Benchmark: lodash/keyBy vs native map

HTML Preparation code:


<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>

Script Preparation code:

var data = [
  { name: 'pi', value: 3.14 },
  { name: 'hundred', value: 100 },
  { name: 'truthy', value: true },
  { name: 'falsy', value: false },
  { name: 'estring', value: '2.71828' },
];


var mapkey = (array, fn) => {
  return new Map(array.map(i => [fn(i), i]));
}

Tests:

lodash/keyBy
_.keyBy(data, (v) => v.name);
map
mapkey(data, (v) => v.name);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lodash/keyBy
map

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (iPhone; CPU iPhone OS 15_5 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/15.5 Mobile/15E148 Safari/604.1

Browser/OS: Mobile Safari 15 on iOS 15.5

View result in a separate tab

Test name	Executions per second
lodash/keyBy	7442611.0 Ops/sec
map	2467604.5 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark compares two approaches for creating a map (an object with key-value pairs) from an array:

1. Using Lodash's `keyBy` function
2. Creating a native Map using a custom `mapkey` function

**Options Compared**

The two options are compared in terms of performance, which is measured by the number of executions per second.

**Pros and Cons of Each Approach**

**Lodash's `keyBy` Function:**

Pros:

* Simpler to write and maintain
* Lodash provides a well-tested and maintained implementation

Cons:

* May have overhead due to the library itself
* May not be optimized for performance, especially for large datasets

**Native Map using `mapkey` Function:**

Pros:

* Customizable and can be optimized for specific use cases
* No external library dependency
* Can potentially outperform Lodash's implementation if well-optimized

Cons:

* Requires more manual effort to write and maintain
* May require additional memory allocation, which can lead to performance issues in some cases

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides a wide range of functions for tasks such as array manipulation, string manipulation, and object transformation. The `keyBy` function is part of Lodash's "utility" module and is used to create an object with key-value pairs from an array.

**Custom `mapkey` Function**

The custom `mapkey` function creates a Map from an array using the `Array.prototype.map()` method and then converts it to a Map using the `Map` constructor. The callback function `(v) => v.name` is applied to each element in the array, which extracts the name property as the key.

**Special JS Feature/ Syntax: None**

There are no special JavaScript features or syntax used in this benchmark.

**Other Alternatives**

If you're interested in exploring alternative approaches for creating maps from arrays, here are a few options:

1. Using `Array.prototype.reduce()`: Instead of using `map()` and `Map` constructors, you can use the `reduce()` method to create an object with key-value pairs.
2. Using `Array.prototype.forEach()`: Similar to `reduce()`, you can use the `forEach()` method to iterate over the array and create a map.
3. Using a library like `fast-queue` or `pako`: These libraries provide optimized functions for creating maps from arrays.

However, keep in mind that these alternatives may not offer significant performance improvements compared to using the native `Map` constructor or Lodash's `keyBy` function.

Related benchmarks:

lodash/keyBy vs native map (version: 0)

Comparing performance of: lodash/keyBy vs map

Created: 2 years ago by: Registered User

Jump to the latest result

lodash/keyBy

map

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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