zhengruifeng commented on code in PR #38468:
URL: https://github.com/apache/spark/pull/38468#discussion_r1019100602
##########
connector/connect/src/main/scala/org/apache/spark/sql/connect/service/SparkConnectStreamHandler.scala:
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@@ -114,10 +120,93 @@ class SparkConnectStreamHandler(responseObserver:
StreamObserver[Response]) exte
responseObserver.onNext(response.build())
}
- responseObserver.onNext(sendMetricsToResponse(clientId, rows))
+ responseObserver.onNext(sendMetricsToResponse(clientId, dataframe))
responseObserver.onCompleted()
}
+ def processAsArrowBatches(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 rows = dataframe.queryExecution.executedPlan.execute()
+ val numPartitions = rows.getNumPartitions
+ var numSent = 0
+
+ if (numPartitions > 0) {
+ type Batch = (Array[Byte], Long)
+
+ val batches = rows.mapPartitionsInternal { iter =>
+ ArrowConverters
+ .toArrowBatchIterator(iter, schema, maxRecordsPerBatch, timeZoneId)
+ }
+
+ val signal = new Object
+ val partitions = collection.mutable.Map.empty[Int, Array[Batch]]
Review Comment:
> can we apply the same idea to JSON batches?
I think so, let's optimize it later
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