Details
Critical Description
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
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
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.
