Benchmark: ArrayBuffer - MeasureThat.net

Script Preparation code:

var str = "aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7aa34a5b6c5acb67acb5acb678acb6ac4b5a6c7b5a4c3b45a67c8b76a54cb34a5c6b7";

var chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";
var encoder = new TextEncoder();

function _base64ToArrayBufferOptimized(base64) {
    return encoder.encode(base64);
}

function _base64ToArrayBuffer(base64) {
    var binary_string =  window.atob(base64);
    var len = binary_string.length;
    var bytes = new Uint8Array( len );
    for (var i = 0; i < len; i++)        {
        bytes[i] = binary_string.charCodeAt(i);
    }
    return bytes.buffer;
}

Tests:

Encoder
_base64ToArrayBufferOptimized(str);
Simple
_base64ToArrayBuffer(str);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Encoder
Simple

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (iPhone; CPU iPhone OS 18_3_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) FxiOS/135.2 Mobile/15E148 Safari/605.1.15

Browser/OS: Mobile Safari 18 on iOS 18.3.1

View result in a separate tab

Test name	Executions per second
Encoder	5024713.0 Ops/sec
Simple	656130.0 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 benchmark and its results in a way that's easy to understand, even for those without deep knowledge of JavaScript.

**What is being tested?**

The provided JSON represents two microbenchmarks: `ArrayBuffer` and two test cases named "Encoder" and "Simple". The tests are designed to measure the performance of converting base64-encoded strings to ArrayBuffer objects.

**Options compared**

Two options are compared:

1. **_base64ToArrayBufferOptimized(base64)**: This function uses the `TextEncoder` API, which is a modern way to work with Uint8Array and ArrayBuffers in JavaScript.
2. **_base64ToArrayBuffer(base64)**: This function uses the `window.atob()` method, which is an older way to decode base64-encoded strings.

**Pros and Cons of each approach**

**TextEncoder API (Optimized):**

Pros:

* More efficient and modern
* Better support for large datasets and streaming data

Cons:

* Requires a relatively new browser version (Firefox 130 or later)
* May have performance variations depending on the browser's implementation

**`window.atob()` method:**

Pros:

* Widespread support across older browsers and versions
* Simpler to implement

Cons:

* Less efficient, especially for large datasets
* May not work well with streaming data

**Other considerations**

* The tests use a large base64-encoded string (`str`) as input, which may affect the results.
* The `TextEncoder` API is generally faster and more efficient than using `window.atob()`.

**Library used**

The `TextEncoder` API is used in the optimized function. It's a built-in JavaScript library that provides a way to encode strings into Uint8Array objects.

**Special JS feature or syntax**

None mentioned, as both test cases use standard JavaScript features.

**Benchmark preparation code**

The provided script preparation code generates a large base64-encoded string (`str`) and defines two functions: `_base64ToArrayBufferOptimized` and `_base64ToArrayBuffer`. These functions are then used in the benchmark definitions.

**Alternatives**

If you need to measure performance of converting base64-encoded strings to ArrayBuffers in older browsers, you may want to consider using a library like `base64-js`, which provides a more efficient and modern way to work with base64 encoding.

Additionally, if you're interested in exploring other approaches or testing different libraries, you can look into:

* Using Web Workers to parallelize the tests
* Measuring performance using other benchmarking tools, such as WebPageTest or Apache JMeter
* Testing different input sizes and scenarios

Related benchmarks:

ArrayBuffer (version: 0)

Comparing performance of: Encoder vs Simple

Created: 9 years ago by: Guest

Jump to the latest result

Encoder

Simple

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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