Benchmark: Map lodash and PureJS

HTML Preparation code:

<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.11/lodash.core.js"></script>

Script Preparation code:

var users = [
  { 'user': 'joey',  'age': 32 },
  { 'user': 'ross',    'age': 41 },
  { 'user': 'chandler', 'age': 39 }
]

Tests:

Native
// Native users.map(function (o) { return o.age = 0; })
Lodash
// Native _.map(users, function (o) { return o.age = 0; })

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Native
Lodash

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark definition and test cases to understand what is being tested.

**Benchmark Definition**

The benchmark measures the performance of two approaches:

1. **Native**: This approach uses the built-in `map()` method in JavaScript, which applies a function to each element in an array and returns a new array with the results.
2. **Lodash**: This approach uses the `_` alias for the Lodash library, which provides a comprehensive set of utility functions, including `map()`. The Lodash `map()` function takes two arguments: the source array (`users`) and a callback function that is applied to each element.

**Options Compared**

The benchmark compares the performance of these two approaches:

* **Native**: Uses the built-in JavaScript `map()` method
* **Lodash**: Uses the `_` alias for the Lodash library's `map()` function

**Pros and Cons**

**Native:**

Pros:

* Faster execution, as it doesn't require loading an additional library
* Lower memory usage, as it only uses native JavaScript functions

Cons:

* May be less efficient for complex operations or large datasets, as it relies on the V8 engine's optimization capabilities
* May not provide the same level of consistency across different browsers and environments

**Lodash:**

Pros:

* Provides a more consistent and predictable performance, as the Lodash library is optimized and tested extensively
* Offers additional functionality beyond just `map()`, such as filtering, reducing, and sorting

Cons:

* Slower execution, due to the overhead of loading the Lodash library
* Higher memory usage, as it requires loading an additional library

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides a comprehensive set of functions for tasks such as:

* Array manipulation (e.g., `map()`, `filter()`, `reduce()`)
* String manipulation (e.g., `trim()`, `replace()`, `split()`)
* Object manipulation (e.g., `keys()`, `values()`, `pairs()`)
* Functional programming utilities (e.g., `curry()`, `partial()`, `memoize()`)

The Lodash library is designed to be fast, efficient, and easy to use, making it a popular choice among developers.

**Special JS Feature/ Syntax**

There are no special JavaScript features or syntax used in this benchmark. Both the Native and Lodash approaches rely on standard JavaScript functions and syntax.

**Other Alternatives**

If you're interested in exploring alternative libraries for array manipulation, here are some options:

* **Underscore.js**: A popular utility library that provides many of the same functions as Lodash.
* **Ramda**: A functional programming library that provides a different set of utilities for working with arrays and objects.
* **ES6 Array Methods**: The native JavaScript array methods (e.g., `map()`, `filter()`, `reduce()`) can often provide faster execution times than external libraries.

Keep in mind that the choice of library or approach ultimately depends on your specific use case, performance requirements, and personal preference.

Related benchmarks:

Map lodash and PureJS (version: 0)

Comparing performance of: Native vs Lodash

Created: 5 years ago by: Guest

Jump to the latest result

Native

Lodash

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):