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Posted to issues@spark.apache.org by "Bill Chambers (JIRA)" <ji...@apache.org> on 2016/11/16 21:05:58 UTC

[jira] [Updated] (SPARK-18424) Single Funct

     [ https://issues.apache.org/jira/browse/SPARK-18424?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Bill Chambers updated SPARK-18424:
----------------------------------
    Summary: Single Funct  (was: Improve Date Parsing Semantics & Functionality)

> Single Funct
> ------------
>
>                 Key: SPARK-18424
>                 URL: https://issues.apache.org/jira/browse/SPARK-18424
>             Project: Spark
>          Issue Type: Improvement
>            Reporter: Bill Chambers
>            Assignee: Bill Chambers
>            Priority: Minor
>
> I've found it quite cumbersome to work with dates thus far in Spark, it can be hard to reason about the timeformat and what type you're working with, for instance:
> say that I have a date in the format
> {code}
> 2017-20-12
> // Y-D-M
> {code}
> In order to parse that into a Date, I have to perform several conversions.
> {code}
>   to_date(
>     unix_timestamp(col("date"), dateFormat)
>     .cast("timestamp"))
>    .alias("date")
> {code}
> I propose simplifying this by adding a to_date function (exists) but adding one that accepts a format for that date. I also propose a to_timestamp function that also supports a format.
> so that you can avoid entirely the above conversion.
> It's also worth mentioning that many other databases support this. For instance, mysql has the STR_TO_DATE function, netezza supports the to_timestamp semantic.



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