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Posted to jira@arrow.apache.org by "Joris Van den Bossche (Jira)" <ji...@apache.org> on 2020/08/04 15:31:00 UTC
[jira] [Closed] (ARROW-9637) [Python] Speed degradation with
categoricals
[ https://issues.apache.org/jira/browse/ARROW-9637?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Joris Van den Bossche closed ARROW-9637.
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Resolution: Not A Problem
> [Python] Speed degradation with categoricals
> --------------------------------------------
>
> Key: ARROW-9637
> URL: https://issues.apache.org/jira/browse/ARROW-9637
> Project: Apache Arrow
> Issue Type: Bug
> Affects Versions: 1.0.0
> Reporter: Larry Parker
> Priority: Major
> Attachments: fact1__c.parquet.zip
>
>
> I have noticed some major speed degradation when using categorical data types. For example, a Parquet file with 1 million rows that sums 10 float columns and groups by two columns (one a date column and one a category column). The cardinality of the category seems to have a major effect. When grouping on category column of cardinality 10, performance is decent (query runs in 150 ms). But with cardinality of 100, the query runs in 10 seconds. If I switch over to my Parquet file that does *not* have categorical columns, the same query that took 10 seconds with categoricals now runs in 350 ms.
> I would be happy to post the Pandas code that I'm using (including how I'm creating the Parquet file), but I first wanted to report this and see if it's a known issue.
> Thanks.
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