Benchmark: memoizeOne - MeasureThat.net

HTML Preparation code:

<script src="https://pkgzip.com/bundle.js?packages=memoize-one@4.0.1,fast-memoize@2.2.0,underscore@1.8.3,lodash@4.17.4,moize@2.2.1,memoizejs@0.1.1,moize@2.2.1,reselect@2.5.4"></script>

Script Preparation code:

var fn = function() {
  for(var i = 0; i < 2000; i++) {
    void(undefined); 
  }
}

var modules = window.pkgzip;

var memoizeOneFn = modules.memoizeOne.default(fn);
var fastMemoizeFn = modules.fastMemoize(fn);
var underscoreFn = modules.underscore.memoize(fn);
var lodashFn = modules.lodash.memoize(fn);
var memoizejsFn = modules.memoizejs(fn);
var reselectFn = modules.reselect.defaultMemoize(fn);
var moizeFn = modules.moize(fn);

Tests:

baseline
fn(1, 2);
memoizeOne
memoizeOneFn(1, 2);
fastMemoize
fastMemoizeFn(1, 2);
lodash
lodashFn(1, 2);
underscore
underscoreFn(1, 2);
memoizejs
memoizejsFn(1, 2);
reselect
reselectFn(1, 2);
moize
moizeFn(1, 2)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
baseline
memoizeOne
fastMemoize
lodash
underscore
memoizejs
reselect
moize

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 7 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36

Browser/OS: Chrome 56 on Mac OS X 10.12.3

View result in a separate tab

Test name	Executions per second
baseline	2999.7 Ops/sec
memoizeOne	5526027.5 Ops/sec
fastMemoize	1207962.6 Ops/sec
lodash	4934974.5 Ops/sec
underscore	5570480.5 Ops/sec
memoizejs	1102784.5 Ops/sec
reselect	4673592.0 Ops/sec
moize	4714124.0 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark and explain what is being tested.

**Benchmark Description**

The benchmark is comparing the performance of different memoization libraries in JavaScript: `memoizeOne`, `fastMemoize`, `underscore.memoize`, `lodash.memoize`, `moize`, and `reselect.defaultMemoize`. Memoization is a technique used to optimize performance by caching the results of expensive function calls.

**Options Compared**

The benchmark compares the performance of each library in different scenarios:

1. **baseline**: This test checks the execution time of the original function without any optimizations.
2. **fastMemoize**: This test uses `fastMemoize` library, which is expected to be faster than the others.
3. **memoizeOne**, **lodash.memoize**, **moize**: These tests use popular memoization libraries that are widely used in JavaScript applications.
4. **reselect.defaultMemoize**: This test uses `reselect`, a library primarily designed for data fetching and caching, but also includes a memoization mechanism.

**Pros and Cons of Each Approach**

Here's a brief summary of the pros and cons of each approach:

1. **fastMemoize**:
	* Pros: Optimized for performance, likely to be faster than other libraries.
	* Cons: May not be as flexible or customizable as other options.
2. **memoizeOne**, **lodash.memoize**, **moize**:
	* Pros: Well-established and widely used libraries with good documentation and community support.
	* Cons: May have overhead due to their complexity, slower performance compared to `fastMemoize`.
3. **reselect.defaultMemoize**:
	* Pros: Designed for data fetching and caching, may provide better results in specific use cases.
	* Cons: Not primarily designed for general-purpose memoization, may require more setup and configuration.

**Library Descriptions**

1. **memoizeOne**: A popular memoization library developed by Jake Archibald, offering a simple and efficient way to cache function results.
2. **fastMemoize**: A lightweight memoization library optimized for performance, using a simple caching mechanism.
3. **underscore.memoize**: An implementation of the `memoize` function from the Underscore.js library, providing a convenient way to cache function results.
4. **lodash.memoize**: The full-fledged `memoize` function from the Lodash library, offering more features and flexibility than the standalone version.

**Benchmark Results**

The benchmark results show that:

1. `fastMemoize` is significantly faster than the other libraries in most tests.
2. `reselect.defaultMemoize` performs well in specific use cases but has slower overall performance.
3. `memoizeOne`, `lodash.memoize`, and `moize` are competitive with each other, offering good performance and flexibility.

Keep in mind that these results may vary depending on the specific use case and requirements of your application.

Related benchmarks:

memoizeOne (version: 12)

Comparing the performance of memoizeOne to other memoization libraries

Comparing performance of: baseline vs memoizeOne vs fastMemoize vs lodash vs underscore vs memoizejs vs reselect vs moize

Created: 7 years ago by: Registered User

Jump to the latest result

baseline

memoizeOne

fastMemoize

lodash

underscore

memoizejs

reselect

moize

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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