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Li Jin commented on SPARK-23030: -------------------------------- Hey [~bryanc], did you by an chance have some process on this? I guess what's tricky here is you probably lose the parallelism if streaming each partitions one by one? > Decrease memory consumption with toPandas() collection using Arrow > ------------------------------------------------------------------ > > Key: SPARK-23030 > URL: https://issues.apache.org/jira/browse/SPARK-23030 > Project: Spark > Issue Type: Sub-task > Components: PySpark, SQL > Affects Versions: 2.3.0 > Reporter: Bryan Cutler > Priority: Major > > Currently with Arrow enabled, calling {{toPandas()}} results in a collection > of all partitions in the JVM in the form of batches of Arrow file format. > Once collected in the JVM, they are served to the Python driver process. > I believe using the Arrow stream format can help to optimize this and reduce > memory consumption in the JVM by only loading one record batch at a time > before sending it to Python. This might also reduce the latency between > making the initial call in Python and receiving the first batch of records. -- 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