Data flow direction

We're creating an information architecture to support the feature-rich application that will ultimately sit on top of this architecture. Data flows into the system and will eventually reach an endpoint, terminating the flow. It's what happens in between the entry point and the termination point that determines the data-flow within a Flux architecture. This is illustrated here:

Data flow is a useful abstraction, because it's easy to visualize data as it enters the system and moves from one point to another. Eventually, the flow stops. But before it does, several side-effects happen along the way. It's that middle block in the preceding diagram that's concerning, because we don't know exactly how the data-flow reached the end.

Let's say that our architecture doesn't pose any restrictions on data flow. Any component is allowed to pass data to any other component, regardless of where that component lives. Let's try to visualize this setup:

As you can see, our system has clearly defined entry and exit points for our data. This is good because it means that we can confidently say that the data-flows through our system. The problem with this picture is with how the data-flows between the components of the system. There's no direction, or rather, it's multidirectional. This isn't a good thing.

Flux is a unidirectional data flow architecture. This means that the preceding component layout isn't possible. The question is—why does this matter? At times, it might seem convenient to be able to pass data around in any direction, that is, from any component to any other component. This in and of itself isn't the issue—passing data alone doesn't break our architecture. However, when data moves around our system in more than one direction, there's more opportunity for components to fall out of sync with one another. This simply means that if data doesn't always move in the same direction, there's always the possibility of ordering bugs.

Flux enforces the direction of data-flows, and thus eliminates the possibility of components updating themselves in an order that breaks the system. No matter what data has just entered the system, it'll always flow through the system in the same order as any other data, as illustrated here:

Predictable root cause

With data entering our system and flowing through our components in one direction, we can more easily trace any effect to it's cause. In contrast, when a component sends data to any other component residing in any architectural layer, it's a lot more difficult to figure how the data reached its destination. Why does this matter? Debuggers are sophisticated enough that we can easily traverse any level of complexity during runtime. The problem with this notion is that it presumes we only need to trace what's happening in our code for the purposes of debugging.

Flux architectures have inherently predictable data-flows. This is important for a number of design activities and not just debugging. Programmers working on Flux applications will begin to intuitively sense what's going to happen. Anticipation is key, because it let's us avoid design dead-ends before we hit them. When the cause and effect are easy to tease out, we can spend more time focusing on building application features—the things the customers care about.

Consistent notifications

The direction in which we pass data from component to component in Flux architectures should be consistent. In terms of consistency, we also need to think about the mechanism used to move data around our system.

For example, publish/subscribe (pub/sub) is a popular mechanism used for inter-component communication. What's neat about this approach is that our components can communicate with one another, and yet we're able to maintain a level of decoupling. In fact, this is fairly common in frontend development because component communication is largely driven by user events. These events can be thought of as fire-and-forget. Any other components that want to respond to these events in some way, need to take it upon themselves to subscribe to the particular event.

While pub/sub does have some nice properties, it also poses architectural challenges, in particular scaling complexities. For example, let's say that we've just added several new components for a new feature. Well, in which order do these components receive update messages relative to pre-existing components? Do they get notified after all the pre-existing components? Should they come first? This presents a data dependency scaling issue.

The other challenge with pub-sub is that the events that get published are often fine-grained to the point where we'll want to subscribe and later unsubscribe from the notifications. This leads to consistency challenges because trying to code lifecycle changes when there's a large number of components in the system is difficult and presents opportunities for missed events.

The idea with Flux is to sidestep the issue by maintaining a static inter-component messaging infrastructure that issues notifications to every component. In other words, programmers don't get to pick and choose the events their components will subscribe to. Instead, they have to figure out which of the events that are dispatched to them are relevant, ignoring the rest. Here's a visualization of how Flux dispatches events to components:

The Flux dispatcher sends the event to every component; there's no getting around this. Instead of trying to fiddle with the messaging infrastructure, which is difficult to scale, we implement logic within the component to determine whether or not the message is of interest. It's also within the component that we can declare dependencies on other components, which helps influence the ordering of messages. We'll cover this in much more detail in later chapters.

Simple architectural layers

Layers can be a great way to organize an architecture of components. For one thing, it's an obvious way to categorize the various components that make up our application. For another thing, layers serve as a means to put constraints around communication paths. This latter point is especially relevant to Flux architectures since it's important that data flow in one direction. It's much easier to apply constraints to layers than it is to individual components. Here is an illustration of Flux layers:

As you can see, the data-flows from one layer to the next, in one direction. Flux only has a few layers, and as our applications scale in terms of component counts, the layer counts remains fixed. This puts a cap on the complexity involved with adding new features to an already large application. In addition to constraining the layer count and the data-flow direction, Flux architectures are strict about which layers are actually allowed to communicate with one another.

For example, the action layer could communicate with the view layer, and we would still be moving in one direction. We would still have the layers that Flux expects. However, skipping a layer like this is prohibited. By ensuring that layers only communicate with the layer directly beneath it, we can rule out bugs introduced by doing something out-of-order.

Loosely coupled rendering

One decision made by the Flux designers that stands out is that Flux architectures don't care how UI elements are rendered. That is to say, the view layer is loosely coupled to the rest of the architecture. There are good reasons for this.

Flux is an information architecture first, and a software architecture second. We start with the former and graduate toward the latter. The challenge with view technology is that it can exert a negative influence on the rest of the architecture. For example, one view has a particular way of interacting with the DOM. Then, if we've already decided on this technology, we'll end up letting it influence the way our information architecture is structured. This isn't necessarily a bad thing, but it can lead to us making concessions about the information we ultimately display to our users.

What we should really be thinking about is the information itself and how this information changes over time. What actions are involved that bring about these changes? How is one piece of data dependent on another piece of data? Flux naturally removes itself from the browser technology constraints of the day so that we can focus on the information first. It's easy to plug views into our information architecture as it evolves into a software product.