Benchmark: Lodash cloneDeep vs JSON Clone 6

HTML Preparation code:

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

Script Preparation code:

var MyObject = {
    categoryName: '',
    mx_internal_uid: 1,
    isCategory: false,
    id: '',
    sourceId: '',
    sourceType: '',
    name: '',
    phone1: '',
    phoneOneCarrier: '',
    phone2: '',
    phoneTwoCarrier: '',
    email1: '',
    email2: '',
    emailNotification: null,
    smsNotification: null,
    contactGroupList: [],
    terminalList: []
  };

var myCopy = null;

Tests:

Deep
myCopy = _.cloneDeep(MyObject);
JSON
myCopy = JSON.parse(JSON.stringify(MyObject));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Deep
JSON

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

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

**Benchmark Definition**

The benchmark is comparing two approaches to clone (copy) an object: `lodash.cloneDeep` and `JSON.parse(JSON.stringify(obj))`. The benchmark aims to measure which approach is faster for deep object cloning.

**Options compared**

There are two options being compared:

1. **Lodash `cloneDeep`**: A function from the Lodash library that creates a shallow copy of an object with the result of recursively calling `cloneDeep` on each detected cyclic reference.
2. **JSON.parse(JSON.stringify(obj))**: A built-in JavaScript method that clones an object by serializing it to JSON and then parsing the resulting string back into a new object.

**Pros and Cons**

1. **Lodash `cloneDeep`**:
	* Pros:
		+ Can handle cyclic references, which can occur when objects reference each other.
		+ Provides more control over the cloning process through its options.
	* Cons:
		+ Requires the Lodash library to be included in the test environment.
2. **JSON.parse(JSON.stringify(obj))**:
	* Pros:
		+ Is a built-in JavaScript method, so no additional libraries are required.
		+ Can handle cyclic references, but may not be as efficient as `cloneDeep`.
	* Cons:
		+ Does not provide control over the cloning process.

**Library and its purpose**

The Lodash library is a popular utility library for JavaScript that provides a wide range of functions for tasks like array manipulation, object manipulation, and more. In this benchmark, `lodash.cloneDeep` is used to create a deep copy of an object.

**Other considerations**

* The benchmark uses Chrome 80 as the test browser, which may affect the results due to differences in JavaScript engine or browser-specific optimizations.
* The benchmark does not account for potential differences in performance between shallow and deep cloning. If the cloned objects are only used with shallow properties, `JSON.parse(JSON.stringify(obj))` might be sufficient and faster.

**Alternatives**

If you need to clone an object, but do not want to use a library like Lodash or rely on built-in methods, you can consider using:

* The `Object.assign()` method with a deep option (`Object.assign(target, source, options)`), which creates a new object by copying the specified properties from one or more source objects.
* The `Array.prototype.slice()` and `Array.prototype.concat()` methods to clone arrays.

However, keep in mind that these alternatives may not provide the same level of control as `cloneDeep` or handle cyclic references as efficiently.

Latest run results:

Run details: (Test run date: 4 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.132 Safari/537.36

Browser/OS: Chrome 80 on Windows

View result in a separate tab

Test name	Executions per second
Deep	368117.3 Ops/sec
JSON	231015.7 Ops/sec

Related benchmarks:

Lodash cloneDeep vs JSON Clone 6 (version: 3)

Comparing performance of: Deep vs JSON

Created: 4 years ago by: Registered User

Jump to the latest result

Deep

JSON

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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