[jira] [Updated] (SPARK-32016) Why spark does not preserve the original timestamp format while writing dataset to file or hdfs

["Xiao Li (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-32016?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Xiao Li updated SPARK-32016:
----------------------------
    Target Version/s:   (was: 3.0.0)

> Why spark does not preserve the original timestamp format while writing dataset to file or hdfs
> -----------------------------------------------------------------------------------------------
>
>                 Key: SPARK-32016
>                 URL: https://issues.apache.org/jira/browse/SPARK-32016
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL, Structured Streaming
>    Affects Versions: 2.3.0, 2.4.0, 2.4.3
>         Environment: Apache spark 2.3 and spark 2.4. May happen in other as well
>            Reporter: Anupam Jain
>            Priority: Major
>             Fix For: 3.0.0
>
>
> Want to write spark dataset having few timestamp columns into hdfs.
>  * While reading, by default spark infers data as timestamp, if format is similar to "*yyyy-MM-dd HH:mm:ss*".
>  * But while writing to file, saves in format as "*yyyy-MM-dd'T'HH:mm:ss.SSSXXX*"
>  * For e.g. source data *2020-06-01 12:10:03* is written as *2020-06-01T12:10:03.000+05:30*.
>  * Expected is to preserve the oroginal timestamp format before writing.
> Why spark does not preserve the original timestamp format while writing dataset to file or hdfs?
> Using simple java code like:
> {color:#4c9aff}Dataset<Row> ds = spark.read().format("csv").option("path",the_path).option("inferSchema","true").load(); {color}
> {color:#4c9aff}ds.write().format("csv").save("path_to_save");{color}
> I know the workaround:
>  * Use "*timestampFormat*" option before save.
>  * But may have performance overhead and also its global for all columns.
>  * So lets say have 2 columns having formats "*yyyy-MM-dd HH:mm:ss*" and "*yyyy-MM-dd HH*". Both can be inferred as timestamp by default, but outputs in a single specified "timestampFormat".
>  * Another way is to use date_format(col, format). But that also may have performance overhead and includes operations to apply, whereas I expect spark to preserve the original format
> Tried with spark2.3 and spark2.4



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