Benchmark: Ramda pipe vs lodash flow

HTML Preparation code:

<script src="//cdnjs.cloudflare.com/ajax/libs/ramda/0.25.0/ramda.min.js"></script>
<script src="//cdnjs.cloudflare.com/ajax/libs/lodash-fp/0.10.4/lodash-fp.min.js"></script>

Script Preparation code:

var numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30];
var isOdd = n => n % 2 === 1;

Tests:

Ramda pipe
R.pipe(R.filter(isOdd),R.map(x => x+1));
lodash flow
_.flow(_.filter(isOdd), _.map(x => x + 1));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Ramda pipe
lodash flow

Fastest: N/A

Slowest: N/A

Latest run results:

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Autogenerated LLM Summary (model llama3.1:latest, generated one year ago):

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Let's break down what's happening in this benchmark.

**What is being tested?**

The benchmark compares the performance of two different approaches to piping functions together: Ramda's `pipe` function and Lodash's `flow` function. Both methods are used to chain multiple functions together, allowing you to compose a series of operations on a value.

**What options are compared?**

Two test cases are being compared:

1. **Ramda pipe**: This uses the `pipe` function from Ramda to chain two functions: `filter` and `map`. The first function (`filter`) takes an array of numbers as input, filters out the even numbers, and returns a new array containing only the odd numbers. The second function (`map`) takes this filtered array and adds 1 to each number.
2. **Lodash flow**: This uses the `flow` function from Lodash to chain two functions: `filter` and `map`. The setup is identical to Ramda pipe, but with Lodash's syntax.

**Pros/Cons of each approach**

Both `pipe` and `flow` serve the same purpose: to compose multiple functions together. However, they have some differences in their usage and behavior:

* **Ramda pipe**: This method uses a more functional programming style, where each function is passed as an argument to the previous one using a right-to-left order (i.e., `pipe(a, b, c)` means `c(b(a(x)))`). Ramda's `pipe` also allows for easy debugging and logging of intermediate results.
* **Lodash flow**: This method uses a more imperative programming style, where each function is passed as an argument to the previous one using a left-to-right order (i.e., `flow(a, b, c)` means `c(b(a(x)))`). Lodash's `flow` also allows for easy debugging and logging of intermediate results.

**Library usage**

Both test cases use external libraries:

1. **Ramda**: This is a popular functional programming library for JavaScript. Ramda provides a wide range of higher-order functions (HOFs) that can be used to compose and manipulate data.
2. **Lodash**: This is another popular utility library for JavaScript, which provides a set of HOFs for tasks like filtering, mapping, and reducing data.

**JS feature or syntax**

No special JS features or syntax are being used in this benchmark.

**Other alternatives**

If you need to chain multiple functions together, there are other alternatives available:

* **ES6's `compose` method**: This is a built-in method in JavaScript that allows you to compose multiple functions together. It's similar to Ramda's `pipe`, but with a more functional programming style.
* **JavaScript's `then()` method**: This is a part of the Promise API, which allows you to chain asynchronous operations together.

In summary, this benchmark compares the performance of two different approaches to piping functions together: Ramda's `pipe` function and Lodash's `flow` function. Both methods are used to compose multiple functions together, but with slightly different syntax and behavior.

Related benchmarks:

Ramda pipe vs lodash flow (version: 0)

check this out

Comparing performance of: Ramda pipe vs lodash flow

Created: 6 years ago by: Guest

Jump to the latest result

Ramda pipe

lodash flow

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.1:latest, generated one year ago):