Benchmark: Spread operator vs Lodash clone for object

HTML Preparation code:

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

Script Preparation code:

function createNestedObject(nestLevel) {
    return {
        a: 'a',
        b: 1,
        c: nestLevel === 0 ? 'c' : createNestedObject(nestLevel - 1)
    };
}
var obj = createNestedObject(100);

Tests:

Spread Operator
var clonedObj = {...obj};
Lodash Clone
var clonedObj = _.clone(obj);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Spread Operator
Lodash Clone

Fastest: N/A

Slowest: N/A

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

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I'd be happy to help explain the benchmark and its results.

**Benchmark Overview**

The benchmark compares two approaches for creating a deep copy of an object using the JavaScript spread operator (`...`) versus Lodash's `clone()` function.

**Spread Operator vs Lodash Clone**

The spread operator is a built-in JavaScript feature introduced in ECMAScript 2018 (ES2018). It allows you to create a new array or object by copying the properties of an existing one. In this benchmark, the test case creates a nested object using the `createNestedObject()` function and then uses the spread operator to create a shallow copy of it.

On the other hand, Lodash is a popular JavaScript library that provides various utility functions for tasks such as data manipulation, string manipulation, and more. The `clone()` function in Lodash creates a deep copy of an object by recursively cloning its properties.

**Pros and Cons of Each Approach**

* **Spread Operator:**
	+ Pros:
		- Lightweight and efficient
		- No additional library dependencies required
	+ Cons:
		- Only works with objects that can be spread (arrays, objects)
		- May not work correctly for nested objects or arrays with complex data structures
		- Can be slower than Lodash's `clone()` function for large datasets
* **Lodash Clone:**
	+ Pros:
		- Provides a robust and efficient way to create deep copies of objects
		- Handles nested objects and arrays with complex data structures correctly
		- Often faster than the spread operator for large datasets
	+ Cons:
		- Requires additional library dependencies (Lodash)
		- May have overhead due to the need to include an external library

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides over 140 functions and methods for tasks such as:

* Data manipulation (e.g., `clone()`, `merge()`)
* String manipulation (e.g., `trim()`, `escape()`)
* Array manipulation (e.g., `map()`, `filter()`)
* Object manipulation (e.g., `pick()`, `omit()`)

Lodash is widely used in the JavaScript community due to its extensive feature set and ease of use.

**Special JS Feature or Syntax**

This benchmark does not use any special JavaScript features or syntax beyond the spread operator, which is a built-in feature introduced in ES2018.

**Alternative Approaches**

Other approaches for creating deep copies of objects include:

* Using `JSON.parse(JSON.stringify(obj))`: This method uses JSON serialization and deserialization to create a deep copy of an object. However, it can be slower than Lodash's `clone()` function and may not work correctly for objects with circular references.
* Using a library like Immer: Immer is a state management library that provides a way to create immutable updates to objects. While it does provide a way to create deep copies of objects, it is not specifically designed for this purpose.

Overall, the benchmark suggests that Lodash's `clone()` function is faster and more efficient than the spread operator for creating deep copies of large objects, while still being easy to use and understand.

Related benchmarks:

Spread operator vs Lodash clone for object (version: 0)

Comparing performance of: Spread Operator vs Lodash Clone

Created: 4 years ago by: Guest

Jump to the latest result

Spread Operator

Lodash Clone

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):