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https://issues.apache.org/jira/browse/SPARK-19289?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15833983#comment-15833983
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Kaushal Prajapati commented on SPARK-19289:
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[~srowen], yes you are right, names are not necessarily unique, then if we are
able to list all the datasets like the same that we are doing for RDDs using
*sc.getPersistentRDDs* to list all the RDDs, it would be useful because if i am
maintaining the unique names for Datasets in a application, i can easily
uncache it or if i have a group of datasets with the same name, i can also
uncache all the datasets using this feature.
> UnCache Dataset using Name
> --------------------------
>
> Key: SPARK-19289
> URL: https://issues.apache.org/jira/browse/SPARK-19289
> Project: Spark
> Issue Type: Wish
> Components: Spark Core, SQL
> Affects Versions: 2.1.0
> Reporter: Kaushal Prajapati
> Priority: Minor
> Labels: features
>
> We can Cache and Uncache any table using its name in Spark Sql.
> {code}
> df.createTempView("myTable")
> sqlContext.cacheTable("myTable")
> sqlContext.uncacheTable("myTable")
> {code}
> Likewise if it is possible to have some kind of uniqueness for names in
> DataSets and an abstraction like the same that we have for tables. It would
> be very useful
> {code}
> scala> val df = sc.range(1,1000).toDF
> df: org.apache.spark.sql.DataFrame = [value: bigint]
> scala> df.setName("MyDataset")
> res0: df.type = MyDataset
> scala> df.cache
> res1: df.type = MyDataset
> sqlContext.getDataSet("MyDataset")
> sqlContext.uncacheDataSet("MyDataset")
> {code}
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