Siddharth Murching created SPARK-21799: ------------------------------------------
Summary: 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]. `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). -- This message was sent by Atlassian JIRA (v6.4.14#64029) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org