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https://issues.apache.org/jira/browse/SPARK-29058?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17051844#comment-17051844
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Hyukjin Kwon commented on SPARK-29058:
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Yeah, so there's kind of tradeoff. If we should have the same results, we
should parse and convert everything always which is pretty costly. Workaround
itself seems simple enough though.
> Reading csv file with DROPMALFORMED showing incorrect record count
> ------------------------------------------------------------------
>
> Key: SPARK-29058
> URL: https://issues.apache.org/jira/browse/SPARK-29058
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
> Affects Versions: 2.3.0
> Reporter: Suchintak Patnaik
> Priority: Minor
>
> The spark sql csv reader is dropping malformed records as expected, but the
> record count is showing as incorrect.
> Consider this file (fruit.csv)
> {code}
> apple,red,1,3
> banana,yellow,2,4.56
> orange,orange,3,5
> {code}
> Defining schema as follows:
> {code}
> schema = "Fruit string,color string,price int,quantity int"
> {code}
> Notice that the "quantity" field is defined as integer type, but the 2nd row
> in the file contains a floating point value, hence it is a corrupt record.
> {code}
> >>> df = spark.read.csv(path="fruit.csv",mode="DROPMALFORMED",schema=schema)
> >>> df.show()
> +------+------+-----+--------+
> | Fruit| color|price|quantity|
> +------+------+-----+--------+
> | apple| red| 1| 3|
> |orange|orange| 3| 5|
> +------+------+-----+--------+
> >>> df.count()
> 3
> {code}
> Malformed record is getting dropped as expected, but incorrect record count
> is getting displayed.
> Here the df.count() should give value as 2
>
>
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