hvanhovell commented on code in PR #38613:
URL: https://github.com/apache/spark/pull/38613#discussion_r1020124708
##########
connector/connect/src/main/scala/org/apache/spark/sql/connect/service/SparkConnectStreamHandler.scala:
##########
@@ -144,36 +144,10 @@ class SparkConnectStreamHandler(responseObserver:
StreamObserver[Response]) exte
.toArrowBatchIterator(iter, schema, maxRecordsPerBatch, timeZoneId)
}
- val signal = new Object
- val partitions = collection.mutable.Map.empty[Int, Array[Batch]]
-
- val processPartition = (iter: Iterator[Batch]) => iter.toArray
-
// This callback is executed by the DAGScheduler thread.
- // After fetching a partition, it inserts the partition into the Map,
and then
- // wakes up the main thread.
- val resultHandler = (partitionId: Int, partition: Array[Batch]) => {
- signal.synchronized {
- partitions(partitionId) = partition
- signal.notify()
- }
- ()
- }
-
- spark.sparkContext.runJob(batches, processPartition, resultHandler)
-
- // The man thread will wait until 0-th partition is available,
- // then send it to client and wait for next partition.
- var currentPartitionId = 0
- while (currentPartitionId < numPartitions) {
- val partition = signal.synchronized {
- while (!partitions.contains(currentPartitionId)) {
- signal.wait()
- }
- partitions.remove(currentPartitionId).get
- }
-
- partition.foreach { case (bytes, count) =>
+ def writeBatches(arrowBatches: Array[Batch]): Unit = {
Review Comment:
The reason why I suggested to use locks and the main thread to write the
results is exactly what this comment is trying to convey. You don't want these
operations to happen inside the DAGScheduler thread. If you keep that blocked
for something none scheduling related, you will stop all other scheduling. This
is particularly bad in an environment where you might have multiple users
running code at the same time.
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