Hi
Alexis from Rabbit here. I hope I am not intruding!
It would be super helpful if people with questions, observations or
moans posted them to the rabbitmq list too :-)
A few comments:
* Along with ZeroMQ, I consider Kafka to be one of the interesting and
useful messaging projects out there. In a world of cruft, Kafka is
cool!
* This is because both projects come at messaging from a specific
point of view that is *different* from Rabbit. OTOH, many other
projects exist that replicate Rabbit features for fun, or NIH, or due
to misunderstanding the semantics (yes, our docs could be better)
* It is striking how few people describe those differences. In a
nutshell they are as follows:
*** Kafka writes all incoming data to disk immediately, and then
figures out who sees what. So it is much more like a database than
Rabbit, in that new consumers can appear well after the disk write and
still subscribe to past messages. Instead, Rabbit which tries to
deliver to consumers and buffers otherwise. Persistence is optional
but robust and a feature of the buffer ("queue") not the upstream
machinery. Rabbit is able to cache-on-arrival via a plugin, but this
is a design overlay and not particularly optimal.
*** Kafka is a client server system with end to end semantics. It
defines order to include processing order, and keeps state on the
client to do this. Group management is via a 3rd party service
(Zookeeper? I forget which). Rabbit is a server-only protocol based
system which maintains order on the server and through completely
language neutral protocol semantics. This makes Rabbit perhaps more
natural as a 'messaging service' eg for integration and other
inter-app data transfer.
*** Rabbit is a general purpose messaging system with extras like
federation. It speaks many protocols, and has core features like HA,
transactions, management, etc. Everything can be switched on or off.
Getting all this to work while keeping the install light and fast, is
quite fiddly. Kafka by contrast comes from a specific set of use
cases, which are interesting certainly. I am not sure if Kafka wants
to be a general purpose messaging system, but it will become a bit
more like Rabbit if that is the goal.
*** Both approaches have costs. In the case of Rabbit the cost is
that more metadata is stored on the broker. Kafka can get performance
gains by storing less such data. But we are talking about some N
thousands of MPS versus some M thousands. At those speeds the clients
are usually the bottleneck anyway.
* Let me also clarify some things:
*** Rabbit does NOT store multiple copies of the same message across
queues, unless they are very small (<60b, iirc). A message delivered
to >1 queue on 1 machine is stored once. Metadata about that message
may be stored more than once, but, at scale, the big cost is the
payload.
*** Rabbit's vanilla install does store some index data in memory when
messages flow to disk. You can change this by using a plugin, but
this is a secret-menu undocumented feature. Very very few people need
any such thing.
*** A Rabbit queue is lightweight. It's just an ordered consumption
buffer that can persist and ack. Don't assume things about Rabbit
queues based on what you know about IBM MQ, JMS, and so forth. Queues
in Rabbit and Kafka are not the same.
*** Rabbit does not use mnesia for message storage. It has its own
DB, optimised for messaging. You can use other DBs but this is
Complicated.
*** Rabbit does all kinds of batching and bulk processing, and can
batch end to end. If you see claims about batching, buffering, etc.,
find out ALL the details before drawing conclusions.
I hope this is helpful.
Keen to get feedback / questions / corrections.
alexis
On Fri, Jun 7, 2013 at 2:09 AM, Marc Labbe <mrla...@gmail.com> wrote:
> We also went through the same decision making and our arguments for Kafka
> where in the same lines as those Jonathan mentioned. The fact that we have
> heterogeneous consumers is really a deciding factor. Our requirements were
> to avoid loosing messages at all cost while having multiple consumers
> reading the same data at a different pace. On one side, we have a few
> consumers being fed with data coming in from most, if not all, topics. On
> the other side, we have a good bunch of consumers reading only from a
> single topic. The big guys can take their time to read while the smaller
> ones are mostly for near real-time events so they need to keep up the pace
> of incoming messages.
>
> RabbitMQ stores data on disk only if you tell it to while Kafka persists by
> design. From the beginning, we decided we would try to use the queues the
> same way, pub/sub with a routing key (an exchange in RabbitMQ) or topic,
> persisted to disk and replicated.
>
> One of our scenario was to see how the system would cope with the largest
> consumer down for a while, therefore forcing the brokers to keep the data
> for a long period. In the case of RabbitMQ, this consumer has it owns queue
> and data grows on disk, which is not really a problem if you plan
> consequently. But, since it has to keep track of all messages read, the
> Mnesia database used by RabbitMQ as the messages index also grows pretty
> big. At that point, the amount of RAM necessary becomes very large to keep
> the level of performance we need. In our tests, we found that this an
> adverse effect on ALL the brokers, thus affecting all consumers. You can
> always say that you'll monitor the consumers to make sure it won't happen.
> That's a good thing if you can. I wasn't ready to make that bet.
>
> Another point is the fact that, since we wanted to use pub/sub with a
> exchange in RabbitMQ, we would have ended up with a lot data duplication
> because if a message is read by multiple consumers, it will get duplicated
> in the queue of each of those consumer. Kafka wins on that side too since
> every consumer reads from the same source.
>
> The downsides of Kafka were the language issues (we are using mostly Python
> and C#). 0.8 is very new and few drivers are available at this point. Also,
> we will have to try getting as close as possible to once-and-only-once
> guarantee. There are two things where RabbitMQ would have given us less
> work out of the box as opposed to Kafka. RabbitMQ also provides a bunch of
> tools that makes it rather attractive too.
>
> In the end, looking at throughput is a pretty nifty thing but being sure
> that I'll be able to manage the beast as it grows will allow me to get to
> sleep way more easily.
>
>
> On Thu, Jun 6, 2013 at 3:28 PM, Jonathan Hodges <hodg...@gmail.com> wrote:
>
>> We just went through a similar exercise with RabbitMQ at our company with
>> streaming activity data from our various web properties. Our use case
>> requires consumption of this stream by many heterogeneous consumers
>> including batch (Hadoop) and real-time (Storm). We pointed out that Kafka
>> acts as a configurable rolling window of time on the activity stream. The
>> window default is 7 days which allows for supporting clients of different
>> latencies like Hadoop and Storm to read from the same stream.
>>
>> We pointed out that the Kafka brokers don't need to maintain consumer state
>> in the stream and only have to maintain one copy of the stream to support N
>> number of consumers. Rabbit brokers on the other hand have to maintain the
>> state of each consumer as well as create a copy of the stream for each
>> consumer. In our scenario we have 10-20 consumers and with the scale and
>> throughput of the activity stream we were able to show Rabbit quickly
>> becomes the bottleneck under load.
>>
>>
>>
>> On Thu, Jun 6, 2013 at 12:40 PM, Dragos Manolescu <
>> dragos.manole...@servicenow.com> wrote:
>>
>> > Hi --
>> >
>> > I am preparing to make a case for using Kafka instead of Rabbit MQ as a
>> > broker-based messaging provider. The context is similar to that of the
>> > Kafka papers and user stories: the producers publish monitoring data and
>> > logs, and a suite of subscribers consume this data (some store it, others
>> > perform computations on the event stream). The requirements are typical
>> of
>> > this context: low-latency, high-throughput, ability to deal with bursts
>> and
>> > operate in/across multiple data centers, etc.
>> >
>> > I am familiar with the performance comparison between Kafka, Rabbit MQ
>> and
>> > Active MQ from the NetDB 2011 paper<
>> >
>> http://research.microsoft.com/en-us/um/people/srikanth/netdb11/netdb11papers/netdb11-final12.pdf
>> >.
>> > However in the two years that passed since then the number of production
>> > Kafka installations increased, and people are using it in different ways
>> > than those imagined by Kafka's designers. In light of these experiences
>> one
>> > can use more data points and color when contrasting to Rabbit MQ (which
>> by
>> > the way also evolved since 2011). (And FWIW I know I am not the first one
>> > to walk this path; see for example last year's OSCON session on the State
>> > of MQ<http://lanyrd.com/2012/oscon/swrcz/>.)
>> >
>> > I would appreciate it if you could share measurements, results, or even
>> > anecdotal evidence along these lines. How have you avoided the "let's use
>> > Rabbit MQ because everybody else does it" route when solving problems for
>> > which Kafka is a better fit?
>> >
>> > Thanks,
>> >
>> > -Dragos
>> >
>>
