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From "Xiangrui Meng (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-15027) ALS.train should use DataFrame instead of RDD
Date Tue, 03 May 2016 18:00:16 GMT

    [ https://issues.apache.org/jira/browse/SPARK-15027?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15269218#comment-15269218
] 

Xiangrui Meng commented on SPARK-15027:
---------------------------------------

Ah, I see the problems now. We do need the hash partitioner to accelerate queries from the
driver and probably joins. What if we convert the factors using `repartition(blocks, "id")`
before we return the factors? It should come with a hash partitioner, but it might be different
from the one we used in ALS. #2 seems like a bug. Could you provide a minimal example that
can reproduce it?

Given the pending issues, it seems that we should target this to 2.1. Sounds good?

> ALS.train should use DataFrame instead of RDD
> ---------------------------------------------
>
>                 Key: SPARK-15027
>                 URL: https://issues.apache.org/jira/browse/SPARK-15027
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML, PySpark
>    Affects Versions: 2.0.0
>            Reporter: Xiangrui Meng
>
> We should also update `ALS.train` to use `Dataset/DataFrame` instead of `RDD` to be consistent
with other APIs under spark.ml and it also leaves space for Tungsten-based optimization.



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