Siddharth Murching created SPARK-21972:
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Summary: 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 (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`.
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