GUI Driven by Control Flow

TL;DR at the end

This post is also published on Geckotech's website.

Over the past few months, I've been working on a data analytics dashboard as an intern at Geckotech.

One functionality we implemented is the ability to have connectors that fetch data from different internet sources and convert them into tabular format to feed into the dashboard.

These connectors tend to have very simple and linear workflow:

A diagram showing the steps: ask user for username & password → send login request → fetch list of available data → ask user which data they would like → fetch the data

Manual Approach#

The contemporary approach to implementing a GUI for this workflow would be to write a state machine.

First, there would need to be a state type that can represent each step in the process

enum State {
	BeforeLogin		{ },
	LoggingIn		{ login_req: AsyncTask },
	FetchingList	{ token: String, list_req: AsyncTask },
	ChoosingData	{ token: String, list: Vec<DataId> },
	FetchingData	{ token: String, chosen: DataId, data_req: AsyncTask }

and then functions to transition between different states

/// transition from the 2nd state to the 3rd
fn when_logged_in(&mut self, token: String) {
	match self {
		Self::LogginIn { .. } => {
			*self = Self::FetchingList {
				list_req: spawn(/*...*/),
		_ => panic!("not in the right state")
// ..........................................
// .‼️ other transition functions omitted ‼️.
// ..........................................

and finally, code to render the UI every time the state is updated

fn display(state: &State) -> Ui {
	match state {
		Self::BeforeLogin => /* login form */,
		Self::ChoosingData => /* options list */,
		_ => /* maybe a loading spinner */

The Problem#

State machines like the one above are extremely common. But hand implementing all of them might not be the best idea. The transition functions are boilerplaty and quite difficult to read.

Is there a more concise way to make state machines?

Control-Flow Approach#

In Rust, async code makes state machines. There's a detailed explainer here. In short, the Future generated from an async block is a state machine in which each state corresponds to an await point, storing the local variables at that point.

Written as async code, the connector state machine is roughly

async fn connector() -> Data {
	// 👇 state 1
	let (username, password) = show_login_form().await;

	// 👇 state 2
	let token = login(&username, &password).await;

	// 👇 state 3
	let list = fetch_list(&token).await;

	// 👇 state 4
	let chosen_data_id = show_list(&list).await;

	// 👇 state 5
	get_data(&token, chosen_data_id).await

No more transition functions! Here, the control flow - the simple advancing of execution from one line to the next - is what transitions the state machine.

Presenting the UI#

Back in the manual approach, there's a display function that takes in the state machine and presents the appropriate UI. That won't work here. Futures generated from async code are opaque; they can't be displayed; they need to present their UI themselves.

I'm using my own framework, Async UI, where presenting UI from async code is trivial. Just add rendering code to show_login_form and show_list. The connector code from above remains the same.

async fn show_login_form() -> (String, String) {
	let (username, password) = (Input::new(), Input::new());
	let button = Button::new();


	(username.value(), password.value())

In other frameworks, the UI might need to be manually "sent out" of the async connector function.

For example, if you're using Dominator, the show_* functions can build Doms and send them through a channel to be displayed.

// `channel` is for sending out UI to be displayed
//                 👇👇👇
async fn connector(channel: Sender<Option<Dom>>) {
	// `show_login_form` can show its UI by
	// sending a `Dom` into the channel
	// `channel.send(Some(html!(...)))`			👇👇👇
	let (username, password) = show_login_form(&channel).await;

	// ...

	let chosen_data_id = show_list(&list, &channel).await;
	// ...

let (tx, rx) = futures_signals::signal::channel(None);

html!("div", {
	.child_signal(rx) // 👈 render what is sent through the channel
	.future(connector(tx)) // 👈 launch the state machine

If you're using a React-like framework, you might do something along the line of

const [ui, setUi] = useState<ReactNode>(null);
useEffect(() => {
	// launch the connector
	// pass in setUi so it can put in its UI to be rendered
}, []);
return ui;


Let's examine the advantages the control-flow approach brings over the manual approach.

Concise & Readable Code#

Not much to say here. The covered example code for manual approach and for control-flow approach speak for themselves.

Language Features#

Rust comes with cool control-flow operators such as match, labeled breaks, and ? (try operator). We can take full advantage of these in the control-flow approach.

For example, I added error handling to the connector

A diagram showing the steps: ask user for username & password → send login request → fetch list of available data → ask user which data they would like → fetch the data. Error in the login step loops back to the start. Errors elsewhere end the flow

async fn connector() -> Result<Data, Box<dyn Error>> {
	// loop until successful login
	let token = loop {
		let (username, password) = show_login_form().await;
		if let Ok(token) = login(&username, &password).await {
			break token;
	let list = fetch_list(&token).await?; // 👈 try operator
	let chosen_data_id = show_list(&list).await;
	Ok(get_data(&token, chosen_data_id).await?) // 👈 try operator

Transition Safety#

In the manual approach, state transition functions can panic. For example, the when_logged_in function above is for transitioning from state 2 to 3, and will panic if called when not in state 2.

In the control-flow approach, this is not a problem. There is not even any need to tell the machine to transition from one state to another. As long as the Future is being awaited, execution of async code advances on its own.



Future objects are opaque and not serializable. They cannot be used in serde. They cannot be persisted to disk.

A partial workaround is to keep the data we want to serialize outside the async code. For example, to support persisting username and password in the connector, I keep the strings outside the async connector function.

// The strings live outside the function.
// The function only borrows the string.
async fn connector(username: &mut String, password: &mut String) -> ... {
	// ...

Coupling of State and UI#

In the control-flow approach, the async code is responsible for both managing the state and presenting the UI. This goes against the prevailing wisdom that state and UI should be kept separate.

But the prevailing wisdom doesn't apply everywhere. Having the UI code and the state code live together can be quite beneficial for cases where encapsulation is more important than separation of concerns.

The connectors of Geckotech's dashboard is one such case. We wanted a simple connector interface, with self-contained connectors. This makes the control-flow approach perfect for us.


GUI programming requires a lot of state machines, and these machines can often be represented by control-flows in async code. Written as async functions, state machine code can be more concise, more expressive, and less error-prone. The tradeoff is less transparency at runtime.


Here's a dummy demo of the connector flow made with Async UI.

Embeded frame not loading? Refresh ↺ or Open externally ↗

View the demo source code here.

See Also#

  • Crank.js uses JS generators (which are also state machines) as components.
  • This post by Vitaliy Akimov explores using JavaScript generators for UI and state management.
  • Self plug: Async UI does "UI as side-effect [of async code]".
  • XState solves the problem of state machine boilerplate in a different way: it introduces statechart - a concise mini-language for state machines. An interpreter is used at runtime.