Sie arbeiten an Ihrem TypeScript-Projekt. Der Code ist sauber und gut strukturiert, Sie sind stolz darauf. Eines Tages taucht ein Fehler auf. Sein Stack-Trace erstreckt sich über eine durchschnittliche NPM-Installation und sprudelt durch zahllose Schichten, die ihn nicht verarbeiten konnten. Ihr Code funktioniert nicht, Sie haben keine Ahnung, wo Sie anfangen sollen, ihn zu reparieren, jeder Versuch fühlt sich wie ein ungeschickter Patch an. Ihre Architektur sieht nicht mehr so ​​sauber aus. Du hasst dein Projekt. Sie schließen Ihren PC und genießen Ihren Freitag.

Das Ödland des Fehlermanagements

JavaScripts Fehlermanagement reicht nicht an die Ausdruckskraft und Entwicklererfahrung heran, die moderne Sprachen wie Rust, Zig und Go bieten. Aufgrund seiner dynamischen Natur und des Fehlens von Leitplanken müssen sich Entwickler häufig mit Ungewissheit auseinandersetzen, ohne über die soliden Grundlagen und Garantien zu verfügen, die strengere Plattformen bieten.

Diese entscheidende Säule der Softwareentwicklung spiegelt sich nur unzureichend in der Kultur und im Ökosystem der Sprache wider, da einige der beliebtesten NPM-Bibliotheken Ausnahmen in ihrer Dokumentation nicht einmal erwähnen.

Dieser Mangel an Standards fördert bei Entwicklern die falsche Vorstellung, dass Ausnahmen selten vorkommen. Infolgedessen führt diese verzerrte Perspektive zu einem mangelnden Interesse an der Etablierung solcher Standards innerhalb der Gemeinschaft.

Try-Catch: Die Kosten der Implizitheit

Das JavaScript-Try-Catch-Modell verbirgt nicht offensichtliche Implikationen. Ausnahmen können überall auftreten, doch es ist überraschend schwierig, sie zu antizipieren. Dieses scheinbar einfache Muster verdeckt oft subtile Fallstricke im alltäglichen Code:

let value;
try {
  value = mayThrow();
} catch (e) {
  // Handle the exception.
}

Das erste Problem, das im Snippet auffällt, ist die Bereichserweiterung, bei der Variablen außerhalb des Try-Catch-Blocks deklariert werden müssen, um einen zusammenhängenden Kontrollfluss aufrechtzuerhalten. Dies führt zu mehr ausführlicherem und schwerer zu verfolgendem Code, der möglicherweise zu subtilen Fehlern führt, wenn die Codebasis komplexer wird.

Die implizite Natur dieser dynamischen Fehlerbehandlung erhöht die kognitive Belastung für die Entwickler und erfordert von ihnen, Ausnahmequellen in der gesamten Codebasis mental zu verfolgen. Im Gegensatz dazu zwingen explizite Fehlerbehandlungsmodelle, wie zum Beispiel das in Go, Entwickler, jeden Fehler anzuerkennen und zu behandeln.

result, err := mayFail();

Dies ist auf lange Sicht ein großer Gewinn, der eine reibungslosere und sicherere Wartung im Zuge der Weiterentwicklung von Projekten ermöglicht.

Zu diesen Herausforderungen kommt noch hinzu, dass die Catch-Klausel von TypeScript nicht in der Lage ist, die Fehler, die ausgelöst werden können, zu verfolgen und strikt einzugeben, was zu einem Verlust der Typsicherheit genau an der Stelle führt, an der sie am kritischsten ist. JavaScript ermöglicht sogar das Auslösen von Nicht-Fehlerwerten, sodass wir praktisch keine Schutzmaßnahmen haben. Sprachen wie Rust zeigen die Leistungsfähigkeit und Eleganz dieses Ansatzes mit seinem Fehlerbehandlungsdesign:

match may_fail() {
  Ok(result) => println!("Success"),
  Err(Error::NotFound) => println!("Not found"),
  Err(Error::PermissionDenied) => println!("Permission denied"),
}

Dem TypeScript-Team wurden verschiedene Vorschläge vorgelegt, die darauf abzielen, eine Grundlage für ein robusteres und vorhersehbareres Ausnahmesystem zu schaffen. Diese Vorschläge wurden jedoch oft durch Einschränkungen in der zugrunde liegenden JavaScript-Plattform blockiert, der die notwendigen Grundelemente zur Unterstützung solcher Architekturverbesserungen fehlen.

Mittlerweile wurden einige Vorschläge zur Behebung dieser Mängel auch dem TC39 (dem Ausschuss für ECMAScript-Standardisierung) vorgelegt, sie befinden sich jedoch noch in der frühen Phase der Prüfung. Wie Matt Pocock betonte, schreitet auch der Hitzetod des Universums stetig voran.

Wenn eine Sprache Innovationen behindert, reagiert die Entwicklergemeinschaft oft mit ausgeklügelten Bibliotheken und User-Land-Lösungen. Viele der aktuellen Vorschläge in diesem Bereich, wie der außergewöhnliche Neverthrow, sind von der funktionalen Programmierung
inspiriert und bieten eine Reihe von Abstraktionen und Dienstprogrammen ähnlich dem Result-Typ von Rust, um das Problem anzugehen:

function mayFail(): Result<string> {
  if (condition) {
    return err("failed");
  }

  return ok("value");
}

Ein weiterer herausragender Ansatz ist der des Effekts
. Dieses leistungsstarke Toolkit befasst sich nicht nur direkt mit dem Fehlermanagement, sondern bietet auch eine umfassende Suite von Dienstprogrammen für die Handhabung asynchroner Vorgänge, Ressourcenmanagement und mehr:

import { Effect } from "effect";

function divide(a: number, b: number): Effect.Effect<number, Error> {
  return b === 0
    ? Effect.fail(new Error("Cannot divide by zero"))
    : Effect.succeed(a / b);
}

const result = Effect.runSync(divide(1, 2));

Outside the joy of a nerd like myself in digging into tech like this, adopting new technologies demands a careful cost-benefit analysis. The JavaScript ecosystem evolves at a breakneck pace, with libraries emerging and becoming obsolete in rapid succession.

Choosing the wrong abstraction can hold your code hostage, create friction in your development process, and demand blood, sweat, and tears to migrate away from. (Also, adding a new package is likely not gonna help with the 200mb bundle size of your React app.)

Error management is a pervasive concern that touches nearly every part of a codebase. Any abstraction that requires rethinking and rewriting such a vast expanse of code demands an enormous amount of trust—perhaps even faith—in its design.

Crafting a Path Forward

We've explored the limitations of user-land solutions, and life's too short to await commit approvals for new syntax proposals. Could there be a middle ground? What if we could push the boundaries of what's currently available in the language, creating something that aspires to be a new standard or part of the standard library, yet is written entirely in user-land and we can use it right now?

As we delve into this concept, let's consider some key principles that could shape our idea:

• Conventions over Abstractions: Minimize abstractions by leveraging existing language features to their fullest.
• Minimal API: Strive for simplicity without sacrificing functionality. Conciseness is often an indicator of robust and lasting design.
• Compatibility and Integrability: Our solution shouldn't depend on universal adoption, and must seamlessly consume and be consumed by code not written with the same principles in mind.
• Intuitive and Ergonomic: The patterns should be self-explanatory, allowing developers to grasp and implement them at a glance, minimizing the risk of misinterpretations that could result in anti-patterns or unexpected behaviors.
• Exploit TypeScript: Leverage TypeScript's type system to provide immediate feedback through IDE features like syntax highlighting, error detection, and auto-completion.

Now, let's dive into the heart of the matter by addressing our first key challenge. Let's introduce the term task for functions that may either succeed or encounter an error.

function task() {
  if (condition) {
    throw new Error("failed");
  }

  return "value";
}

We need an error-handling approach that keeps control flow clean, keeps developers constantly aware of potential failures, and maintains type safety throughout. One idea worth exploring is the concept of returning errors instead of throwing them. Let's see how this might look:

function task() {
  if (condition) {
    // return instead of throwing.
    return new Error("failed");
  }

  return "value";
}

By introducing Errors as values and assigning them specific meaning, we enhance the expressivity of a task's return value, which can now represents either successful or failing outcomes. TypeScript’s type system becomes particularly effective here, typing the result as string | Error, and flagging any attempt to use the result without first checking for errors. This ensures safer code practices. Once error checks are performed, type narrowing allows us to work with the success value free from the Error type.

const result: string | Error = task();

// Handle the error.
if (result instanceof Error) {
  return;
}

result;
// ?^ result: string

Managing multiple errors becomes reliable with TypeScript’s type checker, which guides the process through autocompletion and catches mistakes at compile time, ensuring a type-driven and dependable workflow.

function task() {
  if (condition1) return new CustomError1();
  if (condition2) return new CustomError2();
  return "value";
}

// In another file...
const result = task();

if (result instanceof CustomError1) {
  // Handle CustomError1.
} else if (result instanceof CustomError2) {
  // Handle CustomError2.
}

And since we're just working within plain JavaScript, we can seamlessly integrate existing libraries to enhance our error handling. For example, the powerful ts-pattern library synergize beautifully with this approach:

import { match } from "ts-pattern";

match(result)
  .with(P.instanceOf(CustomError1), () => {
    /* Handle CustomError1 */
  })
  .with(P.instanceOf(CustomError2), () => {
    /* Handle CustomError2 */
  })
  .otherwise(() => {
    /* Handle success case */
  });

We now face 2 types of errors: those returned by tasks adopting our convention and those thrown. As established in our guiding principles, we can't assume every function will follow our convention. This assumption is not only necessary to make our pattern useful and usable, but it also reflects the reality of JavaScript code. Even without explicit throws, runtime errors like "cannot read properties of null" can still occur unexpectedly.

Within our convention, we can classify returned errors as "expected" — these are errors we can anticipate, handle, and recover from. On the other hand, thrown errors belong to the "unexpected" category — errors we can't predict or generally recover from. These are best addressed at the highest levels of our program, primarily for logging or general awareness. Similar distinctions are built into the syntax of some other languages. For example, in Rust:

// Recoverable error.
Err("Task failed")

// Unrecoverable error.
panic!("Fatal error")

For third-party APIs whose errors we want to handle, we can wrap them in our own functions that conform to our error handling convention. This approach also gives us the opportunity to add additional context or transform the error into a more meaningful representation for our specific use case. Let's take fetch as an example, to demonstrate also how this pattern seamlessly extends to asynchronous functions:

async function $fetch(input: string, init?: RequestInit) {
  try {
    // Make the request.
    const response = await fetch(input, init);
    // Return the response if it's OK, otherwise an error.
    return response.ok ? response : new ResponseError(response);
  } catch (error) {
    // ?^ DOMException | TypeError | SyntaxError.
    // Any cause from request abortion to a network error.
    return new RequestError(error);
  }
}

When fetch returns a response with a non-2XX status code, it's often considered an unexpected result from the client's perspective, as it falls outside the normal flow. We can wrap such responses in a custom exception type (ResponseError), while keeping other network or parsing issues in their own type (RequestError).

const response: Response | ResponseError | RequestError = await $fetch("/api");

This is an example of how we can wrap third-party APIs to enrich the expressiveness of their error handling. This approach also allows for progressive enhancement — whether you’re incrementally refactoring existing try/catch blocks or just starting to add proper error types in a codebase that’s never even heard of try/catch. (Yes, we know you’re out there.)

Another important aspect to consider is task composition, where we need to extract the results from multiple tasks, process them, and return a new value. In case any task returns an error, we simply stop the execution and propagate it back to the caller. This kind of task composition can look like this:

function task() {
  // Compute the result and exclude the error.
  const result1: number | Error1 = task1();
  if (result1 instanceof Error1) return result1;

  // Compute the result and exclude the error.
  const result2: number | Error2 = task2();
  if (result2 instanceof Error2) return result2;

  const result = result1 + result2;
}

The return type of the task is correctly inferred as number | Error1 | Error2, and type narrowing allow removing the Error types from the return values. It works, but it's not very concise. To address this issue, languages like Zig have a dedicated operator:

pub fn task() !void {
  const value = try mayFail();
  // ...
}

We can achieve something similar in TypeScript with a few simple tricks. Our goal is to create a more concise and readable way of handling errors while maintaining type safety. Let's attempt to define a similar utility function which we'll call $try, it could look something like this:

function task() {
  const result1: number = $try(task1());
  const result2: number = $try(task2());

  return result1 + result2;
}

This code looks definitely cleaner and more straightforward. Internally, the function could be implemented like this:

function $try<T>(result: T): Exclude<T, Error> {
  if (result instanceof Error) throw result;
  return result;
}

The $try function takes a result of type T, checks if it's an Error, and throws it if so. Otherwise, it returns the result, with TypeScript inferring the return type as Exclude.

We've gained a lot in readability and clarity, but we've lost the ability to type expected errors, moving them to the unexpected category. This isn't ideal for many scenarios.

We need a native way to collect the errors types, perform type narrowing, and terminate execution if an error occurs, but we are running short on JavaScript constructs. Fortunately, Generators can come to our rescue. Though often overlooked, they can effectively handle complex control flow problems.

With some clever coding, we can use the yield keyword to extract the return type from our tasks. yield passes control to another process that determines whether to terminate execution based on whether an error is present. We’ll refer to this functionality as $macro, as if it extends the language itself:

// ?^ result: number | Error1 | Error2
const result = $macro(function* ($try) {
  const result1: number = yield* $try(task1());
  const result2: number = yield* $try(task2());

  return result1 + result2;
});

We'll discuss the implementation details later. For now, we've achieved our compact syntax at the cost of introducing an utility. It accepts tasks following our convention and returns a result with the same convention: this ensures the abstraction remains confined to its intended scope, preventing it from leaking into other parts of the codebase — neither in the caller nor the callee.

As it's still possible to have the "vanilla" version with if statements, paying for slightly higher verbosity, we've struck a good balance between conciseness and keeping everything with no abstraction. Moreover, we've got a potential starting point to inspire new syntax or a new part of the standard library, but that's for another post and the ECMAScript committee will have to wait for now.

Wrapping Up

Our journey could end here: we've highlighted the limitations of current error management practices in JavaScript, introduced a convention that cleanly separates expected from unexpected errors, and tied everything together with strong type definitions.

As obvious as it may seems, the real strength of this approach lies in the fact that most JavaScript functions are just a particular case of this convention, that happens to return no expected error. This makes integrating with code written without this convention in mind as intuitive and seamless as possible.

One last enhancement we can introduce is simplifying the handling of unexpected errors, which up to now still requires the use of try/catch. The key is to clearly distinguish between the task result and unexpected errors. Taking inspiration from Go's error-handling pattern, we can achieve this using a utility like:

const [result, err] = $trycatch(task);

This utility adopts a Go-style tuple approach, where the first element is the task's result, and the second contains any unexpected error. Exactly one of these values will be present, while the other will be null.

But we can take it a step further. By leveraging TypeScript's type system, we can ensure that the task's return type remains unknown until the error is explicitly checked and handled. This prevents the accidental use of the result while an error is present:

const [result, err] = $trycatch(() => "succeed!");
// ?^ result: unknown
// ?^ err: Error | null

if (err !== null) {
  return;
}

result;
// ?^ result: string

Due to JavaScript's dynamic nature, any type of value can be thrown. To avoid falsy values that can create and subtle bugs when checking for the presence of an error, err will be an Error object that encapsulates the thrown values and expose them through Error.cause.

To complete out utility, we can extend it to handle asynchronous functions and promises, allowing the same pattern to be applied to asynchronous operations:

// Async functions.
const [result, err] = await $trycatch(async () => { ... });

// Or Promises.
const [result, err] = await $trycatch(new Promise(...));

That's enough for today. I hope you’ve enjoyed the journey and that this work inspires new innovations in the Javascript and Typescript ecosystem.

How to implement the code in the articles, you ask? Well, of course there's a library! Jokes aside, the code is straightforward, but the real value lies in the design and thought process behind it. The repository serves as a foundation for ongoing discussions and improvements. Feel free to contribute or share your thoughts!

See you next time — peace ✌️.

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