Github user koeninger commented on a diff in the pull request:
https://github.com/apache/spark/pull/15679#discussion_r85641432
--- Diff: docs/streaming-kafka-0-10-integration.md ---
@@ -120,15 +184,24 @@ Kafka has an offset commit API that stores offsets in
a special Kafka topic. By
<div class="codetabs">
<div data-lang="scala" markdown="1">
stream.foreachRDD { rdd =>
- val offsets = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
+ val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
// some time later, after outputs have completed
- stream.asInstanceOf[CanCommitOffsets].commitAsync(offsets)
+ stream.asInstanceOf[CanCommitOffsets].commitAsync(offsetRanges)
}
As with HasOffsetRanges, the cast to CanCommitOffsets will only succeed if
called on the result of createDirectStream, not after transformations. The
commitAsync call is threadsafe, but must occur after outputs if you want
meaningful semantics.
</div>
<div data-lang="java" markdown="1">
+ stream.foreachRDD(new VoidFunction<JavaRDD<ConsumerRecord<String,
String>>>() {
+ @Override
+ public void call(JavaRDD<ConsumerRecord<String, String>> rdd) {
+ OffsetRange[] offsetRanges = ((HasOffsetRanges)
rdd.rdd()).offsetRanges();
+
+ // some time later, after outputs have completed
+ ((CanCommitOffsets)
stream.inputDStream()).commitAsync(offsetRanges);
--- End diff --
I think it's far too late to fix those issues at this point. DStreams
return an RDD, not a parameterized type. KafkaUtils methods return DStreams
and RDDs, not an implementation specific type.
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