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Posted to issues@spark.apache.org by "Siddharth Murching (JIRA)" <ji...@apache.org> on 2017/08/21 18:00:02 UTC
[jira] [Updated] (SPARK-21799) KMeans performance regression (5-6x
slowdown) in Spark 2.2
[ https://issues.apache.org/jira/browse/SPARK-21799?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Siddharth Murching updated SPARK-21799:
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Summary: KMeans performance regression (5-6x slowdown) in Spark 2.2 (was: KMeans Performance Regression (5-6x slowdown) in Spark 2.2)
> 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.cache()` does set the public `df.storageLevel` member properly, so I'd suggest replacing instances of `df.rdd.storageLevel` with df.storageLevel` in MLlib algorithms (the same pattern shows up in LogisticRegression, LinearRegression, and others).
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