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https://issues.apache.org/jira/browse/SYSTEMML-909?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15492666#comment-15492666
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Matthias Boehm commented on SYSTEMML-909:
-----------------------------------------

I just delivered the rework of the dataframe-matrix and matrix-dataframe 
converters. This should fix the performance and OOM issues. 
[~mwdus...@us.ibm.com] could you please give it a try. If this does not solve 
the issue, please provide the schema of the input data frame and maybe write it 
to HDFS before conversion to isolate the problem. Thanks. 

> `determineDataFrameDimensionsIfNeeded(...)` is a bottleneck.
> ------------------------------------------------------------
>
>                 Key: SYSTEMML-909
>                 URL: https://issues.apache.org/jira/browse/SYSTEMML-909
>             Project: SystemML
>          Issue Type: Improvement
>            Reporter: Mike Dusenberry
>
> The {{[determineDataFrameDimensionsIfNeeded(...) | 
> https://github.com/apache/incubator-systemml/blob/master/src/main/java/org/apache/sysml/api/mlcontext/MLContextConversionUtil.java#L585]}}
>  function in {{MLContext}} is a major bottleneck, particularly due to the 
> `javaRDD` call.
> The issue I'm seeing is that the javaRDD.count() function causes execution of 
> the lazy DataFrames I pass in, which are created from another DataFrame via 
> df.randomSplit([0.8, 0.2]), thus a shuffle occurs. I know that this is going 
> to happen anyways in the internal conversion, but it wastes a lot of time by 
> having to also do it in this step too. Assume that I have more data than I 
> can efficiently cache (~7TB with the potential for much more), so I need to 
> incur the shuffle step only once on the way into the engine.



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