Udi Dahan   Udi Dahan – The Software Simplist
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Archive for the ‘DDD’ Category



Life without distributed transactions

Monday, December 31st, 2012

transactionsOccasionally I get questions about the issue of transactional messaging – why is it so important, why does NServiceBus default to this behavior, and if we didn’t use it, what bad things could happen. I’m talking specifically about the ability to enlist a queue in a distributed transaction here.

I think the reason for this interest is the rise in popularity of cloud platforms and queuing systems like RabbitMQ (which don’t support distributed transactions) and the difficulty of setting up distributed transactions even in on-premise.

Of course, there’s also the regular scalability hand-wringing going on even though most people wouldn’t bump up against those limits anyway.

In this post, I’ll talk about the nature of the problem, explain the pitfalls in some of the common solutions, but I’ll put off the description of how to provide consistency without distributed transactions to a future post as this one is already going to be quite long.

I’ll start with the basic fault-tolerance issues and then explain how things spiral out from there.

Starting with the basics

OK, so we have a queuing system in place that dispatches messages to our business logic which does some transactional work against a database.

Let’s say that we completed the transaction against our database but before we could acknowledge to the queue that the message was processed successfully, our machine crashed. What our machine comes up again, the queue will once again dispatch us the same message. Unless we have some logic to detect that we’ve already processed it (called “idempotence” in the REST community), we will end up processing it again.

In short, the problem is duplicates.

Attempted solutions to the duplicate problem

Most queuing systems don’t do anything about duplicates, actually giving it a proper architectural name: At-least-once message delivery, as opposed to the Once-and-only-once model that a queue that supports distributed transaction provides.

The solution often suggested is to have your logic check to see if it has already processed a message with that ID before – in essence storing the ID of each message processed for some period of time. Of course, there is some performance overhead with that, but it might be a small price to pay compared to dealing with it in the logic of every use case.

On the other hand, you’ll often have some messages (like Update commands) for which it looks like you can safely process them multiple times, in which case you might want not to pay the performance overhead there. The thing is, if your logic publishes an event in addition to the regular database work (something that is quite common) and you process the same message twice, you will probably end up publishing the event twice as well.

These duplicates are different in that here we have two distinct messages with different IDs that contain the same business data. This means that recipients of these messages will not be able to filter them out at an infrastructure level anymore.

NOTE: Deduplication abilities in queues

Although the Azure Service Bus doesn’t support distributed transactions meaning you still have the issue mentioned above, Microsoft added the ability to detect and filter out duplicates based on message contents rather than just the ID. This helps quite a bit but it’s important to understand that that doesn’t cover everything for you. Let me explain:

More complex logic

In some of your most important use cases, you may have both entity updates as well as entity creation happening together in your domain model. You might be using some kind of event model (like I wrote about here) to percolate out the information that an entity was created in order to keep your service layer decoupled from the internals of the domain model.

In the callback code from these domain events, you will likely publish out an event on the queuing system containing information like the ID of the entities created as well as other business data. And there’s the rub.

You see, without distributed transactions, you can run into some problematic scenarios:

For example, if you don’t make sure that your event publishing calls to the queuing system include the same transaction object as the one you used when retrieving the original message from the queue, then those calls could “escape” before you know if the database transaction is going to succeed. Deadlocks always happen at the lousiest times. Anyway, if you’re using database generated IDs for your entities, then those IDs will get published out in events despite the database rolling back and your subscribers will now be making decisions on wrong data – not just eventually consistent data.

In this case, processing the message again doesn’t really solve the problem – it just means that you’ll be publishing events with different IDs, so an infrastructure like Azure Service Bus couldn’t really de-duplicate them.

On the other hand, if you do use the same transaction and combine in the infrastructural message ID based de-duplication described above (as identifying duplicate calls for complex business logic is damn hard), you’ll run into another problem.

Consider what would happen if your server crashes right after finishing its database work but before it completes the transaction against the queuing system. When going to retry the message, the infrastructure filtering thing would know not to call your business logic again and that message would be quietly swallowed. Unfortunately, the event publishing calls to the queuing system from the first time the message was processed were rolled back and since your business logic isn’t called again, the event publishing won’t happen again.

Oops.

In closing

I hope I’ve been able to clarify what kind of scenarios distributed transactions solve for you and some of the difficulties in solving them yourself.

Now, to be clear, you could solve these problems by going in-depth on each of your use cases, analyzing the consistency needs and structuring the code differently to address those needs. But give this another thought, if our consistency is dependent on calling otherwise independent APIs in exactly the right order, and that a change in this order would not cause any visible functional effects, what would happen when developers with less expertise maintain this code?

The folks in the event sourcing community have their solution to this which is based on writing their business logic differently. As the adoption of this pattern is still pretty limited (probably still in the Innovator section of the Technology Adoption Curve), it’ll be interesting to see how it holds up with larger teams in the mainstream.

Oh, and in case it wasn’t clear from before, the guys in the REST community haven’t even begun addressing this problem when it comes to server-to-server integration.

We’re working on a solution for this with NServiceBus that won’t require you to change how you write business logic. We’ve got one big release to do before we can roll this in, and that’s coming soon (with all sorts of cool things like support for ActiveMQ and queues in the database). The solution we’ve found is architecturally sound but you’ll have to wait for my next post to hear about it.

Stay tuned.



UI Composition Techniques for Correct Service Boundires

Saturday, June 23rd, 2012

PrismOne 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.

marriott

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.



Don’t try to model the real world, it doesn’t exist.

Monday, March 5th, 2012

Recently I’ve started talking more about modeling and its relation to the real world.

no spoonHere’s where it all starts from:

Don’t try to model the real world, it doesn’t exist.

I know that that sounds like a very Matrix-y kind of statement, so let me explain.

The “Real” World

The problem with the “real” world is that you are limited by the laws of physics. The thing is that somewhere along the history of software development, we got this idea that if only the structure of our software represented physical reality, then our software would be maintainable, flexible, robust, … in short, good.

glassThe thing is that a single physical entity can have multiple meanings to various stakeholders.

Let’s look at something simple, like a glass:

From a developer’s perspective we might call that a Product and not think very much more of it. We’d be happy that we could come up with a single abstraction that allowed us to model all the different kinds of products the same way.

Yet, in talking with our business stakeholders, one might call it inventory, another might call it a liability (think of breakage requiring insurance), and another call it merchandise. The important thing to note is that the data relevant to each of those meanings is so different from one stakeholder to another.

And that brings me to “customer”

One of my least favorite entities – a lingering symptom of the Northwind disease.

When someone walks into your store for the first time (whether that store is physical or virtual), are they a customer? Even if they haven’t ever bought anything? Even if you don’t know their name? Are they even a User then? I mean, it’s not like we’re going to force people to login (or create an account) just to browse the site, right? A term like Visitor, Prospect, or Lead sounds like it would describe this type of concept better.

After wandering around your store for a while, they come up to you and ask for help finding something. If this pattern repeated itself over and over again for the same category of item, would that be meaningful to the business? Don’t you think that should be modeled? I hope your answers are yes, and yes. This is the domain of merchandising, and seems more related to Visitors than to Customers.

Let’s say your selling to other businesses rather than to consumers. In the B2B space, it is common not to receive payment for goods or services for some time – you might have heard terms like Net30, which means you will be paid up to 30 days later (in some cases, this may be from the end of the month of the invoice rather than the date of the invoice).

If you talk to the business folks in charge of these scenarios, you’ll hear them talk about Accounts Payable and Accounts Receivable. Yep, they are the accountants. If you were to go about building a DDD Ubiquitous Language, it sounds like the term Account would be a better choice than Customer. The thing is that accountants use the same language regardless of how quickly an account is settled – like if payment is done by credit card at the time of purchase.

There is no Customer.
There is no Product.

The same goes for so many other problem domains.

I know it feels counter-intuitive to not have a single class representing a single physical thing. It feels like it’s the exact opposite of Domain-Driven Design. It feels anti-object-oriented. But remember, most stakeholders you talk to don’t focus on the physical elements either.

The one thing left to be modeled from “reality”

And that’s identity.

It would be most accurate to say that the physical thing you perceive is nothing more than identity serving to correlate all the separate business concerns to each other. It’s this ID that ties the Visitor on the site, to the Account, to the Addressee (for shipment).

These IDs are needed primarily for reporting and UI reasons – it isn’t likely to have a business action operate on entities correlated this way in the same transaction.

Nouns, Verbs, and Reality

In building your ubiquitous language, look past the nouns and verbs visible on the surface.

Watch out for statements like “in reality…” and “in the real world…” as they are really just one person’s interpretation of their perception of reality. Not one of us is able to see reality clearly – it’s all just perceptions. Recognize that, like models, all perceptions are wrong, but some may be useful.

Model the perceptions – at least you can have first hand experience of those.

Forget about reality – all that exists is perceptions.

In closing

Transcend the physical.

In software there is no gravity, no mass, and as many dimensions as you choose to create.

Break free of the Matrix.

You are the god of your software.



Common CQRS Abuses

Sunday, February 26th, 2012

Abuse #1

“I’m using CQRS because I need to scale.”

While CQRS may be more scalable than other more traditional architectures, the use of asynchronous communication often complicates the user interaction model causing users to not see the changes they made to data in the UI until later. Trying to compensate for this (by writing even more code) digs one deeper into the complexity hole.

When I point to non-collaborative subdomains and state “You don’t need CQRS for that”, the reason is that in these areas you don’t tend to have much read/write contention. While multiple users/actors may be working in parallel, they don’t touch the same set of data (or do so only very rarely).

In these environments, all you need is a scalable data storage technology – something designed to scale-out (unlike most relational databases). This can take the form of NOSQL databases like HBase and Cassandra. Often all you need is the UI to query that directly and show the results, and the same goes for persisting the data back – possibly with some basic validation and calculation code on the side.

No commands, events, DTOs, publish/subscribe, domain model, etc.

As Ayende says – JFHCI, just f-ing hard code it.

You’d be surprised how much of your data this approach can apply to.

With the time you save on all the less important stuff, you’ll have more time to apply CQRS the right way for the high-value/high-complexity parts of your system.

***

Just a final note, as registration for my course in New York is coming to a close in 2 weeks, I wanted to let you all know that the price for the course will be going up this April, after the course in Sydney. The reason for this is that the courses I run myself (at the current rate) have been cannibalizing attendees from the partner companies I do the course with.

I’ll be providing significant discounts to independent consultants (and others paying their own way) to try to keep things fair. Hope to see you there.

Go to the registration page.



Udi & Greg Reach CQRS Agreement

Friday, February 10th, 2012

Lion--Tiger-psd74183Hard 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.



Recording of joint interview with Eric Evans

Wednesday, November 30th, 2011

Last month both myself and Eric Evans spoke at a conference run by the International Association of Software Architects (IASA) in Madrid. Eric talked about DDD and I talked about CQRS. While the talks were recorded, I don’t think they’ve come online yet.

At the end of the conference, we were interviewed by the local .NET magazine dNM and that video is now available here. We covered the background on things like DDD, CQRS, and the Cloud. I don’t think that either of us said anything earth-shattering but if you have half an hour, take a look:



Why you should be using CQRS almost everywhere…

Sunday, October 2nd, 2011

grass… but differently than the way most people have been using it.

I think I’ve just about drove everybody crazy now with my apparent zigzagging on CQRS.

Some people heard about CQRS first from one of my presentations and got all excited about it. Then I did some blogging which further drove people to CQRS (as did Greg Young and some others). As CQRS was just about to hit its stride with the Early Adopters, I started pushing a more balanced view – CQRS not as an answer, but as one of many questions. More recently I’ve pushed more strongly back against CQRS saying that it should be used rarely.

So what’s the missing piece?

If you’re in the Domain-Driven Design camp (as many doing CQRS are), then it’s Bounded Contexts.

If you’re in the Event-Driven SOA camp (a much smaller camp to be sure), then it’s Services.

The problem is the naming, because the DDD guys have their kinds of services which do not fit the definition for Service of the Event-Driven SOA approach.

Let me propose the term Autonomous Business Component for the purposes of this blog post to describe that thing which is both a DDD Bounded Context (have the shared BC part of the acronym) and an SOA Autonomous Services. Resulting in the nice short form: ABC (and everyone knows you need to have a good acronym if you want something to catch on).

What does this have to do with CQRS?

Nothing just yet. Well, at least, nothing directly to do with CQRS.

Although some proponents of CQRS have stated that it can and should be used as the top-most architectural pattern, both myself and Greg Young (arguably the first two to talk about it and the two who ultimately collaborated on naming it – and now Google knows we didn’t means “cars”) always recommended it as a pattern to be used one level down.

Although Greg and I have had many long discussions on the topic and do agree very much about what the overall structure should look like, I’ll try to avoid putting words in his mouth from this point on.

Before talking more about ABCs, let’s discuss the principle upon which they rest: The Single Responsibility Principle (SRP).

What does SRP have to with CQRS?

Many developers are familiar with SRP and have seen good results from using it. What we’re going to do is take this principle to the next level.

In Object Orientation (OO), data is encapsulated in an object. A good object does not expose its data to other objects to do with as they wish. Rather, it exposes methods that other objects can invoke, and those methods operate on the internal data.

SRP would guide us to not have the same data exist in two objects. For example, if we saw the customer’s first name as an internal data member of two objects, we’d be right to question that kind of duplication and move to refactor it away. However, when we see two systems doing the exact same thing – somehow that gets excused.

“Of course we need to be able to see the customer’s first name in the front-end website as well as in the back-end fulfillment system. How could we NOT have the customer’s first name in both those code-bases?”

And there’s the catch.

Who said that a system should be a single code-base?

But what about integration?

Although many times we do need to integrate existing systems together, sometimes we have the ability to change those systems. More importantly, when going to create a new solution, we can avoid getting ourselves into the problems that integration tries to solve.

Integrating with a system that cannot be changed can be done also by composing multiple ABCs, but that’s a topic for another post.

It is better to think of integration as a necessary evil – kind of like regular expressions and multi-threading; things to be avoided unless absolutely necessary.

“If you have a problem that you decide to use a regular expression to solve, you now have 2 problems.” Or so the saying goes. With multi-threading, you have a non-deterministic number of problems to solve.

If you thought you had duplicate responsibilities with 2 systems operating on the same data, how will introducing a 3rd code base (also known as “integration”) help? Remember that Single Responsibility Principle – our goal is to get it down to one.

OK, so how do ABCs do that?

In order for us to get back into alignment with SRP, that would require us to have responsibility for a single piece of data exist in one code base. Note that SRP makes no statements about how many physical places a given code base can be deployed to. Nor does it state that only a single technology can be in play – code that emits HTML can be packaged at design time together with rich-client code in the same solution.

If an ABC is responsible for a piece of data, it is responsible for it everywhere, and forever. No other ABC should see that data. That data should not travel between ABCs via remote procedure call (RPC) or via publish/subscribe. It is the ultimate level of encapsulation – SRP applied at the highest level of granularity.

This results in systems which are the result of deploying the components of multiple ABCs to the same physical place. The ABC which owns the customer name would have the necessary web code to render it in the e-commerce front-end and in the shipping back-end for printing on labels. This would mean that practically every screen in any UI is a composite of widgets owned by their respective ABCs.

This is ultimately what keeps the complexity of each ABC’s code base to a minimum.

But why not just use CQRS as the top-level pattern? ABCs are weird.

Imagine trying to create a single denormalized view model for the entire Amazon.com product page – product name, price, inventory, editorial review, customer comments, other products that customers viewed, other products that customers bought, etc.

Pretty complex, right?

How much duplication would you have for the page shown after you add an item to a cart? Once again, you need to show other products that customers bought, their names, images, prices, and inventory.

And then on the home page – items you might be interested in, names, images, prices.

And that’s only in the front-end system.

It’s not just the duplication, but how complex the code is for each one.

Instead of the duplication that top-level CQRS would bring you, consider an ABC responsible for products names and images that has just about the same view model composed on each of the above screens. The same with another ABC responsible for price.

You may be thinking that this would result in more queries to get the data to show on a page, and you’d be right. But it isn’t necessarily a classical N+1 Select problem, as the queries are bounded to the number of ABCs. Secondly, consider the ability to have well-tuned caching at the granularity of an ABC – something that would be much more difficult when dealing with everything as a single monolithic view model. In short, not only will it not be a performance problem, often it will actually improve performance.

OK – that explains “everywhere”, what about “forever”?

Forever is where things get interesting – or more accurately, when they get interesting.

Let’s talk about things like invoices.

One of the requirements in this area is that immutability. If the customer’s name was Jane Smith when they made their purchase, it doesn’t matter that they’ve since changed their name to Jane Jones, the invoice should still show Jane Smith.

Often developers push these types of requirements on the data warehouse guys – that’s where history gets handled. The only thing is that if your ABC owns the customer’s name, then no other code base can deal with it. If it’s your data, you have to handle all historical representations of it.

On the one hand, this would seem to kill the data warehouse. On the other hand, it means that the principles of data warehouses are now core to every code-base.

This means you don’t ever delete data (see my previous blog post on the subject), and you definitely don’t overwrite it with an update – even if you think you’re in a simple CRUD domain. The only case where you can get away with traditional CRUD is if we’re talking about private data – data that is only ever acted on by a single actor.

This sounds like the collaboration you talk about with CQRS

It’s similar in principle but different in practice.

In a collaborative domain, an inherent property of the domain is that multiple actors operate in parallel on the same set of data. A reservation system for concerts would be a good example of a collaborative domain – everyone wants the “good seats” (although it might be better call that competitive rather than collaborative, it is effectively the same principle).

A customer’s name would not fall under that category. It isn’t an inherent property of the domain for multiple actors to operate on that data. While there can be multiple readers, one can easily enforce a single writer without any adverse effects. Doing that with a reservation system would cause the online system to behave as if users were lining up in front of a box office – not a desirable outcome.

Private data would be something like a user’s shopping cart. Until they make a purchase, that data doesn’t need to be visible anywhere. Here you could theoretically do simple CRUD – that is, until the business realizes that there’s extremely valuable information to be extracted from the historical record of things people do with their carts.

I think you’re ready to make your point, so just make it already

OK – so we now realize that Update and Delete don’t exist in their traditional form. Delete is really just a kind of update, and update is effectively an “upsert” – a combination of update and insert to retain history. This can be done by having ValidFrom and ValidTo columns for our data.

In which case, Create is really just a special case of Upsert, which looks like this:

UPDATE Something SET ValidTo = NOW() WHERE Id=@Id AND ValidTo = NULL; INSERT INTO Something SET { regular values }, Id=@Id, ValidTo = NULL;

And then we’d have 2 forms of Read – reading the current state (ValidTo = NULL), and reading history (ValidFrom <= Instant AND (ValidTo >= Instant OR ValidTo = NULL))

Here we don’t need fancy N-Tier architectures, data transfer objects, service layers, or domain models. A simple 2-Tier approach could probably suffice. We don’t need a task-based UI, events, denormalized view models, or any of that CQRS stuff. This was at the crux of my previous anti-CQRS post.

The only thing is that this is exactly CQRS.

Say what?

Have we not effectively separated the responsibility of commands/upserts and queries/reads?

As Greg Young has said before, “the creation of 2 objects where there previously was one”.

Effectively 2 paths through our ABC.

CQRS.

Let me give you a second to gather your thoughts.

*

You see, CQRS is an approach, a mind-set – not a cookie cutter solution. Frameworks that guide you to applying CQRS exactly the same way everywhere are taking you in the wrong direction. The fact is that you couldn’t possibly know what your Aggregate Roots were before you figured out how to break your system down into ABCs. Attempting to create commands and events for everything will make you overcomplicate your solution.

So the built-in history of this model is event-sourcing?

Well, it’s not event-sourcing in the sense that we don’t necessarily have events. It achieves many of the benefits of event-sourcing by giving us the full history of what happened.

On the whole issue of replaying events to fix bugs – that’s a bit problematic, logically, unless we have a closed system. A closed system is one that doesn’t interact with anything else – no other systems, no users, nothing. As such, closed systems aren’t that common.

In an open system, one with users, let’s say there was a bug. This bug could have caused the wrong data to be written and/or shown to users. As such, users could have submitted subsequent commands based on that erroneous data that they would not have submitted otherwise. There’s no way for us to know.

The problem with replaying events when we fix the bug is that we’re in essence rewriting history – making it as if the user didn’t see the wrong data. The only problem is that we can’t know which events not to replay – we can’t automatically come up with the right events that should have come afterwards. We could try to sit together with our users and have them try to revise history manually, but our organization often isn’t in a bubble. Our users interacted with customers and suppliers. It isn’t feasible to try to undo the real-world impacts of this situation.

Why didn’t you just tell us this from the very beginning?

I did, you just weren’t listening.

You wanted a cookie cutter, and until you tried CQRS out as cookie cutter (and saw it create a bunch of complexity) you wouldn’t listen to anything else.

As developers, we’re trained to solve problems – the faster the better. Unfortunately, this causes us to be blind to things that don’t immediately present themselves as solutions.

When applying CQRS with ABCs, the solutions you end up with are very simple, but the process of getting there is quite hard and takes practice. Finding the boundaries of ABCs such that data isn’t duplicated between them and that data doesn’t travel between them either via RPC or publish/subscribe – it may feel impossible the first several times you try. Keep at it – it is almost always possible.

We haven’t touched on the whole saga/aggregate-root thing yet, but that isn’t as important until you can successfully apply the principles described here.

Also, this post has already gotten long enough, so it looks like now would be a good time to stop.

Until next time…



Inconsistent data, poor performance, or SOA – pick one

Sunday, September 18th, 2011

One of the things that surprises some developers that I talk to is that you don’t always get consistency even with end-to-end synchronous communication and a single database. This goes beyond things like isolation levels that some developers are aware of and is particularly significant in multi-user collaborative domains.

The problem

Let’s start with an image to describe the scenario:

Inconsistency

Image 1. 3 transactions working in parallel on 3 entities

The main issue we have here is that the values transaction 2 gets for A and B are those from T0 – before either transaction 1 or 3 completed. The reason this is an issue is that these old values (usually together with some message data) are used to calculate what the new state of C should be.

Traditional optimistic concurrency techniques won’t detect any problem if we don’t touch A or B in transaction 2.

In short, systems today are causing inconsistency.

Some solutions

1. Don’t have transactions which operate on multiple entities (which probably isn’t possible for some of your most important business logic).

2. Turn on multi-version concurrency control – this is called snapshot isolation in MS Sql Server.

Yes, you need to turn it on. It’s off by default.

The good news is that this will stop the writing of inconsistent data to your database.
The bad news is that it will probably cause your system many more exceptions when going to persist.

For those of you who are using transaction messaging with automatic retrying, this will end up as “just” a performance problem (unless you follow the recommendations below). For those of you who are using regular web/wcf services (over tcp/http), you’re “cross cutting” exception management will likely end up discarding all the data submitted in those requests (but since that’s what you’re doing when you run into deadlocks this shouldn’t be news to you).

The solution to the performance issues

Eventual consistency.

Funny isn’t it – all those people who were afraid of eventual consistency got inconsistency instead.

Also, it’s not enough to just have eventual consistency (like between the command and query sides of CQRS). You need to drastically decrease the size of your entities. And the best way of doing that is to partition those entities across multiple business services (also known in DDD lingo as Bounded Contexts) each with its own database.

This is yet another reason why I say that CQRS shouldn’t be the top level architectural breakdown. Very useful within a given business service, yes – though sometimes as small as just some sagas.

Next steps

It may seem unusual that the title of this post implies that SOA is the solution, yet the content clearly states that traditional HTTP-based web services are a problem. Even REST wouldn’t change matters as it doesn’t influence how transactions are managed against a database.

The SOA solution I’m talking about here is the one I’ve spent the last several years blogging about. It’s a different style of SOA which has services stretch up to contain parts of the UI as well as down to contain parts of the database, resulting in a composite UI and multiple databases. This is a drastically different approach than much of the literature on the topic – especially Thomas Erl’s books.

Unfortunately there isn’t a book out there with all of this in it (that I’ve found), and I’m afraid that with my schedule (and family) writing a book is pretty much out of the question. Let’s face it – I’m barely finding time to blog.

The one thing I’m trying to do more of is provide training on these topics. I’ve just finished a course in London, doing another this week in Aarhus Denmark, and another next month in San Francisco (which is now sold out). The next openings this year will be in Stockholm, London; Sydney Australia and Austin Texas will be coming in January of next year. I’ll be coming over to the US more next year so if you missed San Francisco, keep an eye out.

I wish there was more I could do, but I’m only one guy.

Hmm, maybe it’s time to change that.



When to avoid CQRS

Friday, April 22nd, 2011

which way?It looks like that CQRS has finally “made it” as a full blown “best practice”.

Please accept my apologies for my part in the overly-complex software being created because of it.

I’ve tried to do what I could to provide a balanced view on the topic with posts like Clarified CQRS and Race Conditions Don’t Exist.

It looks like that wasn’t enough, so I’ll go right out and say it:

Most people using CQRS (and Event Sourcing too) shouldn’t have done so.

Should we really go back to N-Tier?

When not using CQRS (which is the majority of the time), you don’t need N-Tier either.

You see, if you’re not in a collaborative domain then you don’t have multiple writers to the same logical set of data as an inherent property of your domain. As such, having a single database where all data lives isn’t really necessary.

Data is inherently partitioned by who owns it.

Let’s take the online shopping cart as an example. There aren’t any use cases where users operate on each others’ carts – ergo, not collaborative, therefore not a good candidate for CQRS. Same goes for user profiles, and tons of other cases.

So why is it that we need a separate tier to run our business logic?

Originally, the application server tier was introduced for improved scalability, but specifically around managing the connection pool to the database. Increasing numbers of clients (when each had its own user/account for connecting to the database) caused problems. Luckily, most web applications side-step this problem – that is, until someone got the idea that the web server was only supposed to run the UI layer, and the Business Logic layer would be on a separate application server tier.

Rubbish – see Fowler’s First Law of Distribution: Don’t.

Keep it all on one tier. Same goes for smart clients.
No, Silverlight, you don’t count – architecturally speaking, you’re a glorified browser.

But what about scalability?

In a non-collaborative domain, where you can horizontally add more database servers to support more users/requests/data at the same time you’re adding web servers – there is no real scalability problem (caveat, until you’re Amazon/Google/Facebook scale).

Database servers can be cheap – if using MySQL/SQL Express/others.

But what about the built-in event-log CQRS/ES gives us?

Architectural gold-plating / stealing from the business.

Who put you in a position to decide that development time and resources should be diverted from short-term business-value-adding features to support a non-functional requirement that the business didn’t ask for?

If you sat down with them, explaining the long-term value of having an archive of all actions in the system, and they said OK, build this into the system from the beginning, that would be fine. Most people who ask me about CQRS and/or Event Sourcing skip this step.

Finally, you can usually implement this specific requirement with some simple interception and logging. Don’t over-engineer the solution. If using messaging, you can get this by turning on journaling, or if you want to centralize this archive, NServiceBus can forward all messages to a specific queue.

Don’t forget that this storage has a cost – including administration. Nothing is free.

What about the “proof of correctness” in Event Sourcing

I’ve heard statements made that when you use the events that flowed into/through your system AS your system’s data, rather than transforming those events to some other schema (relational or otherwise) and storing the result – you can prove that your system behaves correctly.

Let me put it this way:

No programming technique used by humans will prevent those same humans from creating bugs.
No testing technique used by humans will prevent those same humans from not catching those bugs.
* Automated tests – see programming technique.

While having a full archive of all events can allow us to roll the system back to some state, fix a bug, and roll forwards, that assumes that we’re in a closed system. We have users which are outside the system. If a user made a decision based on data influenced by the bug, there’s no automated way for us to know that, or correct for it as we roll forwards.

In short, we’re interested in the business’ behavior – as composed of user and system behavior. No proof can exist.

Umm, so where should we use it

If you’ve uncovered a scenario where you’re wondering “first-one-wins, or last-one-wins”, that’s often a good candidate for a place where CQRS could make sense. Then re-read my Race Conditions Don’t Exist post.

Also, CQRS should not be your top-level architectural pattern – that would be SOA.
CQRS, if used at all, would be used inside a service boundary only.

Given that SOA guides us away from having a given 3rd normal form entity exist in any one service, it is unlikely that the building blocks of your CQRS design will be those kinds of entities. Most 3rd normal form one-to-many and many-to-many relationships simply do not exist when doing SOA and CQRS properly.

Therefore, I’m sorry to say that most sample application you’ll see online that show CQRS are architecturally wrong. I’d also be extremely wary of frameworks that guide you towards an entity-style aggregate root CQRS model.

In Summary

So, when should you avoid CQRS?

The answer is most of the time.

Here’s the strongest indication I can give you to know that you’re doing CQRS correctly: Your aggregate roots are sagas.

And the biggest caveat – the above are generalizations, and can’t necessarily be true for every specific scenario. If you’re Greg Young, then you probably can (and will) decide on your own on these matters. For everybody else, please take these warnings to heart. There have been far too many clients that have come to me all mixed up with their use CQRS in areas where it wasn’t warranted.

If you want to know everything you need to know to apply CQRS appropriately, please come to my course – there is so much unlearning to do first that just can’t happen via a series of blog posts.



Entities, Transactions, and Broken Boundaries

Saturday, March 5th, 2011

One of the things I cover early on in my course is the problem with traditional layered architecture driving people to create a business logic layer made up of a bunch of inter-related entities. I see this happening a lot, even though nowadays people are calling that bunch of inter-related entities a “domain model”.

Let me just say this upfront – most inter-related entity models are NOT a domain model.
Here’s why: most transactions don’t respect entity boundaries.

That being said, you don’t always need a domain model.
The domain model pattern’s context is “if you have complicated and everchanging business rules” – right there on page 119 of Patterns of Enterprise Application Architecture.

Persisting the customer’s first name, last name, and middle initial – and later reading and showing that data does not sound either complicated or that it is really going to change that much.

Then there are things like credit limits, that may be on the customer entity as well. It is likely that there are business requirements that expect that value to be consistent with the total value of unpaid orders – data that comes from other entities.

The problem that is created is one of throughput.

Since databases lock an entire row/entity at a time, if one transaction is changing the customer’s first name, the database would block another transaction that tried to change the same customer’s credit limit.

The bigger your entities, the more transactions will likely need to operate on them in parallel, the slower your system will get as the number of transactions increases. This feeds back in on itself as often those blocked transactions will have operated already on some other entity, leaving those locked for longer periods of times, blocking even more transactions.

And the absurd thing is that the business never demanded that the customer’s first name be consistent with the credit limit.

What if we didn’t have a single Customer entity?

What if we had one that contained first name, last name, middle initial and another that contained things like credit limit, status, and risk rating. These entities would be correlated by the same ID, but could be stored in separate tables in the database. That would do away with much of the cascading locking effects drastically improving our throughput as load increases.

And you know what? That division would still respect the 3rd normal form.

Which of these entities do you think would be classified by the business under the “complicated and everchanging rules” category?

And for those entities that are just about data persistence – do you think it’s justified to use 3 tiers? Do we really need a view model which we transform to data transfer objects which we transform to domain objects which we transform to relational tables and then all the way back? Wouldn’t some simpler 2-tier programming suffice – dare I say datasets? Ruby on Rails?

Are we ready to leave behind the assumption that all elements of a given layer must be built the same way?



   


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“When I attended Udi's SOA Workshop, then it suddenly changed my view of what Service Oriented Architectures were all about. Udi explained complex concepts very clearly and created a very productive discussion environment where all the attendees could learn a lot. I strongly recommend hiring Udi.”

Daniel Jin Daniel Jin, Senior Lead Developer at PJM Interconnection
“Udi is one of the top SOA guru in the .NET space. He is always eager to help others by sharing his knowledge and experiences. His blog articles often offer deep insights and is a invaluable resource. I highly recommend him.”

Pasi Taive Pasi Taive, Chief Architect at Tieto
“I attended both of Udi's "UI Composition Key to SOA Success" and "DDD in Enterprise Apps" sessions and they were exceptionally good. I will definitely participate in his sessions again. Udi is a great presenter and has the ability to explain complex issues in a manner that everyone understands.”

Eran Sagi, Software Architect at HP
“So far, I heard about Service Oriented architecture all over. Everyone mentions it – the big buzz word. But, when I actually asked someone for what does it really mean, no one managed to give me a complete satisfied answer. Finally in his excellent course “Advanced Distributed Systems”, I got the answers I was looking for. Udi went over the different motivations (principles) of Services Oriented, explained them well one by one, and showed how each one could be technically addressed using NService bus. In his course, Udi also explain the way of thinking when coming to design a Service Oriented system. What are the questions you need to ask yourself in order to shape your system, place the logic in the right places for best Service Oriented system.

I would recommend this course for any architect or developer who deals with distributed system, but not only. In my work we do not have a real distributed system, but one PC which host both the UI application and the different services inside, all communicating via WCF. I found that many of the architecture principles and motivations of SOA apply for our system as well. Enough that you have SW partitioned into components and most of the principles becomes relevant to you as well. Bottom line – an excellent course recommended to any SW Architect, or any developer dealing with distributed system.”

Consult with Udi

Guest Authored Books
Chapter: Introduction to SOA    Article: The Enterprise Service Bus and Your SOA

97 Things Every Software Architect Should Know



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