amaliujia commented on code in PR #38468:
URL: https://github.com/apache/spark/pull/38468#discussion_r1015819772
##########
connector/connect/src/main/scala/org/apache/spark/sql/connect/service/SparkConnectStreamHandler.scala:
##########
@@ -117,10 +129,91 @@ 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 spark = dataframe.sparkSession
+ val schema = dataframe.schema
+ // TODO: control the batch size instead of max records
+ val maxRecordsPerBatch = spark.sessionState.conf.arrowMaxRecordsPerBatch
+ val timeZoneId = spark.sessionState.conf.sessionLocalTimeZone
+
+ SQLExecution.withNewExecutionId(dataframe.queryExecution,
Some("collectArrow")) {
+ val pool =
ThreadUtils.newDaemonSingleThreadExecutor("connect-collect-arrow")
+ val tasks = collection.mutable.ArrayBuffer.empty[Future[_]]
+ val rows = dataframe.queryExecution.executedPlan.execute()
+
+ if (rows.getNumPartitions > 0) {
+ val batches = rows.mapPartitionsInternal { iter =>
+ ArrowConverters
+ .toArrowBatchIterator(iter, schema, maxRecordsPerBatch, timeZoneId)
+ }
+
+ val processPartition = (iter: Iterator[(Array[Byte], Long, Long)]) =>
iter.toArray
+
+ val resultHandler = (partitionId: Int, taskResult: Array[(Array[Byte],
Long, Long)]) => {
Review Comment:
@cloud-fan and I had a discussion on this. I remember our initial thought
was to maintain the partition ordering on the server side. For example, there
is `def foreachPartition` API already. The question was whether we can foreach
partitions in the partitioning order.
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