Benchmark: Lodash multiple iteration sort vs multiple array.prototype.sort

HTML Preparation code:


<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.11/lodash.min.js'></script>

Script Preparation code:

function getRandomInt(max) {
  return Math.floor(Math.random() * Math.floor(max));
}

var arr = [];
for(var i = 0; i < 100000; i++){
  arr.push({value1:getRandomInt(100),value2:getRandomInt(100),value3:getRandomInt(100),value4:getRandomInt(100),value5:getRandomInt(100)});
}

Tests:

_.sortBy
_.sortBy(arr,["value1","value2","value3","value4","value5"]);
array.prototype.sort
arr.sort((a,b) => a.value1 - b.value1); arr.sort((a,b) => a.value2 - b.value2); arr.sort((a,b) => a.value3 - b.value3); arr.sort((a,b) => a.value4 - b.value4); arr.sort((a,b) => a.value5 - b.value5);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
_.sortBy
array.prototype.sort

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 5 years ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.90 Safari/537.36

Browser/OS: Chrome 75 on Linux

View result in a separate tab

Test name	Executions per second
_.sortBy	5.0 Ops/sec
array.prototype.sort	3.5 Ops/sec

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

LLMs can make mistakes. Check important info.

**Benchmark Overview**

MeasureThat.net is a website that allows users to create and run JavaScript microbenchmarks. The provided benchmark compares two approaches: using Lodash's `sortBy` function versus multiple array prototype methods for sorting.

**Tested Options**

The benchmark tests the following options:

1. **Lodash's `sortBy` function**: This option uses the `sortBy` function from Lodash to sort the array.
2. **Multiple array prototype methods**: This option sorts the array using four separate `sort` methods, each with a different comparison function.

**Pros and Cons**

**Lodash's `sortBy` function:**

Pros:

* More concise and readable code
* Less error-prone due to Lodash's built-in type checking
* Often faster than manual implementation

Cons:

* Requires an external library (Lodash)
* May not be as performant in extreme cases or with very large datasets
* Can be slower for smaller datasets due to overhead of the function call

**Multiple array prototype methods:**

Pros:

* No external dependencies required
* More control over the sorting process
* Can be faster for very large datasets or specific use cases

Cons:

* Code can become cluttered and harder to read
* Error-prone due to manual implementation
* May require more effort to optimize performance

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides a wide range of functions for tasks such as array manipulation, string formatting, and object creation. The `sortBy` function is one of its most useful tools, allowing developers to easily sort arrays based on specific criteria.

**Special JS Feature/Syntax**

This benchmark does not use any special JavaScript features or syntax beyond what's standard in modern browsers.

**Other Alternatives**

If you're interested in exploring alternative approaches to sorting, consider the following:

1. **Using `Array.prototype.sort()` with a custom comparator**: This approach involves creating a custom function that takes two elements as input and returns a value indicating their order.
2. **Using `Array.prototype.reduce()` for sorting**: While not exactly equivalent to manual sorting, `reduce()` can be used to sort arrays by applying a cumulative operation to each element in the array.

Keep in mind that these alternatives may require more setup and expertise than using Lodash's `sortBy` function or multiple array prototype methods.

Related benchmarks:

Lodash multiple iteration sort vs multiple array.prototype.sort (version: 0)

Comparing performance of: _.sortBy vs array.prototype.sort

Created: 5 years ago by: Guest

Jump to the latest result

_.sortBy

array.prototype.sort

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):