Analyzing Python Compression Libraries: zlib, LZ4, Brotli, and Zstandard
Tags: zlib, lz4, brotli, zstandard
Published: 2024-04-29 20:33:19

---
title: Analyzing Python Compression Libraries: zlib, LZ4, Brotli, and Zstandard
published: true
description: In this blog post, we will dive into a comparison of four popular Python compression libraries—zlib, LZ4, Brotli, and Zstandard—using a real-world dataset to evaluate their performance in terms of compression ratio and time efficiency.
tags: zlib, LZ4, Brotli, Zstandard
# cover_image: https://direct_url_to_image.jpg
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# published_at: 2024-04-29 20:29 +0000
---

GitHub: https://github.com/dhilipsiva/py-compress-compare

When dealing with large volumes of data, compression can be a critical factor in enhancing performance, reducing storage costs, and speeding up network transfers. In this blog post, we will dive into a comparison of four popular Python compression libraries—zlib, LZ4, Brotli, and Zstandard—using a real-world dataset to evaluate their performance in terms of compression ratio and time efficiency.

## The Experiment Setup

Our test involved a dataset roughly 581 KB in size, named sample_data.json. We executed compression and decompression using each library as follows:

* Compression was performed 1000 times.
* Decompression was repeated 10,000 times.

This rigorous testing framework ensures that we obtain a solid understanding of each library's performance under heavy load.

## Compression Ratio

The compression ratio is a key metric that represents how effectively a compression algorithm can reduce the size of the input data. Here’s how each library scored:

* Zlib achieved a compression ratio of 27.84,
* LZ4 came in at 18.23,
* Brotli impressed with a ratio of 64.78,
* Zstandard offered a ratio of 43.42.

From these results, Brotli leads with the highest compression ratio, indicating its superior efficiency in data size reduction. Zstandard also shows strong performance, while LZ4, though lower, still provides a reasonable reduction.

## Compression Time

Efficiency isn't just about space savings; time is equally crucial. Here’s how long each library took to compress the data:

* Zlib: 7.34 seconds,
* LZ4: 0.13 seconds,
* Brotli: 204.18 seconds,
* Zstandard: 0.15 seconds.

LZ4 and Zstandard excel in speed, with LZ4 being slightly faster. Zlib offers a middle ground, but Brotli, despite its high compression efficiency, takes significantly longer, which could be a drawback for real-time applications.

## Decompression Time

Decompression time is vital for applications where data needs to be rapidly restored to its original state:

* Zlib: 11.99 seconds,
* LZ4: 0.46 seconds,
* Brotli: 0.99 seconds,
* Zstandard: 0.46 seconds.

Again, LZ4 and Zstandard show excellent performance, both under half a second. Brotli presents a decent time despite its lengthy compression time, while zlib lags behind in this aspect.

## Conclusion

Each library has its strengths and weaknesses:

* Brotli is your go-to for maximum compression but at the cost of time, making it suitable for applications where compression time is less critical.
* Zstandard offers a great balance between compression ratio and speed, recommended for a wide range of applications.
* LZ4 shines in speed, ideal for scenarios requiring rapid data processing.
* Zlib provides moderate performance across the board.

Choosing the right library depends on your specific needs, whether it’s speed, space, or a balance of both. This experiment provides a clear picture of what to expect from these libraries, helping you make an informed decision based on your application's requirements.