Benchmark: _.map vs array.map

HTML Preparation code:

<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.15/lodash.core.js"></script>

Script Preparation code:

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

Tests:

array.map
users.map(function (u) {return u.age;})
_.map
_.map(users, function (u) {return u.age;})

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
array.map
_.map

Fastest: N/A

Slowest: N/A

Latest run results:

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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 explain what's being tested, compared, and some pros and cons of each approach.

**Benchmark Overview**

The benchmark compares two ways to perform the same operation: iterating over an array and mapping its elements to a new value. In this case, the original array `users` contains objects with `user` and `age` properties.

**Options Compared**

There are two options being compared:

1. **Array.prototype.map()**: This is a built-in JavaScript method that creates a new array with the results of applying a provided function to every element in the original array.
2. **Lodash's _.map()**: Lodash is a utility library for JavaScript that provides additional functions beyond the built-in ones. In this case, it provides an implementation of `_.map()` that can be used as a drop-in replacement for the native method.

**Pros and Cons**

### Array.prototype.map()

Pros:

* Built-in method, so no additional library or setup required
* Generally faster since it's implemented in native code
* Works well with large datasets

Cons:

* Can only be called on arrays (not objects)
* Requires an explicit function signature to handle errors
* May not work as expected if the original array is modified during iteration

### Lodash's _.map()

Pros:

* Can be used on any iterable object (not just arrays), making it more flexible
* Provides a robust implementation that handles edge cases and errors
* Often includes additional utility functions, such as `_.forEach()` or `_.reduce()`

Cons:

* Requires the Lodash library to be included in your project
* May have a slight performance overhead due to the additional function calls

**Library and Purpose**

Lodash is a widely-used JavaScript utility library that provides a comprehensive set of helper functions for common programming tasks. The _.map() function is just one example of its many implementations.

In this benchmark, Lodash's _.map() is used as a drop-in replacement for Array.prototype.map(). This allows users to compare the performance of both methods without having to modify their code.

**Special JS Features or Syntax**

There are no special JavaScript features or syntax mentioned in this benchmark. It only focuses on the difference between two iteration approaches and how they perform in various browsers.

**Other Alternatives**

If you're interested in exploring other alternatives, here are a few options:

* **Underscore.js**: Another popular utility library that provides similar implementations to Lodash.
* **Native implementation**: Some modern JavaScript engines, like V8 (used by Chrome), provide native implementations of Array.prototype.map() that may be faster than the built-in method.
* **Custom implementation**: Depending on your specific use case, you might consider implementing a custom iteration approach using loops and conditional statements.

In summary, this benchmark allows users to compare the performance of two common iteration approaches: `Array.prototype.map()` and Lodash's _.map(). The choice between these methods depends on your specific requirements, such as flexibility, performance, or library dependencies.

Related benchmarks:

_.map vs array.map (version: 0)

Comparing performance of: array.map vs _.map

Created: 5 years ago by: Guest

Jump to the latest result

array.map

_.map

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):