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