[
https://issues.apache.org/jira/browse/SPARK-13534?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wes McKinney updated SPARK-13534:
---------------------------------
Description:
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.
was:
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 the the
null values in the data have to be replace 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 an 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.
> 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.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
