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From "Dongjoon Hyun (Jira)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-26572) Join on distinct column with monotonically_increasing_id produces wrong output
Date Mon, 02 Mar 2020 19:37:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-26572?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Dongjoon Hyun updated SPARK-26572:
----------------------------------
    Affects Version/s: 2.1.3

> 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: 2.1.3, 2.2.2, 2.3.2, 2.4.0
>         Environment: Running on Ubuntu 18.04LTS and Intellij 2018.2.5
>            Reporter: Sören Reichardt
>            Assignee: Peter Toth
>            Priority: Major
>              Labels: correctness
>             Fix For: 2.3.4, 2.4.1, 3.0.0
>
>
> 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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