Github user falaki commented on the issue: https://github.com/apache/spark/pull/17941 @felixcheung we all know that SparkR (and in general R) API is not perfect when it comes to ETLing unstructured data. For example we don't have a great story for nested data, etc. To overcome these limitations many ETL their data in Python or Scala and then analyze them in R. With introduction of sessions that workflow is partially broken. You can still do it but you need to persist the table. The global temp view is to solve that problem. It exists in PySpark, so I think it deserves to exist in SparkR as well.
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