Jarno Seppanen created SPARK-18688:
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Summary: 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
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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