spark-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Sean Owen (JIRA)" <>
Subject [jira] [Commented] (SPARK-2688) Need a way to run multiple data pipeline concurrently
Date Sun, 25 Jan 2015 17:35:34 GMT


Sean Owen commented on SPARK-2688:

[~sandyr] Yes I can appreciate the difference between "pulling" N different RDDs from a single,
persisted RDD, and "pushing" N different RDDs as the result of one pass over an RDD. (It's
kind of interesting to think about what that means in terms of implementation -- it seems
like this only works if the N output RDDs are persisted, since you have to immediately save
the results.) 

This seems to be the topic of SPARK-3622, however. It does seem to me like a different and
valid operation to consider implementing.

This JIRA seems to be only about avoiding recomputing an RDD that is the common parent of
several RDDs. That's already possible, of course. It's possible without persisting to disk
too. Excluding the use cases that seem described by SPARK-3622, then, I'm not sure what this
covers, which is why I suggested closing this in favor of it.

> Need a way to run multiple data pipeline concurrently
> -----------------------------------------------------
>                 Key: SPARK-2688
>                 URL:
>             Project: Spark
>          Issue Type: New Feature
>          Components: Spark Core
>    Affects Versions: 1.0.1
>            Reporter: Xuefu Zhang
> Suppose we want to do the following data processing: 
> {code}
> rdd1 -> rdd2 -> rdd3
>            | -> rdd4
>            | -> rdd5
>            \ -> rdd6
> {code}
> where -> represents a transformation. rdd3 to rrdd6 are all derived from an intermediate
rdd2. We use foreach(fn) with a dummy function to trigger the execution. However, rdd.foreach(fn)
only trigger pipeline rdd1 -> rdd2 -> rdd3. To make things worse, when we call rdd4.foreach(),
rdd2 will be recomputed. This is very inefficient. Ideally, we should be able to trigger the
execution the whole graph and reuse rdd2, but there doesn't seem to be a way doing so. Tez
already realized the importance of this (TEZ-391), so I think Spark should provide this too.
> This is required for Hive to support multi-insert queries. HIVE-7292.

This message was sent by Atlassian JIRA

To unsubscribe, e-mail:
For additional commands, e-mail:

View raw message