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Date: Wed, 15 Mar 2017 22:32:41 +0000 (UTC)
From: "Hyukjin Kwon (JIRA)" <jira@apache.org>
To: issues@spark.apache.org
Message-ID: <JIRA.13056118.1489529579000.31935.1489617161990@Atlassian.JIRA>
In-Reply-To: <JIRA.13056118.1489529579000@Atlassian.JIRA>
References: <JIRA.13056118.1489529579000@Atlassian.JIRA> <JIRA.13056118.1489529579792@jira-lw-us.apache.org>
Subject: [jira] [Resolved] (SPARK-19954) Joining to a unioned DataFrame does
 not produce expected result.
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archived-at: Wed, 15 Mar 2017 22:32:49 -0000


     [ https://issues.apache.org/jira/browse/SPARK-19954?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-19954.
----------------------------------
    Resolution: Duplicate

> Joining to a unioned DataFrame does not produce expected result.
> ----------------------------------------------------------------
>
>                 Key: SPARK-19954
>                 URL: https://issues.apache.org/jira/browse/SPARK-19954
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.1.0
>            Reporter: Arun Allamsetty
>            Priority: Blocker
>
> I found this bug while trying to update from Spark 1.6.1 to 2.1.0. The bug is that when we try to join two DataFrames, one of which is a result of a union operation, the result of the join results in data as if the table was joined only to the first table in the union. This issue is not present in Spark 2.0.0 or 2.0.1 or 2.0.2, only in 2.1.0. Here's how to reproduce it.
> {noformat}
> import spark.implicits._
> import org.apache.spark.sql.functions.lit
> case class A(id: Long, colA: Boolean)
> case class B(id: Long, colB: Int)
> case class C(id: Long, colC: Double)
> case class X(id: Long, name: String)
> val aData = A(1, true) :: Nil
> val bData = B(2, 10) :: Nil
> val cData = C(3, 9.73D) :: Nil
> val xData = X(1, "a") :: X(2, "b") :: X(3, "c") :: Nil
> val aDf = spark.createDataset(aData).toDF
> val bDf = spark.createDataset(bData).toDF
> val cDf = spark.createDataset(cData).toDF
> val xDf = spark.createDataset(xData).toDF
> val unionDf =
>   aDf.select($"id", lit("a").as("name"), $"colA", lit(null).as("colB"), lit(null).as("colC")).union(
>   bDf.select($"id", lit("b").as("name"), lit(null).as("colA"), $"colB", lit(null).as("colC"))).union(
>   cDf.select($"id", lit("c").as("name"), lit(null).as("colA"), lit(null).as("colB"), $"colC"))
> val result = xDf.join(unionDf, unionDf("name") === xDf("name") && unionDf("id") === xDf("id"))
> result.show
> {noformat}
> The result being
> {noformat}
> +---+----+---+----+----+----+----+
> | id|name| id|name|colA|colB|colC|
> +---+----+---+----+----+----+----+
> |  1|   a|  1|   a|true|null|null|
> +---+----+---+----+----+----+----+
> {noformat}
> Force computing {{unionDf}} using {{count}} does not help change the result of the join. However, writing the data to disk and reading it back does give the correct result. But it is definitely not ideal. Interestingly caching the {{unionDf}} also gives the correct result.
> {noformat}
> +---+----+---+----+----+----+----+
> | id|name| id|name|colA|colB|colC|
> +---+----+---+----+----+----+----+
> |  1|   a|  1|   a|true|null|null|
> |  2|   b|  2|   b|null|  10|null|
> |  3|   c|  3|   c|null|null|9.73|
> +---+----+---+----+----+----+----+
> {noformat}


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