Unidirectional Data Flow in Mobile Apps

In mobile development, tracking how data moves and transforms inside an app is critical to building reliable, maintainable features. As apps become more dynamic and reactive, handling user input, fetching data asynchronously, and updating UI across different screens, developers need clear architectural approaches that keep state predictable and code maintainable. One pattern that’s increasingly shaping mobile architecture is unidirectional data flow (UDF).

Before we go further, it’s worth noting that in our previous lesson, we explored Model-View-Intent (MVI), a pattern that enforces unidirectional flow, in detail. If you’re curious about how MVI works under the hood, what a reducer does, and how state flows through an app, you might want to revisit that.

In this lesson, we’ll explore how UDF works in mobile environments, how it connects to patterns like MVI, and why it’s reshaping the way modern mobile apps are built. Rather than focusing on the internal mechanics of MVI again, we’ll explore the architectural philosophy of unidirectional data flow, how it’s adapted for mobile platforms, and why it’s reshaping how we think about building modern apps.

Let’s begin.

Unidirectional data flow

Unidirectional data flow (UDF) means the application state changes in a single, predictable direction. Instead of components reading and writing state freely, the flow is structured: user actions trigger events (or Intents), which are processed to update a central state, which then updates the UI. This makes the logic more transparent and significantly easier to test or debug.

This concept isn’t new. You’ve already seen it in action if you’ve worked with modern web frameworks like React or libraries like Redux. We trigger an action (like clicking a button) that creates a signal (an Intent), which flows into a central system that updates the application’s state. Then the UI re-renders based on that new state. It’s a loop, but a controlled one. The data only moves forward in one direction through that loop, as illustrated below:

This loop is simple, traceable, and makes bugs easier to diagnose. If something goes wrong, we can look at the current state, the last Intent, and how it transformed, without digging through multiple UI callbacks.

A sample pseudocode for the state, View, and reducer is provided below:

Note: An Intent represents a user’s action or a system-triggered event. It’s a declarative way to say, something happened that should lead to a state change, for example, AddItemClicked(itemId), CheckoutPressed, etc. ...

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