Don’t Control, but Observe

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Don’t Control, but Observe

Gregor Hohpe

TodAy’S SySTEMS ARE diSTRiBuTEd And looSEly CouplEd. Build- ing loosely coupled systems is a bit of a drag, so why do we bother? Because we want our systems to be flexible so they do not break apart at the slightest change. This is a critical property in today’s environments, where we may only control a small portion of our application, the remainder living in distributed services or third-party packages, controlled by other departments or external vendors.
So, it looks like the effort to build a system that is flexible and can evolve over time is a good idea. But that also means our system will change over time. As in “today’s system is not what it was yesterday.” Unfortunately, this makes doc- umenting the system challenging. It’s commonly known that documentation is out of date the moment it is printed, but in a system that changes all the time, things can only be worse. Moreover, building a system that is flexible gener- ally means the architecture is more complex and it’s more difficult to get the proverbial “big picture.” For example, if all system components communicate with one another over logical, configurable channels, one better have a look at the channel configuration to get any idea what is going on. Sending messages into the logical la-la-land is unlikely to trigger a compiler error, but it is sure to disappoint the user whose action was encapsulated in that message.
Being a control-freak architect is so yesteryear, leading to tightly coupled and brittle solutions. But letting the software run wild is sure to spawn chaos. You have to supplement the lack of control with other mechanisms to avoid doing an instrument flight without the instruments. But what kind of instruments do
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??we have? Plenty, actually. Today’s programming languages support reflection, and almost all runtime platforms provide runtime metrics. As your system becomes more configurable, the current system configuration is another great source of information. Because so much raw data is difficult to understand, extract a model from it. For example, once you figure out which components send messages to which logical channels, and which components listen to these channels, you can create a graph model of the actual communication between components. You can do this every few minutes or hours, providing an accurate and up-to-date image of the system as it evolves. Think of it as “Reverse MDA.”1 Instead of a model driving the architecture, you build a flex- ible architecture, and extract the model from the actual system state.
In many cases, it’s easy to visualize this model, creating the literal big picture. However, resist the temptation to plot the 3×5-meter billboard of boxes and lines, which contains every class in your system. That picture may pass as con- temporary art, but it’s not a useful software model. Instead, use a 1,000-foot view as described by Erik Doernenburg, a level of abstraction that actually tells you something. On top of that, you can make sure your model passes basic validation rules, such as the absence of circular dependencies in a dependency graph, or no messages being sent to a logical channel no one listens to.
Letting go of control is a scary thing, even when it comes to system architec- ture. But supplemented by observation, model extraction, and validation, it is probably the way only to architect for the 21st century.