Udi Dahan   Udi Dahan – The Software Simplist
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Lost Notifications? No Problem.

Sunday, December 7th, 2008.

One of the most common questions I get on the topic of pub/sub messaging is what happens if a notification is lost. Interestingly enough, there are some who almost entirely write-off this pattern because of this issue, preferring the control of request/response-exception. So, what should be done about lost messages? The short answer is durable messaging. The long answer is design.

Durable Messaging

In order to prevent a message from being lost when it is sent from a publisher to a subscriber, the message is written to disk on the publisher side, and then forwarded to the subscriber, where it is also written to disk. This store-and-forward mechanism enables our systems to gracefully recover from either side being temporarily unavailable.

In my MSDN article on this topic, I outlined some problems with this approach. These problems are exacerbated for publishers. Imagine a publisher with 40 subscribers, publishing 10 messages a second, each containing 1MB of XML. If 10 of the subscribers are unavailable, that’s 100MB of data being written to the publisher’s disk every second, 6GB every minute. That’s liable to bring down a publisher before an administrator brews a cup of coffee.

Publishers have no choice but to throw away messages after a certain period of time.

Publisher Contracts

The whole issue of contracts and schema is considered one of the better understand parts of SOA. Unfortunately, the operational aspects of service contracts is hardly ever taken into account.

On top of the schema of the messages a service publishers, additional information is needed in the contract:

  1. How big will this message be?
  2. How often will it be published?
  3. How long will this message be stored if a subscriber is unavailable?

This first two pieces of information are important for subscribers to do load and capacity planning. The last one is the most important as it dictates the required availability and fault-tolerance characteristic of subscribers.

For Example

In the canonical retail scenario, when our sales service accepts an order, it publishes an order accepted event. Other services subscribed to this event include shipping, billing, and business intelligence.

While shipping and billing are highly available and able to keep up with the rate at which orders are accepted, the business intelligence service is not. BI has two main parts to it – a nightly batch that does the number crunching, and a UI for reporting off of the results of that number crunching. Some even do the reporting in a semi-offline fashion, emailing reports back to the user when they’re ready.

Furthermore, nobody’s going to invest in servers for making BI highly available.

And wasn’t the whole point of this publish/subscribe messaging to keep our services autonomous? That not all services have to have the same level uptime?

Houston, do we have a problem.?

Data Freshness

There is a glimmer of light in all this doom and gloom.

Not all services have the same data freshness requirements.

The business intelligence service above doesn’t need to know about orders the second they’re accepted. A daily roll-up would be fine, and an hourly roll-up bring us that much closer to “real time business intelligence”.

So, while BI is ready to accept the sales message schema, it would like a slightly different contract around it – less messages per unit of time, more data in each message.

From the operational perspective of the sales service, it would be cost effective to have less “online” subscribers. It could even take things a few steps further. Instead of using the regular messaging backbone for transmitting these hourly messages, it could use FTP. The data could even be zipped to take up even less space. Since the total data size is less than the corresponding online stream, is stored on cheaper, large storage, and the number of subscribers for this zipped, hourly update is fairly small, these messages can be kept around far longer.

If you’ve heard about consumer-driven contracts, this is it.

Note that we’re still talking about the same logical message schema.

Summary

It’s not that lost notifications aren’t a problem.

It’s that they feed the design process in such a way that the resulting service ecosystem is set up in such a way that notifications won’t get lost. I know that that sounds kind of recursive, but that’s how it works. Either subscribers take care of their SLA allowing them to process the online stream of events, or they should subscribe to a different pipe (which will have different SLA requirements, but maybe they can deal with those).

It make sense to have multiple pipes for the same logical schema.

It’s practically a necessity to make pub/sub a feasible solution.

 


Related Content

MSDN article on messaging and lost messages

Durable messaging dilemmas

Additional logic required for service autonomy

More in depth example on events and pub/sub between services

Consumer-Driven Contracts

  
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Leave a comment below or email me, whatever works for you.

  1. Ian Robinson Says:

    Hi Udi

    I think this is a great illustration of tailoring solutions to real consumer needs, which is the primary driver behind consumer-driven contracts. As a provider it may not be possible to offer a one-size-fits-all solution that meets the needs of each and every consumer – so specialize on a case-by-case basis, but in each case do “just enough” to meet real as opposed to imagined needs. Or, and this was the first motivation for CDC, if you do have to provide a one-size-fits-all contract, fit the identifiable aggregate needs, and nothing more.

    When I started with CDC, it was for me really about creating and maintaining message schemas that matched the aggregate needs of several consumers – that is, a one-size-fits-all schema, but nothing gold-plated, just something with no more and no less than what was needed. We communicated consumer expectations using tests and assertions – XPath, Schematron, and more recently a representation-format-agnostic DSL (ie. expectations in this DSL can be transformed into assertions against XML, JSON, YAML, form-encoded name-value pairs, etc). By having consumer teams give these assertions and tests to provider teams, we helped providers build into their own test suites and continuous integration environments an understanding of how their services were actually being used. If tests were kept up-to-date, teams could then track their schema footprint and identify possibly redundant elements. Where we were able to do this early enough in the service development lifecycle, these programmatic expectations helped drive out a service contract “by example”.

    So I guess CDC started out as a way to help the one-size-fits-all mentality do the simplest thing that could possibly work, whilst at the same time providing a basis for evolving a service as new expectations and consumer needs were identified and came online. I’m always on the lookout for practices and techniques that allow us to do just enough today – even in the context of SOA – whilst maintaining a duty of care to the overall lifetime of a system, and this seemed to help.

    CDC then, in its original form was quite modest, being concerned mainly with schema and a desire to contain proliferation – ie. not stamp out consumer-specific instances of a message but instead provide a minimally complete single instance. But with experience I’ve learnt there is a case for specializing what a provider has to offer on a consumer-by-consumer basis – and I think this is an equally, if not more, interesting real-world application of the idea. I remember Jesus Rodriguez did something with the WCF LOB adapters along these lines quite some time ago. Your varying the publication rate, message size and transport per consumer is another good case in point. Inside ThoughtWorks, Jim Webber and I did something similar with a client at the beginning of the year. As with your case, it involved BI. Recognizing that BI consumers have quite different needs – and capabilities – from “transactional” consumers, we created a specialized ETL-friendly interface to our service that extended and clarified our business-meaningful service contract in line with a real-world need. As with what you describe, consumer expectations, including quality-of-service expectations, shape the service ecosystem to the problem at hand, rather than compromising the solution by adapting the problem to a narrow, ostensibly consistent but less-than-useful architectural or technical vision.

    Hope this is of some help. On a more general note, your blog and articles have been enormously useful in shaping, testing and refining my own approach to delivering on SOA initiatives over the last few years. Over and against a certain 3-layer-application-architecture-blown-out-to-distributed-proportions school of SOA, your writing, together with Bill Poole’s stuff, which I think I probably found through your blog, steers a far more valuable course.

    Kind regards

    ian


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