Benchmark: deep freeze vs deep clone (version 2)

Script Preparation code:

function deepClone (obj) {
  const result = {}

  for (const key in obj) {
    const value = obj[key]

    if (value !== null && typeof value === 'object') {
      result[key] = deepClone(value)
    } else {
      result[key] = value
    }
  }

  return result
}

function deepFreeze (obj) {
  for (const key in obj) {
    const value = obj[key]

    if (value !== null && typeof value === 'object' && Object.isFrozen(value) === false) {
      deepFreeze(value)
    }
  }

  return Object.freeze(obj)
}

var source = {
  foo: 'foo',
  bar: 'bar',
  baz: 'baz',
  qux: {
    foo: 'foo',
    bar: 'bar',
    baz: 'baz',
    qux: {
      foo: 'foo',
      bar: 'bar',
      baz: 'baz',
      qux: {
        foo: 'foo',
        bar: 'bar',
        baz: 'baz',
        qux: 'qux'
      }
    }
  }
}

Tests:

deepFreeze
deepFreeze(source)
deepClone
deepClone(source)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
deepFreeze
deepClone

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 provided benchmark and explain what's being tested, compared options, pros and cons, and other considerations.

**What is being tested?**

The benchmark is designed to compare the performance of two methods for creating immutable data structures in JavaScript:

1. `deepFreeze`: This function from the `Object` object creates an immutable copy of an object by freezing its prototype chain.
2. `deepClone`: A custom implementation (provided in the script preparation code) creates a deep clone of an object by recursively copying all properties and their values.

**Options compared**

The benchmark compares the performance of:

* `deepFreeze`
* `deepClone`

These two approaches have different characteristics:

* `deepFreeze`:
	+ Pros: Simple, efficient, and widely supported.
	+ Cons: Can be slower than `deepClone` for very large objects due to the overhead of freezing the prototype chain.
* `deepClone` (custom implementation):
	+ Pros: Provides a more thorough clone of the object, including all nested properties and their values.
	+ Cons: Slower and less efficient than `deepFreeze` due to its recursive nature.

**Other considerations**

The benchmark also considers the following:

* The source data structure (`source`) is an object with multiple levels of nesting.
* Both `deepFreeze` and `deepClone` create immutable copies of this object, which can be beneficial for performance-critical applications.

**Library usage**

The `Object` object is used in both test cases. Specifically, the `deepFreeze` function uses the `Object.isFrozen()` method to check if a value is frozen before attempting to freeze it.

**Special JavaScript feature or syntax**

This benchmark does not use any special JavaScript features or syntax that would require specific understanding of modern JavaScript or its ecosystem. The code and concepts used are widely supported across most browsers.

**Alternatives**

Other alternatives for creating immutable data structures in JavaScript include:

* Using the `JSON.parse(JSON.stringify(obj))` method to create a deep clone.
* Utilizing a library like Lodash's `cloneDeep` function, which provides a more comprehensive and efficient implementation of deep cloning.
* Employing a custom implementation similar to the one provided in the benchmark.

Keep in mind that each alternative has its pros and cons, and the choice of approach depends on the specific requirements and constraints of your project.

Related benchmarks:

deep freeze vs deep clone (version 2) (version: 0)

can be used in similar ways when you want immutable data

Comparing performance of: deepFreeze vs deepClone

Created: 4 years ago by: Guest

Jump to the latest result

deepFreeze

deepClone

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):