Yeah there are lots of interesting subtleties with this sort of thing :-)
I've got a bit of a mantra on my project "handle edge cases at the
edges" so my "core" is very simple with federated C++ brokers run on
boxes with lots of memory running nothing but C++ qpid brokers.
Which is why I've been looking into more "off the wall" approaches such
as the one I mention using QMF queueThresholdExceeded.
You make some interesting points wrt "
Flowing
to disk may make sense here as long as the rate at which messages can flow
through the disk is higher than the rate at which they are entering the
broker (and actually high enough so that the backlog can quickly be
reduced)
"
Like I say it's subtle. In my case I can tolerate moderate loss, so the
WAN failure is and edge case for me. Even if the disk rate isn't higher
than the input rate I'll still save *something*. In my case the latest
messages are more valuable than older ones so I'm likely to "replay"
using a LIFO model too.
On 05/01/12 18:57, Rob Godfrey wrote:
On 5 January 2012 19:30, Fraser Adams<[email protected]> wrote:
Just to jump in on this thread.
Re "
but my opinion
is that if you have "millions of messages" then a Message Broker is the
wrong solution to your problem - you want a Database.
"
I can't say I agree with Rob's assertion here!!
Well maybe that's a reasonable comment if the *intention* is to have
millions of messages hanging around, but what if it's due to an unfortunate
circumstance......
Indeed - that is what I meant :-) Planning for failure scenarios should
always be carefully considered... As Robbie mentioned we have had people
build up millions of messages on persistent queues... and if you use the
64-bit JVM and put large amounts of RAM in your box, then the physical
limitations of maximum queue size can be made acceptably large... However I
would always caution against designing a system where you expect to hold
this number of *persistent* messages as part of your regular process -
especially using the Java Broker as it is not designed for this sort of use
case.
The case you describe is somewhat different in that you are looking at
transient messages (where one would expect the rate of
production/consumption to be higher), and you want the broker to be capable
of buffering for N minutes while temporary network outages occur. Flowing
to disk may make sense here as long as the rate at which messages can flow
through the disk is higher than the rate at which they are entering the
broker (and actually high enough so that the backlog can quickly be
reduced). This is where the flow-to-disk work that I hope to be starting
soon on the Java Broker would play.
-- Rob
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