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From "Siddharth Murching (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-21972) Allow users to control input data persistence in ML Estimators via a handlePersistence ml.Param
Date Mon, 11 Sep 2017 03:42:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-21972?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Siddharth Murching updated SPARK-21972:
---------------------------------------
    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 (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`.


> 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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