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https://issues.apache.org/jira/browse/SPARK-13700?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15182978#comment-15182978
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Paulo Costa commented on SPARK-13700:
-------------------------------------
Hello, Sean.
Thanks for looking at it.
I just want to clarify a few points:
This is unrelated to `AsyncRDDActions`:
- `AsyncRDDActions` is about executing RDD actions without blocking the driver
program.
- This request is about executing a `map()` transform on multiple items
concurrently,
Also, my motivation isn't to execute multiple expensive synchronous calls
(Spark's solves that problem really well with partition parallelism)
Instead, I need to execute multiple cheap, high-latency, asynchronous calls
(i.e. anything that requires network communication -- HTTP requests, DB access,
etc)
I agree that anyone can do it themselves using `mapPartitions` (I just did),
but so can `map`, `filter`, and many of the existing RDD transforms.
It doesn't mean they should, as it took me a couple of hours to write it, and
I'm not completely sure I got it right (Concurrency is hard!)
I'll send the idea on a mailing list
> Rdd.mapAsync(): Easily mix Spark and asynchroneous transformation
> -----------------------------------------------------------------
>
> Key: SPARK-13700
> URL: https://issues.apache.org/jira/browse/SPARK-13700
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Reporter: Paulo Costa
> Priority: Minor
> Labels: async, features, rdd, transform
>
> Spark is great for synchronous operations.
> But sometimes I need to call a database/web server/etc from my transform, and
> the Spark pipeline stalls waiting for it.
> Avoiding that would be great!
> I suggest we add a new method RDD.mapAsync(), which can execute these
> operations concurrently, avoiding the bottleneck.
> I've written a quick'n'dirty implementation of what I have in mind:
> https://gist.github.com/paulo-raca/d121cf27905cfb1fafc3
> What do you think?
> If you agree with this feature, I can work on a pull request.
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