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From Bhavesh Mistry <mistry.p.bhav...@gmail.com>
Subject Topic Partitions and Consuming Message at near-real Time
Date Thu, 10 Apr 2014 15:47:16 GMT
We will be using Kafka for log message transport layer.  Log have specific
format (


There are two type of messages  *HEARTBEAT* and *APPLICATION_LOG*

So we have created two topics *HEARTBEATS* AND *APPLICATION_LOG*.

I have following questions on consumer side:

1) We are building near-realtime monitoring application so we need to be
able to drain from *HEARTBEAT *topic as soon as possible.  We  have 6000
servers dumping heartbeats messages at rate of ~1MB per minute from each
servers  ?  How can I partition a message such way that consumer can drain
as soon as message arrives  ?  Basically, we have to meet 30 seconds SLA
from time message was created  to consumer  (we just insert message into
cassandra )  for our monitoring tools )   I was thinking about partitioning
by HOSTNAME as partitioning key  so we can have 6000 consumers draining
from each partition ?  Can a new partition be created dynamically when new
server is added and a consumer be started automatically ?  Please recommend

2) With near-realtime requirement, if the consumer is lag behind  (lets
say by 5 minutes or  more  due to downtime  or code roll or etc ),  when
consumer group restarts , I would like to consume first latest messages (to
meet the SLA) before consuming from last offset ?  How can I achieve this ?
 Also, I need to be able to backfill messages.

Solution We have in mind:

I was thinking about creating new consumer group each time we have down
time and read from the largest offset configuration (to meet the 30 second
SLA) ?  Also,  we wanted to backfill the message using previous group till
we started new consumer and previous consumer  group will die after
backfill is complete.

Is there any alternative solution for consume latest message and backfill
in event of down time  ?  Please let us know what is being done at Linkedin
or other large systems where we have SLA to meet.

By the way, we will be using Kafka 0.8 version.



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