Tag Archives: software architecture

Three Historical Definitions of the Open/Closed Principle and a Claim that it’s Pointless

Bertrand Meyer first published the OCP in his influential 1980’s book Object Oriented Software Construction:

  • “A module is said to be open if it is still available for extension. For example, adding new fields, or performing new functions.
  • A module will be said to be closed if it is available for use by other modules.”

It’s a neat double-definition, not least because the definition of Closed is both useful, and one that might not contradict the definition of Open. But Meyer’s proposed technique—use subclassing to achieve Openness of a Closed module—is widely ignored. Many of us have discovered the pain of working with inheritance hierarchies, so we savour the Gang of Four’s sage dictum: “prefer composition over inheritance.”

Dynamic languages like javascript can do the open/closed trick quite easily. The danger is that, in doing so, you develop inscrutable code and and are left with system that, when it works, you don’t know how it works; and so when it breaks you don’t know how to fix it.

Uncle Bob all-but-redefined the Open/Closed principle by using Interfaces as his technique. The interface is fixed and Closed; modules that depend on it can rely on it not changing. But the Implementation is Open: it can be changed without breaking the interface.

It is worth mentioning a word of wisdom from the .Net team’s Framework Design Guidelines: the weakness of interfaces in Java and .Net is precisely that they are 100% closed. There can be no version 2. Or rather, if there is a InterfaceV2 then it can usually have no useful relationship to InterfaceV1. You might as well call it ICompletelyUnrelatedInterface. (Or perhaps one could put the versioning at the namespace level).
This versioning problem is widely felt in service oriented systems with public interfaces. It is often addressed by creating a new endpoint for a new version of the service. Offering two versions of a service becomes on the whole precisely as expensive as offering two services, which is to say twice as expensive. This is unfortunate.

Contrast this with Meyer’s vision of OCP: On Meyer’s subclassing approach, version 1 clients and version 2 clients would call the same service and get the same responses. Version 2 clients would recognise, and so be able to use, the enhanced v2 capabilities; whereas version 1 clients would only recognise the version 1 capabilities. But here I see a second problem with Meyer’s vision: I’ve almost never seen systems (or even parts of systems) that can achieve it in practise. It’s a beautiful dream. But unachievable. It is a pipedream.

More recently (Dec 2016), Michael Feathers has offered an updated version, towards the bottom of the the page at Towards a galvanizing definition of technical debt:
“our code is better to the degree that we don’t have to change it much when we add features. We should be able to make modifications primarily by adding new classes and functions rather than changing existing ones”
This is much ‘softer’ formulation than Meyer’s or Bob Martin’s and you could take it as just a rule of thumb; something to weigh in the balance against other factors. Feathers implementation in this case (and I’m left with the impression that in a different codebase he’d be happy with a different implementation) is doing event driven code as most people think it should be done: use an AddEventListener() interface, which makes the code Open to all kinds of extension.
This AddEventListener() is exactly the approach used in the HTML spec and other GUI frameworks of the past 20 years. The downside is that the ‘closed’ bit of the interface is so small and weakly-typed that it’s almost non-existent. The interface tells you nothing about the semantics. (What kind of events can I listen to? What information do I get about each event? What can I do with them? I can only find out by reading the HTML spec, which turns out to be quite hard going, or turning to MDN, or, the first port of call for many, StackOverflow. In a bespoke codebase replace this with “ask for documentation; find it is incomplete; and then hunt through the code for examples of how I can use it”).
Strongly typed interfaces are at least somewhat self-documenting—they offer a definitive list of all syntactically valid calls to the service—even if that documentation depends heavily on how well the developers chose their method and parameter names.

These three examples leave me with mixed feelings. OCP seems like trying to square the circle, and Meyer’s choice of name was a well-chosen contradiction. Yet the goals—Openness for extension, Closedness for reliability—are unavoidable.

Dan North, amongst others, has suggest that OCP, and indeed all the SOLID principles, are of limited value and we should drop them in favour of something else. I sympathise—I think that SOLID is a mishmash of mixed value—but I’m willing to wrestle for a couple more years with OCP before I admit defeat.

I’d rather have the above three technique, and others, in my toolkit because my software design still has to address the two contradictory requirements that Meyer identified in the 80s:
–Because my software is still evolving, it has to be open for evolution: it has to change.
–Because my software is already is use, and hence being depended on by some other software or person, it has to be reliable and therefore can’t change.

The Known Unknowns Matrix

I.T. is not the only industry to have happily latched onto the the former Secretary of State’s famous phrase, “the unknown unknowns”. It’s a useful phrase to ponder if you’re responsible for planning or estimating anything because planning & estimating always involve risk. A recent slideshare by Danni Mannes on Agile Architecture pointed out to me that one should really consider the full matrix:

Known Not Known
Knowns Things we know, and we know we know them Things we know but don’t realise we know them; tacit knowledge that we take for granted. Become a problem if we are responsible, and fail, to communicate them to people who don’t know. Also a problem when we start work in a new context and don’t realise that what we ‘know’ is no longer valid, so they become unknown unknowns.
Unknowns Things we know that we don’t know. We can record the risk, and estimate a cost for investigation & discovery Things we don’t know that we don’t know. This is the quadrant most likely to shipwreck plans.

My personal takeaway from this is that I will try using this quadrant when listing risks. Just having a space for the possibility of unknown knowns & unknowns can be an impetus to do a little risk-storming & consultation, to help you discover the as-yet-unknowns.

P.S.

I’ve just read the brief and brilliant mcfunley.com/choose-boring-technology

Coplien & Bjørnvig : Lean Architecture For Agile Developers. A Review

Four years after this book came out, Agile Architecture has at last become a Thing. But as the nuance of its title hints, this book is not fad-driven. It is a carefully-thought out exposition of what architects can learn from lean and agile ideas, and what they can do better as a result.

Well. It’s partly that. If you are a practising architect, it is actually four must-read books in one.

If you are not, you might dismiss this book for two reasons. The first, that judging by other reviews & my own experience, the homespun style of the first half does not suit a bullet-pointed gimme-the-headlines-now generation. The second is that if you have not experienced the pitfalls of architecting and doing software in a real organisation with actual people in it, then Coplien & Bjørnvig’s pearls of wisdom may impress you much as the agile manifesto might impress a cattle rancher.

It is not a beginner’s book. It is the mature distillation of the sweat-soaked notebooks of a fellow-traveller who has stumbled over rocky terrain, been through the tarpits and has some hard-bloody-earned (and, academically researched) wisdom to share as a result.

I said four must-reads in one. They are:

1) The (literally) decades of experience of a leading practitioner & thinker in the field.

2) A thought-through answer to the questions, what can we learn from lean & agile. Whilst value-chains and some technique feature, the authors’ secret conviction is surely that Technology Is All About Human Beings. “Everybody, all together, from early on” is their Lean Secret. “Deferring interaction with stakeholders [users, the business, customers, domain experts, developers], or deferring decisions beyond the responsible moment slows progress, raises cost and increases frustration. A team acts like a team from the start.”

3) Which leads to that which for me, as a more techy-focused reader, was the marvel of the book. Clements et al in “Software Architecture in Practise” offered attribute-driven design, a partitioning of the system based on a priority ordering of, primarily, technical quality requirements. Coplien & Bjørnvig all but deduce a partitioning based on the priority ordering of people: Users first, Development team next.

“The end-users’ mental model” is the refrain on which they start early and never stop hammering. From this they suggest that the first partitioning is What the System Is from What the System Does. I am tempted to paraphrase as, Domain Model from Use Cases. But their point is partly that this also neatly matches the primary partition by rate of change, because the last thing to change in a business is, the business of the business. If you’re in retail, you might change what and how and where you sell, but your business is still Selling Stuff. And at lower levels inside the organisation too: an accounting department, though there has been five thousand years of technology change, still does accounting. The users know this. They understand their domain and if the fundamental form of your software system matches the end user mental model then it can survive–nay, enable!–change and stay fundamentally fit for purpose.

Second, don’t fight Conway’s law. “Organizations which design systems … are constrained to produce designs which are copies of the communication structures of these organizations”. The deduction for agile teams is, partition so as to maximise the long-term autonomy of your self-organising teams. Even when the team divisions are imposed for non-technical reasons–geography, politics, whatever–still allow that fact to trump more ‘technical’ considerations. This may not match your vision of technical perfection, but it will still be the best way. Ruth Malan recently paraphrased Conway’s law as, “if the architecture of the system and the architecture of the organization are at odds, the architecture of the organization wins”. Don’t be a loser.

These points seem to me obvious in hindsight, yet they turn traditional approaches to high-level designs on their head. After considering People, yes we can considering rates of change, quality attributes, technology areas. But it’s always People First.

4) And finally. This is the first book-sized exposition of DCI architecture, which I would describe as an architectural pattern for systems that have users. Having separated What the System Is from What the System Does, DCI provides the design pattern for how What the System Does (the Use Cases) marshals the elements of the What the System Is. There are at least three notable outcomes.

Firstly that use cases are mapped closely to specific pieces of code; in the best case each use case can be encapsulated in its own component.

Secondly, that the relationship between Domain elements and Use Cases are expressed as “In Use Case X Domain element Y plays the Role of Z”. This brings significant clarity to both, and is part of the key to ‘componentising’ use cases; the Roles needed for a use case become, in the code, its public dependencies. In UML-speak, the Required Interfaces for such a UseCaseComponent are the roles needed to ‘play’ the use case, and those roles are Interfaces which are Implemented by Classes in the Domain Model. Coupling is reduced, cohesion is gained, clarity abounds.

Thirdly, the simpleness of the mapping from business architecture to code is greatly increased. Suddenly one can draw simple straight lines between corresponding elements of architecture and code.

The authors say of their work, “This book is about a Lean approach to domain architecture that lays a foundation for agile software change”. To my mind, this hits the agile architecture nail on the head. Agile software development always only ever succeeded at scale because the people doing it either knew, or had given to them, enough architecture to make it work. Software Agility, just like every other software Ility, must either be supported by the architecture or it ain’t gonna happen.

But the best thing I got from this book was the proof, before my very eyes, that correct technical design flows from knowing how to put the human beings central.

Where to Buy

UK



ebay (UK): Lean Architecture for Agile Software Development


Amazon (UK): Lean Architecture for Agile Software Development

USA



Amazon (USA): Lean Architecture for Agile Software Development

Introducing: The Semantic Field. Or, The One Truly Correct Usage of Layered Architecture in the World

Bear with me if you already abandoned layered architecture long ago. You may be quite familiar with the thought that layered architectures often fail to apply the Dependency Inversion principle, and often thereby induce tight coupling of un-modular, un-testable layers.

I wish to do two things in this post. First, I propose that the notion “Semantic Field” better captures the one big idea that layered architecture nearly gets right. Second, I will discuss the One Truly Correct Usage of Layered Architecture In the World in order to show why it’s the wrong choice for nearly all other usages.

Semantic Field

“Semantic Field” or “Semantic Domain” is a term from linguistics. Words are in the same semantic field if they are related to the same area of reality. (The word domain is pretty much what you’d call it as a DDDer). Orange is in the same semantic domain (let’s call it the fruit domain) as Apple. But it’s also with Red in the semantic domain of Colour, whereas Apple isn’t. That’s how natural language rolls.

Kent Beck used the term conceptual symmetry to explain why he didn’t like this code snippet:

void process(){
input();
count++;
output()
}

and wants to change count++; into tally();. Somehow the count++ doesn’t seem to be on the same level as the method calls. Indeed it isn’t. It’s the same feeling you have when you see:

void applyToJoin(Customer customer){
    if(eligibilityRules.validate(customer)){
        membershipList.accept(customer);
        htmlBtnUpdate.setEnabled();
    }
}

that a method dealing in business rules and processes should not also know about html buttons. Semantic Field is the notion we want here. The clean code rule is “One Level of Abstraction per Function” and I propose to rename it as “One semantic field per method”. In fact, one semantic domain per class, namespace, module, or … layer.

This is what layers gets right: The idea that inside a given layer you understand a specific semantic domain, and don’t use vocabulary from the semantic fields of the layers above or below you.

Where layers goes wrong is, well, the layering. The belief that all top-level dependencies in a system can be expressed in one dimension, top to bottom. They just can’t. Squeezing your code into 1 dimension makes you do contortions that are utterly unhelpful. Strict layering adds to this a second failure mode: It makes you write pointless passthrough code, which ought to be deleted.

(Layering does get a second thing right: no cyclic dependencies. Code with mutual dependencies will try to morph into ball of mud architecture. I’m sure this is half the reason why layered architecture become wildly popular. It was a vast improvement on ball-of-mud).

The One Truly Correct Usage of Layered Architecture In The World

The other reason we were entranced by layered architecture for a decade was the ISO OSI 7 layer model for networking. It seemed so obviously, thoroughly, beautifully, correct.
OSI 7 layer architecture

Each layer is clearly (well, it was clear up to about layer 5, after that it got a bit hazy for some of us) and cleanly independent of the other layers. Each layer is a different semantic domain. The bottom layer deals with physical connectors and with what voltage represents a 1 or a 0 and how a byte sequence is encoded as an electrical waveform. The next layer deals with packets, complete with a destination and a source. The next layer deals  in routes: how to get to this destination from the source. The next layer deals in messages: how to turn them into packages and back again. And so on.
And, the layer-cake picture precisely models the dependencies between the layers. At least to layer 5, each layer relies on and adds value to the layer beneath it.

It was beautiful. It made sense. It was what I wanted my software to look like. It was a siren, luring us all to shipwreck our software on the rock of a beautiful but evil vision of how it should always be.

Why a Layered Architecture is Nearly Always Wrong For Any Other Software System

The bit that isn’t wrong

The part of the OSI model that is applicable to 99% of all known software is the separation into semantic fields. This is why we used to say that business logic shouldn’t be in the UI layer; html buttons live in a different semantic domain to customers and invoices. (Except: it was the wrong way to put it. The presentation layer does reference business logic because in an interactive system usability is achieved by having the UI reflect the business logic; for instance by hiding options that are not valid for the current user).

The bit that fails miserably

The part of the OSI model that is applicable to very very few systems is the layering. In the OSI architecture the strict layering works because the language of each layer can be defined in terms of layers beneath it. Session, Frame, Bit are in separate semantic domains, but the model allows Frame to be defined in terms of Bit, Session in terms of Frame, and so on.

This is almost never the case in layered business software. The vocabulary of a UI cannot be defined in terms of the vocabulary of commerce and business administration, and the vocabulary of a business cannot be defined in terms of data entities. They just are separate domains. The fact that that second one sometimes works a little bit (you can define a customer–incorrectly–as rows in data tables) is what seduces you into thinking it should work. But it doesn’t. You cannot define your business in terms of a data layer.

In particular then, a layered architecture with UI on top is always wrong; and business layer on top of data layer is always, but less obviously and more seductively, wrong. Hexagonal architecture (aka ports and adapters) is a much better model for most systems because it doesn’t confine dependencies to a single dimension (in addition to the already well-known fact that it gets your dependencies pointing the right way).

DDD: Treating the UI layer as a domain

Having recognised that user interface is a separate semantic domain, should we apply some DDD thinking and treat it as a bounded context with it’s own domain? The domain of an MVC web UI includes controllers, actions, routes, etc. But it must reference business logic all the time when deciding what to display, whether to accept user input, and ultimately to do anything with that input. To some, making the UI layer it’s own domain context, and giving it adapters to interface with the business domain seems like over-engineering, whilst others advocate almost exactly that.

I recommend that you should at least be aware that if you do not do this de-coupling (and in MVC web apps I personally almost never have) then your UI layer will have two semantic domains inside it. It’s a trade-off, but a sufficiently small one that I would usually come down in favour of which side has fewer total lines of code.

A Single Deployment Target is not a Monolith

Since the dawn of software architecture, we mostly knew that ‘Monolithic Architecture’ was more or less a synonym for ball of mud architecture. It mostly referred to code which was so entangled and coupled that it could not be separated out into components (or layers, or modules, or libraries, or equivalent). God classes, lack of information hiding, a change in one place means 20 other changes. That kind of thing. A failure of Modularity.

Recently the term Monolith has been taken to mean having a Single Deployment Target at runtime. This is a quite different meaning.

If you think that Monolith as described in para 1 above is the same as Monolith in para 2 above, then I suggest that you have confused, not separated, your concerns.

This is easy to understand if you are comfortable with architecture views. The para 1 definition is about the logical and (in 4+1 lingo) development views: the structure and relationships of classes, components, packages, and other kinds of modularity. The para 2 definition is only about the runtime deployment view (in 4+1: physical view).

The point is that you are at liberty in pretty much any operating system, runtime or language devised in the last 30 years, to structure your code and components as carefully and modularly as you like, whilst choosing your runtime deployment scenario independently of that modularity: it’s okay for 2 uncoupled components to run on the same machine. Honestly. *nix does it all the time. Ooh, so does Windows. And .Net and Java and Android and iOS and ….

The CTO at intilery.com showed me a couple of years ago how their server codebase can be deployed as either a single .war for a single webserver or split as separate .wars for separate machines by flicking a switch in the build config.

It’s not rocket science, it’s Separation of Concerns: the codebase is not the runtime.