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Posted to issues@spark.apache.org by "Josh Rosen (JIRA)" <ji...@apache.org> on 2019/06/06 23:17:00 UTC
[jira] [Updated] (SPARK-27969) Non-deterministic expressions in
filters or projects can unnecessarily prevent all scan-time column pruning,
harming performance
[ https://issues.apache.org/jira/browse/SPARK-27969?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Josh Rosen updated SPARK-27969:
-------------------------------
Component/s: Optimizer
> Non-deterministic expressions in filters or projects can unnecessarily prevent all scan-time column pruning, harming performance
> --------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-27969
> URL: https://issues.apache.org/jira/browse/SPARK-27969
> Project: Spark
> Issue Type: Bug
> Components: Optimizer, SQL
> Affects Versions: 2.4.0
> Reporter: Josh Rosen
> Priority: Major
>
> If a scan operator is followed by a projection or filter and those operators contain _any_ non-deterministic expressions then scan column pruning optimizations are completely skipped, harming query performance.
> Here's an example of the problem:
> {code:java}
> import org.apache.spark.sql.functions._
> val df = spark.createDataset(Seq(
> (1, 2, 3, 4, 5),
> (1, 2, 3, 4, 5)
> ))
> val tmpPath = java.nio.file.Files.createTempDirectory("column-pruning-bug").toString()
> df.write.parquet(tmpPath)
> val fromParquet = spark.read.parquet(tmpPath){code}
> If all expressions are deterministic then, as expected, column pruning is pushed into the scan
> {code:java}
> fromParquet.select("_1").explain
> == Physical Plan == *(1) FileScan parquet [_1#68] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[dbfs:/local_disk0/tmp/column-pruning-bug7865798834978814548], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<_1:int>{code}
> However, if we add a non-deterministic filter then no column pruning is performed (even though pruning would be safe!):
> {code:java}
> fromParquet.select("_1").filter(rand() =!= 0).explain
> == Physical Plan ==
> *(1) Project [_1#127]
> +- *(1) Filter NOT (rand(-1515289268025792238) = 0.0)
> +- *(1) FileScan parquet [_1#127,_2#128,_3#129,_4#130,_5#131] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[dbfs:/local_disk0/tmp/column-pruning-bug4043817424882943496], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<_1:int,_2:int,_3:int,_4:int,_5:int>{code}
> Similarly, a non-deterministic expression in a second projection can end up being collapsed such that it prevents column pruning:
> {code:java}
> fromParquet.select("_1").select($"_1", rand()).explain
> == Physical Plan ==
> *(1) Project [_1#127, rand(1267140591146561563) AS rand(1267140591146561563)#141]
> +- *(1) FileScan parquet [_1#127,_2#128,_3#129,_4#130,_5#131] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[dbfs:/local_disk0/tmp/column-pruning-bug4043817424882943496], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<_1:int,_2:int,_3:int,_4:int,_5:int>
> {code}
> I believe that this is caused by SPARK-10316: the Parquet column pruning code relies on the [{{PhysicalOperation}} unapply method|https://github.com/apache/spark/blob/v2.4.3/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala#L43] for extracting projects and filters and this helper purposely fails to match if _any_ projection or filter is non-deterministic.
> It looks like this conservative behavior may have originally been added to avoid pushdown / re-ordering of non-deterministic filter expressions. However, in this case I feel that it's _too_ conservative: even though we can't push down non-deterministic filters we should still be able to perform column pruning.
> /cc [~cloud_fan] and [~marmbrus] (it looks like you [discussed collapsing of non-deterministic projects|https://github.com/apache/spark/pull/8486#issuecomment-136036533] in the SPARK-10316 PR, which is related to why the third example above did not prune).
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