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Felix Cheung commented on SPARK-26858: -------------------------------------- If I understand, this is the case where Spark actually doesn't care much about the schema but sounds like Arrow does. Could we infer the schema from R data.frame? Is there an equivalent way for Python Pandas to Arrow? > Vectorized gapplyCollect, Arrow optimization in native R function execution > --------------------------------------------------------------------------- > > Key: SPARK-26858 > URL: https://issues.apache.org/jira/browse/SPARK-26858 > Project: Spark > Issue Type: Sub-task > Components: SparkR, SQL > Affects Versions: 3.0.0 > Reporter: Hyukjin Kwon > Assignee: Hyukjin Kwon > Priority: Major > > Unlike gapply, gapplyCollect requires additional ser/de steps because it can > omit the schema, and Spark SQL doesn't know the return type before actually > execution happens. > In original code path, it's done via using binary schema. Once gapply is done > (SPARK-26761). we can mimic this approach in vectorized gapply to support > gapplyCollect. -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org