[
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
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}
> 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: [email protected]
For additional commands, e-mail: [email protected]
