Benchmark: TextDecoder vs String.fromCharCode

Script Preparation code:

var bytes = [84,104,105,115,32,105,115,32,97,32,115,97,109,112,108,101,32,112,97,114,97,103,114,97,112,104,46];
var bufferArray = new Uint16Array(bytes);
var decoder = new TextDecoder(); // default 'utf-8' or 'utf8'

Tests:

String.fromCharCode
String.fromCharCode.apply(null, bufferArray);
TextDecoder
decoder.decode(bufferArray);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
String.fromCharCode
TextDecoder

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 17 hours ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/147.0.0.0 Safari/537.36

Browser/OS: Chrome 147 on Linux

View result in a separate tab

Test name	Executions per second
String.fromCharCode	8118327.0 Ops/sec
TextDecoder	7104509.5 Ops/sec

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

LLMs can make mistakes. Check important info.

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

**Benchmark Overview**

The benchmark is comparing two approaches to convert a Uint16Array (an array of 16-bit unsigned integers) to a string:

1. **String.fromCharCode**: This method uses the `String.fromCharCode` function to create a string from individual Unicode code points.
2. **TextDecoder**: This method uses the `TextDecoder` API, which is designed for decoding binary data into text.

**Options Compared**

The benchmark is comparing the performance of these two approaches:

* **String.fromCharCode**: This approach iterates over the Uint16Array and converts each 16-bit integer to a Unicode code point using `String.fromCharCode`. The resulting string is returned.
* **TextDecoder**: This approach creates a new instance of `TextDecoder` with a default encoding (typically 'utf-8' or 'utf8'). It then passes the Uint16Array to the `decode()` method, which decodes the binary data into a text string.

**Pros and Cons**

Here are some pros and cons of each approach:

* **String.fromCharCode**:
	+ Pros: Simple and straightforward.
	+ Cons: Can be slower due to the iteration over the array and conversion to Unicode code points.
* **TextDecoder**:
	+ Pros: Designed for decoding binary data into text, optimized for performance.
	+ Cons: Requires creating an instance of `TextDecoder`, which may incur some overhead.

**Library and Purpose**

The `TextDecoder` API is a built-in JavaScript API designed for decoding binary data into text. It's part of the Web APIs specification and provides a efficient way to convert binary data into strings. In this benchmark, it's used to compare its performance with `String.fromCharCode`.

**Other Considerations**

* **Buffer vs Uint16Array**: The benchmark uses a Uint16Array, which is a typed array designed for storing 16-bit integers. Other buffer types like ArrayBuffer or TypedArray might also be used in real-world scenarios.
* **Encoding**: The `TextDecoder` API can work with various encodings, including 'utf-8', 'utf16le', and others. However, the default encoding used here is typically 'utf-8' or 'utf8'.
* **Browser Support**: Both approaches are widely supported by modern browsers.

**Alternatives**

Some alternative approaches to convert a Uint16Array to a string could include:

* Using a library like `iconv-lite` or `utf8-buffer`, which provide efficient implementations of encoding and decoding.
* Implementing custom logic using bitwise operations and Unicode code points.
* Using WebAssembly or other low-level programming languages for performance-critical scenarios.

Keep in mind that these alternatives might not be as straightforward to implement and may incur additional overhead.

Related benchmarks:

TextDecoder vs String.fromCharCode (version: 0)

Comparing performance of: String.fromCharCode vs TextDecoder

Created: 5 years ago by: Guest

Jump to the latest result

String.fromCharCode

TextDecoder

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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