Benchmark: memoizeOne - complex types - September 2020

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() {
  for(var i = 0; i < 2000; i++) {
    void(undefined); 
  }
}


var memoizeOneFn = window.memoizeOne(fn);
var moizeFn = window.moize.default(fn);


var arg = {foo: 'bar'}

Tests:

baseline
fn(arg);
memoizeOne
memoizeOneFn(arg);
moize
moizeFn(arg)

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:

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

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**Benchmark Overview**

The provided benchmark compares the performance of three memoization libraries: `memoizeOne`, `moize`, and a baseline (native JavaScript function call). The benchmark tests how these libraries optimize the execution of a simple function that performs an expensive operation (`void(undefined);`) 2000 times.

**Library Explanations**

1. **`memoizeOne`**: Memoization is a technique used to cache the results of expensive function calls so that subsequent calls with the same arguments can return the cached result instead of recalculating it. `memoizeOne` is a popular JavaScript library for memoization.
2. **`moize`**: Moize is another popular JavaScript memoization library.

**Options Compared**

The benchmark compares the performance of:

1. **Native JavaScript function call (baseline)**: This option does not use any memoization library and simply calls the function with the provided arguments without caching or optimization.
2. **`memoizeOne`**: This option uses the `memoizeOne` library to memoize the function call, which means it caches the result of the function call for each unique set of arguments.

**Pros and Cons**

* **Native JavaScript function call (baseline)**:
	+ Pros: No additional library dependencies or overhead.
	+ Cons: May perform slower due to repeated calculations without caching.
* **`memoizeOne`**: 
	+ Pros: Caches results, reducing calculation time.
	+ Cons: Introduces an additional library dependency and potential complexity in implementation.
* **`moize`**:
	+ Pros: Similar caching benefits as `memoizeOne`, but potentially more efficient due to its optimized implementation.
	+ Cons: Additional library dependency.

**Special JS Features or Syntax**

There are no specific JavaScript features or syntax mentioned in the benchmark. However, it's essential to note that memoization can help improve performance by avoiding unnecessary computations, which is a fundamental concept in software optimization.

**Alternative Approaches**

If you prefer not to use memoization libraries like `memoizeOne` and `moize`, you could consider implementing manual caching using an object or array to store the results of function calls. This approach would require more code and manual maintenance but can be a viable alternative for small projects or simple use cases.

For larger projects, you may want to explore other optimization techniques, such as:

* Using just-in-time (JIT) compilation or ahead-of-time (AOT) compilation.
* Leveraging parallel processing or concurrency using web workers or async/await.
* Implementing lazy loading or deferring computationally expensive operations until necessary.

Keep in mind that the best approach depends on the specific requirements and constraints of your project.

Related benchmarks:

memoizeOne - complex types - September 2020 (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):