[jira] [Updated] (SPARK-18688) Interpolated time series join

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-18688?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon updated SPARK-18688:
---------------------------------
    Labels: bulk-closed  (was: )

> Interpolated time series join
> -----------------------------
>
>                 Key: SPARK-18688
>                 URL: https://issues.apache.org/jira/browse/SPARK-18688
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 2.0.2
>            Reporter: Jarno Seppanen
>            Priority: Major
>              Labels: bulk-closed
>
> Time series joins are very common in analytics tasks. A simple example would be joining the newest value of number of followers from data frame F with sessions from data frame S. Currently, a cross join is needed for such joins in Spark, making them practically impossible.
> Example syntax:
> {noformat}
> SELECT l.account_id, l.time AS login_time, f.num_followers
> FROM account_login l
> LEFT JOIN follower_count_changed f
>   ON (f.account_id = l.account_id
>       AND l.time INTERPOLATE PREVIOUS VALUE f.time)
> {noformat}
> In essence, I'd like to have support for efficiently running joins like INTERPOLATE PREVIOUS VALUE joins in Vertica [1].
> Thanks for your consideration,
> Jarno
> [1] https://my.vertica.com/docs/7.1.x/HTML/index.htm#Authoring/SQLReferenceManual/LanguageElements/Predicates/INTERPOLATE.htm



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