Benchmark: Lodash cloneDeep vs structuredClone with array values

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 = {
    description: 'Creates a deep copy of source, which should be an object or an array.',
    myNumber: 123456789,
    myBoolean: true,
    jayson: {
        stringify: 'JSON.stringify() method converts a JavaScript value to a JSON string....',
        parse: 'JSON.parse() method parses a JSON string...'
    },
    arrayOfValues: [
        'asdf',
        123,
        {
            bob: 's your uncle',
            nestedArray: [123, 333, {
                prop: 123
            }, ]
        },

    ]
};

var myCopy = null;

Tests:

Lodash cloneDeep
myCopy = _.cloneDeep(MyObject);
Native structuredClone
myCopy = structuredClone(MyObject);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash cloneDeep
Native structuredClone

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's dive into the benchmark.

**What is tested?**

The provided JSON represents a JavaScript microbenchmark that compares the performance of two approaches for creating a deep copy of an object:

1. `_.cloneDeep` from Lodash
2. The native `structuredClone` API (available in modern browsers)

**Options compared:**

The benchmark tests two options:

* **Lodash cloneDeep**: A popular JavaScript library function that creates a deep copy of an object.
* **Native structuredClone**: A new API introduced in modern browsers, which allows creating a deep copy of an object.

**Pros and Cons:**

1. **Lodash cloneDeep**:
	* Pros:
		+ Wide adoption and familiarity among developers
		+ Supports a wide range of data types (arrays, objects, etc.)
		+ Can be easily used in existing codebases
	* Cons:
		+ Adds an extra dependency to the project (Lodash)
		+ May have additional overhead due to library functions
2. **Native structuredClone**:
	* Pros:
		+ Built-in API, no extra dependency required
		+ Optimized for performance by browsers
		+ Growing adoption as modern browsers support it
	* Cons:
		+ Less familiar among developers (not widely adopted yet)
		+ May not work in older browsers or environments

**Library and Purpose:**

The `structuredClone` API is a new, built-in JavaScript function introduced in ECMAScript 2020. Its purpose is to create a deep copy of an object, which can be useful for tasks like:

* Clone and mutate an object without affecting the original
* Use objects as cache keys or identifiers
* Clone complex data structures (arrays, objects, etc.)

**Special JS feature or syntax:**

There are no special features or syntaxes used in this benchmark. The focus is on comparing two existing APIs for creating a deep copy.

**Other alternatives:**

If `structuredClone` is not supported by your browser or environment, you can consider alternative approaches:

* **JSON.parse() + JSON.stringify()**: Create an object from a string representation of the original data and then parse it back to create a copy. (Performance-intensive)
* **for...of loop with Object.assign()**: Use a `for...of` loop to iterate over the properties of the original object and assign each property value to a new object using `Object.assign()`. (Less efficient than `structuredClone`)
* **lodash.cloneDeep() alternative approaches**: If you want to avoid Lodash, you can explore other library functions like `JSON.parse()` + `JSON.stringify()` or implement your own deep copy function.

Keep in mind that these alternatives may not be as efficient or reliable as the native `structuredClone` API.

Related benchmarks:

Lodash cloneDeep vs structuredClone with array values (version: 0)

https://developer.mozilla.org/en-US/docs/Web/API/structuredClone

Comparing performance of: Lodash cloneDeep vs Native structuredClone

Created: 3 years ago by: Guest

Jump to the latest result

Lodash cloneDeep

Native structuredClone

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):