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From "Everett Anderson (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-19586) Incorrect push down filter for double negative in SQL
Date Tue, 14 Feb 2017 02:39:41 GMT
Everett Anderson created SPARK-19586:
----------------------------------------

             Summary: Incorrect push down filter for double negative in SQL
                 Key: SPARK-19586
                 URL: https://issues.apache.org/jira/browse/SPARK-19586
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 2.0.2
            Reporter: Everett Anderson
             Fix For: 2.1.0


Opening this as it's a somewhat serious issue in the 2.0.x tree in case there's a 2.0.3 planned,
but it is fixed in 2.1.0.

While it works in 1.6.2 and 2.1.0, it appears 2.0.2 has a significant filter optimization
error.

Example:

// Create some fake data

import org.apache.spark.sql.Row
import org.apache.spark.sql.Dataset
import org.apache.spark.sql.types._

val rowsRDD = sc.parallelize(Seq(
    Row(1, "fred"),
    Row(2, "amy"),
    Row(3, null)))

val schema = StructType(Seq(
    StructField("id", IntegerType, nullable = true),
    StructField("username", StringType, nullable = true)))
    
val data = sqlContext.createDataFrame(rowsRDD, schema)
val path = "/tmp/test_data"

data.write.mode("overwrite").parquet(path)

val testData = sqlContext.read.parquet(path)

testData.registerTempTable("filter_test_table")

%sql
explain select count(*) from filter_test_table where not( username is not null)

or

spark.sql("select count(*) from filter_test_table where not( username is not null)").explain

In 2.0.2, I'm seeing

== Physical Plan ==
*HashAggregate(keys=[], functions=[count(1)])
+- Exchange SinglePartition
 +- *HashAggregate(keys=[], functions=[partial_count(1)])
 +- *Project
 +- *Filter (isnotnull(username#35) && NOT isnotnull(username#35))
 +- *BatchedScan parquet default.<hive table name>[username#35] Format: ParquetFormat,
InputPaths: <path to parquet>, PartitionFilters: [], PushedFilters: [IsNotNull(username),
Not(IsNotNull(username))], ReadSchema: struct<username:string>

which seems like both an impossible Filter and an impossible pushed filter.

In Spark 1.6.2 it was

== Physical Plan ==
TungstenAggregate(key=[], functions=[(count(1),mode=Final,isDistinct=false)], output=[_c0#1822L])
+- TungstenExchange SinglePartition, None
 +- TungstenAggregate(key=[], functions=[(count(1),mode=Partial,isDistinct=false)], output=[count#1825L])
 +- Project
 +- Filter NOT isnotnull(username#1590)
 +- Scan ParquetRelation[username#1590] InputPaths: <path to parquet>, PushedFilters:
[Not(IsNotNull(username))]

and 2.1.0 it's working again as

== Physical Plan ==
*HashAggregate(keys=[], functions=[count(1)])
+- Exchange SinglePartition
   +- *HashAggregate(keys=[], functions=[partial_count(1)])
      +- *Project
         +- *Filter NOT isnotnull(username#14)
            +- *FileScan parquet [username#14] Batched: true, Format: Parquet, Location: InMemoryFileIndex[file:/tmp/test_table],
PartitionFilters: [], PushedFilters: [Not(IsNotNull(username))], ReadSchema: struct<username:string>

while it's easy for humans in interactive cases to work around this by removing the double
negative, it's a bit harder if it's a programmatic construct.




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