[
https://issues.apache.org/jira/browse/SPARK-26572?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Sören Reichardt updated SPARK-26572:
------------------------------------
Description:
When joining a table with projected monotonically_increasing_id column after
calling distinct with another table the operators do not get executed in the
right order.
Here is a minimal example:
{code:java}
import org.apache.spark.sql.{DataFrame, SparkSession, functions}
object JoinBug extends App {
// Spark session setup
val session = SparkSession.builder().master("local[*]").getOrCreate()
import session.sqlContext.implicits._
session.sparkContext.setLogLevel("error")
// Bug in Spark: "monotonically_increasing_id" is pushed down when it
shouldn't be. Push down only happens when the
// DF containing the "monotonically_increasing_id" expression is on the left
side of the join.
val baseTable = Seq((1), (1)).toDF("idx")
val distinctWithId = baseTable.distinct.withColumn("id",
functions.monotonically_increasing_id())
val monotonicallyOnRight: DataFrame = baseTable.join(distinctWithId, "idx")
val monotonicallyOnLeft: DataFrame = distinctWithId.join(baseTable, "idx")
monotonicallyOnLeft.show // Wrong
monotonicallyOnRight.show // Ok in Spark 2.2.2 - also wrong in Spark 2.4.0
}
{code}
It produces the following output:
{code:java}
Wrong:
+---+------------+
|idx| id |
+---+------------+
| 1|369367187456 |
| 1|369367187457 |
+---+------------+
Right:
+---+------------+
|idx| id |
+---+------------+
| 1|369367187456 |
| 1|369367187456 |
+---+------------+
{code}
We assume that the join operator triggers a pushdown of expressions
(monotonically_increasing_id in this case) which gets pushed down to be
executed before distinct. This produces non-distinct rows with unique id's.
However it seems like this behavior only appears if the table with the
projected expression is on the left side of the join in Spark 2.2.2 (for
version 2.4.0 it fails on both joins).
was:
When joining a table with projected monotonically_increasing_id column after
calling distinct with another table the operators do not get executed in the
right order.
Here is a minimal example:
{code:java}
import org.apache.spark.sql.{DataFrame, SparkSession, functions}
object JoinBug extends App {
// Spark session setup
val session = SparkSession.builder().master("local[*]").getOrCreate()
import session.sqlContext.implicits._
session.sparkContext.setLogLevel("error")
// Bug in Spark: "monotonically_increasing_id" is pushed down when it
shouldn't be. Push down only happens when the
// DF containing the "monotonically_increasing_id" expression is on the left
side of the join.
val baseTable = Seq((1), (1)).toDF("idx")
val distinctWithId = baseTable.distinct.withColumn("id",
functions.monotonically_increasing_id())
val monotonicallyOnRight: DataFrame = baseTable.join(distinctWithId, "idx")
val monotonicallyOnLeft: DataFrame = distinctWithId.join(baseTable, "idx")
monotonicallyOnLeft.show // Wrong
monotonicallyOnRight.show // Ok in Spark 2.2.2 - also wrong in Spark 2.4.0
}
{code}
It produces the following output:
{code:java}
Wrong:
+---+------------+
|idx| id |
+---+------------+
| 1|369367187456 |
| 1|369367187457 |
+---+------------+
Right:
+---+------------+
|idx| id |
+---+------------+
| 1|369367187456 |
| 1|369367187456 |
+---+------------+
{code}
We assume that the join operator triggers a pushdown of expressions
(monotonically_increasing_id in this case) which gets pushed down to be
executed before distinct. This produces non-distinct rows with unique id's.
However it seems like this behavior only appears if the table with the
projected expression is on the left side of the join.
> Join on distinct column with monotonically_increasing_id produces wrong output
> ------------------------------------------------------------------------------
>
> Key: SPARK-26572
> URL: https://issues.apache.org/jira/browse/SPARK-26572
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.2.2, 2.4.0
> Environment: Running on Ubuntu 18.04LTS and Intellij 2018.2.5
> Reporter: Sören Reichardt
> Priority: Major
>
> When joining a table with projected monotonically_increasing_id column after
> calling distinct with another table the operators do not get executed in the
> right order.
> Here is a minimal example:
> {code:java}
> import org.apache.spark.sql.{DataFrame, SparkSession, functions}
> object JoinBug extends App {
> // Spark session setup
> val session = SparkSession.builder().master("local[*]").getOrCreate()
> import session.sqlContext.implicits._
> session.sparkContext.setLogLevel("error")
> // Bug in Spark: "monotonically_increasing_id" is pushed down when it
> shouldn't be. Push down only happens when the
> // DF containing the "monotonically_increasing_id" expression is on the
> left side of the join.
> val baseTable = Seq((1), (1)).toDF("idx")
> val distinctWithId = baseTable.distinct.withColumn("id",
> functions.monotonically_increasing_id())
> val monotonicallyOnRight: DataFrame = baseTable.join(distinctWithId, "idx")
> val monotonicallyOnLeft: DataFrame = distinctWithId.join(baseTable, "idx")
> monotonicallyOnLeft.show // Wrong
> monotonicallyOnRight.show // Ok in Spark 2.2.2 - also wrong in Spark 2.4.0
> }
> {code}
> It produces the following output:
> {code:java}
> Wrong:
> +---+------------+
> |idx| id |
> +---+------------+
> | 1|369367187456 |
> | 1|369367187457 |
> +---+------------+
> Right:
> +---+------------+
> |idx| id |
> +---+------------+
> | 1|369367187456 |
> | 1|369367187456 |
> +---+------------+
> {code}
> We assume that the join operator triggers a pushdown of expressions
> (monotonically_increasing_id in this case) which gets pushed down to be
> executed before distinct. This produces non-distinct rows with unique id's.
> However it seems like this behavior only appears if the table with the
> projected expression is on the left side of the join in Spark 2.2.2 (for
> version 2.4.0 it fails on both joins).
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