[jira] [Updated] (SPARK-32016) Why spark does not preserve the original timestamp format while writing dataset to file or hdfs
[
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 "*-MM-dd HH:mm:ss*".
> * But while writing to file, saves in format as
> "*-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 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 "*-MM-dd HH:mm:ss*" and
> "*-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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[jira] [Updated] (SPARK-32016) Why spark does not preserve the original timestamp format while writing dataset to file or hdfs
[
https://issues.apache.org/jira/browse/SPARK-32016?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiao Li updated SPARK-32016:
Fix 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
>
> 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 "*-MM-dd HH:mm:ss*".
> * But while writing to file, saves in format as
> "*-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 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 "*-MM-dd HH:mm:ss*" and
> "*-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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