Dibyendu,
Thanks for the reply.
I am reading your project homepage now.
One quick question I care about is:
If the receivers failed for some reasons(for example, killed brutally by
someone else), is there any mechanism for the receiver to fail over
automatically?
On Mon, Mar 16, 2015 at 3:25
Guys,
We have a project which builds upon Spark streaming.
We use Kafka as the input stream, and create 5 receivers.
When this application runs for around 90 hour, all the 5 receivers failed
for some unknown reasons.
In my understanding, it is not guaranteed that Spark streaming receiver
will
Akhil,
I have checked the logs. There isn't any clue as to why the 5 receivers
failed.
That's why I just take it for granted that it will be a common issue for
receiver failures, and we need to figure out a way to detect this kind of
failure and do fail-over.
Thanks
On Mon, Mar 16, 2015 at
You need to figure out why the receivers failed in the first place. Look in
your worker logs and see what really happened. When you run a streaming job
continuously for longer period mostly there'll be a lot of logs (you can
enable log rotation etc.) and if you are doing a groupBy, join, etc type
Which version of Spark you are running ?
You can try this Low Level Consumer :
http://spark-packages.org/package/dibbhatt/kafka-spark-consumer
This is designed to recover from various failures and have very good fault
recovery mechanism built in. This is being used by many users and at
present
Yes.. Auto restart is enabled in my low level consumer ..when there is some
unhandled exception comes...
Even if you see KafkaConsumer.java, for some cases ( like broker failure,
kafka leader changes etc ) it can even refresh the Consumer (The
Coordinator which talks to a Leader) which will
I have checked Dibyendu's code, it looks that his implementation has
auto-restart mechanism:
