Benchmark: Jason: flatMap vs Reduce

Script Preparation code:

const assignments = [{
        id: '1',
        tasks: [
            'article', 'video', 'GO'
        ]
    },
    {
        id: '2',
        tasks: [
            'article', 'video', 'GO'
        ]
    },
];

Tests:

flatMap
const assignments = [{ id: '1', tasks: [ 'article', 'video', 'GO' ] }, { id: '2', tasks: [ 'article', 'video', 'GO' ] }, ]; const test = assignments.flatMap(assignment => assignment.tasks.map(task => { return ({ id: assignment.id, task }) }));
reduce
const assignments = [{ id: '1', tasks: [ 'article', 'video', 'GO' ] }, { id: '2', tasks: [ 'article', 'video', 'GO' ] }, ]; const test = assignments.reduce( (test, { tasks, id }) => test .concat(tasks.map(task => ({ id, task }))), [] );

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
flatMap
reduce

Fastest: N/A

Slowest: N/A

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

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Let's break down the provided benchmark and its components.

**Benchmark Purpose**

The benchmark, named "Jason: flatMap vs Reduce", tests two different approaches to flattening an array of objects in JavaScript. The test case uses two built-in methods: `flatMap` (introduced in ECMAScript 2019) and `reduce`.

**Options Compared**

The benchmark compares the performance of two options:

1. **flatMap**: A method that applies a function against an accumulator, reducing it to a single output value. In this case, it's used to flatten the `tasks` array within each object in the `assignments` array.
2. **reduce**: A method that reduces an array or an object to a single value using a callback function.

**Pros and Cons of Each Approach**

1. **flatMap**:
	* Pros: More concise code, easier to read and understand, as it avoids explicit iteration and creates a new array with the desired shape.
	* Cons: May have slower performance due to its creation of an intermediate array, which can be memory-intensive for large datasets.
2. **reduce**:
	* Pros: Highly flexible, allowing the developer to customize the reduction process using the callback function. It's also often more efficient than flatMap because it avoids creating an intermediate array.
	* Cons: Requires explicit iteration and handling of the accumulator value, making it less concise than flatMap.

**Library Usage**

In this benchmark, no libraries are explicitly used. However, modern JavaScript browsers like Chrome 94 have built-in support for the `flatMap` method, which simplifies the code but might be slower due to its intermediate array creation.

**Special JS Features/Syntax**

The benchmark uses ECMAScript 2019's `flatMap` method, a relatively new feature introduced in this version of the standard. This allows the test case to concisely express the flattening operation without explicit iteration. If you're using an older JavaScript environment that doesn't support `flatMap`, you would need to use a different approach.

**Other Alternatives**

If you want to achieve the same result as flatMap, but don't have access to this method:

1. Use `forEach` and `Array.prototype.push`: You can iterate over each task and push it to an accumulator array.
```javascript
const result = [];
assignments.forEach((assignment) => {
  assignment.tasks.forEach((task) => {
    result.push({ id: assignment.id, task });
  });
});
```
2. Use `map` with `Array.prototype.concat`: This approach creates a new array by concatenating the mapped values to an initial value.
```javascript
const result = assignments.map((assignment) => {
  return assignment.tasks.map((task) => ({ id: assignment.id, task })).concat([]);
}).reduce((acc, tasks) => acc.concat(tasks), []);
```
These alternatives are often more verbose and less efficient than using flatMap.

Keep in mind that the benchmark is specific to this particular use case (flattening arrays of objects). Depending on your project's requirements, other approaches might be more suitable or even necessary.

Related benchmarks:

Jason: flatMap vs Reduce (version: 0)

Comparing performance of: flatMap vs reduce

Created: 4 years ago by: Registered User

Jump to the latest result

flatMap

reduce

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):