hvanhovell commented on code in PR #38468:
URL: https://github.com/apache/spark/pull/38468#discussion_r1014269647
##########
connector/connect/src/main/scala/org/apache/spark/sql/connect/service/SparkConnectStreamHandler.scala:
##########
@@ -117,7 +126,70 @@ 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
+
+ val rows = dataframe.queryExecution.executedPlan.execute()
+ var numBatches = 0L
+
+ if (rows.getNumPartitions > 0) {
+ val batches = rows.mapPartitionsInternal { iter =>
+ ArrowConverters
+ .toArrowBatchIterator(iter, schema, maxRecordsPerBatch, timeZoneId)
+ }
+
+ val obj = new Object
+
+ val processPartition = (iter: Iterator[(Array[Byte], Long, Long)]) =>
iter.toArray
Review Comment:
This breaks sorted results. A higher partition can complete earlier than
lower ones thus breaking the order. That is why I the snippet I posted buffered
the partitions in the handler, while the main thread scanned over them 1 by 1.
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