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https://issues.apache.org/jira/browse/SPARK-21799?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16151407#comment-16151407
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Apache Spark commented on SPARK-21799:
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User 'WeichenXu123' has created a pull request for this issue:
https://github.com/apache/spark/pull/19107
> 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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