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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 04:04:14 UTC
[jira] [Updated] (SPARK-18534) Datasets Aggregation with Maps
[ https://issues.apache.org/jira/browse/SPARK-18534?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon updated SPARK-18534:
---------------------------------
Labels: bulk-closed (was: )
> Datasets Aggregation with Maps
> ------------------------------
>
> Key: SPARK-18534
> URL: https://issues.apache.org/jira/browse/SPARK-18534
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.2, 1.6.3
> Reporter: Anton Okolnychyi
> Priority: Major
> Labels: bulk-closed
>
> There is a problem with user-defined aggregations in the Dataset API in Spark 1.6.3, while the identical code works fine in Spark 2.0.
> The problem appears only if {{ExpressionEncoder()}} is used for Maps. The same code with a Kryo-based alternative produces a correct result. If the encoder for a map is defined with the help of {{ExpressionEncoder()}}, Spark is not capable of reading the reduced values in the merge phase of the considered aggregation.
> Code to reproduce:
> {code}
> case class TestStopPoint(line: String, sequenceNumber: Int, id: String)
> // Does not work with ExpressionEncoder() and produces an empty map as a result
> implicit val intStringMapEncoder: Encoder[Map[Int, String]] = ExpressionEncoder()
> // Will work if a Kryo-based encoder is used
> // implicit val intStringMapEncoder: Encoder[Map[Int, String]] = org.apache.spark.sql.Encoders.kryo[Map[Int, String]]
> val sparkConf = new SparkConf()
> .setAppName("DS Spark 1.6 Test")
> .setMaster("local[4]")
> val sparkContext = new SparkContext(sparkConf)
> val sparkSqlContext = new SQLContext(sparkContext)
> import sparkSqlContext.implicits._
> val stopPointDS = Seq(TestStopPoint("33", 1, "id#1"), TestStopPoint("33", 2, "id#2")).toDS()
> val stopPointSequenceMap = new Aggregator[TestStopPoint, Map[Int, String], Map[Int, String]] {
> override def zero = Map[Int, String]()
> override def reduce(map: Map[Int, String], stopPoint: TestStopPoint) = {
> map.updated(stopPoint.sequenceNumber, stopPoint.id)
> }
> override def merge(map: Map[Int, String], anotherMap: Map[Int, String]) = {
> map ++ anotherMap
> }
> override def finish(reduction: Map[Int, String]) = reduction
> }.toColumn
> val resultMap = stopPointDS
> .groupBy(_.line)
> .agg(stopPointSequenceMap)
> .collect()
> .toMap
> {code}
> The code above produces an empty map as a result if the Map encoder is defined as {{ExpressionEncoder()}}. The Kryo-based encoder works fine (commented in the code).
> A preliminary investigation was done to find out possible reasons for this behavior. I am not a Spark expert but hope it will help.
> The Physical Plan looks like:
> {noformat}
> == Physical Plan ==
> SortBasedAggregate(key=[value#55], functions=[(anon$1(line#4,sequenceNumber#5,id#6),mode=Final,isDistinct=false)], output=[value#55,anon$1(line,sequenceNumber,id)#64])
> +- ConvertToSafe
> +- Sort [value#55 ASC], false, 0
> +- TungstenExchange hashpartitioning(value#55,1), None
> +- ConvertToUnsafe
> +- SortBasedAggregate(key=[value#55], functions=[(anon$1(line#4,sequenceNumber#5,id#6),mode=Partial,isDistinct=false)], output=[value#55,value#60])
> +- ConvertToSafe
> +- Sort [value#55 ASC], false, 0
> +- !AppendColumns <function1>, class[line[0]: string, sequenceNumber[0]: int, id[0]: string], class[value[0]: string], [value#55]
> +- ConvertToUnsafe
> +- LocalTableScan [line#4,sequenceNumber#5,id#6], [[0,2000000002,1,2800000004,3333,31236469],[0,2000000002,2,2800000004,3333,32236469]]
> {noformat}
>
> Everything untill the first (from bottom) {{SortBasedAggregate}} step and part of it is handled correctly. In particular, I see that each row correctly updates the mutable aggregation buffer in the {{update()}} method of the {{TypedAggregateExpression}} class. My initial idea was that the problem appeared in the {{ConvertToUnsafe}} step directly after the first {{SortBasedAggregate}}. If I take a look at the {{ConvertToUnsafe}} class, I can see that the first {{SortBasedAggregate}} returns a map with 2 elements (I call {{child.execute().collect()(0).getMap(1)}} in {{doExecute()}} of {{ConvertToUnsafe}} to see this). At the same time, if I examine the output of this {{ConvertToUnsafe}} in the identical way as its input, I see that the result map does not contain any elements. As a consequence, Spark operates on two empty maps in the {{merge()}} method of the {{TypedAggregateExpression}} class. However, my assumption was only partially correct. I did a more detailed investigation and its outcomes are described in comments.
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