[
https://issues.apache.org/jira/browse/SPARK-12469?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15687668#comment-15687668
]
Reynold Xin commented on SPARK-12469:
-------------------------------------
Sorry there is no way to get this in 2.1, given the size of the change.
> Data Property Accumulators for Spark (formerly Consistent Accumulators)
> -----------------------------------------------------------------------
>
> 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
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
