Benchmark: loop vs stringify

Script Preparation code:

window.fdeq = function equal(a, b) {
    Object.entries(a).some(([field, value]) => 
value !== b[field]);

};

window.foo = Array.from({length: 40}, () => ([Math.random() * 40, Math.random() * 40]));
window.bar = Array.from({length: 40}, () => ([Math.random() * 40, Math.random() * 40]));

Tests:

loop
fdeq(foo,bar)
stringify
JSON.stringify(foo) === JSON.stringify(bar)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
loop
stringify

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36 Edg/122.0.0.0

Browser/OS: Chrome 122 on Windows

View result in a separate tab

Test name	Executions per second
loop	644250.1 Ops/sec
stringify	48473.3 Ops/sec

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

LLMs can make mistakes. Check important info.

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

**Benchmark Overview**

The test compares two approaches to compare two arrays of random numbers: using a custom `equal` function (`fdeq`) that loops through each field and value in an object, versus using the built-in `JSON.stringify` method.

**Custom `equal` Function (`fdeq`)**

* Purpose: To create a custom equality check function that compares two objects based on their fields and values.
* Library: None (self-defined)
* Pros:
	+ Allows for fine-grained control over the comparison process, enabling more complex comparisons beyond simple object identity checks.
	+ Can be optimized or modified to improve performance, if needed.
* Cons:
	+ Requires manual implementation of the comparison logic, which can lead to errors and maintenance issues.
	+ May have performance overhead due to the explicit looping through each field.

**`JSON.stringify` Method**

* Purpose: To convert an object into a string representation that can be compared for equality using `===`.
* Library: Built-in JavaScript
* Pros:
	+ Fast and efficient, as it leverages optimized implementation of the serialization process.
	+ Simple to implement, as it relies on the built-in serialization method.
* Cons:
	+ Limited control over the comparison process, as it depends on the internal implementation details of `JSON.stringify`.
	+ May not work correctly for all types of objects (e.g., those with circular references or complex data structures).

**Other Considerations**

* Performance: The test measures the execution rate of each approach. The custom `equal` function might be optimized to outperform `JSON.stringify`, but this depends on the specific implementation and testing conditions.
* Error Handling: Both approaches assume that the input objects are well-formed and have the same structure. If one or both inputs contain errors, the comparison results may not be accurate.

**Test Cases**

The benchmark consists of two test cases:

1. `loop`: Uses the custom `equal` function (`fdeq`) to compare the `foo` and `bar` arrays.
2. `stringify`: Uses the built-in `JSON.stringify` method to compare the `foo` and `bar` arrays.

**Alternative Approaches**

Other possible approaches to compare two objects or arrays include:

* Using a library like Lodash's `isEqual` function, which provides a more robust and flexible comparison mechanism.
* Implementing a custom comparison algorithm using bitwise operations or other low-level techniques.
* Using a different data structure, such as a binary search tree or a heap, to efficiently compare elements.

However, these alternatives may introduce additional complexity, performance overhead, or limitations in terms of control over the comparison process.

Related benchmarks:

loop vs stringify (version: 0)

Comparing performance of: loop vs stringify

Created: 2 years ago by: Guest

Jump to the latest result

loop

stringify

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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