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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2017/09/02 04:34:02 UTC
[jira] [Assigned] (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 ]
Apache Spark reassigned SPARK-21799:
------------------------------------
Assignee: (was: Apache Spark)
> 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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