Benchmark: memoizeOne - 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);

Tests:

baseline
fn(1, 2);
memoizeOne
memoizeOneFn(1, 2);
moize
moizeFn(1, 2)

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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Let's dive into the world of memoization and JavaScript microbenchmarks.

**Benchmark Overview**

The provided JSON represents a benchmark for comparing the performance of two memoization libraries: `memoizeOne` and `moize`. The benchmark consists of three test cases:

1. **Baseline**: This test case measures the execution time of the original function, without any optimization.
2. **Memoize One**: This test case measures the execution time of the modified function using `memoizeOne`.
3. **Moize**: This test case measures the execution time of the modified function using `moize`.

**Script Preparation Code**

The script preparation code includes:

* A simple function `fn` that performs a loop with 2000 iterations.
* The two memoization libraries are imported and used to create new functions: `memoizeOneFn` and `moizeFn`. These functions wrap the original `fn` function, but don't actually modify it.

**Html Preparation Code**

The HTML preparation code includes links to the `memoize-one` and `moize` JavaScript files. These libraries are responsible for implementing the memoization logic.

**Memoization Libraries**

* **`memoizeOne`**: This library provides a simple way to memoize functions using a hash table-based approach.
* **`Moize` (formerly known as `memoize`)**: This library provides a more advanced way to memoize functions, using a combination of caching and reevaluation.

**Pros and Cons**

* **`Memoize One`**:
	+ Pros:
		- Simple to use
		- Fast execution times
	+ Cons:
		- May not be suitable for large datasets or complex functions
		- Not as flexible as other memoization libraries
* **`Moize`**:
	+ Pros:
		- More advanced and flexible than `memoizeOne`
		- Suitable for large datasets and complex functions
	+ Cons:
		- May have slower execution times due to caching overhead

**Other Considerations**

* **Caching**: Both libraries use caching mechanisms to store the results of expensive function calls. However, `Moize` uses a more sophisticated caching approach that reevaluates the function periodically.
* **Reevaluation**: `Moize` also implements reevaluation logic to ensure that cached results are updated when the underlying data changes.

**Special JS Features**

None mentioned in this benchmark.

**Alternatives**

Other memoization libraries available for JavaScript include:

* **LRU Cache**: A popular caching library for Node.js and browser environments.
* **memcached**: A high-performance caching server that can be used as a backend for memoization.
* **Redis**: An in-memory data store that can be used to implement memoization.

These alternatives offer different trade-offs between performance, complexity, and flexibility. The choice of which one to use depends on the specific requirements of your project.

Related benchmarks:

memoizeOne - 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):