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https://issues.apache.org/jira/browse/SPARK-12469?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15072987#comment-15072987
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holdenk commented on SPARK-12469:
---------------------------------
Thanks for the link to the previous attempt :) I think by avoiding depending on
the stage id and instead tracking the rdd being computed (or as one of the
other ones options only allowing each accumulator to be used inside of one
transformation) this approach should work.
> Consistent Accumulators for Spark
> ---------------------------------
>
> Key: SPARK-12469
> URL: https://issues.apache.org/jira/browse/SPARK-12469
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Reporter: holdenk
>
> Tasks executed on Spark workers are unable to modify values from the driver,
> and accumulators are the one exception for this. Accumulators in Spark are
> implemented in such a way that when a stage is recomputed (say for cache
> eviction) the accumulator will be updated a second time. This makes
> accumulators inside of transformations more difficult to use for things like
> counting invalid records (one of the primary potential use cases of
> collecting side information during a transformation). However in some cases
> this counting during re-evaluation is exactly the behaviour we want (say in
> tracking total execution time for a particular function). Spark would benefit
> from a version of accumulators which did not double count even if stages were
> re-executed.
> Motivating example:
> {code}
> val parseTime = sc.accumulator(0L)
> val parseFailures = sc.accumulator(0L)
> val parsedData = sc.textFile(...).flatMap { line =>
> val start = System.currentTimeMillis()
> val parsed = Try(parse(line))
> if (parsed.isFailure) parseFailures += 1
> parseTime += System.currentTimeMillis() - start
> parsed.toOption
> }
> parsedData.cache()
> val resultA = parsedData.map(...).filter(...).count()
> // some intervening code. Almost anything could happen here -- some of
> parsedData may
> // get kicked out of the cache, or an executor where data was cached might
> get lost
> val resultB = parsedData.filter(...).map(...).flatMap(...).count()
> // now we look at the accumulators
> {code}
> Here we would want parseFailures to only have been added to once for every
> line which failed to parse. Unfortunately, the current Spark accumulator API
> doesn’t support the current parseFailures use case since if some data had
> been evicted its possible that it will be double counted.
> See the full design document at
> https://docs.google.com/document/d/1lR_l1g3zMVctZXrcVjFusq2iQVpr4XvRK_UUDsDr6nk/edit?usp=sharing
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