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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2015/05/19 11:06:00 UTC
[jira] [Assigned] (SPARK-7637) StructType.merge slow with large
nenormalised tables O(N2)
[ https://issues.apache.org/jira/browse/SPARK-7637?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-7637:
-----------------------------------
Assignee: (was: Apache Spark)
> StructType.merge slow with large nenormalised tables O(N2)
> ----------------------------------------------------------
>
> Key: SPARK-7637
> URL: https://issues.apache.org/jira/browse/SPARK-7637
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 1.3.1
> Reporter: Rowan Chattaway
> Priority: Minor
> Original Estimate: 24h
> Remaining Estimate: 24h
>
> StructType.merge does a linear scan through the left schema and for each element scans the right schema. This results in a O(N2) algorithm.
> I have found this to be very slow when dealing with large denormalised parquet files.
> I would like to make a small change to this function to map the fields of both the left and right schemas resulting in O(N).
> This has a sizable increase in performance for large denormalised schemas.
> 10000x10000 column merge
> 2891ms Original
> 32ms with mapped field approach.
> This merge can be called many times depending upon the number of files that you need to merge the schemas for, compounding the performance.
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