Benchmark: Lodash deep clone (v4.17.20) vs JSON.stringfy

HTML Preparation code:


<script src='https://cdn.jsdelivr.net/npm/lodash@4.17.20/lodash.min.js'></script>

Script Preparation code:

window.obj = { foo: 'some string', bar: 'another', subObj: { hey: 'you', what: 'are you doing' } }

Tests:

Lodash deep clone
const objClone = _.cloneDeep(window.obj);
JSON.stringfy
const objClone = JSON.parse(JSON.stringify(window.obj));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash deep clone
JSON.stringfy

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36

Browser/OS: Chrome 124 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Lodash deep clone	1986753.4 Ops/sec
JSON.stringfy	1775136.5 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 the performance of two approaches for creating a deep clone of an object: Lodash's `cloneDeep` function and JSON stringification using `JSON.parse(JSON.stringify())`.

**Options Being Compared**

There are two options being compared:

1. **Lodash's `cloneDeep` function**: This is a utility function from the Lodash library that creates a deep copy of an object.
2. **JSON stringification with `JSON.parse(JSON.stringify())`**: This approach serializes the original object to a JSON string, and then parses the resulting string back into an object.

**Pros and Cons**

1. **Lodash's `cloneDeep` function**:
	* Pros:
		+ Creates a deep copy of the object, preserving all nested properties and their values.
		+ Often faster than manual serialization and parsing.
	* Cons:
		+ Requires including the Lodash library, which may add overhead for some use cases.
2. **JSON stringification with `JSON.parse(JSON.stringify())`**:
	* Pros:
		+ Only requires a lightweight JSON parser (built-in in modern browsers).
		+ Can be faster for very large objects or in situations where memory is limited.
	* Cons:
		+ May not preserve all nested properties and their values, as some data types may not be serializable.
		+ Requires manual handling of circular references.

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides a wide range of functions for tasks such as array manipulation, object manipulation, and string manipulation. The `cloneDeep` function is one of the many utilities provided by Lodash, designed to create deep copies of objects.

**Special JS Feature/Syntax**

There doesn't appear to be any special JavaScript features or syntax being used in this benchmark.

**Other Alternatives**

If you need a faster alternative to `JSON.parse(JSON.stringify())`, you might consider using:

1. **Object.assign()**: This method creates a new object by copying all own enumerable properties from one or more source objects.
2. **Array.prototype.slice() + JSON.parse()**: You can use `Array.prototype.slice()` to create an array copy of the object's values, and then parse the resulting array into an object using `JSON.parse()`.
3. **Manual serialization/deserialization**: For very large objects or performance-critical applications, you might consider implementing your own manual serialization and deserialization mechanism.

Keep in mind that these alternatives may not be as convenient or efficient as Lodash's `cloneDeep` function, but they can provide a good starting point for customizing the cloning process to suit your specific needs.

Related benchmarks:

Lodash deep clone (v4.17.20) vs JSON.stringfy (version: 0)

Comparing performance of: Lodash deep clone vs JSON.stringfy

Created: 5 years ago by: Guest

Jump to the latest result

Lodash deep clone

JSON.stringfy

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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