[ https://issues.apache.org/jira/browse/SPARK-26567?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
eaton updated SPARK-26567: -------------------------- Description: 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} // code placeholder 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} was: 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} // code placeholder {code} 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) > 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} > // code placeholder > 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} > -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org