Benchmark: sumOfEventNumbers-vanilla-vs-rxjs

HTML Preparation code:

<script src='https://unpkg.com/rxjs@6.5.5/bundles/rxjs.umd.min.js'></script>

Script Preparation code:

function onlyEven(value) {return value % 2 == 0;}
function timesTwo(value) {return value * 2;}
function sum(a, b) {return a + b;}

Tests:

vanilla
Array.from(Array(1000000).keys()) .filter(onlyEven) .map(timesTwo) .reduce(sum, 0);
rxjs
let { from } = rxjs; let { filter, map, reduce } = rxjs.operators; from(Array.from(Array(1000000).keys())).pipe( filter(onlyEven), map(timesTwo), reduce(sum, 0) );

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
vanilla
rxjs

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**

MeasureThat.net is a website that allows users to create and run JavaScript microbenchmarks. The provided benchmark definition consists of two test cases: "vanilla" and "rxjs". The purpose of this benchmark is to compare the performance of JavaScript code using traditional imperative programming (vanilla) versus reactive programming with the RxJS library.

**Test Case 1: Vanilla**

The first test case uses only vanilla JavaScript. It creates an array of 1 million numbers, filters out the odd numbers using a custom function `onlyEven`, doubles each remaining number using another custom function `timesTwo`, and finally sums up all these doubled numbers using the built-in `sum` function.

**Test Case 2: RxJS**

The second test case uses RxJS library to achieve similar functionality. It creates an observable from an array of 1 million numbers, filters out the odd numbers using the `filter` operator, doubles each remaining number using the `map` operator, and finally sums up all these doubled numbers using the `reduce` operator.

**Comparison of Approaches**

Both approaches have pros and cons:

* **Vanilla JavaScript:**
	+ Pros:
		- Simple and straightforward code.
		- No additional library dependencies.
	+ Cons:
		- Can be error-prone due to manual memory management.
		- May lead to performance issues if not optimized properly.
* **RxJS:**
	+ Pros:
		- Encapsulates complex operations in a composable and modular way.
		- Reduces the risk of errors through operator chaining.
		- Can improve performance by avoiding unnecessary computations.
	+ Cons:
		- Adds an additional library dependency.
		- May introduce overhead due to creation and disposal of observables.

**Library: RxJS**

RxJS (Reactive Extensions for JavaScript) is a library that provides a way to compose and process data streams using the observer pattern. It allows developers to write more scalable, modular, and efficient code by abstracting away low-level details such as memory management and event handling.

**Special JS Feature/Syntax: Custom Functions**

The benchmark uses custom functions `onlyEven` and `timesTwo`, which are defined in the `Script Preparation Code`. These functions are not built-in JavaScript features or syntax but are used to simulate real-world scenarios where developers might need to perform specific calculations.

**Other Alternatives**

If you're looking for alternatives to RxJS, some options include:

* **Promise-chaining**: Similar to RxJS, but with a more functional programming style.
* **Cooperative scheduling libraries**: These libraries use cooperative scheduling instead of preemptive scheduling, which can lead to better performance in certain scenarios.
* **Web workers**: If the computation is CPU-bound and requires parallelization, web workers might be a suitable alternative.

Keep in mind that each alternative has its own trade-offs and requirements. The choice ultimately depends on the specific needs and constraints of your project.

Related benchmarks:

sumOfEventNumbers-vanilla-vs-rxjs (version: 0)

Comparing performance of: vanilla vs rxjs

Created: 4 years ago by: Guest

Jump to the latest result

vanilla

rxjs

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):