It’s quite common for our systems to need to expose an API for external parties to call that isn’t exactly aligned with our service boundaries – at least, when you follow the “vertical services” model rather than the “layered services” approach. I’ve blogged many times about the problems with layering, so I won’t go into that now beyond to say that you really, REALLY, should avoid it.

A short intro to SOA, done right

In the “vertical services” approach I espouse, you often see components from multiple services deployed to any given endpoint. While these services usually don’t need to communicate with each other at all, occasionally you’ll see them leaving “breadcrumbs” behind for each other – things like UserId or OrderId in the session. What’s especially important is that these IDs are accessible even before the entity is finalized – this enables each service to collect its own data without needing any other service to know about that data.

In an ecommerce environment, we would see one service owning money, another the product catalog (excluding prices, those would be owned by the previous service), another service owning the customers’ payment info (credit cards, etc), and yet another owning shipping addresses – all of these separate from the one that owns the shopping cart. Let’s call these Finance, Catalog, Payment, Shipping, and finally Shopping – just so that we have something to reference later.

The API

While we can do all sorts of cool browser composition with the UI in our own system, enabling each service to collect and display its own information, if we want to expose an API for clients to call, we wouldn’t want to force those clients to have to make a separate call to each service in order to make a purchase. Instead, we’d want something that looks like:

MakePurchase(Guid orderId, Dictionary cart, CreditCardInfo cc, Address shippingAddress)

In case you were wondering, the service which owns the definition of this API is different from all of the above services – it is a service that is primarily technical in nature and is responsible for things like integration and data transformations. I call this service IT/Ops.

Getting the data from the API to the services

So, we don’t want any of our business-centric services to know about anybody else’s data structures, so that leaves it to IT/Ops to pass the data to them. The thing is that we still want to do that in the most loosely-coupled way possible – with messaging being a good candidate for that.

So, what we’ll do is have IT/Ops send a message containing the data to the other services, but with a slight twist.

Here’s what the code would look like with NServiceBus:

Bus.SendLocal(new Order { Id = orderId, Cart = cart, 
                          CreditCard = cc, ShippingAddress = shippingAddress });

Why the SendLocal?

So that the components from the other services can all run together with IT/Ops in the same process giving a nice tight deployment model.

UPDATE:

If you’re using a transport like RabbitMQ that is set up as a remote broker, the overhead of going back through the broker might not be worth the improved reliability you’d get by going through messaging. In that case, you might want to consider the Domain Events approach. This would give you a similar level of decoupling but then IT/Ops would need to set up an surrounding transaction, well, that is if you want all the services processing to succeed/fail as one unit. If you don’t, you’d probably be better off just sending messages from IT/Ops to endpoints that host each of the components of the other services.

End Update

Before we get to the services, let me show you the Order class – specifically, the interfaces it implements:

public class Order : ShoppingOrder, PaymentsOrder, ShippingOrder
{
    public Guid OrderId { get; set; }
    public Dictionary<Guid, int> Cart { get; set; }
    public CreditCardInfo CreditCard { get; set; }
    public Address ShippingAddress { get; set; }
}

public interface ShoppingOrder
{
    Guid OrderId { get; set; }
    Dictionary<Guid, int> Cart { get; set; }
}

public interface PaymentsOrder
{
    Guid OrderId { get; set; }
    CreditCardInfo CreditCard { get; set; }
}

public interface ShippingOrder
{
    Guid OrderId { get; set; }
    Address ShippingAddress { get; set; }
}

Each of the above interfaces represents the data that each service cares about. Therefore, each service will provide an assembly that handles that message/data and persists it to its database, like this:

public class ShoppingAPIHandler : IHandleMessages<ShoppingOrder>
{
    public void Handle(ShoppingOrder message)
    {
        //persist to shopping service db
    }
}

public class PaymentsAPIHandler : IHandleMessages<PaymentsOrder>
{
    public void Handle(PaymentsOrder message)
    {
        //persist to payment service db
    }
}

public class ShippingAPIHandler : IHandleMessages<ShippingOrder>
{
    public void Handle(ShippingOrder message)
    {
        //persist to shipping service db
    }
}

Since the Order object being sent is a polymorphic match for all of these interfaces, NServiceBus knows to invoke all of these handlers. By the way, if you care about the order of invocation, then you can control that as well (but I won’t get into that here).

Also, since all of these handler assemblies are deployed to the same endpoint, and the Order object is sent just once, this means that all the handlers will be invoked in a single transaction on a single thread – either all of them succeeding, or all failing. Since they’re all connected on the same Order Id, referential integrity can be preserved as well.

Wrapping up

When you are building a system on SOA principles, you’ll often find that you need a service like IT/Ops to handle data transformation and other broker-centric tasks. While much of SOA is based on the Bus Architectural Style – meaning primarily publish/subscribe interaction between services – that doesn’t mean that your business-centric services cannot have their components deployed in the same process.

I’d go so far as to say that if you aren’t deploying components from multiple services in process with each other at least some of the time then it’s quite likely that your service boundaries are probably incorrect.

Anyway, I hope you found this post interesting. Shout out to Slawek who gave me the idea for this post.

By the way, if you’d like to learn more about these kinds of patterns, the next batch of courses is open for registration – but the early bird prices are almost over, so you’d better hurry.

Register for   Denver CO, USA,     Bad Ems, Germany,     Perth, Australia.

I thought I’d give a bit of a shout-out to Dennis van der Stelt whose been applying many of the principles from my course on his project and now has something of a success story to share. Although all he put out was a little tweet about it, I think if more people bug him, we can tease some more out:

First big update released of our system based on @udidahan ADSD training, makes a maintainable system & site updates in <1sec instead of 24h

— Dennis van der Stelt (@dvdstelt) September 1, 2012

It’s not just about getting the system built quickly the first time, and it’s not just about having a maintainable code base longer term, equally important is the ability to quickly deploy major releases to the system without downtime – ‘cuz when you’re system is down, it’s not providing any business value (which is what being Agile is all about).

What do you say, Dennis? Tell us some more!

UPDATE

Dennis gives the full story here.

Occasionally I’ll get questions from people who have been going down the CQRS path about why I’m so against data duplication. Aren’t the performance benefits of a denormalized view model justified, they ask. This is even more pronounced in geographically distributed systems where the “round-trip” may involve going outside your datacenter over a relatively slow link to another site.

CQRS

As his been said several times before by many others, it’s not the denormalized view model that defines CQRS.

One of the things that sometimes surprising people after going through my course is that in most cases you don’t need a denormalized view model, or at least, not the kind you think. Yes, that’s right: MOST cases.

But I don’t want to get too deep into the CQRS thing in this post – that can wait.

SOA

The big thing I’m against is raw business data being duplicated between services.

Data that can be expected to be accessible in multiple services includes things like identifiers, status information, and date-times. These date-times are used to anchor the status changes in time so that our system will behave correctly even if data/messages are processed out of order. Not all status information necessarily needs to be anchored in time explicitly – sometimes this can be implicit to the context of a given flow through the system.

For example, the Amazon.com checkout workflow.

In that flow, if you provide a shipping address that is in the US, you are presented with one set of options for shipping speed, whereas an international address will lead you to a different set of options.

Assuming that the address information of the customer and the shipping speed options are in different services, we need to propagate the status InternationalAddress(true/false) between these services in that same flow. In this case, there isn’t a need to explicitly anchor that status in time.

But what’s so bad about duplication of data between services?

The danger is that functionality ultimately follows raw business data.

You start with something small like having product prices in the catalog service, the order service, and the invoice service. Then, when you get requirements around supporting multiple currencies, you now need to implement that logic in multiple places, or create a shared library that all the services depend on.

These dependencies creep up on you slowly, tying your shoelaces together, gradually slowing down the pace of development, undermining the stability of your codebase where changes to one part of the system break other parts. It’s a slow death by a thousand cuts, and as a result nobody is exactly sure what big decision we made that caused everything to go so bad.

That’s the thing, it wasn’t viewed as a “big decision” but rather as just one “pragmatic choice” for that specific case. The first one excuses the second, which paves the way for third, and from that point on, it’s a “pattern” – how we do things around here; the proverbial slippery slope.

So what’s with the word “Replication” in the title of this post?

While data duplication between services is very dangerous, replication of business data WITHIN a service is perfectly alright.

Let’s get back into multi-site scenarios, like a retail chain that has a headquarters (HQ) and many stores. Prices are pushed out from the HQ and orders are pushed back from the stores according to some schedule.

We know that we can’t guarantee a perfect connection between all stores and the HQ at all times, therefore we copy the prices published from the HQ and store them locally in the store. Also, since we want to perform top-level analytics on the orders made at the various stores, that would be best done by having all of those orders copied locally at the HQ as well.

We should not view this movement of data from one physical location to another as duplication, but rather as replication done for performance reasons. If there were some magical always-on zero-latency network that existed, we wouldn’t need to do any of this replication.

And that’s just the thing – logical boundaries should not be impacted by these types of physical infrastructure choices (generally speaking). Since services are aligned with logical boundaries, we should expect to see them cross physical boundaries – this includes SYSTEM boundaries (since a system is really nothing more than a unit of deployment).

I know that you might be reading that and thinking “What!?” but there isn’t enough time to get into this in any more depth here. You can read some of my previous posts on the topic of SOA for more info here.

Cross-site integration without replication

There are some domains where sensitive data cannot be allowed to “rest” just anywhere. Let’s look at a healthcare environment where we’re integrating data from multiple hospitals and care providers. While all of these partners are interested in working together to make sure that patients get the best care, which means that they need to share their data with each other, they don’t want any of THEIR data to remain at any partner sites afterwards (and are quite adamant about this).

In these cases, the decision was made that performance is less important than data ownership. Personally, I don’t agree with this mindset. The fact that data is “at rest” in a location as opposed to “in flight” does not change ownership. It could be stored in an encrypted manner so that only a certain application could use it, resulting in the same overall effect, but this is an argument that I’ve never won.

People (as physical beings) put a great deal of emphasis on the physical locations of things. It’s understandable but quite counterproductive when dealing with the more abstract domain of software.

In closing

By virtue of the fact that we don’t duplicate raw business data between services, that means that the regular data structures inside a service already look very different from what they would have looked like in a traditional layered architecture with an ORM-persisted entity model.

In fact, you probably wouldn’t see very many relationships between entities at all.

Going beyond that, you probably wouldn’t see the same entities you had before. An Order wouldn’t exist the way you expect; addresses (billing and shipping) would be stored (indexed by OrderID) in one service whereas the shipping speed (also indexed by OrderId) would be in another, and the prices may well be in yet another.

It is in this manner that data does not end up being duplicated between services, but rather is composed by many services whether that is in the UI of one system, the print-outs down by a second system, or in the integration with 3rd parties done by a third system.

If performance needs to be improved, look at having these services replicate their data from one physical system to another – in-memory caching is one way of doing this, denormalized view models might be though of as another (until you realize there isn’t very much normalization within a service to begin with).

And a word from our sponsor 🙂

For those of you on “rewrite that big-ball-of-mud” projects looking to use these principles, I strongly suggest coming on one of my courses. The next one is in San Francisco and I’ve just opened up the registration for Miami.

For those of you on the other side of the Atlantic, the next courses will be in Stockholm in October and in London this December.

The schedule for next year is also coming together and it will include South Africa and Australia too.

Anyway, here’s what one attendee had to say after taking the course earlier this month:

I wanted to thank you for the excellent workshop in Toronto last week. I spent the better part of the weekend reflecting over what was presented, the insights we learned through the group exercises, and how my preconceptions of SOA have changed. By the end of the course, all the tidbits of (usually) rather ambiguous information that I’ve collected from various blogs, books, and other sources, finally coalesced into something more intelligible – one big A-HA moment if you will. Overall, I found the content of the workshop to be incredibly enlightening and it left me feeling invigorated and excited to learn more.
– Joel from Canada

Hope you’ll be able to make it.

If travel is out of the question for you, you can also look at get a recording of the course here.

One final thing

If your employer won’t foot the bill for these, please get in touch with me.
I wouldn’t want you not to be able to come just because you’re paying out of pocket.

There are very substantial discounts available.

Contact me.

Here’s a small quick tip that can help you improve the performance of important use cases in your systems. It doesn’t require very many changes to your code and can improve matters when your system is under load but won’t make much of a difference when you have capacity to spare.

This is something I talk about in the first day of my course when going through the fallacies of distributed computing – specifically fallacy #3 which talks about bandwidth.

What about bandwidth?

When it comes to network bandwidth in your datacenter, there’s a pretty good chance you’re still on gigabit ethernet. When most developers hear that prefix, “giga”, there is an instantaneous translation in their brain to “so much I don’t need to worry about it”.

The thing is that it’s GigaBIT, not GigaByte, so we’re talking about 128 MBps.

Also, keep in mind that hardly anybody programs at the level of ethernet, we’re several layers up the stack. You can expect roughly 40% the bandwidth of ethernet up in TCP land due to its collision detection, exponential backoff, etc. So that’s roughly 50MBps, not counting overhead for serialization (which can be very significant if it’s text-based like XML or JSON).

In practice, you might be getting something like 25MBps – definitely not so much that you don’t even need to think about it.

Everybody’s talking scalability in terms of number of servers and memory, storage, CPU per server – but what about the network? More importantly, what happens when (not if) you run out? Well, the latency of your calls increase – and that can be quite substantial.

Business Priority

And now we get to the crux of the matter.

Consider a “Customer” web service with a bunch of methods on it, including these two: GenerateTopCustomersByRegionReport and MarkCustomerAsFraud.

Now, your system is under significant load and there’s just enough bandwidth left for one call to make it across the network without timing out. Two users invoke the functionality above – one doing the report, the other doing the fraud. Should the fact that one user clicked the button a millisecond before the other mean that the MarkCustomerAsFraud should be delayed to the point of failure?

I’m fairly sure that if we asked a business stakeholder the answer would be a clear no.

While we could try asking the network engineers to give higher priority to that webmethod, let’s face it, that’s never going to happen. Since both methods are on the same web service, clients are bound to the address where that web service is hosted, regardless of which methods they call.

The problem with “the” network

If there’s one word I loathe in the English language, it’s the word “the”.

Such a small word, tacked on in front of so many other words that, without you even noticing it, traps your mind into thinking you can have only one of that thing.

The network.
The database.

But you CAN have more than one – it’s YOUR system. Design it however you like.

Most servers (if not all) can have more than one network card these days. And even if yours couldn’t – you can ask your network engineers to set up multiple virtual networks on top of the physical network and divide up the bandwidth between them.

Putting it together

The next step is to simply put the MarkAsFraud method on a different web service. This way, you can decide at deployment time which web service should be hosted over which network.

When your system is under load, you will then be guaranteed that even if there are a large number of low priority calls being invoked, they will not use up the network bandwidth reserved for your higher priority calls. You will likely still need to take care of processing and other IO concerns on your servers, but if worst comes to worst, you can partition your server farm as well.

While this may sound a bit CQRS-ish but it would be more accurate to say that CQRS is a more specific case of this pattern – that of partitioning the API according to business priority.

One of the interesting things about messaging is that we tend to forgo the traditional “service contracts” where many methods are put together on a single “service”. Instead, each message definition stands on its own and can be routed to any destination.

In summary

If you are still using WCF and web services, be aware that these apparently little things can have an impact on how your system behaves under load. Even if you do use MSMQ under WCF, the traditional service contract made of multiple methods will still govern your routing.

If you do go all the way with this pattern, you’ll see that each of your service contracts ends up with only one method on it. This might make you wonder what’s the point of the whole service contract thing in WCF – that would be a very good issue to resolve.

Remember the first rule of remote communication – Don’t.

We’re more than half-way through 2012, more than 16 years since the term SOA was introduced, and still people are treating the term “service” as if it were nothing more than a remotely-callable component (like a web service).

Take this “Bare-knuckle SOA” thing that’s started making the rounds. Not to say that the approach doesn’t have its merits (regardless of the SOA thing), but it seems to be talking about little more than regular XML and HTTP communication. Personally, I think that the functional testing done over HTTP in that post would have been much better done directly against the API of the component, bypassing HTTP entirely.

Appending the word “service” to a concept doesn’t change any of its architectural properties.

EmailService, CurrencyService, GeoLocationService – all of them just components which have methods that can be invoked remotely. If anything, the design would probably have been better if all the XML and HTTP was removed – if the components were all hosted in the same process.

In fact, in a well designed Service-Oriented Architecture, we tend to see components from many services deployed in process with each other (as I showed in my recent post UI Composition for Correct Service Boundaries).

But I’ve got to admit – the “bare knuckle” prefix, that’s some good link-bait.

Following on my last post called UI composition techniques for correct service boundaries, one commentor didn’t seem to like the approach I described saying:

“I’m sorry, but with all due respect I must strongly disagree. You haven’t avoided any orchestration work at all, you’ve just moved it in to client side script!

How are you going to deal with the scenario that one of the service calls fails? Say a failed credit card payment, or no more rooms left? In more javascript??

I would much rather take the less brittle approach of introducing an orchestration service. Like it or not, however trivial it may be, there is a relationship between these services, if one call fails, they both fail. This should be reflected in the architecture, not hidden in javascript. With an orchestration service you also either get transactions for free provided by infrastructure, or alternatively if the underlying service doesnt support this, explicit and unit testable control over recovery.”

Since this is a common point of view, I thought I’d take the time to explain a bit more.

Let’s start at a fairly high level.

On failures

I’ve talked many times in the past about how to handle technical causes for failure like server crashes, database deadlocks, and even deserialization exceptions. Messaging and queuing solutions like NServiceBus can help overcome these issues such that things don’t actually fail – they just take a little longer to succeed.

On the logical side of things, the CQRS patterns I talk about describe an approach where aggressive client-side validation is done to prevent almost all logical causes for failure. The only thing that can’t be mitigated client-side are race conditions resulting in actions taken by other users at the same time.

In short, it really is uncommon for things to fail when being processed server-side.

Back to the specific example

The concerns raised in the comment specifically talked about a failed credit card payment or no rooms left in the hotel, so let’s start with the credit card thing:

In my last post I talked about collecting guest and credit card information from the user as a part of the “checkout” process when making a reservation for a hotel room. Just to be clear – there is a final “confirm your reservation” step that happens after all information has been collected.

What this means is that we aren’t actually charging the customer’s card when we collect that data, therefore there is no real issue with a failed credit card payment that needs to be handled by the client-side javascript. When the customer confirms their reservation, yes, there might be a failure when charging the card though there are only some specific types of rates for which the hotel charges your card when you make a reservation.

In general, failed credit card payments are handled pretty much the same way for all ecommerce – an email is sent to the customer asking for an alternative form of payment, also saying that their purchase won’t be processed until payment is made.

In any case, it is only after the reservation is placed that the responsible service would publish an event about that. The service which collected the credit card information would be subscribed to that event and initiate the charge of the card when that event arrives (or not, depending on the rate rules mentioned).

With regards to there not being any rooms left, well, first of all, there’s overbooking – hotels accept more reservations than rooms available because they know that customers sometimes need to cancel, and some just don’t show up. Secondly, there is a manual compensation process if more people show up than there are actual rooms to put them in. In some cases, a hotel will bump you up to a higher class of room (assuming there aren’t too many reservations for those), and in others they will call a “partner” hotel nearby and put you up there instead.

In summary

While arguments can be made that yes, these issues have been addressed in this specific example, there may be other domains where it is not possible to do these kinds of “tricks”. Although I do agree with that in theory, I’ve spent the better part of 5 years travelling around the world talking to hundreds of people in quite a few business domains, and every single time I’ve found it possible to apply these principles.

In short, the use of UI composition allows services to collect their own data, making it so anything outside that service doesn’t depend on those data structures which makes both development and versioning much easier. Technical failure conditions can be mitigated at infrastructure levels in most cases and other business logic concerns can be addressed asynchronously with respect to the data collection.

Give it a try.

One of the things which often throws people off when looking to identify their service boundaries is the UI design. Even those who know that the screen a user is looking at is the result of multiple services working together sometimes stumble when dealing with forms that users enter data into.

Let’s take for example a screen from the Marriott.com online reservation system (below). This screen collects information about the guest staying at the hotel (name, phone number, address, etc) and credit card information.

While we might have wanted to keep guest information in a separate service from the credit card information (which may very well be the corporate card of someone responsible for travel), the above screen would seem to indicate that the data would be collected together, validated together, and would also have to be processed together.

The traditional way

In standard layered architectures you would have all the data submitted by the user passed in a single call from a controller to some “service layer” (possibly running on a different machine), which would then persist that data in one transaction.

Even if some attempt was made to separate things out, there likely would be some “orchestration service” that received the full set of data and it would make calls to the other “services”, passing in the specific data that each “service” is responsible for.

I am putting quotes around the word “service” to indicate that I don’t consider these proper services in the SOA sense (as they lack the necessary autonomy) – they are more like functions or procedures, whether or not they’re invoked XML over HTTP is besides the point.

What to do?

Like so many other things, the solution is simple but a bit counter-intuitive as it doesn’t follow the way most web development is done, i.e. one submit button => one call to the server.

Let’s say the “Red” service is responsible for guest information and the “Blue” service is responsible for credit card data. In this case, each service would have its own javascript come down with the page and that script would register itself for a callback on the click of the submit button. Each service would take the data the user entered into its part of the page and independently make a call to “the” server (could be to 2 separate servers) where the data is persisted (potentially to 2 different databases).

This raises other questions, of course.

Now that the data submitted is being processed in 2 transactions rather than just one, we may need to figure out how to correlate the data. In this specific case, it’s not such a big deal as there is no direct relationship between the guest and the credit card – both need to be independently correlated to some reservation ID.

That reservation ID would likely have been “created” on a button click on a previous screen by some other service. The reason why I put the word “created” in quotes is that this could be as simple as having the client generate a new GUID and put that in a cookie (which would cause the reservation ID to end up being submitted along with subsequent requests). Another alternative would be to put the reservation ID in the session.

It’s quite possible that the reservation ID would only be persisted much later in the service that owns it when the user actually confirms the reservation on the website.

In any case, what we can see is that each of the commands of our respective services can now be processed independently of the others in an entirely asynchronous fashion thus vastly improving the autonomy of our services.

Some words on CQRS

This style of UI composition where services leverage javascript code running in the browser isn’t technically difficult in the slightest. The rest of the implementation of each service – having a controller that takes that data and passes it on for persistence can be quite simple.

I’d say even more strongly, most of the time you shouldn’t need to use any fancy-dancy messaging to get that data persisted – that is, unless you’re still stuck with the big relational database behind 23 firewalls type data tier. Embrace NoSQL databases for the simplicity and scalability they provide – don’t try to re-invent that using messaging, CQRS, persistent view models, event-sourcing, and other crap.

There are other very valid business reasons to embrace CQRS, but they have nothing to do with persistence.

Also notice, this is all happening within a service boundary / bounded context.

In closing

If you aren’t leveraging these types of composite UI techniques, it’s quite likely that your service boundaries aren’t quite right. Do be aware of the UI design and use it to inform your choices around boundaries, but be aware of certain programming “best practices” that may lead you astray with your architecture.

Also, if you’re planning on coming to my course in Toronto to learn more about these topics, just wanted to let you know that there’s one week left for the early-bird discount.

Finally, it’s good I have a birthday that comes around once a year to remind me that my time here isn’t unlimited and that I had better get off my rear and do something meaningful with the time I do have. If you get value from these posts, leave a comment or send me a tweet to let me know – it does wonders for my motivation.

Thanks a bunch.

Last week I gave a presentation at Skills Matter titled Modeling Distributed Systems with NServiceBus Studio.

The recording is online here.

If you’d like to see the list of all presentations on NServiceBus, you can find those here.

I’m not putting NServiceBus Studio online just yet – we’ve got a fair bit more work to cover use cases like authentication and logging, but it will be coming online soon. Update: In the past year, we’ve been able to get this ready and it’s now available for beta. Check out ServiceMatrix.

I don’t think we’ll be able to offer the full round-tripping model-to-code-back-to-model abilities for some time (as they’re horrendously difficult to do well), but I do hope we’ll be able to find an economic model that will allow us to offer most of it for free.

Getting more companies willing to go on the record about their usage of NServiceBus.

Update: Check out full list of customers.

Here’s a testimonial from a large insurance company whose name always had me wondering…

If P & C Insurance is the leading property and casualty insurance company in the Nordic countries with 3.6 million customers and 6400 employees. NServiceBus was chosen as the ESB for one of our core insurance systems and is currently processing around 100,000 insurance policies a month. We expect that number to double within a year.

The support NServiceBus provides around asynchronous messaging and publish/subscribe communication makes the implementation of Service Oriented Architectures very straightforward. While the performance and scalability of the platform is able to handle the massive loads we’re under, we’re just as pleased with how quickly developers are able to get started with NServiceBus – even those coming into the project later on benefit from the maintainability of NServiceBus-centric code. We already have over 30 developers using it.

Looking forwards, we will be using NServiceBus on more systems at ever larger scales.

Hard to believe, isn’t it?

Although both myself and Greg have been saying (quite publicly) for a long time now that we’re in agreement in about 99% of the DDD/CQRS content we talk about, it turns out the terminology we use has made it very difficult for everybody else to see that.

Anyway, on a recent call with Greg and the Microsoft Patterns & Practices team working on the CQRS guidance, I think we finally ironed out the terminological differences.

First of all, both of us clearly stated that CQRS is not meant to be the top-level architecture of a system.

The use of Bounded Contexts from Domain Driven Design is a good way to *start* handling that top-level.

The area of some contention was how big a Bounded Context should be. After going back and forth a bit, Greg brought the concept of Business Component into the conversation, and that really cleared things up all around. I was quite pleased as I’ve been going on and on about these business components for years (I think 2006 was one of my earlier posts on the topic, though the mp3 has disappeared since then).

Anyway, here’s the meat:

A given Bounded Context should be divided into Business Components, where these Business Components have full UI through DB code, and are ultimately put together in composite UI’s and other physical pipelines to fulfill the system’s functionality.

A Business Component can exist in only one Bounded Context.

CQRS, if it is to be used at all, should be used within a Business Component.

There you have it – terminological agreement in addition to the philosophical agreement that was always there.

You can find the history of my posts mentioning Business Components here.