[
https://issues.apache.org/jira/browse/SPARK-10493?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14737050#comment-14737050
]
Sean Owen commented on SPARK-10493:
-----------------------------------
I think you still have the same issue with zipPartitions, unless you have an
ordering on the RDD, since the partitions may not appear in any particular
order, in which case zipping them may give different results. It may still not
be the issue though, since a lot of partitionings will happen to have the
assumed, same order anyway.
Why would this necessarily be better than union()? if you have the same # of
partitions and same partitioning you shouldn't have a shuffle. That's also by
the by.
I can't reproduce this in a simple, similar local example. I think there's
something else different between what you're doing and the code snippet here.
> reduceByKey not returning distinct results
> ------------------------------------------
>
> Key: SPARK-10493
> URL: https://issues.apache.org/jira/browse/SPARK-10493
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Reporter: Glenn Strycker
>
> I am running Spark 1.3.0 and creating an RDD by unioning several earlier RDDs
> (using zipPartitions), partitioning by a hash partitioner, and then applying
> a reduceByKey to summarize statistics by key.
> Since my set before the reduceByKey consists of records such as (K, V1), (K,
> V2), (K, V3), I expect the results after reduceByKey to be just (K,
> f(V1,V2,V3)), where the function f is appropriately associative, commutative,
> etc. Therefore, the results after reduceByKey ought to be distinct, correct?
> I am running counts of my RDD and finding that adding an additional
> .distinct after my .reduceByKey is changing the final count!!
> Here is some example code:
> rdd3 = tempRDD1.
> zipPartitions(tempRDD2, true)((iter, iter2) => iter++iter2).
> partitionBy(new HashPartitioner(numPartitions)).
> reduceByKey((a,b) => (math.Ordering.String.min(a._1, b._1), a._2 + b._2,
> math.max(a._3, b._3), math.max(a._4, b._4), math.max(a._5, b._5)))
> println(rdd3.count)
> rdd4 = rdd3.distinct
> println(rdd4.count)
> I am using persistence, checkpointing, and other stuff in my actual code that
> I did not paste here, so I can paste my actual code if it would be helpful.
> This issue may be related to SPARK-2620, except I am not using case classes,
> to my knowledge.
> See also
> http://stackoverflow.com/questions/32466176/apache-spark-rdd-reducebykey-operation-not-returning-correct-distinct-results
--
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
