[jira] [Commented] (HIVEMALL-44) Support Top-K joins for DataFrame/Spark

["Takeshi Yamamuro (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/HIVEMALL-44?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15928279#comment-15928279 ] 

Takeshi Yamamuro commented on HIVEMALL-44:
------------------------------------------

Resolved by https://github.com/apache/incubator-hivemall/pull/33

> Support Top-K joins for DataFrame/Spark
> ---------------------------------------
>
>                 Key: HIVEMALL-44
>                 URL: https://issues.apache.org/jira/browse/HIVEMALL-44
>             Project: Hivemall
>          Issue Type: New Feature
>            Reporter: Takeshi Yamamuro
>            Priority: Blocker
>              Labels: Spark
>
> In Hivemall, `each_top_k` is useful for practical use cases. On the other hand, there are some cases we need to join tables then compute Top-K entries.You know we can compute this query by using regular joins + `each_top_k`. However, we have space to improve this query more; that is, we compute Top-K entries while processing joins. This optimization avoids a substantial amount of  I/O for joins.
> An example query is as follows;
> {code}
> val inputDf = Seq(
>   ("user1", 1, 0.3, 0.5),
>   ("user2", 2, 0.1, 0.1),
>   ("user3", 3, 0.8, 0.0),
>   ("user4", 1, 0.9, 0.9),
>   ("user5", 3, 0.7, 0.2),
>   ("user6", 1, 0.5, 0.4),
>   ("user7", 2, 0.6, 0.8)
> ).toDF("userId", "group", "x", "y")
> val masterDf = Seq(
>   (1, "pos1-1", 0.5, 0.1),
>   (1, "pos1-2", 0.0, 0.0),
>   (1, "pos1-3", 0.3, 0.3),
>   (2, "pos2-3", 0.1, 0.3),
>   (2, "pos2-3", 0.8, 0.8),
>   (3, "pos3-1", 0.1, 0.7),
>   (3, "pos3-1", 0.7, 0.1),
>   (3, "pos3-1", 0.9, 0.0),
>   (3, "pos3-1", 0.1, 0.3)
> ).toDF("group", "position", "x", "y")
> // Compute top-1 rows for each group
> val distance = sqrt(
>   pow(inputDf("x") - masterDf("x"), lit(2.0)) +
>   pow(inputDf("y") - masterDf("y"), lit(2.0))
> )
> val top1Df = inputDf.join_top_k(
>   lit(1), masterDf, inputDf("group") === masterDf("group"),
>   distance.as("score")
> )
> {code}



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