Benchmark: DeepClone vs JSON Stringify/Parse

HTML Preparation code:

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

Script Preparation code:

var o = {
 a: {
   b: 1,
   c: 2,
   d: 3,
   j: {
     k: [1,2,3],
     l: [4,5,6]
   },
 },
 e: [1,2,3,4,5,6],
 f: 1,
 g: {
   h: 1,
 }
}

Tests:

Lodash cloneDeep
const a = _.cloneDeep(o)
Stringify
const a = JSON.parse(JSON.stringify(o))

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash cloneDeep
Stringify

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 8 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/139.0.0.0 Safari/537.36

Browser/OS: Chrome 139 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Lodash cloneDeep	886507.7 Ops/sec
Stringify	2013257.6 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark measures the performance of two approaches: using Lodash's `cloneDeep` function to create a deep copy of an object, and using JSON stringification and parsing to achieve the same result.

**Options Compared**

Two options are compared:

1. **Lodash cloneDeep**: This method uses Lodash's `cloneDeep` function to create a deep copy of the input object `o`. Lodash is a utility library that provides various functions for tasks like array manipulation, object creation, and more.
2. **JSON Stringify/Parse**: This approach involves stringifying (converting) the entire input object `o` into a JSON string using `JSON.stringify(o)`, and then parsing (converting back to an object) the resulting string using `JSON.parse(JSON.stringify(o))`. This method is often used for serializing and deserializing data in JavaScript applications.

**Pros and Cons of Each Approach**

1. **Lodash cloneDeep**:
	* Pros:
		+ More efficient, as it only clones a subset of the object's properties.
		+ Less memory-intensive, as it doesn't create an entire new JSON string.
	* Cons:
		+ Requires Lodash to be included in the script, which may add unnecessary overhead.
2. **JSON Stringify/Parse**:
	* Pros:
		+ Simple and straightforward implementation.
		+ No additional dependencies are required.
	* Cons:
		+ Inefficient, as it creates an entire new JSON string, which can lead to increased memory usage.
		+ May not handle cyclic references (i.e., objects referencing themselves) correctly.

**Library: Lodash**

Lodash is a popular utility library for JavaScript that provides various functions for tasks like array manipulation, object creation, and more. In this benchmark, Lodash's `cloneDeep` function is used to create a deep copy of an object.

**Special JS Feature or Syntax**

There are no special JavaScript features or syntaxes being tested in this benchmark. The focus is on comparing the performance of two different approaches: using a library (Lodash) versus a built-in method (`JSON.stringify()` and `JSON.parse()`).

**Other Alternatives**

If you're looking for alternative methods to create deep copies of objects, some other options include:

1. **Spread operator**: You can use the spread operator (`{...o}`) to create a shallow copy of an object.
2. **Object.assign()**: You can use `Object.assign()` to create a shallow copy of an object.
3. **Array.prototype.slice()**: You can use `Array.prototype.slice()` to create a shallow copy of an array.

Keep in mind that these alternatives may not provide the same level of functionality as Lodash's `cloneDeep` function, which is designed specifically for creating deep copies of objects and arrays.

Related benchmarks:

DeepClone vs JSON Stringify/Parse (version: 0)

Comparing performance of: Lodash cloneDeep vs Stringify

Created: 4 years ago by: Guest

Jump to the latest result

Lodash cloneDeep

Stringify

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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