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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 04:15:41 UTC
[jira] [Resolved] (SPARK-16073) Performance of Parquet encodings on
saving primitive arrays
[ https://issues.apache.org/jira/browse/SPARK-16073?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-16073.
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Resolution: Incomplete
> Performance of Parquet encodings on saving primitive arrays
> -----------------------------------------------------------
>
> Key: SPARK-16073
> URL: https://issues.apache.org/jira/browse/SPARK-16073
> Project: Spark
> Issue Type: Task
> Components: MLlib, SQL
> Affects Versions: 2.0.0
> Reporter: Xiangrui Meng
> Priority: Major
> Labels: bulk-closed
>
> Spark supports both uncompressed and compressed (snappy, gzip, lzo) Parquet data. However, Parquet also has its own encodings to compress columns/arrays, e.g., dictionary encoding: https://github.com/apache/parquet-format/blob/master/Encodings.md.
> It might be worth checking the performance overhead of Parquet encodings on saving large primitive arrays, which is a machine learning use case. If the overhead is significant, we should expose a configuration in Spark to control the encoding levels.
> Note that this shouldn't be tested under Spark until SPARK-16043 was fixed.
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