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https://issues.apache.org/jira/browse/SPARK-22240?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16200333#comment-16200333
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Hyukjin Kwon commented on SPARK-22240:
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This is an unfortunate limitation when {{multiLine}} is enabled. Because some
rows could spawn across multiple blocks, the partition are created per file if
I haven't missed something (I implemented that option). Just to double check, I
guess you are only reading reading a single file?
> 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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