Benchmark: Array.prototype.map vs Lodash.map on large data

HTML Preparation code:

<script src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.js"></script>

Script Preparation code:

var data = Array(1000000);

Tests:

Array.prototype.map
data.map(function (number) { return number * 2});
Lodash.map
_.map(data, function (number) { return number * 2});

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Array.prototype.map
Lodash.map

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 14 days ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:133.0) Gecko/20100101 Firefox/133.0

Browser/OS: Firefox 133 on Mac OS X 10.15

View result in a separate tab

Test name	Executions per second
Array.prototype.map	1144.3 Ops/sec
Lodash.map	171.7 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the benchmark test case provided by MeasureThat.net.

**What is being tested?**

The benchmark tests the performance difference between two approaches:

1. `Array.prototype.map` (native JavaScript method)
2. `_map` function from Lodash library

In particular, both methods are applied to an array of 1,000,000 elements and perform a simple transformation on each element: multiplying it by 2.

**Options compared**

The benchmark compares the performance of two approaches:

* `Array.prototype.map`: This is a native JavaScript method that applies a specified function to every element in an array.
* `_map` from Lodash library: This is a higher-order function that takes a callback function and applies it to each element in an array. It's essentially a wrapper around the native JavaScript `map` method.

**Pros and Cons**

Here are some pros and cons of each approach:

* `Array.prototype.map`:
	+ Pros:
		- Native JavaScript, so it should be faster and more efficient.
		- Less overhead compared to using a library like Lodash.
	+ Cons:
		- Can be less readable due to the use of `this` context and binding.
		- May have limitations in terms of functional programming capabilities.
* `_map` from Lodash library:
	+ Pros:
		- Provides a more concise and readable way to perform transformations on arrays.
		- Offers additional features like memoization, caching, and support for more complex data structures.
	+ Cons:
		- Introduces an external dependency (Lodash library), which can increase the bundle size and complexity.
		- May have a slight performance overhead compared to native JavaScript `map`.

**Other considerations**

In terms of special JavaScript features or syntax, neither approach uses anything that's not standard in modern JavaScript. However, if you're using ES6+ features like `const`, `let`, or `class` declarations, you might want to consider those when writing your own benchmark code.

**Alternatives**

If you need to compare the performance of other array methods or libraries, here are some alternatives:

* For native JavaScript methods:
	+ `Array.prototype.forEach`
	+ `Array.prototype.filter`
	+ `Array.prototype.reduce`
* For other library-based approaches:
	+ Use functions like `forEach`, `filter`, and `reduce` from the `array` method in Lodash.
	+ Consider using functional programming libraries like Ramda or Purescript.

In terms of other benchmarking frameworks, MeasureThat.net is quite unique because it allows users to create their own microbenchmarks with a simple HTML and JavaScript setup. However, if you're looking for alternative frameworks, you might want to explore options like:

* Benchmark.js
* JSPerf
* Webperf

These tools provide more features and flexibility for creating complex benchmarks, but they often require more setup and configuration compared to MeasureThat.net's simple approach.

Related benchmarks:

Array.prototype.map vs Lodash.map on large data (version: 0)

Approaches performance

Comparing performance of: Array.prototype.map vs Lodash.map

Created: 4 years ago by: Guest

Jump to the latest result

Array.prototype.map

Lodash.map

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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