I think you can use SPM - http://sematext.com/spm - it will give you all Spark and all Kafka metrics, including offsets broken down by topic, etc. out of the box.  I see more and more people using it to monitor various components in data processing pipelines, a la http://blog.sematext.com/2015/04/22/monitoring-stream-processing-tools-cassandra-kafka-and-spark/

Otis

On Mon, Jun 1, 2015 at 5:23 PM, dgoldenberg <dgoldenberg123@gmail.com> wrote:
Hi,

What are some of the good/adopted approached to monitoring Spark Streaming
from Kafka?  I see that there are things like
http://quantifind.github.io/KafkaOffsetMonitor, for example.  Do they all
assume that Receiver-based streaming is used?

Then "Note that one disadvantage of this approach (Receiverless Approach,
#2) is that it does not update offsets in Zookeeper, hence Zookeeper-based
Kafka monitoring tools will not show progress. However, you can access the
offsets processed by this approach in each batch and update Zookeeper
yourself".

The code sample, however, seems sparse. What do you need to do here? -
 directKafkaStream.foreachRDD(
     new Function<JavaPairRDD&lt;String, String>, Void>() {
         @Override
         public Void call(JavaPairRDD<String, Integer> rdd) throws
IOException {
             OffsetRange[] offsetRanges =
((HasOffsetRanges)rdd).offsetRanges
             // offsetRanges.length = # of Kafka partitions being consumed
             ...
             return null;
         }
     }
 );

and if these are updated, will KafkaOffsetMonitor work?

Monitoring seems to center around the notion of a consumer group.  But in
the receiverless approach, code on the Spark consumer side doesn't seem to
expose a consumer group parameter.  Where does it go?  Can I/should I just
pass in group.id as part of the kafkaParams HashMap?

Thanks



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