Benchmark: .filter().map() vs .reduce test

Script Preparation code:

var array = [];
for (var i = 0; i < 10000; i++) {
  array[i] = i;
}

var firstTestResults = [];
var secondTestResults = [];

Tests:

.map().filter()
firstTestResults = array .filter(value => { return (value + 10) % 2 === 0; }) .map(value => { return value + 10; });
.reduce()
secondTestResults = array.reduce((acc, value) => { value = value + 10; if (value % 2 === 0) { acc.push(value); } return acc; }, []);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
.map().filter()
.reduce()

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/128.0.0.0 Safari/537.36

Browser/OS: Chrome 128 on Linux

View result in a separate tab

Test name	Executions per second
.map().filter()	20116.7 Ops/sec
.reduce()	31043.5 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark compares the performance of two approaches to process an array of numbers:

1. `.map()` followed by `.filter()`
2. `.reduce()`

Both tests are executed on a large array of 10,000 elements, where each element is initialized with its index (`i`).

**Options Being Compared**

Two options are being compared:

* `Array.prototype.map()`: This method creates a new array with the results of applying a provided function to every element in the original array.
* `Array.prototype.reduce()`: This method reduces an array to a single value, executing a callback function on each element in the array.

**Pros and Cons of Each Approach**

* `.map()`:
	+ Pros: Simple, easy to understand, and intuitive. It creates a new array with the desired output, making it easier to reason about the code.
	+ Cons: Can be slower for large arrays since it creates a new array with all the results, which can lead to memory issues.
* `.reduce()`:
	+ Pros: More flexible and efficient than `map()` when dealing with large arrays. It avoids creating a new array with intermediate results.
	+ Cons: Less intuitive and more complex to understand compared to `map()`.

**Library Usage**

There is no explicit library usage in the provided benchmark code, but it's worth noting that both `.map()` and `.reduce()` are built-in methods of the Array prototype in JavaScript.

**Special JS Feature/Syntax**

None of the benchmarks explicitly use any special JavaScript features or syntax. However, if you're interested in exploring other options, there are some alternative approaches, such as using `forEach()`, `for...of` loops, or even using a custom loop to iterate through the array.

**Alternatives**

If you want to explore alternative approaches, here are a few options:

* Using `forEach()`:
```javascript
array.forEach(function(value) {
  // process value
});
```
This approach uses an inline function and does not create any new arrays or objects.
* Using a `for...of` loop:
```javascript
for (const value of array) {
  // process value
}
```
This approach is similar to the `forEach()` method but uses a more modern syntax.

**Other Considerations**

When choosing between these approaches, consider the following:

* **Performance**: If you need to process large arrays and memory efficiency is crucial, `.reduce()` might be a better choice. However, if simplicity and ease of understanding are more important, `.map()` followed by `.filter()` might be a better fit.
* **Code readability**: If code readability is essential, consider using `map()` or `forEach()`, as they provide clear and concise ways to process arrays.

I hope this explanation helps you understand the provided benchmark and provides a solid foundation for exploring JavaScript performance optimizations!

Related benchmarks:

.filter().map() vs .reduce test (version: 0)

Comparing performance of: .map().filter() vs .reduce()

Created: 5 years ago by: Guest

Jump to the latest result

.map().filter()

.reduce()

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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