What Makes a Monolith Monolithic?

 

It seems like everybody throws around the term “monolith”, but what do we mean by that?

Sam Newman started the ball rolling yesterday with this tweet:

I'm at that point in the new book where I have to define what monolith means. Bugger.

— Sam Newman (@samnewman) July 25, 2017

My first response was a (semi) joke:

that's just semantics...[looks at the definition of "semantics"]...bugger

— Gene Hughson (@GeneHughson) July 25, 2017

I say semi joke because, in truth, semantics (i.e. meaning) is critical. The English language has a horrible tendency to overload terms as it is, and in our line of work we tend to make it even worse. Lack of specificity obscures, rather than enlightens. The problem with the term “monolith” is that, while it’s a powerfully evocative term, it isn’t a simple one to define. My second response was closer to an actual definition:

yep...IMO it's more about meta-coupling - even multiple binaries/process could still be monolithic, particularly w/ shared data stores

— Gene Hughson (@GeneHughson) July 25, 2017

The purpose of this post is to expand on that a bit.

The “mono” portion of the term is, in my opinion, the crucial part. I believe that quality of oneness is what defines a monolithic system. As I noted in the second tweet, it’s a matter of meta-coupling, whether that coupling exists in the form of deployment, data architecture, or execution style (Jeppe Cramon‘s post “Microservices: It’s not (only) the size that matters, it’s (also) how you use them – part 3” shows how temporal coupling can turn a distributed system into a runtime monolith). The following tweets between Anne Currie and Sam illustrate the amorphous nature of what is and isn’t a monolith:

know at least 2 companies who code independent microservices but compile services together as a single process to deploy (dev vs run speed)

— Anne Currie (@anne_e_currie) July 25, 2017

They consider they produce microservices. They deploy separately where resilience needs outweigh execution speed ones. We do need terms 😉

— Anne Currie (@anne_e_currie) July 25, 2017

I think in this example you have three things - service endpoints, modules, and microservices.

— Sam Newman (@samnewman) July 25, 2017

When lots of them bundled together, you have multiple modules each exposing a service endpoint. When deployed separately - microservices

— Sam Newman (@samnewman) July 25, 2017

Modules that can be deployed to run in a single process need not be considered monolithic, if they’re not tightly coupled. Likewise, running distributed isn’t a guarantee against being monolithic if the components are tightly coupled in any way. The emphasis on “in any way” is due to the fact that any of the types of coupling I mentioned above can be a deal killer. If all the “microservices” must be deployed simultaneously for the system to work, it’s a distributed monolith. If the communication is both synchronous and fault intolerant, it’s a distributed monolith. If there’s a single data store backing the entire system, it’s a distributed monolith. It’s not the modularity that defines it (you can have a modular monolith), but the inability to separate the parts without damaging the whole system.

I would also point out that I don’t consider “monolithic” to be derogatory, in and of itself. There is a trade-off involved in terms of coupling and complexity (and cost). While I generally prefer more flexibility, there is always the danger of over-engineering. If we’re hand-carving marble gargoyles to stick on a tool shed, chances are the customer won’t be pleased. The solution should bear at least a passing resemblance to the problem context it’s supposed to address.

Designing Communication, Communicating Design

We work in a communications industry.

We create and maintain systems to move information around in order to get things done. That information moves between people and systems in combinations and configurations too numerous to count. In spite of that, we don’t do that great a job of communicating what should be, for us, extremely important information. We tend to be really bad at communicating the architecture of our systems – structure, behavior, and most importantly, the reasons for the decisions made. It’s bad enough when we fail to adequately communicate that information to others, it’s really bad when we fail to communicate it to ourselves. I know I’ve let myself down more than once (“What was I thinking here?!”).

Over the past few days, I’ve been privileged to follow (and even contribute a bit to) a set of conversations on Twitter. Grady Booch, Ivar Jacobson, Ruth Malan, Simon Brown, and others have been discussing the need for architectural awareness and the state of communicating architecture.

This exchange between Simon, Chris Carroll and Eoin Woods sums it up well:

I've run architecture katas for 10K+ people and they do struggle to communicate architecture 😞

— Simon Brown (@simonbrown) October 23, 2016

For want of a language or for want of understanding what needs to be communicated?

— Chris F. Carroll (@chrisfcarroll) October 23, 2016

both in my experience

— Eoin Woods (@eoinwoodz) October 24, 2016

First and foremost, an understanding of what the role of a software architect is and why it’s important is needed. Any organization where the role is seen as either just a senior developer or (heaven help us!) some sort of Taylorist “thinker” who designs everything for the “worker bee” coders to implement, is almost guaranteed to be challenged in terms of application architecture. Resting on that foundation of shifting sand, the organization’s enterprise IT architecture (EITA) is likewise almost guaranteed to be challenged barring a remarkable series of “happy accidents”. The role (not necessarily position) of software architect is required, because software architecture is a distinct set of concerns that can either be addressed intentionally or left to emerge haphazardly out of the construction of the system.

Before we can communicate the architecture of a system, it’s necessary to understand what that is. In “Software Architecture: Central Concerns, Key Decisions”, Ruth Malan and Dana Bredemeyer defined it as high impact, systemic decisions involving (at a minimum):

• system priority setting
• system decomposition and composition
• system properties, especially cross-cutting concerns
• system fit to context
• system integrity

I don’t think it’s possible to over-emphasize the use of “system” and “systemic” in the preceding paragraphs. That being said, it’s important to understand that architectural concerns do not exist in a void. There is a cyclic relationship between the architectural concerns of a system and the system’s code. The architectural concerns guide the implementation, while the implementation defines the current state of the architecture and constrains the evolution of future state of the architecture. Code is a necessary, but insufficient source of architectural knowledge – it’s not enough. As Ruth Malan noted in the Visual Design portion (part II) of her presentation at the Software Architect Conference in London a year ago:

While the code serves as a foundation of the system, it’s also important to realize that the system exists within a larger context. There is a fractal set of systems within systems within ecosystems. Ruth illustrated this in the Intention and Reflection portion (part III) of the presentation reference above:

[Note: Take the time to view the entirety of the Intention and Reflection presentation. It’s an excellent overview of how to design the architecture of a system.]

The fractal nature of systems within systems within ecosystems is illustrated by the image at the top of the post (h/t to Ric Phillips for the reblog of it). Richard Sage‘s humorous (though only partly, I’m sure) suggestion of it as a meta-model goes a long way towards portraying the problem of a language to communicate architecture.

Not only are we dealing with a nested set of “things”, but the understanding of those things differ according to the stakeholder. For example, while the business owner might see a “web site” as one monolithic thing, the architect might see an application made up of code components depending on other applications and services running on a collection of servers. Maintaining a coherent, normalized object model of the system yet being able to present it in multiple ways (some of which might be difficult to relate) is not a trivial exercise.

Lower-level aspects of design lend themselves to automated solutions, which can increase reliability of the model by avoiding “documentation rot”. An interesting (in my opinion) aspect that can also be automated is the evolution of code over time. What can’t be parsed from the code, however, is intention and reasoning.

Another barrier to communication is the need to be both expressive and flexible (also well illustrated by Richard’s meta-model) while also being simple enough to use. UML works well on the former, but (rightly or wrongly) is perceived to fail on the latter. Simon Brown’s C4 model aims to achieve a better balance in that aspect.

At present, I don’t think we have one tool that does it all. I suspect that even with a suite of tools, that narrative documents will still be way some aspects are captured and communicated. Having a centralized store for the non-code bits (with a way to relate them back to the code) would be a great thing.

All in all, it is encouraging to see people talking about the need for architectural design and the need to communicate the aspects of that design.

A Service by Any Other Name

I remember when reuse was the Holy Grail. First it was object-oriented languages, then object modeling, then components, then services. While none of these has lived up to the promise of rampant reuse, one thing has – language. We have learned to overload and reuse terms to the point that most are no more descriptive than using the word “thing”. “Service” is one of these.

The OASIS Reference Model for Service Oriented Architecture 1.0 defines a service as “…a mechanism to enable access to one or more capabilities, where the access is provided using a prescribed interface and is exercised consistent with constraints and policies as specified by the service description”. This definition includes nothing about protocol, media types, messaging patterns, etc. Essentially, this definition applies to anything that exposes something to consumers over in a defined interface in a defined manner.

Well, that’s helpful. It doesn’t even resolve whether it’s referring to the application providing services or the specific endpoints exposed by that application. In the context of Domain-Driven Design, a service may even be share the same process with its client.

Some services are meant to be interactive. A request is made and a response is returned. Such a service is extremely useful when the response is being presented to an actual human user in response to some action of theirs. This immediate gratification provides an excellent user experience provided latency is minimized. When interactive services are composed within another interactive service, this latency burden quickly increases as the remote client can’t regain control until the service regains it from each remote service it’s called (barring a timeout).

Some services are meant to work asynchronously. These work well particularly well in system to system scenarios when there’s no need to for temporal coupling. A significant trade-off for this model is the additional complexity involved, particularly around error condition feedback.

Some services (typically SOAP-based) are meant to receive and return highly structured and well-defined messages invoking tasks to be performed. This works extremely well for many system to system communications. Others (typically RESTful services) provide more of a variation of media types which are dealt with using CRUD operations. These work particularly well when the client is mainly presenting the response (perhaps with some minimal transformation) to an end user. If the messages are less well-defined, then the level of client complexity increases. Services using well-defined messages that are exposed externally will typically have different versioning requirements than services meant for consumption by internal clients (where “internal” is defined as built and deployed contemporaneously with the service).

The points behind this litany of service definitions and usage patterns? Different styles are appropriate to different situations and precision in communication is important to avoid conflating those styles.

Providing functionality via a service, regardless of the type of service, is not sufficient to meet a need. Functionality should be exposed via the flavor of service that corresponds to the needs of the client. Otherwise, the client may incur more effort and complexity dealing with the mismatch than the functionality is worth.

The concept of a message-oriented API for the back-end allows it to meet all of these needs without violating the DRY principle. Just like UI components, service endpoints (SOAP or REST style) should not contain domain logic, but delegate to domain services. As such, this provides a thin layer primarily concerned with translation between the format and messaging pattern required externally and that required by the domain services. This allows internal consumers (again, where “internal” is defined as built and deployed contemporaneously with the back-end) to work either directly in-process with domain services or remotely via a very thin shim that retains the same message structure and solely provides the network communication capabilities. External consumers should have their own endpoints (perhaps even separate sites) that cater to their needs.

Having the thinnest possible service layer not only prevents duplication of logic but also lessens the burden of standing up new endpoints. Lowering the cost of an endpoint makes it easier to justify providing tailored APIs. APIs that are tailored to specific client types are simpler to consume, further reducing costs.

In my last post, I asked “do you need a service layer?” The answer remains, “it depends”. But now there’s a new wrinkle, “if you need a service layer, does it need to be a remote one?”

Hatin’ on Nulls

When I first read Christian Neumanns’ “Why We Should Love ‘null'”, I found myself agreeing with his position. Yes, null references have “…led to innumerable errors, vulnerabilities, and system crashes, which have probably caused a billion dollars of pain and damage…” per Sir C. A. R. Hoare. Yes, many people heartily dislike null references and will go to great lengths to work around the problem. Finally, yes, these workarounds may be more detrimental than the problem they are intended to solve. While I agreed with the position that null references are a necessary inconvenience (the ill effects are ultimately the result of failure to check for null, not the null condition itself), I didn’t initially see the issue as being particularly “architectural”.

Further on in the article, however, Christian covered why null references and the various workarounds, become architecturally significant. The concept of null, nothing, is semantically important. A price of zero dollars is not intrinsically the same as a missing price. A date several millenia into the future does not universally convey “unknown” or “to be determined”. Using the null object pattern may eliminate errors due to unchecked references, but it’s far from “safe”. According to Wikipedia, “…a Null Object is very predictable and has no side effects: it does nothing“. That, however, is untrue. A Null Object masks a potential error condition and allows the user to continue on in ignorance. That, in my opinion, is very much doing something.

A person commenting on Christian’s post stated that “…a crash is the worst kind of experience a user can have”. That person argued that masking a null reference error may not be as bad for the user as a crash. There’s a kernel of truth there, but it’s a matter of risk. If an application continues on and the result is a misunderstanding of what’s been done or worse, corrupted data, how bad is that? If the application in question is a game, there’s little real harm. What if the application in question is dealing with health information? I stand by the position that where there is an error, no news is bad news.

As more and more applications become platforms via being service enabled, semantic issues gain importance. Versioning strategies can ensure structural compatibility, but semantic issues can still break clients. Coherence and consistency should be considered hallmarks of an API. As Erik Dietrich noted in “Notes on Writing Discoverable Framework Code”, a good API should “make screwing up impossible”. Ambiguity makes screwing up very possible.

What do we mean by “architecture”?

Every systematic development of any subject ought to begin with a definition, so that everyone may understand what the discussion is about.

Marcus Tullius Cicero (196BC ‒ 16BC), De Officiis, Book 1, Moral Goodness (h/t to Glen Alleman, October 8, 2013 Quote of the Day)

One of the joys of the English language is the overloading of words with multiple meanings. While it’s not hard to start an argument over technical issues under normal circumstances, having multiple definitions to conflate makes it that much easier. Take, for example, the word “architecture”. While different dictionaries give slightly different definitions (e.g. Merriam-Webster Dictionary.com and Wiktionary), the common set of definitions boil down to:

• the practice of architecture (i.e. the discipline of architectural design)
• an architectural style (e.g. layered, RESTful, etc.)
• the result of architectural work (i.e. the architecture of a thing – the “decisions of consequence” that define the structure and behavior of a system as well as constraining its future evolution)

While there is a relationship between these concepts, a person practicing architecture might create an architectural design using one or more architectural styles, there is enough differentiation to cause trouble if we don’t clarify our terms. Far from being an academic exercise, paying attention to semantics can actually save time and frustration when there is ambiguity present.