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https://issues.apache.org/jira/browse/SPARK-6319?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15126668#comment-15126668
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Cheng Lian commented on SPARK-6319:
-----------------------------------
One possible but not necessarily the best workaround is to have a UDF that maps
your binary field to some other data type, which can be used in GROUP BY/JOIN.
> 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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