Hi Mahendra, thanks for your reply. I was planning on using the Atmosphere
Framework (http://async-io.org/) to handle the web push stuff (I've never used
it before but we use PrimeFaces a little and that's what they use for their
components). I thought that I would have the JVM that the user is connected to
just be a Kafka consumer. Given the topic limitations, I think I am back to
having a single topic that all guest data is placed on and have all JVMs
publish and consume to that same topic. So it would look something like this:
- I have 20 Web JVMs.
- Every minute 100 people log in per JVM. So 2,000 log ins per minute. Each
Web JVM publishes a single message per log in.
- My data services consume the log in event and then create about 1,000
messages per user containing data about that user. Each data message will
probably be between 500 byte and 2k. Let's assume an average of 1k per message
so that would be 1 MB per user or about 2GB per minute.
- The Recommendation service would consume all 2GB of data per minute and only
end up using small amount of the data and then it would add it's recommendation
messages to the same topic.
- Each Web JVM also would consume the 2GB of data plus the handful of
recommendation messages per minute and end up ignoring everything but the
recommendation messages (especially since the 2GB represents the data for all
the guests but each JVM only has 1/20 of the guest logged in).
It seems wasteful to put 2 GB of data per minute in Kafka only to have the
Recommendation service consume all this data and only end up using a few k of
data and also have the web consume all this data when it just wants the few
recommendation messages. However, the benefit of using a single topic is that
in the future other services could consume more of the data or the
recommendation messages and since everything is on the same topic the order is
guaranteed. In our immediate use case we could put the recommendation messages
on its own topics but in a sense we would be coupling our use case to our
choice of topics. If we want the web to start also showing a little bit of the
data from the data messages, we would be back to consuming the 2GB of data in
the Web JVMs.
Traditionally we would just have the Web call a service on the Recommendation
system (possibly asynchronously) which in turn would call the database to load
just the data it needs. But we are thinking that by publishing all the data we
have about the user (whether in the immediate future the existing systems need
that data or not, future systems might) we are creating a system where we can
easily add new consumers to do new things with all this data. The main
downside seems to be that most of the consumers are processing millions of
messages that they have no interest in. Do you think that the benefits
outweigh the cons? Is there a better way to achieve similar results?
Thanks
Josh
________________________________
From: Mahendra M <mahendr...@gmail.com>
To: users@kafka.apache.org; Josh Foure <user...@yahoo.com>
Sent: Friday, June 14, 2013 8:03 AM
Subject: Re: Using Kafka for "data" messages
Hi Josh,
Thanks for clarifying the use case. The idea is good, but I see the following
three issues
1. Creating a queue for each user. There could be limits on this
2. Removing old queues
3. If the same user logs in from multiple browsers, things get a bit
more complex.Can I suggest an alternate approach than using Kafka?
Using a combination of Kafka and XMPP-BOSH/Comet for this.
1. User logs in. Message is sent on a Kafka queue.
2. Web browser starts a long polling connection to a server (XMPP-BOSH
/ Comet)
3. Consumers pick up message in (1) and do their job. They push their
results to a results queue and to an XMPP end-point (u...@domain.com)
4. Recommender can pick up from the results queue and push it's result
to the XMPP end-point
5. Web front-end picks up the messages and does the displaying job.
If you plan it more, you can avoid using Kafka in this use case and just do
with XMPP (for steps 1 and 3)
Also, you don't have to take care of large number of queues, removing them etc.
Also XMPP is really good in handling multiple end-points for a single user.
(There are good XMPP servers like ejabberd and tigase. Also good lightweight JS
libraries for handling connections).
PS: I think my reply is going off-topic. So, I will stop.
Regards,
Mahendra
On Thu, Jun 13, 2013 at 11:17 PM, Josh Foure <user...@yahoo.com> wrote:
Hi Mahendra, I think that is where it gets a little tricky. I think it would
work something like this:
>
>1. Web sends login event for user "user123" to topic "GUEST_EVENT".
>2. All of the systems consume those messages and publish the data messages to
>topic "GUEST_DATA.user123".
>3. The Recommendation system gets all of the data from "GUEST_DATA.user123",
>processes and then publishes back to the same topic "GUEST_DATA.user123".
>4. The Web consumes the messages from the same topic (there is a different
>topic for every user that logged in) "GUEST_DATA.user123" and when it finds
>the recommendation messages it pushes that to the browser (note it will need
>to read all the other data messages and discard those when looking for the
>recommendation messages). I have a concern that the Web will be flooded with
>a ton of messages that it will promptly drop but I don't want to create a new
>"response" or "recommendation" topic because then I feel like I am tightly
>coupling the message to the functionality and in the future different systems
>may want to consume those messages as well.
>
>Does that make sense?
>Josh
>
>
>
>
>
>
>
>________________________________
> From: Mahendra M <mahendr...@gmail.com>
>To: users@kafka.apache.org; Josh Foure <user...@yahoo.com>
>Sent: Thursday, June 13, 2013 12:56 PM
>Subject: Re: Using Kafka for "data" messages
>
>
>
>Hi Josh,
>
>The idea looks very interesting. I just had one doubt.
>
>1. A user logs in. His login id is sent on a topic
>2. Other systems (consumers on this topic) consumer this message and
>publish their results to another topic
>
>This will be happening without any particular order for hundreds of users.
>
>Now the site being displayed to the user.. How will you fetch only messages
>for that user from the queue?
>
>Regards,
>Mahendra
>
>
>
>On Thu, Jun 13, 2013 at 8:51 PM, Josh Foure <user...@yahoo.com> wrote:
>
>>
>> Hi all, my team is proposing a novel
>> way of using Kafka and I am hoping someone can help do a sanity check on
>> this:
>>
>> 1. When a user logs
>> into our website, we will create a “logged in” event message in Kafka
>> containing the user id.
>> 2. 30+ systems
>> (consumers each in their own consumer groups) will consume this event and
>> lookup data about this user id. They
>> will then publish all of this data back out into Kafka as a series of data
>> messages. One message may include the user’s name,
>> another the user’s address, another the user’s last 10 searches, another
>> their
>> last 10 orders, etc. The plan is that a
>> single “logged in” event may trigger hundreds if not thousands of
>> additional data
>> messages.
>> 3. Another system,
>> the “Product Recommendation” system, will have consumed the original
>> “logged in”
>> message and will also consume a subset of the data messages (realistically
>> I
>> think it would need to consume all of the data messages but would discard
>> the
>> ones it doesn’t need). As the Product
>> Recommendation consumes the data messages, it will process recommended
>> products
>> and publish out recommendation messages (that get more and more specific
>> as it
>> has consumed more and more data messages).
>> 4. The original
>> website will consume the recommendation messages and show the
>> recommendations to
>> the user as it gets them.
>>
>> You don’t see many systems implemented this way but since
>> Kafka has such a higher throughput than your typical MOM, this approach
>> seems
>> innovative.
>>
>> The benefits are:
>>
>> 1. If we start
>> collecting more information about the users, we can simply start publishing
>> that in new data messages and consumers can start processing those messages
>> whenever they want. If we were doing
>> this in a more traditional SOA approach the schemas would need to change
>> every time
>> we added a field but with this approach we can just create new messages
>> without
>> touching existing ones.
>> 2. We are looking to
>> make our systems smaller so if we end up with more, smaller systems that
>> each
>> publish a small number of events, it becomes easier to make changes and
>> test
>> the changes. If we were doing this in a
>> more traditional SOA approach we would need to retest each consumer every
>> time
>> we changed our bigger SOA services.
>>
>> The downside appears to be:
>>
>> 1. We may be
>> publishing a large amount of data that never gets used but that everyone
>> needs
>> to consume to see if they need it before discarding it.
>> 2. The Product Recommendation
>> system may need to wait until it consumes a number of messages and keep
>> track
>> of all the data internally before it can start processing.
>> 3. While we may be
>> able to keep the messages somewhat small, the fact that they contain data
>> will
>> mean they will be bigger than your tradition EDA messages.
>> 4. It seems like we
>> can do a lot of this using SOA (we already have an ESB than can do
>> transformations to address consumers expecting an older version of the
>> data).
>>
>> Any insight is appreciated.
>> Thanks,
>> Josh
>
>
>
>
>--
>Mahendra
>
>http://twitter.com/mahendra
--
Mahendra
http://twitter.com/mahendra
