Github user maropu commented on a diff in the pull request:
https://github.com/apache/spark/pull/16114#discussion_r90600966
--- Diff:
external/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisRecordProcessor.scala
---
@@ -56,6 +56,38 @@ private[kinesis] class
KinesisRecordProcessor[T](receiver: KinesisReceiver[T], w
logInfo(s"Initialized workerId $workerId with shardId $shardId")
}
+ private def addRecords(batch: List[Record], checkpointer:
IRecordProcessorCheckpointer): Unit = {
+ 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.
+ */
+ private def processRecordsWithLimit(
+ batch: List[Record], checkpointer: IRecordProcessorCheckpointer):
Unit = {
+ val maxRecords = receiver.getCurrentLimit
+ if (batch.size() <= maxRecords) {
+ addRecords(batch, checkpointer)
+ } else {
+ val numIter = batch.size / maxRecords
--- End diff --
Thanks! I'll fix
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