One of the questions that came up from my NServiceBus – .NET Service Bus Smackdown post was about the Polymorphic Message Dispatch and Polymorphic Message Routing features. People wanted to know what those are, why they’re important, and if other technologies (specifically WCF and BizTalk) support them.

Messaging Basics

First of all, when building a system using messaging, you don’t have methods that are invoked on some remote object (a.k.a “service”) to which you pass parameters. Instead, you use some generic piece of infrastructure (in the world of Java, this is most commonly a Message Broker) to send a message where a message can be thought of as a serializable class. Here’s an example of a message:

public class UserCreated : IMessage
{
    public Guid UserId { get; set; }
    public string Name { get; set; }
}

This message would be published using NServiceBus like this:

bus.Publish<UserCreated>( m =>
{
    m.UserId = Guid.NewGuid();
    m.Name = "John Smith";
});

This can be contrasted with RPC models like WCF where you need to define a “service” that has methods on it, where those methods accepts parameters. Sometimes developers try to make this more generic by having a single “service” with one method on it named something like “Process” where the parameter it accepts is of the type “object”, or they introduce generics like this: Process<T>(T message);

This is where Polymorphic Message Dispatch comes in:

Polymorphic Message Dispatch

While you can pull of the WCF generics thing, one thing that is more difficult (without writing your own dispatch model) is to have a pipeline of classes which can be invoked based on their relationship to the type passed in. Using NServiceBus, both of the following message handlers will be invoked when UserCreated arrives:

public class Persistence : IHandleMessages<UserCreated>
{
    public void Handle(UserCreated message) { }
}

public class Audit : IHandleMessages<IMessage>
{
    public void Handle(IMessage message) { }
}

Now some might say that WCF, BizTalk, and the .NET Service Bus allow you to do auditing in their own internal pipeline, and that’s true. The place where this becomes more powerful is when you need to build V2 of your system, and the publisher now publishes a slightly different event – that a user was created as a part of a campaign, requiring the subscriber to register statistics about the campaign. Of course, this event also means that a user was created. Here’s how you’d do that with NServiceBus:

public class UserCreatedFromCampaign : UserCreated
{
    public Guid CampaignId { get; set; }
}

//publisher code
bus.Publish<UserCreatedFromCampaign>( m =>
{
    m.UserId = Guid.NewGuid();
    m.Name = "John Smith";
    m.CampaignId = theCampaignId;
}

//subscriber code
public class Statistics : IHandleMessages<UserCreatedFromCampaign>
{
    public void Handle(UserCreatedFromCampaign message) { }
}

The important part is what you don’t see – since UserCreatedFromCampaign inherits from UserCrated, the Persistence handler we had from V1 will also be invoked, and so will the Audit handler of course. You don’t have to make your new code call the old code like you would in a method based dispatch model. This makes sure that the coupling in your service layer code remains constant over time as you grow the functionality of your system.

This was one of the main benefits mentioned by Rackspace in their use of NServiceBus (here):

“The main benefit NServiceBus has brought us so far is developer scalability due to lower coupling and higher consistency in our code.”

But, when looking at the above scenario, we can obviously expect that all sorts of things can happen in relation to campaigns – it is a separate concern, and thus should be handled by a separate subscriber. And this bring us to…

Polymorphic Message Routing

The challenge that we have here is that we no longer have a hierarchy where something clearly belongs on top of something else. We have users created and activities happening related to campaigns – that may happen in any combination. By having separate subscribers, we could then introduce new handlers/subscribers to our environment without touching or taking down any of the other subscribers. Here’s what the subscribers would look like:

public class Persistence : IHandleMessages<UserCreated>
{
    public void Handle(UserCreated message) { }
}

public class Statistics : IHandleMessages<CampaignActivityOccurred>
{
    public void Handle(CampaignActivityOccurred message) { }
}

But if each of the above messages were a class, how could we define a message which inherited from both?

Before answering that, we need to understand why the publisher wouldn’t just publish both of the above messages. You see, the publisher can’t make any assumptions about its subscribers – it could be that one of them has logic that correlates across both of these messages that could end up counting the occurrence as happening twice rather than once, possibly charging the account associated with the campaign twice. Publishing two messages results in two transactions when there really should have been one.

So, here’s how to define messages so that we can have multiple inheritance:

public interface UserCreated : IMessage
{
    Guid UserId { get; set; }
    string Name { get; set; }
}

public interface CampaignActivityOccurred : IMessage
{
    Guid CampaignId { get; set; }
    Guid ActivityId { get; set; }
}

public interface UserCreatedFromCampaign
                 : UserCreated,
                   CampaignActivityOccurred
{
}

And when the publisher publishes UserCreatedFromCampaign, the event would be routed to both the UserCreated subscriber and the CampaignActivityOccurred subscriber. The power of this approach is felt as we handle new requirements around purchases made related to a campaign. Now we can have another event which inherits from CampaignActivityOccurred and not have to worry since the existing subscriber will be routed those messages automatically.

Since WCF doesn’t have publish/subscribe capabilities, we might as well move along.

Not to throw a burning match on an ocean of oil, but REST doesn’t really support this either.

Not Content-Based Routing

This may sound like the content-based router pattern from EIP (CBR), but it’s not. The important difference is that there isn’t some part of the routing that depends on the structure of the messages. The major drawback of CBR is that it creates a central place in your system that needs to be changed any time *syntactic* changes happen to message structure *in addition to* to changes in the subscribers.

Now, this is where the BizTalk guys would say that “that’s why we can do message transformations”, and then the subscribers wouldn’t need to be changed. However, can we really know when getting a requirement that the change is syntactic and not semantic? I mean, it’s quite common that changes to message structures happen together with changes to processing logic.

You may be beginning to get the feeling that more and more logic is being sucked out of the subscribers into some monolithic black hole that is likely going to be unmaintainable and quite slow.

This is one of the main differences between using a bus and a broker – a bus supports the correct distribution of logic keeping the system loosely coupled; brokers are useful integration engines when you absolutely can’t change the applications being integrated. Enterprise Application Integration (EAI) brokers don’t usually make good Enterprise Service Bus (ESB) technology.

In Closing

NServiceBus has all sorts of features you didn’t know you needed until you saw what life could be like when you had them. Most of these features don’t have snazzy drag-and-drop demos that make people ooh-and-aah and TechEds and PDCs, but they’re really necessary to avoid finding yourself in yet another big-ball-of-mud code base telling your manager/customer (again) that it would be faster to rebuild the system from scratch than to implement that new requirement in the old one.

Take NServiceBus for a spin and see for yourself.

Just before we usher in the new year, I’m happy to announce the release of NServiceBus version 2.5.

Yes, there’s a new logo, and the website’s been redesigned.
It’s been a long time coming – the previous version (2.0) was released in March.

I’m really quite excited about this version as it rolls up all the bug fixes and enhancements that customers have asked for as they ran version 2.0 under the most severe types of production environments. The next thing that is a big deal that many have been asking for is a licensed version of NServiceBus – that is, the ability to purchase a commercial license and receive support.

We all know how managers like having a throat to choke.

And now they’ll have one – NServiceBus Ltd is the company that will be providing licensing, services, and support for all customers’ NServiceBus needs. After more than 33,000 downloads and over 1000 developers in the community, the demand has really grown. Who would’ve thought all this would happen when I started NServiceBus 4 years ago (before it even had a name).

Why NServiceBus is better than WCF for your distributed systems

This question comes up repeatedly for people hearing about NServiceBus for the first time.

The answer is simple – reliability.

A system built with NServiceBus is so much more reliable to all kinds of production conditions than WCF that it’s hardly a fair comparison at all. While WCF can be configured to provide something kind of close to the same level of reliability, you need to do a fair amount of spelunking through the various options of netMsmqBinding to get it right.

The second reason to use NServiceBus instead of WCF is publish/subscribe.

The ability to make use of events and the observer pattern not just to achieve loose coupling within a single process, but across many processes, machines, and sites. Can you imagine going back to programming without events? Shudder. But that’s exactly what it’s like to use WCF in your distributed system. NServiceBus brings you the best of object-oriented programming but in a distributed and reliable infrastructure.

Don’t wait any longer

Take NServiceBus for a spin.

But things may look a bit different after you do…

http://www.NServiceBus.com

And have a happy New Year.

One of the better known analysts in the enterprise software area, JP Morgenthal, wrote this post about the relationship between SOA, BPM, and EA. In it he defines SOA as follows:

“SOA is a practice that focuses on modeling the entities, and relationships between entities, that comprise the business as a set of services. This can be done on a small or large scale. Typically, the relationships in this model represent consumer/provider relationships.”

I have some serious concerns about the ramifications of this definition/description.

First of all, when reading “entities”, many people will interpret that to mean the entities found in Entity Relationship Diagrams [ERD] or in Object Oriented Analysis & Design [OOAD]. In both, these entities are identified as the “nouns” of the domain. Examples of these ERD/OOAD-type entities include things like Customer, Order, and Product.

These are almost always the wrong place to start for identifying services in SOA.

Second, on the consumer/provider relationship: on the one had, this fits very well with how web services can consume (or call) other web services. However, the downsides of using web services as services in SOA is becoming well enough known that even in the same post we see this warning:

“Web Services is not SOA, it is merely a standardized approach to accessing functionality on remote systems.”

But the question remains, if a producer/consumer relationship is OK for SOA-type services, why doesn’t that hold for web services? And the answer is… it depends on the type of producer/consumer relationship. The typical relationship is one of synchronous calls from consumer to producer, this is not OK for SOA-type services either.

You see, this synchronous producer/consumer implies a model where services are not able to fulfill their objectives without calling other services. In order for us to achieve the IT/Business alignment promised by SOA, we need services which are autonomous, ie. able to fulfill their objectives without that kind of external help.

Instead, we need to look for a more loosely coupled producer/consumer relationship – like publish/subscribe, where the producer emits events, and the consumer subscribes and handles those events. The reason that this kind of relationship doesn’t hurt autonomy is that it disconnects services on the dimension of time. In order for a service to be able to make a decision autonomously without synchronously calling any other service, using only information provided by events it received in the past, it must be strongly aligned with the business.

Most projects which bandy about the SOA acronym aren’t actually made up of services – they’re made up of XML over HTTP functions calling other XML over HTTP functions, eventually calling XML over HTTP databases. You can layer as much XML and HTTP as you want on top of it, but at the end of the day, most projects are just functions calling functions calling databases – in other words, procedural programming in the large, and no amount of SOAP will wash away the stink.

Here’s a different definition of services for SOA that may communicate a bit better what it’s all about:

A service is the technical authority for a specific business capability.
Any piece of data or rule must be owned by only one service.

What this means is that even when services are publishing and subscribing to each other’s events, we always know what the authoritative source of truth is for every piece of data and rule.

Also, when looking at services from the lense of business capabilities, what we see is that many user interfaces present information belonging to different capabilities – a product’s price alongside whether or not it’s in stock. In order for us to comply with the above definition of services, this leads us to an understanding that such user interfaces are actually a mashup – with each service having the fragment of the UI dealing with its particular data.

Ultimately, process boundaries like web apps, back-end, batch-processing are very poor indicators of service boundaries. We’d expect to see multiple business capabilities manifested in each of those processes.

I know that this may be more confusing than the traditional web services approach but, to paraphrase Donald Rumsfeld, it is better to know that you don’t know, than to not know that you don’t know

Not in the business world anyway.

The problem is that, as software developers, we’re all too quick to accept them at face value. We don’t question the requirements – in all fairness, it was never our job to do so. We were the ones that implemented them, preferably quickly.

For example

Let’s say we get the requirement the following requirements:

1. If the order was already shipped, don’t let the user cancel the order.
2. If the order was already cancelled, don’t let the user ship the order.

The race condition here is when we have two users who are looking at the same order, which is neither cancelled nor shipped yet, and each submits a command – one to ship the order, the other to cancel it.

In these cases, the code is simple – just an if statement before performing the relevant command.

So what’s the problem

A microsecond difference in timing shouldn’t make a difference to core business behaviors. Which means that we’ve actually got here is a bug in the requirements. Users are actually dictating solutions here rather than requirements.

Let’s ask our stakeholders, “why shouldn’t we let users cancel a shipped order? I mean, the users don’t want the products.”

And the stakeholders would respond with something like, “well, we don’t want to refund the user’s money then. Or, at least, not all their money. Well, maybe if they return the products in their original packaging, *then* we could give a full refund.”

And as we drilled deeper, “when do refunds need to be given? Right away, in the same transaction?”

The stakeholders would explain, “no, refunds don’t need to be given right away.”

It turns out we were missing the concept of a refund, as well as assuming that all things needed to be processed and enforced immediately. Once we dug into the requirements, we found that there is actually plenty of time to allow both transactions to go through. We just need to add some checks during shipping’s long-running process to see if the order was cancelled, and then to cut the process short.

So is everything a long-running process then?

That’s actually a fair question – long-running processes are a lot more common than at first appears.

What we’re seeing is that cancellation is now a command that has no reason to fail – just like CQRS tells us. When this command is performed, it publishes the OrderCancelled event, which the billing service subscribes to.

Billing then starts a long-running process (a saga, in NServiceBus lingo), also listening to events from the shipping process, ultimately making a decision when a refund should be given, and for how much.

Deeper business analysis

As we discuss matters more with our business stakeholders, we hear that most orders are actually cancelled within an hour of being submitted. It is quite rare for orders to be cancelled days later.

In which case, we could look at modeling the acceptance of an order as a long-running process itself.

When a user places an order, we don’t immediately publish an event indicating the acceptance of an order, instead a saga is kicked off – which opens up a timeout for an hour later. If a cancellation command arrives during that period of time, the user gets a full refund (seeing as we didn’t charge anything since billing didn’t get the accepted event to begin with), and the saga just shuts itself down. If the timeout occurs an hour later, and the saga didn’t get a cancel command, then the order is actually accepted and the event is published.

Yes, sagas are everywhere, once you learn to see with business eyes, and no race conditions are left.

In closing

Any time you see requirements that indicate a race condition, dig deeper.

What you’re likely to find are some additional business concepts as well as the introduction of time and the creation of long-running business processes. The implementation at that point will pivot from being trivial if-statements to being richer sagas.

Keep an eye out.

Last April I was in Bergen Norway for some consulting and training and I also gave my first NServiceBus presentation to a user group. I don’t particularly like giving NServiceBus-specific presentations, preferring to talk about the patterns and concepts of service-based architectures and service buses – NServiceBus is just an implementation. Ultimately, that’s what happened in the presentation – in the first half (or so) I talked about the theory, and in the second I demonstrated that theory with NServiceBus.

Currently, the video is being graciously hosted by Jon Torresdal on his blog, so let’s hope that the bandwidth holds up.

Get it here.

At QCon London a couple of weeks ago I had a chat with Ross Mason, the founder of Mule – the open source Java ESB. After a while, I realized that NServiceBus is a bit different from Mule ESB in terms of scope.

While Mule has many features in terms of connectivity and integration, NServiceBus provides platform interop only. One could say that this is a product of the different backgrounds I and Ross come from.

On the other hand, the saga capabilities in NServiceBus for handling long-running processes are considered to be BPM functionality on the Java side of the industry, and as such, Mule does not have them.

In terms of other enterprise features like management and monitoring, Mule is more mature, but NServiceBus holds its own in terms of high availability and actually surpasses Mule with the grid and scale-out capabilities of its distributor.

Anyway, I think it’s about time each of these parts was explicitly described so that companies already invested in Java ESB tools will know what they’re getting with NServiceBus.

Until then, I hope this podcast describing the full spectrum of NServiceBus, from top level SOA services to in-the-weeds transaction management, will provide more information about what it is and why you might want to use it:

Deep Fried Bytes, episode 49 – Getting the right message about NServiceBus with Udi Dahan.

Comments most welcome.

A couple of weeks ago I gave a talk on Command/Query Responsibility Segregation in London.

The recording of the talk is online here.

There is one important thing that I didn’t have enough time to cover, but I want you to keep in mind as you’re watching this. It is that CQRS is applicable only *within* the context of a single service/BC – NOT across or between them.

Let me know what you think.

So it’s been about 6 months since my last NServiceBus post and since then about 1000 new people have subscribed to this blog so they might not know anything about it. For a bit of history, see the post (from almost exactly a year ago) describing the 1.9 release of NServiceBus here.

What’s New

The quickly approaching next release of NServiceBus will be version 2.0 and is a big step from 1.9. After 2 betas and 2 release candidates, this version has had a longer stabilization period than any of the versions so far (1.4-1.9). Many of my clients are already using it in production and are very pleased with it. I’ve heard similar reports from others in the community (now with over 500 members in the discussion group). There have been almost 10,000 downloads since the version 1.9 release and in every country I visit I meet people using NServiceBus in new and interesting applications.

With my appearance on Hanselminutes, many in the mainstream .NET industry have started taking a look at NServiceBus. That, and the fact that Microsoft’s Oslo technology has now taken a very data-driven turn (rather than its original service-oriented direction).

Interestingly enough, I’ve been hearing more and more reports about people using NServiceBus as a developer-friendly API on top of other technologies. This includes BizTalk and even Neuron. I never thought that people would take the pluggability of NServiceBus that far.

So, what is NServiceBus?

Well, it’s a service bus, y’know, like an ESB – just an open-source one.

All kidding aside, in a nutshell, it gives you an easy way to integrate transactional messaging into your applications.

One of the reasons why you might want to do that is so that you don’t lose messages containing valuable data when IIS recycles your AppDomain, every 15-20 minutes (as I wrote about in this MSDN magazine article).

There are many other nice things in there, like the ability to unit test your service layers and long-running processes but you can read more about that here…

Documentation

One of the biggest differences to NServiceBus in this release is documentation.

A lot of work has gone into the NServiceBus.com site to help developers hit the ground running with NServiceBus, including the more advanced aspects of transparent scale-out with the distributor and multi-site communications.

There is still work to be done in this area but feedback so far has been extremely positive (except for some grumblings from certain old-timers saying that if they could figure it out by themselves, well, you know the rest).

In Closing

If you’re building a distributed enterprise .NET system, take 5 minutes, download it, and see transactional publish/subscribe messaging working on your machine without any big heavy-weight middleware.

www.NServiceBus.com

After listening how the community has interpreted Command-Query Responsibility Segregation I think that the time has come for some clarification. Some have been tying it together to Event Sourcing. Most have been overlaying their previous layered architecture assumptions on it. Here I hope to identify CQRS itself, and describe in which places it can connect to other patterns.

Download as PDF – this is quite a long post.

Why CQRS

Before describing the details of CQRS we need to understand the two main driving forces behind it: collaboration and staleness.

Collaboration refers to circumstances under which multiple actors will be using/modifying the same set of data – whether or not the intention of the actors is actually to collaborate with each other. There are often rules which indicate which user can perform which kind of modification and modifications that may have been acceptable in one case may not be acceptable in others. We’ll give some examples shortly. Actors can be human like normal users, or automated like software.

Staleness refers to the fact that in a collaborative environment, once data has been shown to a user, that same data may have been changed by another actor – it is stale. Almost any system which makes use of a cache is serving stale data – often for performance reasons. What this means is that we cannot entirely trust our users decisions, as they could have been made based on out-of-date information.

Standard layered architectures don’t explicitly deal with either of these issues. While putting everything in the same database may be one step in the direction of handling collaboration, staleness is usually exacerbated in those architectures by the use of caches as a performance-improving afterthought.

A picture for reference

I’ve given some talks about CQRS using this diagram to explain it:

The boxes named AC are Autonomous Components. We’ll describe what makes them autonomous when discussing commands. But before we go into the complicated parts, let’s start with queries:

Queries

If the data we’re going to be showing users is stale anyway, is it really necessary to go to the master database and get it from there? Why transform those 3rd normal form structures to domain objects if we just want data – not any rule-preserving behaviors? Why transform those domain objects to DTOs to transfer them across a wire, and who said that wire has to be exactly there? Why transform those DTOs to view model objects?

In short, it looks like we’re doing a heck of a lot of unnecessary work based on the assumption that reusing code that has already been written will be easier than just solving the problem at hand. Let’s try a different approach:

How about we create an additional data store whose data can be a bit out of sync with the master database – I mean, the data we’re showing the user is stale anyway, so why not reflect in the data store itself. We’ll come up with an approach later to keep this data store more or less in sync.

Now, what would be the correct structure for this data store? How about just like the view model? One table for each view. Then our client could simply SELECT * FROM MyViewTable (or possibly pass in an ID in a where clause), and bind the result to the screen. That would be just as simple as can be. You could wrap that up with a thin facade if you feel the need, or with stored procedures, or using AutoMapper which can simply map from a data reader to your view model class. The thing is that the view model structures are already wire-friendly, so you don’t need to transform them to anything else.

You could even consider taking that data store and putting it in your web tier. It’s just as secure as an in-memory cache in your web tier. Give your web servers SELECT only permissions on those tables and you should be fine.

Query Data Storage

While you can use a regular database as your query data store it isn’t the only option. Consider that the query schema is in essence identical to your view model. You don’t have any relationships between your various view model classes, so you shouldn’t need any relationships between the tables in the query data store.

So do you actually need a relational database?

The answer is no, but for all practical purposes and due to organizational inertia, it is probably your best choice (for now).

Scaling Queries

Since your queries are now being performed off of a separate data store than your master database, and there is no assumption that the data that’s being served is 100% up to date, you can easily add more instances of these stores without worrying that they don’t contain the exact same data. The same mechanism that updates one instance can be used for many instances, as we’ll see later.

This gives you cheap horizontal scaling for your queries. Also, since your not doing nearly as much transformation, the latency per query goes down as well. Simple code is fast code.

Data modifications

Since our users are making decisions based on stale data, we need to be more discerning about which things we let through. Here’s a scenario explaining why:

Let’s say we have a customer service representative who is one the phone with a customer. This user is looking at the customer’s details on the screen and wants to make them a ‘preferred’ customer, as well as modifying their address, changing their title from Ms to Mrs, changing their last name, and indicating that they’re now married. What the user doesn’t know is that after opening the screen, an event arrived from the billing department indicating that this same customer doesn’t pay their bills – they’re delinquent. At this point, our user submits their changes.

Should we accept their changes?

Well, we should accept some of them, but not the change to ‘preferred’, since the customer is delinquent. But writing those kinds of checks is a pain – we need to do a diff on the data, infer what the changes mean, which ones are related to each other (name change, title change) and which are separate, identify which data to check against – not just compared to the data the user retrieved, but compared to the current state in the database, and then reject or accept.

Unfortunately for our users, we tend to reject the whole thing if any part of it is off. At that point, our users have to refresh their screen to get the up-to-date data, and retype in all the previous changes, hoping that this time we won’t yell at them because of an optimistic concurrency conflict.

As we get larger entities with more fields on them, we also get more actors working with those same entities, and the higher the likelihood that something will touch some attribute of them at any given time, increasing the number of concurrency conflicts.

If only there was some way for our users to provide us with the right level of granularity and intent when modifying data. That’s what commands are all about.

Commands

A core element of CQRS is rethinking the design of the user interface to enable us to capture our users’ intent such that making a customer preferred is a different unit of work for the user than indicating that the customer has moved or that they’ve gotten married. Using an Excel-like UI for data changes doesn’t capture intent, as we saw above.

We could even consider allowing our users to submit a new command even before they’ve received confirmation on the previous one. We could have a little widget on the side showing the user their pending commands, checking them off asynchronously as we receive confirmation from the server, or marking them with an X if they fail. The user could then double-click that failed task to find information about what happened.

Note that the client sends commands to the server – it doesn’t publish them. Publishing is reserved for events which state a fact – that something has happened, and that the publisher has no concern about what receivers of that event do with it.

Commands and Validation

In thinking through what could make a command fail, one topic that comes up is validation. Validation is different from business rules in that it states a context-independent fact about a command. Either a command is valid, or it isn’t. Business rules on the other hand are context dependent.

In the example we saw before, the data our customer service rep submitted was valid, it was only due to the billing event arriving earlier which required the command to be rejected. Had that billing event not arrived, the data would have been accepted.

Even though a command may be valid, there still may be reasons to reject it.

As such, validation can be performed on the client, checking that all fields required for that command are there, number and date ranges are OK, that kind of thing. The server would still validate all commands that arrive, not trusting clients to do the validation.

Rethinking UIs and commands in light of validation

The client can make of the query data store when validating commands. For example, before submitting a command that the customer has moved, we can check that the street name exists in the query data store.

At that point, we may rethink the UI and have an auto-completing text box for the street name, thus ensuring that the street name we’ll pass in the command will be valid. But why not take things a step further? Why not pass in the street ID instead of its name? Have the command represent the street not as a string, but as an ID (int, guid, whatever).

On the server side, the only reason that such a command would fail would be due to concurrency – that someone had deleted that street and that that hadn’t been reflected in the query store yet; a fairly exceptional set of circumstances.

Reasons valid commands fail and what to do about it

So we’ve got a well-behaved client that is sending valid commands, yet the server still decides to reject them. Often the circumstances for the rejection are related to other actors changing state relevant to the processing of that command.

In the CRM example above, it is only because the billing event arrived first. But “first” could be a millisecond before our command. What if our user pressed the button a millisecond earlier? Should that actually change the business outcome? Shouldn’t we expect our system to behave the same when observed from the outside?

So, if the billing event arrived second, shouldn’t that revert preferred customers to regular ones? Not only that, but shouldn’t the customer be notified of this, like by sending them an email? In which case, why not have this be the behavior for the case where the billing event arrives first? And if we’ve already got a notification model set up, do we really need to return an error to the customer service rep? I mean, it’s not like they can do anything about it other than notifying the customer.

So, if we’re not returning errors to the client (who is already sending us valid commands), maybe all we need to do on the client when sending a command is to tell the user “thank you, you will receive confirmation via email shortly”. We don’t even need the UI widget showing pending commands.

Commands and Autonomy

What we see is that in this model, commands don’t need to be processed immediately – they can be queued. How fast they get processed is a question of Service-Level Agreement (SLA) and not architecturally significant. This is one of the things that makes that node that processes commands autonomous from a runtime perspective – we don’t require an always-on connection to the client.

Also, we shouldn’t need to access the query store to process commands – any state that is needed should be managed by the autonomous component – that’s part of the meaning of autonomy.

Another part is the issue of failed message processing due to the database being down or hitting a deadlock. There is no reason that such errors should be returned to the client – we can just rollback and try again. When an administrator brings the database back up, all the message waiting in the queue will then be processed successfully and our users receive confirmation.

The system as a whole is quite a bit more robust to any error conditions.

Also, since we don’t have queries going through this database any more, the database itself is able to keep more rows/pages in memory which serve commands, improving performance. When both commands and queries were being served off of the same tables, the database server was always juggling rows between the two.

Autonomous Components

While in the picture above we see all commands going to the same AC, we could logically have each command processed by a different AC, each with it’s own queue. That would give us visibility into which queue was the longest, letting us see very easily which part of the system was the bottleneck. While this is interesting for developers, it is critical for system administrators.

Since commands wait in queues, we can now add more processing nodes behind those queues (using the distributor with NServiceBus) so that we’re only scaling the part of the system that’s slow. No need to waste servers on any other requests.

Service Layers

Our command processing objects in the various autonomous components actually make up our service layer. The reason you don’t see this layer explicitly represented in CQRS is that it isn’t really there, at least not as an identifiable logical collection of related objects – here’s why:

In the layered architecture (AKA 3-Tier) approach, there is no statement about dependencies between objects within a layer, or rather it is implied to be allowed. However, when taking a command-oriented view on the service layer, what we see are objects handling different types of commands. Each command is independent of the other, so why should we allow the objects which handle them to depend on each other?

Dependencies are things which should be avoided, unless there is good reason for them.

Keeping the command handling objects independent of each other will allow us to more easily version our system, one command at a time, not needing even to bring down the entire system, given that the new version is backwards compatible with the previous one.

Therefore, keep each command handler in its own VS project, or possibly even in its own solution, thus guiding developers away from introducing dependencies in the name of reuse (it’s a fallacy). If you do decide as a deployment concern, that you want to put them all in the same process feeding off of the same queue, you can ILMerge those assemblies and host them together, but understand that you will be undoing much of the benefits of your autonomous components.

Whither the domain model?

Although in the diagram above you can see the domain model beside the command-processing autonomous components, it’s actually an implementation detail. There is nothing that states that all commands must be processed by the same domain model. Arguably, you could have some commands be processed by transaction script, others using table module (AKA active record), as well as those using the domain model. Event-sourcing is another possible implementation.

Another thing to understand about the domain model is that it now isn’t used to serve queries. So the question is, why do you need to have so many relationships between entities in your domain model?

(You may want to take a second to let that sink in.)

Do we really need a collection of orders on the customer entity? In what command would we need to navigate that collection? In fact, what kind of command would need any one-to-many relationship? And if that’s the case for one-to-many, many-to-many would definitely be out as well. I mean, most commands only contain one or two IDs in them anyway.

Any aggregate operations that may have been calculated by looping over child entities could be pre-calculated and stored as properties on the parent entity. Following this process across all the entities in our domain would result in isolated entities needing nothing more than a couple of properties for the IDs of their related entities – “children” holding the parent ID, like in databases.

In this form, commands could be entirely processed by a single entity – viola, an aggregate root that is a consistency boundary.

Persistence for command processing

Given that the database used for command processing is not used for querying, and that most (if not all) commands contain the IDs of the rows they’re going to affect, do we really need to have a column for every single domain object property? What if we just serialized the domain entity and put it into a single column, and had another column containing the ID? This sounds quite similar to key-value storage that is available in the various cloud providers. In which case, would you really need an object-relational mapper to persist to this kind of storage?

You could also pull out an additional property per piece of data where you’d want the “database” to enforce uniqueness.

I’m not suggesting that you do this in all cases – rather just trying to get you to rethink some basic assumptions.

Let me reiterate

How you process the commands is an implementation detail of CQRS.

Keeping the query store in sync

After the command-processing autonomous component has decided to accept a command, modifying its persistent store as needed, it publishes an event notifying the world about it. This event often is the “past tense” of the command submitted:

MakeCustomerPerferredCommand -> CustomerHasBeenMadePerferredEvent

The publishing of the event is done transactionally together with the processing of the command and the changes to its database. That way, any kind of failure on commit will result in the event not being sent. This is something that should be handled by default by your message bus, and if you’re using MSMQ as your underlying transport, requires the use of transactional queues.

The autonomous component which processes those events and updates the query data store is fairly simple, translating from the event structure to the persistent view model structure. I suggest having an event handler per view model class (AKA per table).

Here’s the picture of all the pieces again:

Bounded Contexts

While CQRS touches on many pieces of software architecture, it is still not at the top of the food chain. CQRS if used is employed within a bounded context (DDD) or a business component (SOA) – a cohesive piece of the problem domain. The events published by one BC are subscribed to by other BCs, each updating their query and command data stores as needed.

UI’s from the CQRS found in each BC can be “mashed up” in a single application, providing users a single composite view on all parts of the problem domain. Composite UI frameworks are very useful for these cases.

Summary

CQRS is about coming up with an appropriate architecture for multi-user collaborative applications. It explicitly takes into account factors like data staleness and volatility and exploits those characteristics for creating simpler and more scalable constructs.

One cannot truly enjoy the benefits of CQRS without considering the user-interface, making it capture user intent explicitly. When taking into account client-side validation, command structures may be somewhat adjusted. Thinking through the order in which commands and events are processed can lead to notification patterns which make returning errors unnecessary.

While the result of applying CQRS to a given project is a more maintainable and performant code base, this simplicity and scalability require understanding the detailed business requirements and are not the result of any technical “best practice”. If anything, we can see a plethora of approaches to apparently similar problems being used together – data readers and domain models, one-way messaging and synchronous calls.

Although this blog post is over 3000 words (a record for this blog), I know that it doesn’t go into enough depth on the topic (it takes about 3 days out of the 5 of my Advanced Distributed Systems Design course to cover everything in enough depth). Still, I hope it has given you the understanding of why CQRS is the way it is and possibly opened your eyes to other ways of looking at the design of distributed systems.

Questions and comments are most welcome.

My latest article has been published in issue 21 of the Microsoft Architecture Journal:

EDA: SOA Through The Looking Glass

Please leave questions and comments here.