[jira] [Comment Edited] (ARROW-504) [Python] Add adapter to write pandas.DataFrame in user-selected chunk size to streaming format

["Matthew Rocklin (JIRA)" <ji...@apache.org>]

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

Matthew Rocklin edited comment on ARROW-504 at 1/23/17 2:53 PM:
----------------------------------------------------------------

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 overflowing 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.  


was (Author: mrocklin):
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}}.



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)