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Kafka >> mail # user >> Partitioning and scale


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Re: Partitioning and scale
Hi Neha/Chris,

Thanks for the reply, so if I set a fixed number of partitions and just add
brokers to the broker pool, does it rebalance the load to the new brokers
(along with the data)?

Tim
On Wed, May 22, 2013 at 1:15 PM, Neha Narkhede <[EMAIL PROTECTED]>wrote:

> - I see that Kafka server.properties allows one to specify the number of
> partitions it supports. However, when we want to scale I wonder if we add #
> of partitions or # of brokers, will the same partitioner start distributing
> the messages to different partitions?
>  And if it does, how can that same consumer continue to read off the
> messages of those ids if it was interrupted in the middle?
>
> The num.partitions config in server.properties is used only for topics that
> are auto created (controlled by auto.create.topics.enable). For topics that
> you create using the admin tool, you can specify the number of partitions
> that you want. After that, currently there is no way to change that. For
> that reason, it is a good idea to over partition your topic, which also
> helps load balance partitions onto the brokers. You are right that if you
> change the number of partitions later, then previously messages that stuck
> to a certain partition would now get routed to a different partition, which
> is undesirable for applications that want to use sticky partitioning.
>
> - I'd like to create a consumer per partition, and for each one to
> subscribe to the changes of that one. How can this be done in kafka?
>
> For your use case, it seems like SimpleConsumer might be a better fit.
> However, it will require you to write code to handle discovery of leader
> for the partition that your consumer is consuming. Chris has written up a
> great example that you can follow -
>
> https://cwiki.apache.org/confluence/display/KAFKA/0.8.0+SimpleConsumer+Example
>
> Thanks,
> Neha
>
>
> On Wed, May 22, 2013 at 12:37 PM, Chris Curtin <[EMAIL PROTECTED]
> >wrote:
>
> > Hi Tim,
> >
> >
> > On Wed, May 22, 2013 at 3:25 PM, Timothy Chen <[EMAIL PROTECTED]> wrote:
> >
> > > Hi,
> > >
> > > I'm currently trying to understand how Kafka (0.8) can scale with our
> > usage
> > > pattern and how to setup the partitioning.
> > >
> > > We want to route the same messages belonging to the same id to the same
> > > queue, so its consumer will able to consume all the messages of that
> id.
> > >
> > > My questions:
> > >
> > >  - From my understanding, in Kafka we would need to have a custom
> > > partitioner that routes the same messages to the same partition right?
> >  I'm
> > > trying to find examples of writing this partitioner logic, but I can't
> > find
> > > any. Can someone point me to an example?
> > >
> > >
> https://cwiki.apache.org/confluence/display/KAFKA/0.8.0+Producer+Example
> >
> > The partitioner here does a simple mod on the IP address and the # of
> > partitions. You'd need to define your own logic, but this is a start.
> >
> >
> > > - I see that Kafka server.properties allows one to specify the number
> of
> > > partitions it supports. However, when we want to scale I wonder if we
> > add #
> > > of partitions or # of brokers, will the same partitioner start
> > distributing
> > > the messages to different partitions?
> > >  And if it does, how can that same consumer continue to read off the
> > > messages of those ids if it was interrupted in the middle?
> > >
> >
> > I'll let someone else answer this.
> >
> >
> > >
> > > - I'd like to create a consumer per partition, and for each one to
> > > subscribe to the changes of that one. How can this be done in kafka?
> > >
> >
> > Two ways: Simple Consumer or Consumer Groups:
> >
> > Depends on the level of control you want on code processing a specific
> > partition vs. getting one assigned to it (and level of control over
> offset
> > management).
> >
> > https://cwiki.apache.org/confluence/display/KAFKA/Consumer+Group+Example
> >
> >
> >
> https://cwiki.apache.org/confluence/display/KAFKA/0.8.0+SimpleConsumer+Example

 
NEW: Monitor These Apps!
elasticsearch, apache solr, apache hbase, hadoop, redis, casssandra, amazon cloudwatch, mysql, memcached, apache kafka, apache zookeeper, apache storm, ubuntu, centOS, red hat, debian, puppet labs, java, senseiDB