[GitHub] [spark] hvanhovell commented on a diff in pull request #38468: [WIP][CONNECT][PYTHON] Arrow-based collect

[GitBox <gi...@apache.org>]

hvanhovell commented on code in PR #38468:
URL: https://github.com/apache/spark/pull/38468#discussion_r1012428719


##########
connector/connect/src/main/scala/org/apache/spark/sql/connect/service/SparkConnectStreamHandler.scala:
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@@ -117,7 +121,38 @@ class SparkConnectStreamHandler(responseObserver: StreamObserver[Response]) exte
       responseObserver.onNext(response.build())
     }
 
-    responseObserver.onNext(sendMetricsToResponse(clientId, rows))
+    responseObserver.onNext(sendMetricsToResponse(clientId, dataframe))
+    responseObserver.onCompleted()
+  }
+
+  def processRowsAsArrowBatches(clientId: String, dataframe: DataFrame): Unit = {
+    import org.apache.arrow.vector.ipc.message.{IpcOption, MessageSerializer}
+
+    val schema = dataframe.schema
+    val maxRecordsPerBatch = dataframe.sparkSession.sessionState.conf.arrowMaxRecordsPerBatch
+    val timeZoneId = dataframe.sparkSession.sessionState.conf.sessionLocalTimeZone
+    val arrowSchema = ArrowUtils.toArrowSchema(schema, timeZoneId)
+    val schemaBuffer = MessageSerializer.serializeMetadata(arrowSchema, IpcOption.DEFAULT)
+
+    val rows = dataframe.queryExecution.executedPlan.execute().map(_.copy()).collect()

Review Comment:
   - Wouldn't it be better to do the heavy lifting on the executors? IMO it is better to convert to arrow directly. `Dataset.toArrowBatchRdd` seems to be a good start.
   - It would also be nice if we can avoid materializing the entire result on the driver. We should be able to forward the batches for a partition immediately when we receive them.



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