Benchmark: Lodash cloneDeep vs JSON Clone vs Spread

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...'
  }
};

var myCopy = null;

Tests:

Lodash cloneDeep
myCopy = _.cloneDeep(MyObject);
Json clone
myCopy = JSON.parse(JSON.stringify(MyObject));
Spread
myCopy = { ...MyObject };

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash cloneDeep
Json clone
Spread

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks.

**Benchmark Overview**

The provided benchmark compares three approaches to create a deep copy of an object: Lodash's `cloneDeep`, JSON cloning using `JSON.parse` and `JSON.stringify`, and spread operator syntax (`{ ...MyObject }`). The test case creates a complex object `MyObject` with nested properties, including a string value that contains special keywords.

**Options Compared**

1. **Lodash cloneDeep**: Uses the Lodash library to create a deep copy of the object.
	* Pros: Generally faster and more reliable for large objects, as it handles recursion and can handle complex data structures like arrays and objects.
	* Cons: Requires an external dependency (the Lodash library) and might not be suitable for very large objects due to its recursive nature.
2. **JSON Clone**: Uses `JSON.parse` and `JSON.stringify` to create a copy of the object.
	* Pros: Fast, lightweight, and doesn't require any external dependencies.
	* Cons: Can lead to issues with circular references, as it uses the same parsing algorithm for both creation and deserialization.
3. **Spread Operator**: Uses the spread operator syntax (`{ ...MyObject }`) to create a copy of the object.
	* Pros: Fast, lightweight, and doesn't require any external dependencies.
	* Cons: Only suitable for objects with non-iterable properties, as it won't work with objects containing functions or other iterables.

**Library Usage**

The benchmark uses the Lodash library (`lodash.js`) for the `cloneDeep` approach. Lodash is a popular utility library that provides a wide range of functional programming helpers and functional building blocks. In this case, `cloneDeep` is used to create a deep copy of the object, which is essential for handling complex data structures.

**Special JS Feature/Syntax**

The benchmark uses special keywords in the string value (`jayson: { ... }`) that contain information about the purpose of each approach. This is likely used as metadata or documentation within the benchmarking framework to provide context and insights into the results.

**Other Alternatives**

If you're interested in exploring alternative approaches, here are a few options:

1. **JSONata**: A JSON-based scripting language that can be used for object manipulation and cloning.
2. **Immutable.js**: A library that provides immutable data structures and methods for creating copies of objects.
3. **Pure Function Composition**: A programming paradigm that emphasizes the use of pure functions to create predictable and composable code.

Keep in mind that each alternative has its own trade-offs, pros, and cons, which may affect performance, readability, and maintainability of your code.

Related benchmarks:

Lodash cloneDeep vs JSON Clone vs Spread (version: 0)

Comparing performance of: Lodash cloneDeep vs Json clone vs Spread

Created: 6 years ago by: Guest

Jump to the latest result

Lodash cloneDeep

Json clone

Spread

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):