Benchmark: test123-pschild1

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:

js
Array.from(Array(10000000).keys()) .filter(onlyEven) .map(timesTwo) .reduce(sum, 0);
rxjs
let { from } = rxjs; let { filter, map, reduce } = rxjs.operators; from(Array.from(Array(10000000).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
js
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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Let's dive into the world of JavaScript microbenchmarks on MeasureThat.net.

**Benchmark Definition JSON**

The provided JSON defines two benchmark tests: `test123-pschild1` and its corresponding test cases. The benchmark definition includes:

* A script preparation code that defines three functions:
	+ `onlyEven(value)`: returns `true` if the input value is even, and `false` otherwise.
	+ `timesTwo(value)`: returns twice the input value.
	+ `sum(a, b)`: returns the sum of two inputs.
* An HTML preparation code that includes a reference to the RxJS library (version 6.5.5).
* Two individual test cases:
	1. A vanilla JavaScript implementation using the defined functions and Array methods (`Array.from`, `filter`, `map`, and `reduce`).
	2. An RxJS-based implementation, which uses the same functions but leverages RxJS operators (`from`, `filter`, `map`, and `reduce`) to process the data.

**Test Case Options**

The two test cases differ in how they process the array of numbers:

1. **Vanilla JavaScript**: The test case uses traditional JavaScript methods to filter, map, and reduce the array.
2. **RxJS-based**: The test case utilizes RxJS operators to achieve the same processing pipeline.

**Pros and Cons**

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

* **Vanilla JavaScript**:
	+ Pros:
		- Easy to understand and implement for developers familiar with traditional JavaScript.
		- No dependencies on external libraries (beyond the included RxJS).
	+ Cons:
		- May be slower due to the overhead of manual array iteration and method calls.
* **RxJS-based**:
	+ Pros:
		- Often faster and more efficient, thanks to optimized library implementations and pipeline composition.
		- Easier to write complex pipelines and manage side effects.
	+ Cons:
		- Requires familiarity with RxJS and its operators.
		- May introduce additional dependencies and overhead.

**Library: RxJS**

RxJS (Reactive Extensions for JavaScript) is a popular library for working with asynchronous data streams. It provides a set of operators that can be chained together to process data in a declarative manner. In the context of this benchmark, RxJS is used to create an observable stream from the array and apply filters, mappings, and reductions.

**Special JS Features or Syntax**

The test cases do not explicitly use any special JavaScript features or syntax beyond what's typically available in modern browsers. However, it's worth noting that some JavaScript engines, like WebAssembly, may introduce additional optimizations or limitations due to their design.

**Alternative Approaches**

Other alternatives for processing large arrays might include:

* **Loops**: Using traditional loops (e.g., `for` loops) instead of array methods.
* **Native Arrays**: Leveraging native Array.js features, such as `at()`, `fill()`, or `forEach()`.
* **Web Assembly (WASM)**: Using WebAssembly's optimized array and loop implementations.

Keep in mind that the choice of approach depends on the specific requirements, performance needs, and compatibility constraints of your project.

Related benchmarks:

test123-pschild1 (version: 0)

test123

Comparing performance of: js vs rxjs

Created: 4 years ago by: Guest

Jump to the latest result

js

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):