Benchmark: Compare clone object: Spread vs Lodash vs Object assign

HTML Preparation code:

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

Script Preparation code:

var posts = {};
for (var i = 0; i < 10000; i++) {
    posts[i] = {
        id: i,
        content: "test posts " + i,
        commentsCount: 1,
        comments: {
            meta: {
                total: 1,
            },
            list: [{
                content: "just a comment"
            }]
        }
    }
}

Tests:

Spread
var newPosts = {...posts};
Lodash clone
var newPosts = _.clone(posts);
Lodash cloneDeep
var newPosts = _.cloneDeep(posts);
Object assign
var newPosts = Object.assign({}, posts)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Spread
Lodash clone
Lodash cloneDeep
Object assign

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

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I'll break down what's being tested in the provided JSON.

**Benchmark Definition**

The benchmark compares three ways to create a new copy of an object:

1. **Spread**: Using the spread operator (`{...posts}`) to create a shallow copy of the `posts` object.
2. **Lodash clone**: Using Lodash's `clone()` function to create a deep copy of the `posts` object.
3. **Lodash cloneDeep**: Using Lodash's `cloneDeep()` function to create a deep copy of the `posts` object.
4. **Object assign**: Using `Object.assign()` to create a new object and then assigning the properties of `posts` to it.

**Options Compared**

The benchmark compares these four options for creating a new copy of an object:

* Shallow copying (spread operator)
* Deep copying using Lodash's `clone()` function
* Deep copying using Lodash's `cloneDeep()` function
* Creating a new object and then assigning its properties from the original object (`Object.assign()`)

**Pros and Cons**

Here are some pros and cons of each approach:

1. **Spread**: Pros - simple, concise, and efficient for shallow copies.
Cons - may not work correctly if the object contains cyclic references or large arrays.
2. **Lodash clone**: Pros - provides a reliable way to create deep copies of objects, handling cyclic references and large arrays.
Cons - adds external dependency (Lodash) and may have performance overhead due to function call.
3. **Lodash cloneDeep**: Pros - similar to `clone()` but designed specifically for deep copying and handling edge cases.
Cons - also adds external dependency (Lodash) and may have similar performance overhead.
4. **Object assign**: Pros - simple and efficient, especially for shallow copies.
Cons - may not work correctly if the object contains cyclic references or large arrays.

**Other Considerations**

When deciding which approach to use, consider the following:

* If you need a deep copy of an object with complex nested structures, Lodash's `cloneDeep()` might be a good choice.
* If you're working with very large objects and performance is critical, using the spread operator or `Object.assign()` might be more efficient.
* If you need to handle cyclic references in your objects, consider using one of the deep copying approaches (Lodash's `clone()` or `cloneDeep()`).

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides many functions for working with objects, arrays, and other data structures. In this benchmark, Lodash is used to provide the `clone()` and `cloneDeep()` functions.

**Special JS Feature/Syntax: None mentioned**

The benchmark uses standard JavaScript features and syntax, without introducing any special or experimental features.

Related benchmarks:

Compare clone object: Spread vs Lodash vs Object assign (version: 0)

Comparing performance of: Spread vs Lodash clone vs Lodash cloneDeep vs Object assign

Created: 3 years ago by: Registered User

Jump to the latest result

Spread

Lodash clone

Lodash cloneDeep

Object assign

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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