom checkpointing with an external function as parameter
commit 9c2adce783dbb61c81aff775fdb0f3216a36412f
Author: András Barják <[email protected]>
Date: 2014-07-09T16:53:53Z
parallelize -> makeRDD
to stay consistent with the other tests
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