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From "Paul Rogers (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (DRILL-5282) Rationalize record batch sizes in all readers and operators
Date Mon, 19 Jun 2017 18:30:00 GMT

     [ https://issues.apache.org/jira/browse/DRILL-5282?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Paul Rogers reassigned DRILL-5282:
----------------------------------

    Assignee: Paul Rogers

> Rationalize record batch sizes in all readers and operators
> -----------------------------------------------------------
>
>                 Key: DRILL-5282
>                 URL: https://issues.apache.org/jira/browse/DRILL-5282
>             Project: Apache Drill
>          Issue Type: Improvement
>    Affects Versions: 1.10.0
>            Reporter: Paul Rogers
>            Assignee: Paul Rogers
>
> Drill uses record batches to process data. A record batch consists of a "bundle" of vectors
that, combined, hold the data for some number of records.
> The key consideration for a record batch is memory consumed. Various operators and readers
have vastly different ideas of the size of a batch. The text reader can produce batches of
100s of K, while the flatten operator produces batches of half a GB. Other operators are randomly
in between. Some readers produce batches of unlimited size driven by average row width.
> Another key consideration is record count. Batches have a hard physical limit of 64K
(the number indexed by a two-byte selection vector.) Some operators produce this much, others
far less. In one case, we saw a reader that produced 64K+1 records.
> A final consideration is the size of individual vectors. Drill incurs severe memory fragmentation
when vectors grow above 16 MB.
> In some cases, operators (such as the Parquet reader) allocate large batches, but only
partially fill them, creating a large amount of wasted space. That space adds up when we must
buffer it during a sort.
> This ticket asks to research an optimal batch size. Create a framework to build such
batches. Retrofit all operators that produce batches to use that framework to produce uniform
batches.



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