How to Decouple Business Logic using Async Generators

Modern web applications are growing ever more complex, with rich user interactions, real-time updates, and sophisticated business logic. As the complexity grows, it becomes increasingly difficult to manage the codebase in a way that is understandable, maintainable, and extensible.

Tightly coupled, monolithic architectures quickly become unwieldy as new features and edge cases are added. Debugging and testing become a nightmare when a single change can have far-reaching and unpredictable effects across the entire application.

The solution lies in breaking down the complexity into smaller, more focused pieces that can be composed together in a flexible manner. This is where async generators come into play.

The Rise of Async Iterators

Async iterators were introduced in ECMAScript 2018 as a way to represent asynchronous data streams. They allow you to write code that can pause execution while waiting for the next value to become available, and resume when it does.

The for-await-of loop consumes an async iterable, while the yield keyword is used to emit values from an async generator function. Together, they provide an intuitive way to describe asynchronous workflows in a sequential, synchronous-looking manner.

Here‘s a simple example that demonstrates the basic concepts:

async function* numberGenerator() {
  yield 1;
  yield 2;
  yield 3;
}

async function main() {
  for await (const num of numberGenerator()) {
    console.log(num);
  }
}

main();

This will output:

1
2
3

Async generators are a powerful tool on their own, but their true potential shines when used to transform and compose streams of data.

Embracing Streams

The async iteration protocol is just one manifestation of a larger shift towards stream-based programming. The idea is to view an application as a series of data flows, where values are emitted over time rather than stored in memory all at once.

Streams offer several benefits over traditional data structures like arrays:

  1. Lazy evaluation: Values are only computed when needed, which can lead to significant performance improvements when dealing with large datasets or expensive computations.

  2. Memory efficiency: Only a small window of data needs to be kept in memory at any given time, making it possible to process datasets that would otherwise exceed available RAM.

  3. Composition: Streams can be transformed, filtered, merged, and split in endless ways, encouraging a highly modular architecture.

  4. Async I/O handling: Streams provide a natural way to handle asynchronous I/O like network requests, file reads, and database queries.

JavaScript has had support for synchronous iterators since ES2015, but async iterators complete the picture by allowing streams to incorporate asynchronous operations.

Thinking in Transducers

Transducers are a powerful abstraction for transforming and composing data streams. They were first introduced in the Clojure programming language, but the concept is universally applicable.

A transducer is simply a function that accepts an iterator and returns a new iterator. It encapsulates some transformation or side effect to be applied to each value flowing through the stream.

Transducers are highly composable. They can be chained together to build complex data pipelines from simple, reusable pieces. This fits perfectly with the async generator paradigm in JavaScript.

Consider a simple example of parsing and transforming a CSV stream:

async function* parseCsv(input) {
  let headers;
  for await (const line of input) {
    if (!headers) {
      headers = line.split(‘,‘);
    } else {
      const values = line.split(‘,‘);
      yield Object.fromEntries(headers.map((h, i) => [h, values[i]]));
    }
  }
}

async function* pluckField(input, fieldName) {
  for await (const item of input) {
    yield item[fieldName];
  }
}

async function* mapAsync(input, fn) {
  for await (const item of input) {
    yield fn(item);
  }
}

async function sendEmail(address) {
  // Send an email to the given address
}

async function main() {
  const fileStream = fs.createReadStream(‘users.csv‘);
  const pipeline = mapAsync(
    pluckField(
      parseCsv(fileStream), 
      ‘email‘
    ),
    sendEmail  
  );

  for await (const _ of pipeline) {
    // Consume the stream to trigger side effects
  }
}

In this example, we have three transducers: parseCsv, pluckField, and mapAsync. Each accepts an async iterator as input and returns a new async iterator reflecting the transformation.

We can compose these transducers together into a pipeline that will parse the CSV, pluck out the email field, and map each email to an async sendEmail operation. Finally, we consume the pipeline to trigger the actual execution.

The beauty of this approach is that each piece can be developed, tested, and reasoned about in isolation. The transducers don‘t know or care about the overall shape of the data flow – they simply receive an iterator as input and return a new iterator as output.

Transducers can be easily reused and recombined in endless ways to build sophisticated data processing pipelines. And because they are just functions, they can be manipulated using all the usual functional programming techniques like currying, partial application, and composition.

Reactive Extensions

The concept of async iterators and transducers fits nicely into the broader paradigm of reactive programming. Reactive programming is a way of modeling data flows and propagation of change through a system.

There are several popular reactive programming libraries in the JavaScript ecosystem, such as RxJS, Most.js, and xstream. These libraries provide powerful abstractions for working with streams of data, including a wide variety of operators for transforming, filtering, combining, and multicasting streams.

While async generators and transducers can be used directly, reactive libraries offer a more complete and battle-tested solution for complex use cases. They typically handle concerns like error propagation, cancellation, backpressure, and concurrency in a consistent and efficient way.

Here‘s how we might rewrite the CSV example using RxJS Observables:

import { from } from ‘rxjs‘;
import { map, mergeMap } from ‘rxjs/operators‘;

function parseCsv(input) {
  let headers;
  return new Observable(subscriber => {
    const subscription = input.subscribe({
      next(line) {
        if (!headers) {
          headers = line.split(‘,‘);
        } else {
          const values = line.split(‘,‘);
          subscriber.next(Object.fromEntries(headers.map((h, i) => [h, values[i]])));
        }
      },
      error(err) { subscriber.error(err); },
      complete() { subscriber.complete(); }
    });

    return () => subscription.unsubscribe();
  });
}

function sendEmail(address) {
  return new Observable(subscriber => {
    // Send the email async, emit when done
    // ...
  });
}

from(fs.createReadStream(‘users.csv‘))
  .pipe(
    parseCsv,
    map(user => user.email),
    mergeMap(sendEmail)
  )
  .subscribe(
    () => {},
    err => console.error(err), 
    () => console.log(‘Done!‘)
  );

The reactive version is a bit more verbose, but it provides a more comprehensive and flexible set of tools for handling complex async flows. Observables are a powerful abstraction that can express both synchronous and asynchronous data streams in a unified way.

Testing and Debugging

One of the biggest benefits of breaking an application into small, focused transducers is that it makes the code much easier to test and debug.

Each transducer can be tested in isolation by simply providing it with a known input stream and asserting on the output stream. This can be done with simple, synchronous data for many cases:

function* inputData() {
  yield {name: ‘Alice‘, email: ‘[email protected]‘};
  yield {name: ‘Bob‘, email: ‘[email protected]‘};
  yield {name: ‘Charlie‘, email: ‘[email protected]‘};
}

const result = [];
for (const user of pluckField(inputData(), ‘email‘)) {
  result.push(user);
}

expect(result).toEqual([
  ‘[email protected]‘,
  ‘[email protected]‘,
  ‘[email protected]‘
]);

For transducers that include async behavior, the testing is a bit more involved but still straightforward. You can use a library like async-iterator-to-array to collect the output into an array for easy assertion:

import toArray from ‘async-iterator-to-array‘;

async function* inputData() {
  yield {name: ‘Alice‘, email: ‘[email protected]‘};
  yield {name: ‘Bob‘, email: ‘[email protected]‘};
  yield {name: ‘Charlie‘, email: ‘[email protected]‘};
}

const result = await toArray(pluckField(inputData(), ‘email‘));

expect(result).toEqual([
  ‘[email protected]‘,  
  ‘[email protected]‘,
  ‘[email protected]‘
]);

Because each stage of the pipeline is decoupled from the others, you can test the edge cases and failure modes of each piece independently from the rest of the system. This makes it much easier to locate and fix bugs, as well as to ensure the correctness of the system as a whole.

Debugging is also greatly simplified by the use of small, focused transducers. When an issue arises, you can easily tap into the data stream at any point in the pipeline to inspect the flow of data and pinpoint the source of the problem.

Many reactive libraries provide rich tools for instrumenting and visualizing data flows, such as RxJS‘s tap operator and the RxJS Marbles testing toolkit. These tools can be invaluable for understanding and debugging complex async behaviors.

Real-World Applications

The techniques we‘ve covered – async generators, transducers, and reactive programming – are not just academic exercises. They have real, practical applications across a wide range of domains.

User Interface Programming

In the realm of user interface programming, these techniques can be used to great effect for managing complex user interactions and data flows.

Consider a drag-and-drop UI where the user can drag items from a palette onto a canvas, move them around, and delete them. Traditionally, this would involve a tangled web of event listeners and callbacks, with state scattered throughout the codebase.

With async generators and transducers, we can model this interaction as a stream of input events that gets transformed into a stream of application state changes. Each stage of the pipeline can be defined as a focused, testable transducer:

  • parseDragEvents: Converts raw mouse/touch events into higher-level drag events
  • applyConstraints: Enforces any constraints on the drag (e.g. snapping to grid, bounding to parent)
  • commitChanges: Applies the drag to the application state and emits a state change event

These transducers can be composed together into a single pipeline that provides a clear, declarative description of the drag-and-drop behavior. The rest of the application can then react to the state change events as needed, without any knowledge of the internal details of the drag-and-drop implementation.

This architecture leads to a much more modular, maintainable, and flexible codebase. New features can be added by simply inserting new transducers into the pipeline, without having to modify any existing code.

Data Processing Pipelines

Another common use case for these techniques is in building data processing pipelines. Whether you‘re processing log files, transforming database records, or analyzing sensor data, the pattern of composing small, focused transformations is incredibly powerful.

With async generators and transducers, you can build pipelines that are:

  • Modular: Each stage of the pipeline is defined as a separate, self-contained unit.
  • Reusable: Transducers can be easily shared and reused across multiple pipelines.
  • Composable: Transducers can be combined in arbitrary ways to build complex behaviors from simple pieces.
  • Lazy: Data is only processed as needed, which can lead to significant performance gains.
  • Memory-efficient: Only a small window of data needs to be kept in memory at any given time.

This makes it possible to build highly efficient, scalable data processing systems that can handle enormous volumes of data with ease.

Microservices Orchestration

The same principles can be applied at the architectural level to orchestrate communication between microservices.

In a microservices architecture, each service is responsible for a specific domain or capability, and services communicate with each other via well-defined APIs. However, managing the flow of data between services can quickly become complex, especially when dealing with async operations and error handling.

Async generators and transducers provide a powerful abstraction for modeling these data flows. Each service can expose an async iterator interface, allowing other services to consume its data as a stream. Transducers can then be used to transform, filter, and combine these streams as needed.

This leads to a highly decoupled architecture where each service can evolve independently, without needing to know the internal details of other services. It also makes it easy to insert new behavior (such as logging, metrics, or error handling) into the communication pipeline without modifying the services themselves.

Conclusion

Async generators and transducers are a powerful addition to the JavaScript toolbox, enabling a more modular, composable, and maintainable approach to async programming.

By modeling async operations as streams of data that can be transformed and composed using simple, focused transducers, we can break down complex behaviors into small, understandable pieces. This leads to code that is easier to reason about, test, and debug.

While these techniques are not a silver bullet, they provide a valuable set of tools for managing complexity and building robust, scalable systems. Whether you‘re building user interfaces, data pipelines, or microservices, the principles of stream-based programming and composition can help you write clearer, more maintainable code.

As with any tool, there is a learning curve involved in mastering async generators and transducers. But the benefits are well worth the investment. By embracing these techniques, you‘ll be able to build more modular, flexible, and robust systems that can scale with the demands of modern software development.

So next time you find yourself tangled in a web of callbacks and state management, take a step back and ask yourself: could this be expressed as a stream of data transformations? The answer might just surprise you!

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