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https://issues.apache.org/jira/browse/SPARK-25643?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Dongjoon Hyun updated SPARK-25643:
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Affects Version/s: (was: 3.0.0)
3.1.0
> Performance issues querying wide rows
> -------------------------------------
>
> Key: SPARK-25643
> URL: https://issues.apache.org/jira/browse/SPARK-25643
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 3.1.0
> Reporter: Bruce Robbins
> Priority: Major
>
> Querying a small subset of rows from a wide table (e.g., a table with 6000
> columns) can be quite slow in the following case:
> * the table has many rows (most of which will be filtered out)
> * the projection includes every column of a wide table (i.e., select *)
> * predicate push down is not helping: either matching rows are sprinkled
> fairly evenly throughout the table, or predicate push down is switched off
> Even if the filter involves only a single column and the returned result
> includes just a few rows, the query can run much longer compared to an
> equivalent query against a similar table with fewer columns.
> According to initial profiling, it appears that most time is spent realizing
> the entire row in the scan, just so the filter can look at a tiny subset of
> columns and almost certainly throw the row away. The profiling shows 74% of
> time is spent in FileSourceScanExec, and that time is spent across numerous
> writeFields_0_xxx method calls.
> If Spark must realize the entire row just to check a tiny subset of columns,
> this all sounds reasonable. However, I wonder if there is an optimization
> here where we can avoid realizing the entire row until after the filter has
> selected the row.
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