[GitHub] [spark] hvanhovell commented on a diff in pull request #38468: [SPARK-41005][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_r1013184548


##########
connector/connect/src/main/scala/org/apache/spark/sql/connect/service/SparkConnectStreamHandler.scala:
##########
@@ -117,7 +131,36 @@ 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 = {
+    val schema = dataframe.schema
+    val maxRecordsPerBatch = dataframe.sparkSession.sessionState.conf.arrowMaxRecordsPerBatch
+    val timeZoneId = dataframe.sparkSession.sessionState.conf.sessionLocalTimeZone
+
+    val batches = dataframe.queryExecution.executedPlan
+      .execute()
+      .mapPartitionsInternal { iter =>
+        ArrowConverters
+          .toArrowBatchIterator(iter, schema, maxRecordsPerBatch, timeZoneId, TaskContext.get)
+      }
+
+    batches.toLocalIterator.foreach { case (bytes, count, size) =>

Review Comment:
   Let's also avoid using local iterator. That will make things unnecessarily slow because it limits parallelism. I was thinking doing the following:
   ```scala
   def collectArrow(callback: Array[Array[Byte]] => ()): Unit = {
       withAction("collectArrow", queryExecution) { plan =>
         val rdd = toArrowBatchRdd(plan)
         val signal = new Object
         val numPartitions = rdd.getNumPartitions
         val availablePartitions = mutable.Map.empty[Int, Array[Array[Byte]]]
         def onNewPartition(partitionId: Int, batches: Array[Array[Byte]]): Unit = {
           signal.synchronized {
             availablePartitions(partitionId) = batches
             signal.notify()
           }
         }
         sparkSession.sparkContext.submitJob(
           rdd = rdd,
           processPartition = (i: Iterator[Array[Byte]]) => i.toArray,
           partitions = 0 until numPartitions,
           resultHandler = onNewPartition,
           resultFunc = () => ())
   
         var currentPartitionId = 0
         while (currentPartitionId < numPartitions) {
           val batches = signal.synchronized {
             var result = availablePartitions.remove(currentPartitionId)
             while (result.isEmpty) {
               signal.wait()
               result = availablePartitions.remove(currentPartitionId)
             }
             result.get
           }
           callback(batches)
           currentPartitionId += 1
         }
       }
     }
   ```
   This was hacked together in Dataset, but IMO it is better to do this somewhere in here. 



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