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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-10169) Evaluating AggregateFunction1 (old code path) may return wrong answers when grouping expressions are used as arguments of aggregate functions
Date Sun, 23 Aug 2015 04:34:45 GMT

     [ https://issues.apache.org/jira/browse/SPARK-10169?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-10169:
------------------------------------

    Assignee: Yin Huai  (was: Apache Spark)

> Evaluating AggregateFunction1 (old code path) may return wrong answers when grouping
expressions are used as arguments of aggregate functions
> ---------------------------------------------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-10169
>                 URL: https://issues.apache.org/jira/browse/SPARK-10169
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 1.1.1, 1.2.2, 1.3.1, 1.4.1
>            Reporter: Yin Huai
>            Assignee: Yin Huai
>            Priority: Critical
>
> Before Spark 1.5, if an aggregate function use an grouping expression as input argument,
the result of the query can be wrong. The reason is we are using transformUp when we do aggregate
results rewriting (see https://github.com/apache/spark/blob/branch-1.4/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala#L154).

> To reproduce the problem, you can use
> {code}
> import org.apache.spark.sql.functions._
> sc.parallelize((1 to 1000), 50).map(i => Tuple1(i)).toDF("i").registerTempTable("t")
> sqlContext.sql(""" 
> select i % 10, sum(if(i % 10 = 5, 1, 0)), count(i)
> from t
> where i % 10 = 5
> group by i % 10""").explain()
> == Physical Plan ==
> Aggregate false, [PartialGroup#234], [PartialGroup#234 AS _c0#225,SUM(CAST(HiveGenericUdf#org.apache.hadoop.hive.ql.udf.generic.GenericUDFIf((PartialGroup#234
= 5),1,0), LongType)) AS _c1#226L,Coalesce(SUM(PartialCount#233L),0) AS _c2#227L]
>  Exchange (HashPartitioning [PartialGroup#234], 200)
>   Aggregate true, [(i#191 % 10)], [(i#191 % 10) AS PartialGroup#234,SUM(CAST(HiveGenericUdf#org.apache.hadoop.hive.ql.udf.generic.GenericUDFIf(((i#191
% 10) = 5),1,0), LongType)) AS PartialSum#232L,COUNT(1) AS PartialCount#233L]
>    Project [_1#190 AS i#191]
>     Filter ((_1#190 % 10) = 5)
>      PhysicalRDD [_1#190], MapPartitionsRDD[93] at mapPartitions at ExistingRDD.scala:37
> sqlContext.sql(""" 
> select i % 10, sum(if(i % 10 = 5, 1, 0)), count(i)
> from t
> where i % 10 = 5
> group by i % 10""").show
> _c0 _c1 _c2
> 5   50  100
> {code}
> In Spark 1.5, new aggregation code path does not have the problem. The old code path
is fixed by https://github.com/apache/spark/commit/dd9ae7945ab65d353ed2b113e0c1a00a0533ffd6.



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