Benchmark: memoized object assignment

HTML Preparation code:


  <script src="https://cdn.jsdelivr.net/npm/memoize-one@5.1.1/dist/memoize-one.js"></script>
  <script src="https://cdn.jsdelivr.net/npm/moize@5.4.7/dist/moize.js"></script>

Script Preparation code:

var fn = function(a, b) {
  Object.assign(a, b);
}


var memoizeOneFn = window.memoizeOne(fn);
var moizeFn = window.moize.default(fn);

var assignee = {a: 1};
var objToAssign = {foo: 'bar', bar: 'foo', foobar: { z: 26, y: 25, x: 24 }};

Tests:

baseline
fn(assignee, objToAssign);
memoizeOne
memoizeOneFn(assignee, objToAssign);
moize
moizeFn(assignee, objToAssign)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
baseline
memoizeOne
moize

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

**Overview of the Benchmark**

The provided benchmark is designed to compare the performance of two memoization libraries, `memoizeOne` and `moize`, in JavaScript. The benchmark is focused on testing the performance of object assignment using the `Object.assign()` method.

**Options Being Compared**

Two options are being compared:

1. **Baseline**: This represents the basic case where no optimization or caching is applied.
2. **Memoized Object Assignment**: This option uses the `memoizeOne` library to memoize the `fn` function, which assigns properties from an object `objToAssign` to another object `assignee`. The `memoizeOne` library caches the results of the function call to avoid redundant computations.

**Pros and Cons**

1. **Memoized Object Assignment (memoizeOne)**:
	* Pros:
		+ Reduces the number of times the `Object.assign()` method is called, which can be computationally expensive.
		+ Caching mechanism reduces overhead by storing results of previous calls.
	* Cons:
		+ May increase memory usage due to caching.
2. **Baseline**:
	* Pros:
		+ No additional memory usage or caching required.
	* Cons:
		+ May result in slower performance due to repeated computations.

**Library: memoizeOne**

`memoizeOne` is a JavaScript library that provides a simple way to memoize functions using an array-based caching mechanism. It's designed to be lightweight and easy to use, making it suitable for small-scale optimizations or large-scale applications where memory usage needs to be optimized.

**Library: moize**

`moize` is another JavaScript library that provides a more advanced caching mechanism than `memoizeOne`. It uses a combination of hash maps and arrays to store cached results, providing better performance and flexibility. However, it may have slightly higher memory usage compared to `memoizeOne`.

**Special JS Feature or Syntax: Object.assign()**

`Object.assign()` is a JavaScript method that copies properties from one or more source objects into a target object. It's a built-in method in modern JavaScript environments.

**Other Alternatives**

For building and running microbenchmarks, you can also consider the following alternatives:

1. **Benchmark.js**: A lightweight JavaScript benchmarking library that provides a simple way to create and run benchmarks.
2. **Benchpress.js**: A popular benchmarking library for Node.js applications that allows you to create and run benchmarks with ease.
3. **Google Benchmark**: A high-performance benchmarking framework developed by Google that provides advanced features for building and running benchmarks.

Keep in mind that the choice of benchmarking library depends on your specific use case, project requirements, and personal preferences.

Related benchmarks:

memoized object assignment (version: 0)

Comparing the performance of memoizeOne to other memoization libraries

Comparing performance of: baseline vs memoizeOne vs moize

Created: 5 years ago by: Registered User

Jump to the latest result

baseline

memoizeOne

moize

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):