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https://issues.apache.org/jira/browse/SPARK-29101?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16933822#comment-16933822
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Dongjoon Hyun commented on SPARK-29101:
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This is backported to branch-2.4 for Apache Spark 2.4.5 via
https://github.com/apache/spark/pull/25843
> CSV datasource returns incorrect .count() from file with malformed records
> --------------------------------------------------------------------------
>
> Key: SPARK-29101
> URL: https://issues.apache.org/jira/browse/SPARK-29101
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.4.4
> Reporter: Stuart White
> Assignee: Sandeep Katta
> Priority: Minor
> Labels: correctness
> Fix For: 2.4.5, 3.0.0
>
>
> Spark 2.4 introduced a change to the way csv files are read. See [Upgrading
> From Spark SQL 2.3 to
> 2.4|https://spark.apache.org/docs/2.4.0/sql-migration-guide-upgrade.html#upgrading-from-spark-sql-23-to-24]
> for more details.
> In that document, it states: _To restore the previous behavior, set
> spark.sql.csv.parser.columnPruning.enabled to false._
> I am configuring Spark 2.4.4 as such, yet I'm still getting results
> inconsistent with pre-2.4. For example:
> Consider this file (fruit.csv). Notice it contains a header record, 3 valid
> records, and one malformed record.
> {noformat}
> fruit,color,price,quantity
> apple,red,1,3
> banana,yellow,2,4
> orange,orange,3,5
> xxx
> {noformat}
>
> With Spark 2.1.1, if I call .count() on a DataFrame created from this file
> (using option DROPMALFORMED), "3" is returned.
> {noformat}
> (using Spark 2.1.1)
> scala> spark.read.option("header", "true").option("mode",
> "DROPMALFORMED").csv("fruit.csv").count
> 19/09/16 14:28:01 WARN CSVRelation: Dropping malformed line: xxx
> res1: Long = 3
> {noformat}
> With Spark 2.4.4, I set the "spark.sql.csv.parser.columnPruning.enabled"
> option to false to restore the pre-2.4 behavior for handling malformed
> records, then call .count() and "4" is returned.
> {noformat}
> (using spark 2.4.4)
> scala> spark.conf.set("spark.sql.csv.parser.columnPruning.enabled", false)
> scala> spark.read.option("header", "true").option("mode",
> "DROPMALFORMED").csv("fruit.csv").count
> res1: Long = 4
> {noformat}
> So, using the *spark.sql.csv.parser.columnPruning.enabled* option did not
> actually restore previous behavior.
> How can I, using Spark 2.4+, get a count of the records in a .csv which
> excludes malformed records?
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