[jira] [Updated] (SPARK-6319) Should throw analysis exception when using binary type in groupby/join

["Reynold Xin (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-6319?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Reynold Xin updated SPARK-6319:
-------------------------------
    Summary: Should throw analysis exception when using binary type in groupby/join  (was: DISTINCT doesn't work for binary type)

> Should throw analysis exception when using binary type in groupby/join
> ----------------------------------------------------------------------
>
>                 Key: SPARK-6319
>                 URL: https://issues.apache.org/jira/browse/SPARK-6319
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 1.0.2, 1.1.1, 1.2.1, 1.3.0
>            Reporter: Cheng Lian
>            Assignee: Liang-Chi Hsieh
>            Priority: Critical
>             Fix For: 1.5.0
>
>
> Spark shell session for reproduction:
> {noformat}
> scala> import sqlContext.implicits._
> scala> import org.apache.spark.sql.types._
> scala> Seq(1, 1, 2, 2).map(i => Tuple1(i.toString)).toDF("c").select($"c" cast BinaryType).distinct.show()
> ...
> CAST(c, BinaryType)
> [B@43f13160
> [B@5018b648
> [B@3be22500
> [B@476fc8a1
> {noformat}
> Spark SQL uses plain byte arrays to represent binary values. However, arrays are compared by reference rather than by value. On the other hand, the DISTINCT operator uses a {{HashSet}} and its {{.contains}} method to check for duplicated values. These two facts together cause the problem.



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