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https://issues.apache.org/jira/browse/STORM-855?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14906575#comment-14906575
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ASF GitHub Bot commented on STORM-855:
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Github user mjsax commented on the pull request:
https://github.com/apache/storm/pull/694#issuecomment-142977052
I did not have time yet. However, I am still on it... I did already some
more work for this PR that is not pushed yet (for example to support different
output batches for different output streams and some "clean up" of my changes).
I think I have an overall cleaner design now of my code. As a next step, I
actually want to investigate why this PR only gives factor two throughput
improvement (because Aeolus give factor five). But if you prefer, I can also
work on the ack problem first to get a fully running PR for now. (I just
disabled acking in my current tests ;))
> Add tuple batching
> ------------------
>
> Key: STORM-855
> URL: https://issues.apache.org/jira/browse/STORM-855
> Project: Apache Storm
> Issue Type: New Feature
> Reporter: Matthias J. Sax
> Assignee: Matthias J. Sax
> Priority: Minor
>
> In order to increase Storm's throughput, multiple tuples can be grouped
> together in a batch of tuples (ie, fat-tuple) and transfered from producer to
> consumer at once.
> The initial idea is taken from https://github.com/mjsax/aeolus. However, we
> aim to integrate this feature deep into the system (in contrast to building
> it on top), what has multiple advantages:
> - batching can be even more transparent to the user (eg, no extra
> direct-streams needed to mimic Storm's data distribution patterns)
> - fault-tolerance (anchoring/acking) can be done on a tuple granularity
> (not on a batch granularity, what leads to much more replayed tuples -- and
> result duplicates -- in case of failure)
> The aim is to extend TopologyBuilder interface with an additional parameter
> 'batch_size' to expose this feature to the user. Per default, batching will
> be disabled.
> This batching feature has pure tuple transport purpose, ie, tuple-by-tuple
> processing semantics are preserved. An output batch is assembled at the
> producer and completely disassembled at the consumer. The consumer output can
> be batched again, however, independent of batched or non-batched input. Thus,
> batches can be of different size for each producer-consumer pair.
> Furthermore, consumers can receive batches of different size from different
> producers (including regular non batched input).
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