Benchmark: Deep copy : JSON vs Specified-Method

Script Preparation code:

function copyX(x) {
  return {
    a: x.a,
    b: x.b,
    c: {
      a: x.c.a
    }
  }
}

function cloneObject(obj) {
    var clone = {};
    for(var i in obj) {
        if(obj[i] != null &&  typeof(obj[i])=="object")
            clone[i] = cloneObject(obj[i]);
        else
            clone[i] = obj[i];
    }
    return clone;
}

var x = {
  a: 444,
  b: 666,
  c: {
    a: "dddd"
  }
}

Tests:

JSON
JSON.parse(JSON.stringify(x))
Copy (no generic)
copyX(x)
Copy (generic)
cloneObject(x)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
JSON
Copy (no generic)
Copy (generic)

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):

LLMs can make mistakes. Check important info.

I'll break down the benchmark and its test cases.

**Benchmark Overview**

The benchmark measures how fast three approaches are to create a deep copy of an object: using JSON, using a custom `copyX` function, and using a generic cloning function (`cloneObject`).

**Approaches Compared**

1. **JSON**: Using `JSON.parse(JSON.stringify(x))`, where `x` is the original object.
2. **Custom `copyX`**: A simple, non-generic function that manually copies the properties of `x`.
3. **Generic `cloneObject`**: A function that recursively clones an object's properties.

**Pros and Cons**

1. **JSON**: Pros:
	* Easy to implement
	* Fast, as it only involves serializing and deserializing the object
Cons:
	* May not work correctly with complex objects or cyclic references
	* Can be slower than custom implementations for large objects
2. **Custom `copyX`**: Pros:
	* Control over the copying process
	* May be faster than JSON for large objects
Cons:
	* Requires manual implementation, which can lead to errors
	* May not handle complex or cyclic references correctly
3. **Generic `cloneObject`**: Pros:
	* Handles complex and cyclic references correctly
	* Less prone to errors compared to custom implementations
Cons:
	* Can be slower than JSON for very large objects due to recursive cloning

**Library Usage**

The benchmark uses the `JSON.parse()` and `JSON.stringify()` methods, which are part of the JavaScript Standard Library. These methods provide a simple way to serialize and deserialize objects.

**Special JS Feature or Syntax**

None mentioned explicitly in this benchmark. However, it's worth noting that the `let` and `var` declarations used in the script preparation code are not strictly necessary and can be omitted if you're targeting modern browsers or environments that support ES6 syntax.

**Other Alternatives**

If you want to explore other approaches, consider these:

* Using a library like Lodash or Underscore.js, which provide functions for deep cloning objects.
* Implementing a custom cloning function using a different algorithm, such as iterating over the object's properties and recursively cloning each value.
* Comparing performance with other methods, such as using `Array.prototype.slice()` to create a shallow copy of an array-like object or using `Object.assign()` to create a shallow copy of an object.

These alternatives can provide more insight into optimal cloning strategies, but may require additional code and experimentation.

Latest run results:

Run details: (Test run date: 10 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/17.2.1 Safari/605.1.15

Browser/OS: Safari 17 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
JSON	1529252.0 Ops/sec
Copy (no generic)	6160773.5 Ops/sec
Copy (generic)	2802696.0 Ops/sec

Related benchmarks:

Deep copy : JSON vs Specified-Method (version: 0)

Comparing performance of: JSON vs Copy (no generic) vs Copy (generic)

Created: 5 years ago by: Guest

Jump to the latest result

JSON

Copy (no generic)

Copy (generic)

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):