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