[jira] [Created] (SPARK-19032) Non-deterministic results using aggregation first across multiple workers

["Harry Weppner (JIRA)" <ji...@apache.org>]

Harry Weppner created SPARK-19032:
-------------------------------------

             Summary: Non-deterministic results using aggregation first across multiple workers
                 Key: SPARK-19032
                 URL: https://issues.apache.org/jira/browse/SPARK-19032
             Project: Spark
          Issue Type: Bug
          Components: Optimizer
    Affects Versions: 1.6.1
         Environment: Standalone Spark 1.6.1 cluster on EC2 with 2 worker nodes, one executor each.
            Reporter: Harry Weppner


We've come across a situation results aggregated using {{first}} on a sorted df are non-deterministic. Given the explanation for the plan there appears to be a plausible explanation but creates more question on the usefulness of these aggregation functions in a spark cluster.

Here's a minimal example to reproduce:

{code}
val df = sc.parallelize(Seq(("a","prod1",0.6),("a","prod2",0.4),("a","prod2",0.4),("a","prod2",0.4),("a","prod2",0.4))).toDF("account","product","probability")
var p = df.sort($"probability".desc).groupBy($"account").agg(first($"product"),first($"probability")).show();

+-------+----------------+--------------------+
|account|first(product)()|first(probability)()|
+-------+----------------+--------------------+
|      a|           prod1|                 0.6|
+-------+----------------+--------------------+

p: Unit = ()

// Repeat and notice that result will occasionally be different

+-------+----------------+--------------------+
|account|first(product)()|first(probability)()|
+-------+----------------+--------------------+
|      a|           prod2|                 0.4|
+-------+----------------+--------------------+

p: Unit = ()

scala> df.sort($"probability".desc).groupBy($"account").agg(first($"product"),first($"probability")).explain(true);
== Parsed Logical Plan ==
'Aggregate ['account], [unresolvedalias('account),(first('product)(),mode=Complete,isDistinct=false) AS first(product)()#523,(first('probability)(),mode=Complete,isDistinct=false) AS first(probability)()#524]
+- Sort [probability#5 DESC], true
   +- Project [_1#0 AS account#3,_2#1 AS product#4,_3#2 AS probability#5]
      +- LogicalRDD [_1#0,_2#1,_3#2], MapPartitionsRDD[1] at rddToDataFrameHolder at <console>:27

== Analyzed Logical Plan ==
account: string, first(product)(): string, first(probability)(): double
Aggregate [account#3], [account#3,(first(product#4)(),mode=Complete,isDistinct=false) AS first(product)()#523,(first(probability#5)(),mode=Complete,isDistinct=false) AS first(probability)()#524]
+- Sort [probability#5 DESC], true
   +- Project [_1#0 AS account#3,_2#1 AS product#4,_3#2 AS probability#5]
      +- LogicalRDD [_1#0,_2#1,_3#2], MapPartitionsRDD[1] at rddToDataFrameHolder at <console>:27

== Optimized Logical Plan ==
Aggregate [account#3], [account#3,(first(product#4)(),mode=Complete,isDistinct=false) AS first(product)()#523,(first(probability#5)(),mode=Complete,isDistinct=false) AS first(probability)()#524]
+- Sort [probability#5 DESC], true
   +- Project [_1#0 AS account#3,_2#1 AS product#4,_3#2 AS probability#5]
      +- LogicalRDD [_1#0,_2#1,_3#2], MapPartitionsRDD[1] at rddToDataFrameHolder at <console>:27

== Physical Plan ==
SortBasedAggregate(key=[account#3], functions=[(first(product#4)(),mode=Final,isDistinct=false),(first(probability#5)(),mode=Final,isDistinct=false)], output=[account#3,first(product)()#523,first(probability)()#524])
+- ConvertToSafe
   +- Sort [account#3 ASC], false, 0
      +- TungstenExchange hashpartitioning(account#3,200), None
         +- ConvertToUnsafe
            +- SortBasedAggregate(key=[account#3], functions=[(first(product#4)(),mode=Partial,isDistinct=false),(first(probability#5)(),mode=Partial,isDistinct=false)], output=[account#3,first#532,valueSet#533,first#534,valueSet#535])
               +- ConvertToSafe
                  +- Sort [account#3 ASC], false, 0
                     +- Sort [probability#5 DESC], true, 0
                        +- ConvertToUnsafe
                           +- Exchange rangepartitioning(probability#5 DESC,200), None
                              +- ConvertToSafe
                                 +- Project [_1#0 AS account#3,_2#1 AS product#4,_3#2 AS probability#5]
                                    +- Scan ExistingRDD[_1#0,_2#1,_3#2]
{code}

My working hypothesis is that after {{TungstenExchange hashpartitioning}} the  _global_ sort order on {{probability}} is lost leading to non-deterministic results.

If this hypothesis is valid, then how useful are aggregation functions such as {{first}}, {{last}} and possibly others in Spark?

It appears that the use of window functions could address the ambiguity by making the partitions explicit but I'd be interested in your assessment. Thanks!



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