Benchmark: deep clone JSON - MeasureThat.net

HTML Preparation code:


<script src='https://cdn.jsdelivr.net/npm/lodash@4.17.10/lodash.min.js'></script>

Tests:

json stringify
var obj = {a: "hello", c: "test", po: 33, arr: [1, 2, 3, 4], anotherObj: {a: 33, str: "whazzup"}}; var obj2 = JSON.parse(JSON.stringify(obj));
lodash deep clone
var obj = {a: "hello", c: "test", po: 33, arr: [1, 2, 3, 4], anotherObj: {a: 33, str: "whazzup"}}; var obj2 = _.clone(obj, true);
Object.assign
var obj = {a: "hello", c: "test", po: 33, arr: [1, 2, 3, 4], anotherObj: {a: 33, str: "whazzup"}}; var obj2 = Object.assign({}, obj);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
json stringify
lodash deep clone
Object.assign

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.

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

**Benchmark Context**

The benchmark measures the performance of three different methods for creating a deep clone of a JavaScript object:

1. `JSON.parse(JSON.stringify(obj))`
2. `_._clone(obj, true)` (using Lodash library)
3. `Object.assign({}, obj)`

**Options Compared**

These are the three options being compared:

* `JSON.parse(JSON.stringify(obj))`: This method uses JSON serialization to create a deep clone of the object.
* `_._clone(obj, true)` (Lodash): This method is part of the Lodash library and provides a function for creating deep clones. The second argument (`true`) indicates that we want to perform a "deep" copy, which means it will recursively clone nested objects as well.
* `Object.assign({}, obj)`: This method uses the spread operator (`{}`) to create a shallow copy of the object, and then recursively copies nested objects.

**Pros and Cons**

1. **JSON.parse(JSON.stringify(obj))**:
	* Pros: Simple and widely supported; doesn't require any external libraries.
	* Cons: Can be slower than other methods due to the overhead of JSON serialization/deserialization.
2. **Lodash's _._clone(obj, true)**:
	* Pros: Efficient and optimized for deep cloning; provides a more comprehensive solution for cloning complex objects.
	* Cons: Requires including the Lodash library, which adds extra dependencies.
3. **Object.assign({}, obj)**:
	* Pros: Simple and widely supported; can be faster than other methods due to its use of spread operator.
	* Cons: Creates a shallow copy, so nested objects may not be cloned recursively.

**Library and Special JS Features**

In this benchmark, the Lodash library is used for its `_._clone` function. This library provides various utility functions for working with JavaScript objects, arrays, and other data structures.

No special JS features or syntax are being tested in this benchmark.

**Other Alternatives**

If you were looking for alternative methods to create a deep clone of an object in JavaScript, some options could be:

* Using the `lodash` library with `_._cloneDeep` (note: not used in this benchmark)
* Using the `lodash` library with `_.extend({}, obj)` (not used in this benchmark)
* Implementing your own recursive function to clone objects
* Using a library like Immer for updating objects

However, `Object.assign` is often a good default choice when you just need a shallow copy of an object.

**Benchmark Preparation Code**

The script preparation code includes the following:

```javascript
<script src='https://cdn.jsdelivr.net/npm/lodash@4.17.10/lodash.min.js'></script>
```

This loads the Lodash library, which is used for the `_._clone` function in one of the benchmark cases.

Please note that you may need to adjust this code based on your specific use case and requirements.

Related benchmarks:

deep clone JSON (version: 0)

Comparing performance of: json stringify vs lodash deep clone vs Object.assign

Created: 7 years ago by: Guest

Jump to the latest result

json stringify

lodash deep clone

Object.assign

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):