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https://issues.apache.org/jira/browse/SPARK-13534?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15171273#comment-15171273
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Wes McKinney commented on SPARK-13534:
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SPARK-13391 would need to have its scope more narrowly defined. Perhaps we can
modify that issue scope to encompass UDF evaluation using Arrow for data
interchange? Otherwise we can close it.
This issue is a first step in that direction, but UDF evaluation is not in
scope for this issue (this is only about raw table data movement).
> Implement Apache Arrow serializer for Spark DataFrame for use in
> DataFrame.toPandas
> -----------------------------------------------------------------------------------
>
> Key: SPARK-13534
> URL: https://issues.apache.org/jira/browse/SPARK-13534
> Project: Spark
> Issue Type: New Feature
> Components: PySpark
> Reporter: Wes McKinney
>
> The current code path for accessing Spark DataFrame data in Python using
> PySpark passes through an inefficient serialization-deserialiation process
> that I've examined at a high level here:
> https://gist.github.com/wesm/0cb5531b1c2e346a0007. Currently, RDD[Row]
> objects are being deserialized in pure Python as a list of tuples, which are
> then converted to pandas.DataFrame using its {{from_records}} alternate
> constructor. This also uses a large amount of memory.
> For flat (no nested types) schemas, the Apache Arrow memory layout
> (https://github.com/apache/arrow/tree/master/format) can be deserialized to
> {{pandas.DataFrame}} objects with comparatively small overhead compared with
> memcpy / system memory bandwidth -- Arrow's bitmasks must be examined,
> replacing the corresponding null values with pandas's sentinel values (None
> or NaN as appropriate).
> I will be contributing patches to Arrow in the coming weeks for converting
> between Arrow and pandas in the general case, so if Spark can send Arrow
> memory to PySpark, we will hopefully be able to increase the Python data
> access throughput by an order of magnitude or more. I propose to add an new
> serializer for Spark DataFrame and a new method that can be invoked from
> PySpark to request a Arrow memory-layout byte stream, prefixed by a data
> header indicating array buffer offsets and sizes.
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