Josh Rosen created SPARK-27969:
----------------------------------
Summary: 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: SQL
Affects Versions: 2.4.0
Reporter: Josh Rosen
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).
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
