Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/16114#discussion_r91184750
--- Diff:
external/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisRecordProcessor.scala
---
@@ -68,9 +69,16 @@ private[kinesis] class
KinesisRecordProcessor[T](receiver: KinesisReceiver[T], w
override def processRecords(batch: List[Record], checkpointer:
IRecordProcessorCheckpointer) {
if (!receiver.isStopped()) {
try {
- receiver.addRecords(shardId, batch)
- logDebug(s"Stored: Worker $workerId stored ${batch.size} records
for shardId $shardId")
- receiver.setCheckpointer(shardId, checkpointer)
+ // Limit the number of processed records from Kinesis stream. This
is because the KCL cannot
+ // control the number of aggregated records to be fetched even if
we set `MaxRecords`
+ // in `KinesisClientLibConfiguration`. For example, if we set 10
to the number of max
+ // records in a worker and a producer aggregates two records into
one message, the worker
+ // possibly 20 records every callback function called.
+ batch.asScala.grouped(receiver.getCurrentLimit).foreach { batch =>
--- End diff --
Sorry, one last comment -- `batch` is used for the overall data set and
each subset. They should be named differently for clarity.
It's also my fault for not realizing the collections here were Java not
Scala, and you have to convert to use the nice Scala idiom. I think it's OK as
it's just going to wrap and not copy the class, but it does bear being careful
about performance here.
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