<<Steve Vinoski <http://steve.vinoski.net/blog/> nominates RPC as an
historically bad idea
<http://qconlondon.com/london-2009/presentation/RPC+and+its+Offspring%3A+Convenient%2C+Yet+Fundamentally+Flawed>,
yet the synchronous request reply message pattern is undeniably the most
common pattern out there in our client-server world. Steve puts this
down to "convenience" but I actually think it goes deeper than that.
Because RPC is actually not convenient - it causes an awful lot of problems.
In addition to the usual problems cited by Steve, RPC makes heavy
demands on scalability. Consider the SLA requirements for an RPC service
provider. An RPC request must respond in a reasonable time interval -
typically a few tens of seconds at most. But what happens when the
service is under heavy load? What strategies can we use to ensure a
reasonable level of service availability?
1. Keep adding capacity to the service so as to maintain the required
responsiveness...damn the torpedoes and the budget!
2. The client times-out, leaving the request in an indeterminate
state. In the worst case, the service may continue working only to
return to find the client has given up. Under continued assault,
the availability of both the client and the server continues to
degrade.
3. The service stops accepting requests beyond a given threshold.
Clients which have submitted a request are responded to within the
SLA. Later clients are out of luck until the traffic drops back to
manageable levels. They will need to resubmit later (if it is
still relevant).
4. The client submits the request to a proxy (such as a message queue
<http://www.soabloke.com/2008/09/20/the-architectural-role-of-messaging/>)
and then carries on with other work. The service responds when it
can and hopefully the response is still relevant at that point.
Out of all these coping strategies, it seems that Option 2 is the most
common, even when in many cases one of the other strategies is more
efficient or cost-effective. Option 1 might be the preferred option
given unlimited funds (and ignoring the fact it is often technically
infeasible). In the real world Option 2 more often becomes the default.
The best choice depends on what the service consumer represents and what
is the cost of any of the side-effects when the service fails to meet
its SLA.
When the client is a human - say ordering something at our web site:
* Option 2 means that we get a pissed off user. That may represent a
high, medium or low cost to the organization
<http://www.soabloke.com/2008/01/13/cost-vs-benefit-occams-razor-for-the-enterprise-architect/>
depending on the value of that user. In addition there is the cost
of indeterminate requests. What if a request was executed after
the client timed-out? There may be a cost of cleaning up or
reversing those requests.
* Option 3 means that we also get a pissed off user - with the
associated costs. We may lose a lot of potential customers who
visit us during the "outage". On the positive side, we minimise
the risk/cost of indeterminate outcomes.
* Option 4 is often acceptable to users - they know we have received
their request and are happy to wait for a notification in the
future. But there are some situations where immediate
gratification is paramount.
On the other hand, if the client is a system participating in a
long-running BPM flow, then we have a different cost/benefit equation.
* For Option 2, we don't have a "pissed off" user. But the
transaction times out into an "error bucket" and is left in an
indeterminate state. We must spend time and effort (usually costly
human effort) to determine where that request got to and remediate
that particular process. This can be very costly.
* Option 3 once again has no user impact, and we minimise the risk
of indeterminate requests. But what happens to the halted
processes? Either they error out and must be restarted - which is
expensive. Alternatively they must be queued up in some way - in
which case Option 3 becomes equivalent to Option 4.
* In the BPM scenario, option 4 represents the smoothest path.
Requests are queued up and acted upon when the service can get to
it. All we need is patience and the process will eventually
complete without the need for unusual process rollbacks or error
handling. If the queue is persistent then we can even handle a
complete outage and restoration of the service.
So if I am a service designer planning to handle service capacity
constraints, for human clients I would probably choose (in order) Option
3, 4 and consider the costs
<http://www.soabloke.com/2008/01/13/cost-vs-benefit-occams-razor-for-the-enterprise-architect/>
of option 2. For BPM processes where clients are "machines" then I would
prefer Option 4 every time. Why make work for myself handling timeouts?
One problem I see so often is that solution designers go for Option 2 by
default - the worst of all the options available to them.>>
You can read this blog at:
http://www.soabloke.com/2009/05/05/the-power-of-later/
Gervas
