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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2018/01/05 01:00:00 UTC
[jira] [Updated] (SPARK-21972) Allow users to control input data
persistence in ML Estimators via a handlePersistence ml.Param
[ https://issues.apache.org/jira/browse/SPARK-21972?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Joseph K. Bradley updated SPARK-21972:
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Target Version/s: 2.4.0 (was: 2.3.0)
> Allow users to control input data persistence in ML Estimators via a handlePersistence ml.Param
> -----------------------------------------------------------------------------------------------
>
> Key: SPARK-21972
> URL: https://issues.apache.org/jira/browse/SPARK-21972
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
> Affects Versions: 2.2.0
> Reporter: Siddharth Murching
>
> Several Spark ML algorithms (LogisticRegression, LinearRegression, KMeans, etc) call {{cache()}} on uncached input datasets to improve performance.
> Unfortunately, these algorithms a) check input persistence inaccurately ([SPARK-18608|https://issues.apache.org/jira/browse/SPARK-18608]) and b) check the persistence level of the input dataset but not any of its parents. These issues can result in unwanted double-caching of input data & degraded performance (see [SPARK-21799|https://issues.apache.org/jira/browse/SPARK-21799]).
> This ticket proposes adding a boolean {{handlePersistence}} param (org.apache.spark.ml.param) so that users can specify whether an ML algorithm should try to cache un-cached input data. {{handlePersistence}} will be {{true}} by default, corresponding to existing behavior (always persisting uncached input), but users can achieve finer-grained control over input persistence by setting {{handlePersistence}} to {{false}}.
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