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Posted to issues@spark.apache.org by "Siddharth Murching (JIRA)" <ji...@apache.org> on 2017/09/11 03:42:00 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 ]
Siddharth Murching updated SPARK-21972:
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Description:
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 (see [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) to the abovementioned estimators 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`.
was:
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 (as described in [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; both of 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) to the abovementioned estimators 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`.
> 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 (see [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) to the abovementioned estimators 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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