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https://issues.apache.org/jira/browse/SPARK-14408?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15227090#comment-15227090
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Joseph K. Bradley commented on SPARK-14408:
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fold seems better to me. PairRDDFunctions.foldByKey does not explicitly say
the function can modify the first argument, but comparing its implementation
with aggregateByKey, I would guess it is fine. What do you think?
RDD.fold should be fine, based on its docs.
This seems like a difficult thing to cover in unit tests...
> Is RDD.treeAggregate implemented correctly?
> -------------------------------------------
>
> Key: SPARK-14408
> URL: https://issues.apache.org/jira/browse/SPARK-14408
> Project: Spark
> Issue Type: Question
> Components: Spark Core
> Reporter: Joseph K. Bradley
>
> **Issue**
> In MLlib, we have assumed that {{RDD.treeAggregate}} allows the {{seqOp}} and
> {{combOp}} functions to modify and return their first argument, just like
> {{RDD.aggregate}}. However, it is not documented that way.
> I started to add docs to this effect, but then noticed that {{treeAggregate}}
> uses {{reduceByKey}} and {{reduce}} in its implementation, neither of which
> technically allows the seq/combOps to modify and return their first arguments.
> **Question**: Is the implementation safe, or does it need to be updated?
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