[jira] [Updated] (SPARK-17168) CSV with header is incorrectly read if file is partitioned

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

     [ https://issues.apache.org/jira/browse/SPARK-17168?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon updated SPARK-17168:
---------------------------------
    Labels: bulk-closed  (was: )

> CSV with header is incorrectly read if file is partitioned
> ----------------------------------------------------------
>
>                 Key: SPARK-17168
>                 URL: https://issues.apache.org/jira/browse/SPARK-17168
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.0.0
>            Reporter: Mathieu DESPRIEE
>            Priority: Minor
>              Labels: bulk-closed
>
> If a CSV file is stored in a partitioned fashion, the DataframeReader.csv with option header set to true skips the first line of *each partition* instead of skipping only the first one.
> ex:
> {code}
> // create a partitioned CSV file with header : 
> val rdd=sc.parallelize(Seq("hdr","1","2","3","4","5","6"), numSlices=2)
> rdd.saveAsTextFile("foo")
> {code}
> Now, if we try to read it with DataframeReader, the first row of the 2nd partition is skipped.
> {code}
> val df = spark.read.option("header","true").csv("foo")
> df.show
> +---+
> |hdr|
> +---+
> |  1|
> |  2|
> |  4|
> |  5|
> |  6|
> +---+
> // one row is missing
> {code}
> I more or less understand that this is to be consistent with the save operation of dataframewriter which saves header on each individual partition.
> But this is very error-prone. In our case, we have large CSV files with headers already stored in a partitioned way, so we will lose rows if we read with header set to true. So we have to manually handle the headers.
> I suggest a tri-valued option for header, with something like "skipOnFirstPartition"



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