hvanhovell commented on code in PR #38468:
URL: https://github.com/apache/spark/pull/38468#discussion_r1018938395
##########
connector/connect/src/main/scala/org/apache/spark/sql/connect/service/SparkConnectStreamHandler.scala:
##########
@@ -114,10 +123,97 @@ 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 rows = dataframe.queryExecution.executedPlan.execute()
+ val numPartitions = rows.getNumPartitions
+ var numSent = 0
+
+ if (numPartitions > 0) {
+ type Batch = (Array[Byte], Long, Long)
+
+ val batches = rows.mapPartitionsInternal { iter =>
+ ArrowConverters
+ .toArrowBatchIterator(iter, schema, maxRecordsPerBatch, timeZoneId)
+ }
+
+ val signal = new Object
+ val partitions = Array.fill[Array[Batch]](numPartitions)(null)
Review Comment:
You could use a map here. That will be a bit more friendly on memory when
you have a task with many tasks. The insight is that it is unlikely that you
have to buffer much since spark executes partitions in order (from low to high).
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