Benchmark: various compression libraries

HTML Preparation code:

<script src="https://unpkg.com/fflate"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/pako/2.0.3/pako.min.js"></script>
<script src="https://cdn.jsdelivr.net/gh/zhipeng-jia/snappyjs@0.7.0/dist/snappyjs.js"></script>

Script Preparation code:

var a = "";
for (var i = 0; i < 100; i++) {
    a += 'The quick brown fox jumps over the lazy dog';
    for (var j = Math.random(100); j >= 0; j--) {
        a += String.fromCharCode(Math.random() * 1000);
    }
}
var enc = new TextEncoder();
var uncompressed = enc.encode(a);
var compressed_pako = pako.gzip(uncompressed);
var compressed_snappy = SnappyJS.compress(uncompressed);

Tests:

pako: compress
pako.deflate(uncompressed)
pako: decompress
pako.inflate(compressed_pako)
fflate: compress
fflate.gzipSync(uncompressed)
fflate: decompress
fflate.decompressSync(compressed_pako);
snappyjs: compress
SnappyJS.compress(uncompressed)
snappyjs: decompress
SnappyJS.uncompress(compressed_snappy)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
pako: compress
pako: decompress
fflate: compress
fflate: decompress
snappyjs: compress
snappyjs: decompress

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36

Browser/OS: Chrome 115 on Linux

View result in a separate tab

Test name	Executions per second
pako: compress	4057.2 Ops/sec
pako: decompress	22327.3 Ops/sec
fflate: compress	5610.4 Ops/sec
fflate: decompress	45245.1 Ops/sec
snappyjs: compress	37297.0 Ops/sec
snappyjs: decompress	57095.9 Ops/sec

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

LLMs can make mistakes. Check important info.

I'll dive into explaining the benchmark.

**Benchmark Overview**

The provided JSON defines a benchmark that tests four different compression libraries: pako, fflate, snappyjs, and their respective decompression functions. The test case generates a large string of random characters, encodes it as a byte array using TextEncoder, and then compresses or decompresses the data using each library.

**Compression vs Decompression**

The benchmark compares different approaches to compression and decompression:

1. **Pako**: pako.deflate(uncompressed) compresses the data using the DEFLATE algorithm, which is a lossless compression format that combines two algorithms: LZ77 (lossless compression) and Huffman coding.
2. **FFlatle**: fflate.gzipSync(uncompressed) compresses the data using GZIP, a lossless compression format that uses a combination of Huffman coding and LZ77.
3. **Snappyjs**: SnappyJS.compress(uncompressed) compresses the data using Snappy, a fast and lightweight compression algorithm.

**Pros and Cons**

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

1. **Pako**: Pros:
	* Fast compression and decompression speeds
	* Supports both lossless and lossy compression modes
	* Simple implementation
Cons:
	* May not be as efficient as other algorithms for very large datasets
2. **FFlatle**: Pros:
	* High compression ratio, making it suitable for smaller datasets
	* Supports multiple compression formats (GZIP, zlib)
Cons:
	* Slower than pako and snappyjs for compression/decompression speeds
3. **Snappyjs**: Pros:
	* Fast compression and decompression speeds
	* Lightweight implementation
Cons:
	* May not be as effective for very large datasets

**Library Explanation**

The test case uses the following libraries:

1. **Pako**: A JavaScript port of the DEFLATE algorithm, which is a widely used lossless compression format.
2. **FFlatle**: A JavaScript implementation of GZIP, a popular lossless compression format.
3. **SnappyJS**: A JavaScript port of the Snappy algorithm, which is a fast and lightweight compression algorithm.

**Special JS Feature**

The benchmark uses a special JavaScript feature called `String.fromCharCode()` to generate random ASCII characters for the test string.

**Other Considerations**

When choosing a compression library, consider factors such as:

* Compression ratio: How much data can be reduced?
* Decompression speed: How quickly can the decompressed data be retrieved?
* Memory usage: How much memory will the compressed data require?
* Implementation complexity: How easy is it to implement and maintain?

**Alternatives**

Other compression libraries that could be used in this benchmark include:

1. **Zlib**: A widely used JavaScript port of the DEFLATE algorithm.
2. **lz-string**: A lightweight JavaScript library for lossless compression.
3. **zlib-js**: Another JavaScript implementation of the DEFLATE algorithm.

These alternatives would require modifications to the benchmark code and results analysis, but could provide additional insights into the performance of different compression algorithms.

Related benchmarks:

various compression libraries (version: 0)

emulate file compression/decompression with pako, fflate and snappyjs

Comparing performance of: pako: compress vs pako: decompress vs fflate: compress vs fflate: decompress vs snappyjs: compress vs snappyjs: decompress

Created: one year ago by: Registered User

Jump to the latest result

pako: compress

pako: decompress

fflate: compress

fflate: decompress

snappyjs: compress

snappyjs: decompress

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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