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From "Hyukjin Kwon (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-22240) S3 CSV number of partitions incorrectly computed
Date Thu, 12 Oct 2017 04:02:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-22240?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16201407#comment-16201407
] 

Hyukjin Kwon commented on SPARK-22240:
--------------------------------------

There were multiple JIRAs for this feature. I believe SPARK-19610 contains all other duplicated
JIRAs.

> S3 CSV number of partitions incorrectly computed
> ------------------------------------------------
>
>                 Key: SPARK-22240
>                 URL: https://issues.apache.org/jira/browse/SPARK-22240
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core
>    Affects Versions: 2.2.0
>         Environment: Running on EMR 5.8.0 with Hadoop 2.7.3 and Spark 2.2.0
>            Reporter: Arthur Baudry
>
> Reading CSV out of S3 using S3A protocol does not compute the number of partitions correctly
in Spark 2.2.0.
> With Spark 2.2.0 I get only partition when loading a 14GB file
> {code:java}
> scala> val input = spark.read.format("csv").option("header", "true").option("delimiter",
"|").option("multiLine", "true").load("s3a://<s3_path>")
> input: org.apache.spark.sql.DataFrame = [PARTY_KEY: string, ROW_START_DATE: string ...
36 more fields]
> scala> input.rdd.getNumPartitions
> res2: Int = 1
> {code}
> While in Spark 2.0.2 I had:
> {code:java}
> scala> val input = spark.read.format("csv").option("header", "true").option("delimiter",
"|").option("multiLine", "true").load("s3a://<s3_path>")
> input: org.apache.spark.sql.DataFrame = [PARTY_KEY: string, ROW_START_DATE: string ...
36 more fields]
> scala> input.rdd.getNumPartitions
> res2: Int = 115
> {code}
> This introduces obvious performance issues in Spark 2.2.0. Maybe there is a property
that should be set to have the number of partitions computed correctly.
> I'm aware that the .option("multiline","true") is not supported in Spark 2.0.2, it's
not relevant here.



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