Benchmark: deep-copy-compare

HTML Preparation code:

<script src="//cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.4/lodash.min.js"></script>

Script Preparation code:

var address = {
  "prefix": "",
  "suffix": "",
  "firstName": "Bryan",
  "middleName": null,
  "lastName": "Nichols",
  "line1": "13256 NE 20TH ST",
  "line2": "",
  "city": "BELLEVUE",
  "stateCode": "WA",
  "postalCode": "98005-2021",
  "countryCode": "US",
  "phoneNumber": "4254445555",
  "scrubStatus": "SCRUBBED_ADDRESS",
  "isPayPalAddress": false,
  "storeId": null,
  "storeDisplayName": null,
  "storeTimeZoneId": null,
  "storeShipToNumber": null,
  "isDefault": false
}

Tests:

JSON.parse
var foo = JSON.parse(JSON.stringify(address));
cloneDeep
var foo = _.cloneDeep(address);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
JSON.parse
cloneDeep

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

Let's dive into the Benchmark Definition and explain what's being tested.

**Benchmark Definition**

The benchmark is designed to compare two approaches for deep copying an object: `JSON.parse` and `_.cloneDeep` from Lodash.

**Options Compared**

There are two options being compared:

1. **`JSON.parse(JSON.stringify(address))`**: This approach uses the `JSON.stringify()` method to convert the address object to a JSON string, and then parses it back into an object using `JSON.parse()`. The resulting object is a shallow copy of the original address.
2. **_.cloneDeep(address)**: This approach uses the `_cloneDeep` function from Lodash to create a deep copy of the address object.

**Pros and Cons**

1. **`JSON.parse(JSON.stringify(address))`**:
	* Pros:
		+ Simple and easy to understand
	* Cons:
		+ Only creates a shallow copy of the object (i.e., it only copies the top-level properties)
		+ Can be slow for large objects due to the overhead of stringification and parsing
2. **_.cloneDeep(address)**:
	* Pros:
		+ Creates a deep copy of the object, including nested properties
		+ Generally faster than `JSON.parse(JSON.stringify())` for large objects
	* Cons:
		+ Requires Lodash library to be included

**Lodash Library**

The `_cloneDeep` function is part of the Lodash library, which provides a set of utility functions for working with JavaScript data structures. The purpose of this library is to provide a consistent and efficient way to perform common operations on objects, arrays, and other data types.

**Other Considerations**

* If you're using Node.js, `JSON.parse(JSON.stringify())` can be slow due to the overhead of stringification and parsing.
* In general, it's a good practice to use libraries like Lodash for complex data manipulation tasks to avoid potential performance issues and ensure consistency across different environments.

**Alternatives**

If you don't want to use the Lodash library, you can also consider using:

1. **`Object.assign()`**: This method creates a shallow copy of an object by copying its enumerable properties.
2. **`.forEach()`**: You can use the `.forEach()` method to iterate over the properties of an object and create a new object with those properties.

However, these alternatives may not provide the same level of consistency and performance as using Lodash's `_cloneDeep` function.

In summary, the benchmark is testing two approaches for deep copying an object: `JSON.parse(JSON.stringify(address))` and _.cloneDeep(address). The latter approach from Lodash provides a consistent and efficient way to create a deep copy, but requires including the library.

Related benchmarks:

deep-copy-compare (version: 0)

Comparing performance of: JSON.parse vs cloneDeep

Created: 8 years ago by: Guest

Jump to the latest result

JSON.parse

cloneDeep

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):