[jira] [Updated] (SPARK-18108) Partition discovery fails with explicitly written long partitions

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

     [ https://issues.apache.org/jira/browse/SPARK-18108?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

holdenk updated SPARK-18108:
----------------------------
    Component/s:     (was: Spark Core)
                 SQL

> Partition discovery fails with explicitly written long partitions
> -----------------------------------------------------------------
>
>                 Key: SPARK-18108
>                 URL: https://issues.apache.org/jira/browse/SPARK-18108
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.0.1
>            Reporter: Richard Moorhead
>            Priority: Minor
>         Attachments: stacktrace.out
>
>
> We have parquet data written from Spark1.6 that, when read from 2.0.1, produces errors.
> {code}
> case class A(a: Long, b: Int)
> val as = Seq(A(1,2))
> //partition explicitly written
> spark.createDataFrame(as).write.parquet("/data/a=1/")
> spark.read.parquet("/data/").collect
> {code}
> The above code fails; stack trace attached. 
> If an integer used, explicit partition discovery succeeds.
> {code}
> case class A(a: Int, b: Int)
> val as = Seq(A(1,2))
> //partition explicitly written
> spark.createDataFrame(as).write.parquet("/data/a=1/")
> spark.read.parquet("/data/").collect
> {code}
> The action succeeds. Additionally, if 'partitionBy' is used instead of explicit writes, partition discovery succeeds. 
> Question: Is the first example a reasonable use case? [PartitioningUtils|https://github.com/apache/spark/blob/branch-2.0/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala#L319] seems to default to Integer types unless the partition value exceeds the integer type's length.



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org