Benchmark: reduce lodash/fp vs pure javascripta

HTML Preparation code:

<script src='https://cdn.jsdelivr.net/g/lodash@4(lodash.min.js+lodash.fp.min.js)'></script>

Script Preparation code:


var users = [
  { 'user': 'barney',  'age': 36, 'active': true },
  { 'user': 'fred',    'age': 40, 'active': false },
  { 'user': 'pebbles', 'age': 1,  'active': true }
];

Tests:

lodash
_.find(users, ({ age }) => age < 40)
javascript
users.find(({ age }) => age < 40)
lodash/fp
_.flow( _.find({ age: 40 }), )(users)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lodash
javascript
lodash/fp

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 its options.

**Benchmark Overview**

The benchmark measures the performance of three JavaScript approaches:

1. `lodash` (a utility library)
2. Plain JavaScript
3. `lodash/fp` (a functional programming variant of `lodash`)

**Test Cases**

Each test case represents a specific scenario, where users are filtered based on their age. The three test cases are:

1. `lodash`: Uses the `find()` method from `lodash` to filter users with an age less than 40.
2. Plain JavaScript: Uses the `find()` method ( native to JavaScript) to achieve the same result as above.
3. `lodash/fp`: Uses the `flow()` function from `lodash/fp` to compose a pipeline of functions, including filtering using `find()`, and applies it to the users array.

**Options Compared**

The options compared are:

* `lodash`: A utility library that provides various algorithms for tasks like string manipulation, array manipulation, and more.
* Plain JavaScript: Uses native JavaScript methods and does not rely on a library.
* `lodash/fp`: A functional programming variant of `lodash`, which encourages a declarative style of programming.

**Pros and Cons**

Here are some pros and cons for each approach:

* **Lodash (plain)**:
	+ Pros:
		- Well-maintained and widely-used library
		- Provides a vast array of algorithms for various tasks
		- Easy to use, with a simple API
	+ Cons:
		- Adds external dependency (library)
		- May introduce unnecessary overhead due to the library's complexity
* **Plain JavaScript**:
	+ Pros:
		- No external dependencies or overhead
		- Native methods are optimized for performance
	+ Cons:
		- Requires more manual effort and boilerplate code
		- May be less readable and maintainable, especially for complex tasks
* **Lodash (fp)**:
	+ Pros:
		- Encourages a declarative style of programming, making code more concise and expressive
		- Simplifies the composition of functions for complex operations
	+ Cons:
		- May be less efficient than plain JavaScript or `lodash` due to the additional abstraction layer
		- Requires familiarity with functional programming concepts

**Library Usage**

The test cases use the following libraries:

* `lodash`: Provides various utility functions, including `find()`.
* `lodash/fp`: A functional programming variant of `lodash`, which includes a `flow()` function for composing pipelines.

**Special JS Features/Syntax**

None of the provided benchmark options require any special JavaScript features or syntax. They are all standard JavaScript syntax.

**Alternatives**

If you're looking for alternatives to these approaches, consider:

* Using other utility libraries (e.g., `underscore`, `ramda`) instead of `lodash`.
* Implementing custom algorithms for specific tasks using native JavaScript methods.
* Exploring functional programming concepts and libraries like `fp.js` or `curry`.

Keep in mind that the choice of approach depends on your project's requirements, performance needs, and personal preference.

Related benchmarks:

reduce lodash/fp vs pure javascripta (version: 4)

Comparing performance of: lodash vs javascript vs lodash/fp

Created: 4 years ago by: Registered User

Jump to the latest result

lodash

javascript

lodash/fp

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):