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https://issues.apache.org/jira/browse/BEAM-135?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15826462#comment-15826462
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Kenneth Knowles commented on BEAM-135:
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There are probably multiple use cases here that might deserve different
treatment. The easiest way to achieve the abstract goal of "iterables of _n_
elements" is using the new state API, which allows buffering across bundles,
which may be smaller than _n_. That API has preliminary support in the direct
runner and Dataflow runner.
> Utilities for "batching" elements in a DoFn
> -------------------------------------------
>
> Key: BEAM-135
> URL: https://issues.apache.org/jira/browse/BEAM-135
> Project: Beam
> Issue Type: New Feature
> Components: sdk-java-core
> Reporter: Ben Chambers
> Assignee: Etienne Chauchot
>
> We regularly receive questions about how to write a {{DoFn}} that operates on
> batches of elements. Example answers include:
> http://stackoverflow.com/questions/35065109/can-datastore-input-in-google-dataflow-pipeline-be-processed-in-a-batch-of-n-ent/35068341#35068341
> http://stackoverflow.com/questions/30177812/partition-data-coming-from-csv-so-i-can-process-larger-patches-rather-then-indiv/30178170#30178170
> Possible APIs could be to wrap a {{DoFn}} and include a batch size, or to
> create a utility like {{Filter}}, {{Partition}}, etc. that takes a
> {{SerializableFunction}} or a {{SimpleFunction}}.
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