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https://issues.apache.org/jira/browse/SPARK-26567?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16736683#comment-16736683
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Hyukjin Kwon commented on SPARK-26567:
--------------------------------------
I don't think we should necessarily follow Hive. The current behaviour looks
making sense already. Let's don't fix.
> Should we align CSV query results with hive text query results: an int field,
> if the input value is 1.0, hive text query results is 1, CSV query results is
> null
> ----------------------------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-26567
> URL: https://issues.apache.org/jira/browse/SPARK-26567
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.4.0
> Reporter: eaton
> Priority: Minor
>
> If we want to be consistent, we can modify the makeConverter function in
> UnivocityParser, but the performance may get worse.The modified code is as
> follows:
>
> {code:java}
> def makeConverter(
> name: String,
> dataType: DataType,
> nullable: Boolean = true,
> options: CSVOptions): ValueConverter = dataType match {
> case _: ByteType => (d: String) =>
> nullSafeDatum(d, name, nullable, options)(_.toDouble.intValue().toByte)
> case _: ShortType => (d: String) =>
> nullSafeDatum(d, name, nullable, options)(_.toDouble.intValue().toShort)
> case _: IntegerType => (d: String) =>
> nullSafeDatum(d, name, nullable, options)(_.toDouble.intValue())
> case _: LongType => (d: String) =>
> nullSafeDatum(d, name, nullable, options)(_.toDouble.intValue().toLong)
> {code}
>
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