Larry Parker created ARROW-9637: ----------------------------------- Summary: 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
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. -- This message was sent by Atlassian Jira (v8.3.4#803005)