[ 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) 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) 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}} (algorithms will not try to persist uncached input). > 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) 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}}. -- This message was sent by Atlassian JIRA (v6.4.14#64029) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org