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https://issues.apache.org/jira/browse/SPARK-21799?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16161992#comment-16161992
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Joseph K. Bradley commented on SPARK-21799:
-------------------------------------------

Now that I've caught up on these, this is just a special case of the bug in 
[SPARK-18608].  I'm going to close this issue and ask for a PR like 
[~podongfeng]'s original PR be sent for [SPARK-18608], fixing the use of 
{{dataset.rdd.getStorageLevel}}.  I think we should fix it for all algorithms, 
not just K-Means.

> KMeans performance regression (5-6x slowdown) in Spark 2.2
> ----------------------------------------------------------
>
>                 Key: SPARK-21799
>                 URL: https://issues.apache.org/jira/browse/SPARK-21799
>             Project: Spark
>          Issue Type: Bug
>          Components: MLlib
>    Affects Versions: 2.2.0
>            Reporter: Siddharth Murching
>
> I've been running KMeans performance tests using 
> [spark-sql-perf|https://github.com/databricks/spark-sql-perf/] and have 
> noticed a regression (slowdowns of 5-6x) when running tests on large datasets 
> in Spark 2.2 vs 2.1.
> The test params are:
> * Cluster: 510 GB RAM, 16 workers
> * Data: 1000000 examples, 10000 features
> After talking to [~josephkb], the issue seems related to the changes in 
> [SPARK-18356|https://issues.apache.org/jira/browse/SPARK-18356] introduced in 
> [this PR|https://github.com/apache/spark/pull/16295].
> It seems `df.cache()` doesn't set the storageLevel of `df.rdd`, so 
> `handlePersistence` is true even when KMeans is run on a cached DataFrame. 
> This unnecessarily causes another copy of the input dataset to be persisted.
> As of Spark 2.1 ([JIRA 
> link|https://issues.apache.org/jira/browse/SPARK-16063]) `df.storageLevel` 
> returns the correct result after calling `df.cache()`, so I'd suggest 
> replacing instances of `df.rdd.getStorageLevel` with df.storageLevel` in 
> MLlib algorithms (the same pattern shows up in LogisticRegression, 
> LinearRegression, and others). I've verified this behavior in [this 
> notebook|https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/5211178207246023/950505630032626/7788830288800223/latest.html]



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