Benchmark: Lodash cloneDeep vs structuredClone vs JSON PARSE Test

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);
Native structuredClone
myCopy = structuredClone(MyObject);
JSON String parse
myCopy = JSON.parse(JSON.stringify(MyObject))

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash cloneDeep
Native structuredClone
JSON String parse

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 break down the benchmark and explain what's being tested, compared, and their pros and cons.

**Benchmark Overview**

The benchmark compares three methods to create a deep copy of an object: `_.cloneDeep` from Lodash, `structuredClone` (a native JavaScript method introduced in ES2022), and `JSON.parse(JSON.stringify(MyObject))`.

**Test Cases**

1. **Lodash cloneDeep**: This test uses the `_.cloneDeep` function from Lodash to create a deep copy of the `MyObject`. `_` is an alias for the Lodash library, which provides utility functions for functional programming.
2. **Native structuredClone**: This test uses the `structuredClone` method (introduced in ES2022) to create a deep copy of the `MyObject`.
3. **JSON String parse**: This test uses the `JSON.parse(JSON.stringify(MyObject))` syntax to create a deep copy of the `MyObject`. Note that this method does not support cyclic references, which can lead to incorrect results.

**Comparison**

The benchmark compares the performance of these three methods across different browsers and execution rates. The results show that:

* Lodash's `_.cloneDeep` is generally faster than the native `structuredClone` method.
* The `JSON String parse` method is slower and less reliable, especially with large input sizes.

**Pros and Cons**

1. **Lodash cloneDeep**: Pros:
	* Highly optimized and performant.
	* Supports cyclic references out of the box.
Cons:
	* Requires Lodash library inclusion.
2. **Native structuredClone**: Pros:
	+ Native implementation, no additional library required.
	+ Supports cyclic references.
Cons:
	+ Limited support in older browsers (pre-ES2022).
3. **JSON String parse**: Pros: None significant.

**Other Considerations**

* The `structuredClone` method is designed to work with structured data formats like JSON and XML. It's optimized for performance and safety, but might not be suitable for all use cases.
* Lodash's `_.cloneDeep` is a well-maintained and widely adopted solution, but it may have performance overhead due to its complexity.

**Alternatives**

If you don't want to use Lodash or rely on the native `structuredClone` method, you can consider:

1. **lodash-es**: A lightweight version of Lodash that's compatible with modern browsers.
2. **JSON.parse(JSON.stringify()) + cyclic references workarounds**: If you need to support older browsers, you might consider implementing a workaround for cyclic references using techniques like JSON string manipulation or caching.

Keep in mind that these alternatives may introduce additional complexity and performance overhead.

I hope this explanation helps!

Related benchmarks:

Lodash cloneDeep vs structuredClone vs JSON PARSE Test (version: 1)

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

Comparing performance of: Lodash cloneDeep vs Native structuredClone vs JSON String parse

Created: 2 years ago by: Registered User

Jump to the latest result

Lodash cloneDeep

Native structuredClone

JSON String parse

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):