Benchmark: clone vs json.pare json.stringify

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 = _.clone(MyObject);
Json clone
myCopy = JSON.parse(JSON.stringify(MyObject));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash cloneDeep
Json clone

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 explanation of the provided benchmark.

**Benchmark Overview**

The benchmark measures the performance difference between two approaches: cloning an object using Lodash's `cloneDeep` function and stringifying/ parsing JSON manually using the `JSON.stringify` and `JSON.parse` methods, respectively. The benchmark is designed to test the creation of a deep copy of an object with nested properties.

**Benchmark Definition JSON**

The benchmark definition json consists of three main parts:

1. **Script Preparation Code**: This section defines the JavaScript object `MyObject` with various properties, including a nested object `jayson`. It also initializes the `myCopy` variable to null.
2. **Html Preparation Code**: This section includes a script tag that loads the Lodash library (version 4.17.5) from a CDN.
3. **Benchmark Definitions**: This section defines two individual test cases:
	* **Lodash cloneDeep**: Creates a deep copy of `MyObject` using Lodash's `cloneDeep` function and assigns it to `myCopy`.
	* **Json clone**: Creates a deep copy of `MyObject` by stringifying the original object, parsing the resulting JSON string, and assigning it to `myCopy`.

**Options Compared**

The benchmark compares two approaches:

1. **Lodash cloneDeep**: Uses Lodash's `cloneDeep` function to create a deep copy of the object.
2. **Json clone**: Manually creates a deep copy by stringifying the original object using `JSON.stringify` and parsing the resulting JSON string using `JSON.parse`.

**Pros and Cons**

1. **Lodash cloneDeep**:
	* Pros: Fast, efficient, and easy to use.
	* Cons: Requires an external library (Lodash) to be loaded.
2. **Json clone**:
	* Pros: No external dependencies required; can be used without libraries.
	* Cons: Manual implementation may lead to errors or performance issues due to unnecessary string manipulations.

**Library - Lodash**

Lodash is a popular JavaScript utility library that provides a collection of functional programming helpers, including cloning functions like `cloneDeep`. The `cloneDeep` function creates a deep copy of an object, recursively copying all nested properties and arrays.

**Special JS Feature/ Syntax**

None mentioned in this benchmark.

**Other Alternatives**

If you need to create a deep copy without using Lodash, other alternatives include:

1. **Using `Array.prototype.slice()`**: Create a shallow copy by slicing the original array.
2. **Using `Object.assign()` with `...` spread operator**: Create a shallow copy by spreading the original object's properties onto a new object.
3. **Manual recursive cloning**: Implement a custom function to recursively clone nested objects and arrays.

Keep in mind that these alternatives may not provide the same level of performance or ease of use as Lodash's `cloneDeep`.

Related benchmarks:

clone vs json.pare json.stringify (version: 0)

Comparing performance of: Lodash cloneDeep vs Json clone

Created: 7 years ago by: Registered User

Jump to the latest result

Lodash cloneDeep

Json 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):