Bruce Robbins created SPARK-25643:
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Summary: 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: 2.4.0
Reporter: Bruce Robbins
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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