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Posted to issues@spark.apache.org by "Herman van Hovell (JIRA)" <ji...@apache.org> on 2018/07/18 11:27:00 UTC
[jira] [Updated] (SPARK-23612) Specify formats for individual
DateType and TimestampType columns in schemas
[ https://issues.apache.org/jira/browse/SPARK-23612?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Herman van Hovell updated SPARK-23612:
--------------------------------------
Labels: DataType date spree sql (was: DataType date sql)
> Specify formats for individual DateType and TimestampType columns in schemas
> ----------------------------------------------------------------------------
>
> Key: SPARK-23612
> URL: https://issues.apache.org/jira/browse/SPARK-23612
> Project: Spark
> Issue Type: Improvement
> Components: PySpark, SQL
> Affects Versions: 2.3.0
> Reporter: Patrick Young
> Priority: Minor
> Labels: DataType, date, spree, sql
>
> [https://github.com/apache/spark/blob/407f67249639709c40c46917700ed6dd736daa7d/python/pyspark/sql/types.py#L162-L200]
> It would be very helpful if it were possible to specify the format for individual columns in a schema when reading csv files, rather than one format:
> {code:java|title=Bar.python|borderStyle=solid}
> # Currently can only do something like:
> spark.read.option("dateFormat", "yyyyMMdd").csv(...)
> # Would like to be able to do something like:
> schema = StructType([
> StructField("date1", DateType(format="MM/dd/yyyy"), True),
> StructField("date2", DateType(format="yyyyMMdd"), True)
> ]
> read.schema(schema).csv(...)
> {code}
> Thanks for any help, input!
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