[ 
https://issues.apache.org/jira/browse/SPARK-17618?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Josh Rosen updated SPARK-17618:
-------------------------------
    Target Version/s: 1.6.3

> Dataframe except returns incorrect results when combined with coalesce
> ----------------------------------------------------------------------
>
>                 Key: SPARK-17618
>                 URL: https://issues.apache.org/jira/browse/SPARK-17618
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 1.6.1, 1.6.2
>            Reporter: Graeme Edwards
>            Priority: Blocker
>              Labels: correctness
>
> We were getting incorrect results from the DataFrame except method - all rows 
> were being returned instead of the ones that intersected. Calling subtract on 
> the underlying RDD returned the correct result.
> We tracked it down to the use of coalesce - the following is the simplest 
> example case we created that reproduces the issue:
> {code}
> val schema = new StructType().add("test", types.IntegerType )
> val t1 = sql.createDataFrame(sql.sparkContext.parallelize(1 to 100).map(i=> 
> Row(i)), schema)
> val t2 = sql.createDataFrame(sql.sparkContext.parallelize(5 to 10).map(i=> 
> Row(i)), schema)
> val t3 = t1.join(t2, t1.col("test").equalTo(t2.col("test")), "leftsemi")
> println("Count using normal except = " + t1.except(t3).count())
> println("Count using coalesce = " + 
> t1.coalesce(8).except(t3.coalesce(8)).count())
> {code}
> We should get the same result from both uses of except, but the one using 
> coalesce returns 100 instead of 94.



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org

Reply via email to