Benchmark: RxJs vs Array - fixed2

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/rxjs/6.6.2/rxjs.umd.min.js'></script>

Script Preparation code:

var source = Array(1000000).fill(1).map((_, i) => Math.random());

Tests:

Array
const arrayResult = source .map(n => n * 2) .filter(n => n > 0.5) .reduce((m, c) => Math.max(m, c));
RxJs
const transducerResult = rxjs.from(source).pipe( rxjs.operators.map(n => n * 2), rxjs.operators.filter(n => n > 0.5), rxjs.operators.reduce((m, c) => Math.max(m, c)) ).subscribe(console.log);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Array
RxJs

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/133.0.0.0 Safari/537.36

Browser/OS: Chrome 133 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Array	27.7 Ops/sec
RxJs	64.7 Ops/sec

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark definition and test cases for you.

**Benchmark Definition**

The benchmark is designed to compare the performance of two approaches: using an array and using RxJS (Reactive Extensions for JavaScript). The benchmark measures the time it takes to perform a series of operations on an array of random numbers.

**Options being compared**

Two options are being compared:

1. **Array approach**: This uses a traditional JavaScript array to store and manipulate data.
2. **RxJs approach**: This uses RxJS, a library for reactive programming in JavaScript, to handle the data manipulation and processing.

**Pros and Cons of each approach**

**Array approach:**

Pros:

* Familiarity with traditional JavaScript arrays
* Easy to implement and understand

Cons:

* Can lead to performance issues due to array manipulation (e.g., filtering, mapping)
* May not be suitable for complex or real-time data processing tasks

**RxJs approach:**

Pros:

* Designed for reactive programming and handling complex data flows
* Provides a more declarative and functional programming style
* Can handle large datasets and high-performance requirements

Cons:

* Steeper learning curve due to new concepts and syntax
* May require additional setup and dependencies (e.g., RxJS library)

**Library used**

In the test cases, the `rxjs` library is used. RxJS provides a set of operators that can be chained together to process data in a reactive manner.

**Special JavaScript feature or syntax**

None mentioned explicitly in this benchmark definition.

**Other considerations**

When choosing between these two approaches, consider the following:

* Complexity of your use case: If you're working with simple datasets and need to perform basic operations, an array approach might be sufficient. However, for more complex data processing tasks or high-performance requirements, RxJS is likely a better choice.
* Learning curve: Be prepared to invest time in learning about reactive programming concepts and the RxJS library if you choose the latter.

**Alternative approaches**

Other alternatives to consider when comparing array and RxJs approaches include:

1. **Pipes**: A pipe-based approach that combines multiple operations into a single pipeline, similar to RxJS.
2. **Closures**: Using closures to encapsulate data and behavior can provide a more concise alternative to RxJS.
3. **Promises**: Using promises for asynchronous programming can offer an alternative to RxJS for some use cases.

Keep in mind that these alternatives may not provide the same level of performance or flexibility as RxJS, but they can be viable options depending on your specific requirements.

Related benchmarks:

RxJs vs Array - fixed2 (version: 0)

Comparing performance of: Array vs RxJs

Created: 5 years ago by: Guest

Jump to the latest result

Array

RxJs

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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