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From "Stephen Boesch (JIRA)" <j...@apache.org>
Subject [jira] [Closed] (SPARK-4775) Possible problem in a simple join? Getting duplicate rows and missing rows
Date Sun, 14 Dec 2014 19:56:13 GMT

     [ https://issues.apache.org/jira/browse/SPARK-4775?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Stephen Boesch closed SPARK-4775.
---------------------------------
    Resolution: Not a Problem

> Possible problem in a simple join?  Getting duplicate rows and missing rows
> ---------------------------------------------------------------------------
>
>                 Key: SPARK-4775
>                 URL: https://issues.apache.org/jira/browse/SPARK-4775
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 1.3.0
>         Environment: Run on Mac but should be agnostic
>            Reporter: Stephen Boesch
>            Assignee: Michael Armbrust
>
> I am working on testing of HBase joins. As part of this work some simple vanilla SparkSQL
tests were created.  Some of the results are surprising: here are the details:
> ------------------------------------
> Consider the following schema that includes two columns:
> {code}
> case class JoinTable2Cols(intcol: Int, strcol: String)
> {code}
> Let us register two temp tables using this schema and insert 2 rows and 4 rows respectively:
> {code}
>     val rdd1 = sc.parallelize((1 to 2).map { ix => JoinTable2Cols(ix, s"valA$ix")})
>     rdd1.registerTempTable("SparkJoinTable1")
>     val ids = Seq((1, 1), (1, 2), (2, 3), (2, 4))
>     val rdd2 = sc.parallelize(ids.map { case (ix, is) => JoinTable2Cols(ix, s"valB$is")})
>     val table2 = rdd2.registerTempTable("SparkJoinTable2")
> {code}
> Here is the data in both tables:
> {code}
> Table1 Contents:
> [1,valA1]
> [2,valA2]
> Table2 Contents:
> [1,valB1]
> [1,valB2]
> [2,valB3]
> [2,valB4]
> {code}
> Now let us join the tables on the first column:
> {code}
> select t1.intcol t1intcol, t2.intcol t2intcol, t1.strcol t1strcol,
>                 t2.strcol t2strcol from SparkJoinTable1 t1 JOIN
>                     SparkJoinTable2 t2 on t1.intcol = t2.intcol
> {code}
> What results do we get:
>  came back with 4 results
> {code}
> Results
> [1,1,valA1,valB2]
> [1,1,valA1,valB2]
> [2,2,valA2,valB4]
> [2,2,valA2,valB4]
> {code}
> Huh??
> Where did valB1 and valB3 go? Why do we have duplicate rows?
> Note: the expected results were:
> {code}
>       Seq(1, 1, "valA1", "valB1"),
>       Seq(1, 1, "valA1", "valB2"),
>       Seq(2, 2, "valA2", "valB3"),
>       Seq(2, 2, "valA2", "valB4"))
> {code}
> A standalone testing program is attached  SparkSQLJoinSuite. An abridged version of the
actual output is also attached.



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