Benchmark: lodash deepClone vs JSON parse stringify

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.21/lodash.min.js'></script>

Tests:

Lodash
const a = {test:{val:[1]}} const b = _.cloneDeep(a); b.test.val[0] = 2; if(a.test.val[0] === b.test.val[0] ){ throw new Error("Shallow Cloned") }
JSON.stringify.parse
const a = {test:{val:[1]}} const b = JSON.parse(JSON.stringify(a)) b.test.val[0] = 2; if(a.test.val[0] === b.test.val[0] ){ throw new Error("Shallow Cloned") }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash
JSON.stringify.parse

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:106.0) Gecko/20100101 Firefox/106.0

Browser/OS: Firefox 106 on Mac OS X 10.15

View result in a separate tab

Test name	Executions per second
Lodash	1297234.8 Ops/sec
JSON.stringify.parse	2321556.0 Ops/sec

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

LLMs can make mistakes. Check important info.

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

**Overview**

The website MeasureThat.net allows users to create and run JavaScript microbenchmarks. The provided JSON represents two test cases: `Lodash` and `JSON.stringify.parse`. These test cases compare the performance of two approaches for creating a deep copy of an object: using Lodash's `cloneDeep` function versus using `JSON.parse(JSON.stringify(a))`.

**Options being compared**

The two options being compared are:

1. **Lodash's `cloneDeep` function**: This function creates a deep copy of an object by recursively cloning its properties.
2. **`JSON.parse(JSON.stringify(a))`**: This method uses JSON serialization and deserialization to create a deep copy of an object.

**Pros and Cons**

Here are some pros and cons of each approach:

* **Lodash's `cloneDeep` function**:
	+ Pros: Efficient, handles complex data structures (e.g., arrays, objects), and is widely used.
	+ Cons: Requires Lodash to be included in the project, adds an extra dependency.
* **`JSON.parse(JSON.stringify(a))`**:
	+ Pros: Simple, lightweight, and doesn't add any dependencies. However, it may not handle complex data structures as efficiently as `cloneDeep`.
	+ Cons: Can lead to unexpected behavior if not used correctly (e.g., circular references), and may produce incorrect results for certain data types.

**Library and purpose**

In the provided benchmark, Lodash is a popular JavaScript library that provides utility functions for tasks like array manipulation, object creation, and more. `cloneDeep` is one of these utility functions, designed to create deep copies of objects.

**Special JS feature or syntax**

There are no special JavaScript features or syntax mentioned in the provided benchmark JSON.

**Other alternatives**

If you're looking for alternative methods to create a deep copy of an object, here are some options:

* **Object.assign()**: While not as efficient as `cloneDeep`, `Object.assign()` can be used to create a shallow copy of an object.
* **Array.prototype.slice()`: For arrays, you can use `slice()` to create a shallow copy. However, for more complex data structures (e.g., objects), this approach may not work as expected.

Keep in mind that creating deep copies of complex objects can be challenging and may require manual implementation or the use of libraries like Lodash.

In summary, the provided benchmark compares the performance of two approaches to create a deep copy of an object: using Lodash's `cloneDeep` function versus using `JSON.parse(JSON.stringify(a))`. While both methods have their pros and cons, Lodash's `cloneDeep` is generally considered more efficient and widely used.

Related benchmarks:

lodash deepClone vs JSON parse stringify (version: 1)

Comparing performance of: Lodash vs JSON.stringify.parse

Created: 3 years ago by: Registered User

Jump to the latest result

Lodash

JSON.stringify.parse

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):