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Posted to issues@spark.apache.org by "Josh Rosen (JIRA)" <ji...@apache.org> on 2016/09/21 19:12:20 UTC
[jira] [Commented] (SPARK-17618) Dataframe except returns incorrect
results when combined with coalesce
[ https://issues.apache.org/jira/browse/SPARK-17618?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15510874#comment-15510874 ]
Josh Rosen commented on SPARK-17618:
------------------------------------
It looks like this affects 1.6.2 as well, but I was unable to reproduce in 2.x.
Comparing the two physical plans, I wonder if the issue has to do with Tungsten vs. regular internal row formats.
For {{t1.except(t3).explain(true)}}:
{code}
== Physical Plan ==
Except
:- Scan ExistingRDD[test#35]
+- ConvertToSafe
+- LeftSemiJoinHash [test#35], [test#36], None
:- TungstenExchange hashpartitioning(test#35,200), None
: +- ConvertToUnsafe
: +- Scan ExistingRDD[test#35]
+- TungstenExchange hashpartitioning(test#36,200), None
+- ConvertToUnsafe
+- Scan ExistingRDD[test#36]
{code}
whereas {{t1.coalesce(8).except(t3.coalesce(8)).explain(true)}} produces
{code}
Except
:- Coalesce 8
: +- Scan ExistingRDD[test#35]
+- Coalesce 8
+- LeftSemiJoinHash [test#35], [test#36], None
:- TungstenExchange hashpartitioning(test#35,200), None
: +- ConvertToUnsafe
: +- Scan ExistingRDD[test#35]
+- TungstenExchange hashpartitioning(test#36,200), None
+- ConvertToUnsafe
+- Scan ExistingRDD[test#36]
{code}
My hunch is that Except is inappropriately mixing Tungsten and non-Tungsten row formats due to a bug in the row format conversion rules.
> 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: Minor
> 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.
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