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From "Matthew Rocklin (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (ARROW-504) [Python] Add adapter to write pandas.DataFrame in user-selected chunk size to streaming format
Date Mon, 23 Jan 2017 14:53:26 GMT

    [ https://issues.apache.org/jira/browse/ARROW-504?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15834694#comment-15834694
] 

Matthew Rocklin commented on ARROW-504:
---------------------------------------

At the moment I don't have any active use cases for this.  We tend to handle pandas dataframes
as atomic blocks of data.

However generally I agree that streaming chunks in a more granular way is probably a better
way to go.  Non-blocking IO quickly becomes blocking IO if data starts overflows local buffers.
 This is the sort of technology that might influence future design decisions.

>From a pure Dask perspective my ideal serialization interface is Python object -> iterator
of memoryview objects.  

> [Python] Add adapter to write pandas.DataFrame in user-selected chunk size to streaming
format
> ----------------------------------------------------------------------------------------------
>
>                 Key: ARROW-504
>                 URL: https://issues.apache.org/jira/browse/ARROW-504
>             Project: Apache Arrow
>          Issue Type: New Feature
>            Reporter: Wes McKinney
>
> While we can convert a {{pandas.DataFrame}} to a single (arbitrarily large) {{arrow::RecordBatch}},
it is not easy to create multiple small record batches -- we could do so in a streaming fashion
and immediately write them into an {{arrow::io::OutputStream}}.



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