Brownfield CQRS Part 1 – Commands

One question that came up several times at DDD eXchange last week was CQRS: now we understand all the benefits, how do we begin migrating our existing applications towards this sort of architecture?

It’s something we’ve been chipping away at at work recently, and over a short series of posts I’d like to share some of the conventions and patterns we’ve been using to migrate traditional WPF client/WCF server systems towards a CQRS-aware architecture.

Note that WCF isn’t strictly required here — these patterns are equally applicable to most other RPC-based services (e.g. old ASMX web services).

CQRS recap

Command-Query Responsibility Segregation (CQRS) is based around a few key assumptions:

  • All system behaviour is either a command that changes the state of the system, or a query that provides a view of that state (e.g. to display on screen).
  • In most systems, the number of reads (queries) is typically an order of magnitude higher than than the number of writes (commands) — particularly on the web. It is therefore useful to be able to scale each side independently.
  • Commands and queries have fundamentally different needs — commands favour a domain model, consistency and normalization, where reads are faster when highly denormalized e.g. table-per-screen with as little processing as possible in between. They often also portray the same objects differently — commands are typically small, driven by the needs of business transactions, where queries are larger, driven by UX requirements and sometimes including projections, flattening and aggregation.
  • Using the same underlying model and storage mechanism for reads and writes couples the two sides together, and ensures at least one will suffer as a result.

CQRS completely separates (segregates) commands and queries at an architectural level, so each side can be designed and scaled independently of the other.


The best way to to begin refactoring your architecture is to define clear interfaces — contracts — between components. Even if secretly, the components are huge messes on the inside, getting their interfaces (commands and queries) nailed down first sets the tone of the system, and allows you to begin refactoring each component at your discretion, without affecting others.

Each method on our WCF service must be either a command or a query. Let’s start with commands.


Each command method on your service must:

  • Take a single command argument — a simple DTO/parameter object that encapsulates all the information required to execute the command.
  • Return void — commands do not have return values. Only fault contracts may be used to throw an exception when a command fails.

Here’s an example:

public class BookTableCommand : ICommand
    public DateTime TimeAndDay { get; set; }

    public int PartySize { get; set; }

    public string PartyName { get; set; }

    public string ContactPhoneNumber { get; set; }

    public string SpecialRequests { get; set; }

Commands carry all the information they need for someone to execute them — e.g. a command for booking a restaurant would tell us who is coming, when the booking is for, contact details, and any special requests (e.g. someone’s birthday). Commands like these are a special case of the Parameter Object pattern.

Now we’ve got our command defined, here’s the corresponding method on the WCF service endpoint:

public interface IBookingService
    void BookTable(BookTableCommand command);

One class per command

Command DTO classes are never re-used outside their single use case. For example, in the situation a customer wishes to change their booking (e.g. change it to a different day, or invite more friends), you would create a whole new ChangeBookingCommand, even though it may have all the same properties as the original BookTableCommand.

Why bother? Why not just create a single, general-purpose booking DTO and use it everywhere? Because:

  1. Commands are more than just the data they carry. The type communicates intent, and its name describes the context under which the command would be sent. This information would be lost with a general-purpose object.
  2. Using the same command DTO for two use cases couples them together. You couldn’t add a parameter for one use case without adding it for the other, for example.

What if I only need one parameter? Do I need a whole command DTO for that?

Say you had a command that only carried a reference to an object — its ID:

public class CancelBookingCommand : ICommand
    public Guid BookingReference { get; set; }

Is it still worth creating an entire command DTO here? Why not just pass the GUID directly?

Actually, it doesn’t matter how many parameters there are:

  • The intent of the command (in this case, cancelling a restaurant booking) is more important than the data it carries.
  • Having a named command object makes this intent explicit. Not possible just with a GUID argument on your operation contract.
  • Adding another parameter to the command (say, for example, an optional reason for cancelling) would require you to change the signature of the service contract. Ading another property to a command object would not.
  • Command objects are much easier to pass around than a bunch of random variables (as we will see in the next post). For example, you can queue commands on a message bus to be processed later, or dispatch them out to a cluster of machines.

Why not just one overloaded Execute() method?

Instead of having one operation contract per command, why don’t you just use a single overloaded method like this?

public interface IBookingService
    void Execute<T>(T command) where T : ICommand;

You can but I wouldn’t recommend it. We’re still doing SOA here — a totally-generic contract like this makes it much harder for things like service discovery and other clients to see the endpoint’s capabilities. Discover more solutions like this one!

Factory Factory pattern: easier than using a container

Here’s a simple example of a class that creates file transfers using different protocols, which can be combined into a big queue later:

public class TransferFactory : ITransferFactory{    public Transfer CreateFtpTransfer(string path)    {        return new Transfer(path, ftpStreamFactory);    }        public Transfer CreateHttpTransfer(string path)    {        return new Transfer(path, httpStreamFactory);    }    public Transfer CreateSmbTransfer(string path)    {        return new Transfer(path, smbStreamFactory);    }    public Transfer CreateSftpTransfer(string path)    {        return new Transfer(path, sftpStreamFactory);    }}

It’s based loosely on something I wrote last week at work, and you can see some pretty classic examples of the Strategy and Abstract Factory patterns here. But where do all the stream factories come from? You could inject them all separately:

public class TransferFactory : ITransferFactory{    public TransferFactory(IStreamFactory ftpStreamFactory,        IStreamFactory httpStreamFactory,        IStreamFactory smbStreamFactory,        IStreamFactory sftpStreamFactory)    {        this.ftpStreamFactory = ftpStreamFactory;        this.httpStreamFactory = httpStreamFactory;        this.smbStreamFactory = smbStreamFactory;        this.sftpStreamFactory = sftpStreamFactory;    }    ...}

But classes like this are horrible to register in an IoC container. Named instances, kilometers of identical positional parameters, and nothing preventing you putting them in the wrong order by accident… messy.

A tidier approach would be to employ the factory factory pattern, where instead of injecting all the factories separately, you just provide one that can create them all:

public class TransferFactory : ITransferFactory{    public TransferFactory(IStreamFactoryFactory streamFactories)    {        this.streamFactories = streamFactories;    }    public Transfer CreateFtpTransfer(string path)    {        return new Transfer(path, streamFactories.CreateFtpStreamFactory());    }        public Transfer CreateHttpTransfer(string path)    {        return new Transfer(path, streamFactories.CreateHttpStreamFactory());    }    ...}

Now some would argue that having factories to create factories is a sure sign of being an architect astronaut, and noone should ever need such a thing. But in examples like this I think they are entirely appropriate, and make your code that much easier to work with.

The Trouble with Soft Delete

Soft delete is a commonly-used pattern amongst database-driven business applications. In my experience, however, it usually ends up causing more harm than good. Here’s a few reasons why it can fail in bigger applications, and some less-painful alternatives to consider.

Tomato, Tomato

I’ve seen a few different implementations of this pattern in action. First is the standard deleted flag to indicate an item should be ignored:

SELECT * FROM Product WHERE IsDeleted = 0

Another style uses meaningful status codes:

SELECT * FROM Task WHERE Status = 'Pending'

You can even give an item a fixed lifetime that starts and ends at a specific time (it might not have started yet):


All of these styles are all flavors of the same concept: instead of pulling dead or infrequently-used items out of active set, you simply mark them and change queries to step over the corpses at runtime.

This is a trade-off: soft delete columns are easy to implement, but incur a cost to query complexity and database performance later down the track.


To prevent mixing active and inactive data in results, all queries must be made aware of the soft delete columns so they can explicitly exclude them. It’s like a tax; a mandatory WHERE clause to ensure you don’t return any deleted rows.

This extra WHERE clause is similar to checking return codes in programming languages that don’t throw exceptions (like C). It’s very simple to do, but if you forget to do it in even one place, bugs can creep in very fast. And it is background noise that detracts away from the real intention of the query.


At first glance you might think evaluating soft delete columns in every query would have a noticeable impact on performance.

However, I’ve found that most RDBMSs are actually pretty good at recognizing soft delete columns (probably because they are so commonly used) and does a good job at optimizing queries that use them. In practice, filtering inactive rows doesn’t cost too much in itself.

Instead, the performance hit comes simply from the volume of data that builds up when you don’t bother clearing old rows. For example, we have a table in a system at work that records an organisations day-to-day tasks: pending, planned, and completed. It has around five million rows in total, but of that, only a very small percentage (2%) are still active and interesting to the application. The rest are all historical; rarely used and kept only to maintain foreign key integrity and for reporting purposes.

Interestingly, the biggest problem we have with this table is not slow read performance but writes. Due to its high use, we index the table heavily to improve query performance. But with the number of rows in the table, it takes so long to update these indexes that the application frequently times out waiting for DML commands to finish.

This table is becoming an increasing concern for us — it represents a major portion of the application, and with around a million new rows being added each year, the performance issues are only going to get worse.

Back to the original problem

The trouble with implementing soft delete via a column is that it simply doesn’t scale well for queries targeting multiple tables — we need a different strategy for larger data models.

Let’s take a step back and examine the reasons why you might want to implement soft deletes in a database. If you think about it, there really are only four categories:

  1. To provide an ‘undelete’ feature.
  2. Auditing.
  3. For soft create.
  4. To keep historical items.

Let’s look at each of these and explore what other options are available.

Soft delete to enable undo

Human error is inevitable, so it’s common practice to give users the ability to bring something back if they delete it by accident. But this functionality can be tricky to implement in a RDBMS, so first you need to ask an important question — do you really need it?

There are two styles I have encountered that are achieved via soft delete:

  1. There is an undelete feature available somewhere in the UI, or
  2. Undelete requires running commands directly against the database.

If there is an undo delete button available somewhere for users, then it is an important use case that needs to be factored into your code.

But if you’re just putting soft delete columns on out of habit, and undelete still requires a developer or DBA to run a command against the database to toggle the flags back, then this is a maintenance scenario, not a use case. Implementing it will take time and add significant complexity to your data model, and add very little benefit for end users, so why bother? It’s a pretty clear YAGNI violation — in the rare case you really do need to restore deleted rows, you can just get them from the previous night’s backup.

Otherwise, if there really is a requirement in your application for users to be able to undo deletes, there is already a well-known pattern specifically designed to take care of all your undo-related scenarios.

The memento pattern

Soft delete only supports undoing deletes, but the memento pattern provides a standard means of handling all undo scenarios your application might require.

It works by taking a snapshot of an item just before a change is made, and putting it aside in a separate store, in case a user wants to restore or rollback later. For example, in a job board application, you might have two tables: one transactional for live jobs, and an undo log that stores snapshots of jobs at previous points in time:

If you want to restore one, you simply deserialize it and insert it back in. This is much cleaner than messing up your transactional tables with ghost items, and lets you handle all undo operations together using the same pattern.

Soft delete for auditing

Another common practice I have seen is using soft delete as a means of auditing: to keep a record of when an item was deleted, and who deleted it. Usually additional columns are added to store this information:

As with undo, you should ask a couple of questions before you implement soft delete for auditing reasons:

  • Is there a requirement to log when an item is deleted?
  • Is there a requirement to log any other significant application events?

In the past I have seen developers (myself included) automatically adding delete auditing columns like this as a convention, without questioning why it’s needed (aka cargo cult programming).

Deleting an object is only one event we might be interested in logging. If a rogue user performs some malicious acts in your application, updates could be just as destructive so we should know about those too.

One possible conclusion from this thought is that you simply log all DML operations on the table. You can do this pretty easily with triggers:

-- Log all DELETE operations on the Product table
-- ...etc

Contextual logging

If something goes wrong in the application and we want to retrace the series of steps that led to it, CREATES, UPDATES and DELETES by themselves don’t really explain much of what the user was trying to achieve. Getting useful audit logs from DML statements alone is like trying to figure out what you did last night from just your credit card bill.

It would be more useful if the logs were expressed in the context of the use case, not just the database commands that resulted from it. For example, if you were tracking down a bug, would you rather read this:

[09:30:24] DELETE ProductCategory 142 13 dingwallr

… or this?

[09:30:24] Product 'iPhone 3GS' (#142) was removed from the catalog by user dingwallr. Categories: 'Smart Phones' (#13), 'Apple' (#15).

Logging at the row level simply cannot provide enough context to give a true picture of what the user is doing. Instead it should be done at a higher level where you know the full use-case (e.g. application services), and the logs should be kept out of the transactional database so they can be managed separately (e.g. rolling files for each month).

Soft create

The soft delete pattern can also be extended for item activation — instead of simply creating an item as part of the active set, you create it in an inactive state and flick a switch or set a date for when it should become active.

For example:

-- Get employees who haven't started work yet
SELECT * FROM Employee WHERE GETDATE() &lt; StartDate

This pattern is most commonly seen in publishing systems like blogs and CMSs, but I’ve also seen it used as an important part of an ERP system for scheduling changes to policy before it comes into effect.

Soft delete to retain historical items

Many database-backed business applications are required to keep track of old items for historical purposes — so users can go back and see what the state of the business was six months ago, for example.

(Alternatively, historical data is kept because the developer can’t figure out how to delete something without breaking foreign key constraints, but this really amounts to the same thing.)

We need to keep this data somewhere, but it’s no longer immediately interesting to the application because either:

  • The item explicitly entered a dormant state – e.g. an expired eBay listing or deactivated Windows account, or
  • The item was implicitly dropped from the active set – e.g. my Google Calendar appointments from last week that I will probably never look at again

I haven’t included deleted items here because there are very few cases I can think of in business applications where data is simply deleted (unless it was entered in error) — usually it is just transformed from one state to another. Udi Dahan explains this well in his article Don’t Delete — Just’ Don’t:

Orders aren’t deleted – they’re cancelled. There may also be fees incurred if the order is canceled too late.Employees aren’t deleted – they’re fired (or possibly retired). A compensation package often needs to be handled.

Jobs aren’t deleted – they’re filled (or their requisition is revoked).

Unless your application is pure CRUD (e.g. data grids), these states most likely represent totally different use cases. For example, in a timesheet application, complete tasks may be used for invoicing purposes, while incomplete tasks comprise your todo list.

Different use cases, different query needs

Each use case has different query requirements, as the information the application is interested in depends on the context. To achieve optimal performance, the database should reflect this — instead of lumping differently-used sets of items together in one huge table with a flag or status code as a discriminator, consider splitting them up into separate tables.

For example, in our job board application, we might store open, expired and filled listings using a table-per-class strategy:

Physically separating job listings by state allows us to optimize them for different use cases — focusing on write performance for active items, and read performance for past ones, with different columns, indexes and levels of (de)normalization for each.

Isn’t this all overkill?

Probably. If you’re already using soft delete and haven’t had any problems then you don’t need to worry — soft delete was a sensible trade-off for your application that hasn’t caused any serious issues so far.

But if you’re anticipating growth, or already encountering scalability problems as dead bodies pile up in your database, you might like to look at alternatives that better satisfy your application’s requirements.

The truth is soft delete is a poor solution for most of the problems it promises to solve. Instead, focus on what you’re actually trying to achieve. Keep everything simple and follow these guidelines:

  • Primary transactional tables should only contain data that is valid and active right now.
  • Do you really need to be able to undo deletes? If so, there are dedicated patterns to handle this.
  • Audit logging at the row level sucks. Do it higher up where you know the full story.
  • If a row doesn’t apply yet, put it in a queue until it does.
  • Physically separate items in different states based on their query usage.

Above all, make sure you’re not gold plating tables with soft delete simply out of habit!

Three common ASP.NET MVC URL routing issues

Following on from my post last week about consistent ASP.NET MVC URLs, here are a few tips that helped when I was first getting started with ASP.NET MVC and its URL routing system.

Have you got your routes registered in the correct order?

ASP.NET MVC checks incoming URL requests against routes in the order they were registered, so you need to add them in the order of most to least specific. Thus, even though you might have a very specific route defined later on that matches the incoming URL perfectly, ASP.NET MVC will choose whichever route matches first. You can’t just drop in extra rules at the bottom of the list and expect them to work.

For example, if you have ten routes, with an {*anything} pattern registered first, the other nine routes will always be ignored, because the anything route will always be matched first.

In a real scenario, one application I am working on uses the default ASP.NET MVC route for most pages, but uses some extra routing rules as well.

  • Ignore any request for the Charts directory, and let ASP.NET handle those URLs as normal.
  • Allow browsing one controller’s resources by name, instead of by numeric ID.
  • Anything else can go to the 404 handler.

Here’s the code I use to implement these rules, in the correct order:

public static void RegisterRoutes(RouteCollection routes)
	// Ignore dynamic image handlers e.g. Charts/Summary.ashx?year=2008&month=08

	// Allow browsing categories by name, instead of by ID.
	routes.MapRoute("Categories", "Categories/{action}/{name}",
		new { controller = "Categories", action = "Index", name = "" }

	// All other pages use the default route.
	routes.MapRoute("Default", "{controller}/{action}/{id}",
		new { controller = "Applications", action = "Index", id = "" }

	// Show a 404 error page for anything else.
	routes.MapRoute("Error", "{*url}",
		new { controller = "Error", action = "404" }

Have you set the default redirection in Default.aspx.cs?

So you’ve changed the default controller, but your application still directs to /Home. If you take a look in Default.aspx.cs, you’ll see the following:

public void Page_Load(object sender, System.EventArgs e)

This code gets called when a user browses to the directory where your application is installed, but doesn’t specify any controller (i.e. an empty URL path). It redirects requests to a default controller, which, in the case for a new ASP.NET MVC Web Application project, is called Home.

If you want to use something other than HomeController, this path needs to be changed as well.

Do your parameter names match?

Here’s another one that might catch you off-guard. The ASP.NET MVC routing system uses Reflection to assign parameter values by argument name when a controller action is called — not by argument index as I originally thought. For example:

routes.MapRoute("Product", "Product/{action}/{id}",
    new { controller = "Product", action = "Detail" });
public class ProductController : Controller
    public ActionResult Detail(int? productID) { .. }

Here, the product ID can’t be passed through automatically because the Detail() method’s parameter is called productID, where in the route it is defined as simply id.

When browsed to, the first URL will work, because you’ve explicitly specified a query string parameter with the right name. The second (desired) URL will not work, because the name of the parameter in the route doesn’t match that of the controller method.

If you want the parameter to be passed through automatically, the name in the controller action must match the name in the route.

C# 3.0′s var keyword: Jeff Atwood gets it all wrong | Richard Dingwall

C# 3.0′s var keyword: Jeff Atwood gets it all wrong

One of the new features of C# 3.0 is the var keyword, which can be used to implicitly declare a variable’s type. For example, instead of declaring the type, you can just write var instead, and the compiler will figure out what type name should be from the value assigned to it:

// A variable explicitly declared as a certain type.string name = "Richard"; // A variable implicitly declared as the type being assigned to it.var name = "Richard";

Note that a var isn’t a variant, so it’s quite different from say, a var in javascript. C# vars are strongly typed, so you can’t re-assign them with values of different types:

var name = "Richard"name = 62; // error, can't assign an int to a string

Vars were introduced to support the use of anonymous types, which don’t have type names. For example:

var me = { Name: "Richard", Age: 22 };

But there’s nothing to stop you from using vars with regular types. In a recent article on, Jeff Atwood recommends using vars “whenever and wherever it makes [your] code more concise”. Guessing from his examples, this means everywhere you possibly can.

The logic behind this is that it improves your code by eliminating the redundancy of having to write type names twice — once for the variable declaration, and again for a constructor.

There’s always a tradeoff between verbosity and conciseness, but I have an awfully hard time defending the unnecessarily verbose way objects were typically declared in C# and Java.

BufferedReader br = new BufferedReader (new FileReader(name));

Who came up with this stuff?

Is there really any doubt what type of the variable br is? Does it help anyone, ever, to require another BufferedReader on the front of that line? This has bothered me for years, but it was an itch I just couldn’t scratch. Until now.

I don’t think he’s right on this one — my initial reaction is that using var everywhere demonstrates laziness by the programmer. And I don’t mean the good sort of laziness which works smarter, not harder, but the bad sort, which can’t be bothered writing proper variable declarations.

Jeff is right though – writing the type name twice does seem a little excessive. However, as Nicholas Paldino notes, the correct way to eliminate this redundancy would be to make the type name on the right hand-side optional, not the left:

While I agree with you that redundancy is not a good thing, the better way to solve this issue would have been to do something like the following:

MyObject m = new();

Or if you are passing parameters:

Person p = new("FirstName", "LastName");

Where in the creation of a new object, the compiler infers the type from the left-hand side, and not the right.

Microsoft’s C# language reference page for var also warns about the consequences of using var everywhere.

Overuse of var can make source code less readable for others. It is recommended to use var only when it is necessary, that is, when the variable will be used to store an anonymous type or a collection of anonymous types.

In the following example, the balance variable could now theoretically contain an Int32, Int64, Single (float), Double, Decimal, or even a ‘Balance’ object instance, depending on how the GetBalance() method works.

var balance = account.GetBalance();

This is rather confusing. Plus, unless you explicitly down cast, vars are always the concrete type being constructed. Let’s go back to Jeff’s BufferedReader example, of which he asks, “is there really any doubt what type of the variable br is?”

BufferedReader br = new BufferedReader (new FileReader(name));

Actually, yes there is, because polymorphism is used quite extensively in .NET’s IO libraries. The fact that br is being implemented with a BufferedReader is most likely irrelevant. All we need is something that satisfies a Reader base class contract. So br might actually look like this:

Reader br = new BufferedReader (new FileReader(name));

Just because a language allows you to do something, doesn’t mean it’s a good idea to do so. Sometimes new features adapt well to solving problems they weren’t designed for, but this is not one of those situations. Stick to using vars for what they were designed for!

Richard Dingwall » Flyweights .NET library

This morning I put the finishing touches to Flyweights, a proof-of-concept .NET memory management library hosted at Google Code. The purpose of the library is simple; if your application stores a number of equal objects that never change, why not just store one object and share a reference to it?

Flyweight<string> a = "Richard";Flyweight<string> b = "Richard";Flyweight<string> c = "Richard";Flyweight<string> d = "Richard";Flyweight<string> e = "Richard";

With Flyweights, only one copy of “Richard” is stored, and each of the objects share a reference to it. Flyweights can be used in a similar manner to Nullable<T>, with a Value property. They are also implicitly castable to and from T.

string sentence = "The quick brown fox jumps over the lazy dog.";Flyweight<string> word = "fox";int n = sentence.IndexOf(word); // implicit cast to string

Documentation, examples and downloads are available from the flyweights project page.