[jira] [Commented] (SPARK-21021) Reading partitioned parquet does not respect specified schema column order

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

    [ https://issues.apache.org/jira/browse/SPARK-21021?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16047815#comment-16047815 ] 

jin xing commented on SPARK-21021:
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I think the reason of the incompatibility is that the {{userSpecifiedSchema}} is split to two parts -- {{dataSchema}}(f3) and {{partitionSchema}}(f1, f2) in {{HadoopFsRelation}}. And the schema of {{HadoopFsRelation}} is {{dataSchema}} ++ {{partitionSchema}}.
I guess this is not a problem?
You can rectify the incompatibility by {{dfRead.select("f1", "f2", "f3")}}

> Reading partitioned parquet does not respect specified schema column order
> --------------------------------------------------------------------------
>
>                 Key: SPARK-21021
>                 URL: https://issues.apache.org/jira/browse/SPARK-21021
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.1.0
>            Reporter: Michel Lemay
>            Priority: Minor
>
> When reading back a partitioned parquet folder, column order gets messed up.
> Consider the following example:
> {code}
> case class Event(f1: String, f2: String, f3: String)
> val df = Seq(Event("v1", "v2", "v3")).toDF
> df.write.partitionBy("f1", "f2").parquet("out")
> val schema: StructType = StructType(StructField("f1", StringType, true) :: StructField("f2", StringType, true) :: StructField("f3", StringType, true) :: Nil)
> val dfRead = spark.read.schema(schema).parquet("out")
> dfRead.show
> +---+---+---+
> | f3| f1| f2|
> +---+---+---+
> | v3| v1| v2|
> +---+---+---+
> dfRead.columns
> Array[String] = Array(f3, f1, f2)
> schema.fields
> Array(StructField(f1,StringType,true), StructField(f2,StringType,true), StructField(f3,StringType,true))
> {code}
> This makes it really hard to have compatible schema when reading from multiple sources.



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