pako vs snappy vs zlib vs lz4
Compression Libraries for Web Development Comparison
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What's Compression Libraries for Web Development?

Compression libraries are essential tools in web development that help reduce the size of data transmitted over networks, improving performance and efficiency. These libraries implement various algorithms to compress and decompress data, making them crucial for optimizing web applications, especially when dealing with large datasets or files. Each library has its unique strengths and use cases, catering to different requirements in terms of speed, compression ratio, and compatibility with various data formats.

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pako29,407,7325,7211.64 MB262 years ago(MIT AND Zlib)
snappy365,85217114.2 kB12-MIT
zlib351,82862-1114 years ago-
lz4105,240437-414 years agoMIT
Feature Comparison: pako vs snappy vs zlib vs lz4

Compression Speed

  • pako:

    Pako offers good compression speeds, especially for gzip and deflate formats. While not as fast as LZ4, it strikes a balance between speed and compression efficiency, making it suitable for web applications that require quick data handling.

  • snappy:

    Snappy is designed for high-speed compression, often outperforming other algorithms in terms of speed. It is optimized for low-latency applications, ensuring rapid data processing without significant overhead.

  • zlib:

    Zlib provides a moderate compression speed that is generally slower than LZ4 and Snappy but offers a good trade-off between speed and compression ratio. It is suitable for applications where both factors are important.

  • lz4:

    LZ4 is known for its exceptional compression and decompression speeds, making it one of the fastest algorithms available. It is particularly advantageous in scenarios where speed is more critical than achieving the highest compression ratio.

Compression Ratio

  • pako:

    Pako delivers a competitive compression ratio, especially for gzip and deflate formats. It is effective in reducing data size while maintaining reasonable speeds, making it a solid choice for web applications.

  • snappy:

    Snappy focuses on speed rather than achieving the highest compression ratio. Its compression may not be as efficient as others, but it is sufficient for many real-time applications where speed is paramount.

  • zlib:

    Zlib offers a good compression ratio, often achieving better size reductions than LZ4 and Snappy. It is suitable for applications where minimizing data size is critical, such as in network transmissions.

  • lz4:

    LZ4 achieves a lower compression ratio compared to some other algorithms, which means the size reduction may not be as significant. However, its speed compensates for this in many use cases where quick access to data is essential.

Use Cases

  • pako:

    Pako is best used in web applications that need to handle compressed data from APIs or servers, particularly when working with gzip streams. It is also suitable for client-side decompression of data received from the server.

  • snappy:

    Snappy is commonly used in big data applications, such as those involving Hadoop or databases like Google Bigtable, where speed is critical for performance and latency is a concern.

  • zlib:

    Zlib is versatile and can be used in various applications, including web servers, data storage, and network communications, where a balance of compression efficiency and speed is required.

  • lz4:

    LZ4 is ideal for scenarios requiring fast data processing, such as in-memory databases, real-time analytics, or when dealing with large datasets that need quick access without significant delays.

Compatibility

  • pako:

    Pako is specifically designed for compatibility with gzip and deflate formats, making it a great choice for web applications that need to interact with standard compressed data streams.

  • snappy:

    Snappy is used in many modern data processing frameworks and is compatible with systems like Hadoop, making it a popular choice in big data environments.

  • zlib:

    Zlib is a well-established library with broad compatibility across different platforms and programming languages, supporting multiple compression formats.

  • lz4:

    LZ4 is widely supported in various programming environments, but it may not be as universally compatible with existing compressed data formats as some other libraries.

Ease of Use

  • pako:

    Pako is easy to use, especially for developers familiar with gzip and deflate formats. Its API is intuitive, making it accessible for quick integration into web applications.

  • snappy:

    Snappy has a simple API, but its focus on speed may require developers to consider trade-offs in compression efficiency, which could add complexity in some scenarios.

  • zlib:

    Zlib is well-documented and widely used, making it easy to find resources and examples for implementation. However, its comprehensive feature set may introduce some complexity for new users.

  • lz4:

    LZ4 is straightforward to implement, with a simple API that allows developers to quickly integrate it into their applications without a steep learning curve.

How to Choose: pako vs snappy vs zlib vs lz4
  • pako:

    Opt for Pako if you require a robust solution for gzip and deflate compression formats. Pako is particularly useful for applications that need to handle compressed data from web servers or APIs, as it provides compatibility with standard gzip streams.

  • snappy:

    Select Snappy when you prioritize speed over compression ratio. It is designed for high-speed compression and is particularly effective in scenarios where low latency is crucial, such as in database applications or real-time analytics.

  • zlib:

    Use Zlib if you need a comprehensive solution for data compression that supports multiple formats, including gzip and deflate. It is a well-established library with a balance of speed and compression efficiency, making it suitable for a wide range of applications.

  • lz4:

    Choose LZ4 if you need extremely fast compression and decompression speeds with a reasonable compression ratio. It's ideal for scenarios where speed is critical, such as real-time data processing or when working with large volumes of data that require quick access.

Similar Npm Packages to pako

pako is a fast and efficient JavaScript library for data compression and decompression using the DEFLATE algorithm. It is particularly useful for web applications that need to handle large amounts of data, allowing developers to compress data before sending it over the network and decompress it on the client side. Pako is designed to be compatible with both Node.js and browser environments, making it a versatile choice for various JavaScript projects.

While pako is a popular choice for compression tasks, there are other libraries that offer similar functionality. Here are a few alternatives:

  • node-gzip is a simple and lightweight library for Gzip compression and decompression in Node.js applications. It provides a straightforward API for compressing and decompressing data using Gzip, making it a good choice for server-side applications that require basic Gzip functionality. If you're looking for a minimalistic solution specifically for Node.js, node-gzip can be a suitable alternative to pako.
  • zlib is a built-in Node.js module that provides compression and decompression functionalities using the DEFLATE and Gzip algorithms. It is a powerful and efficient option for handling compressed data in Node.js applications. While zlib is not as user-friendly as pako for browser environments, it is a robust choice for server-side applications that require high-performance compression. If you are working exclusively within a Node.js environment and need a native solution, zlib is an excellent option.

To see how pako compares with node-gzip and zlib, check out the comparison: Comparing node-gzip vs pako vs zlib.

Similar Npm Packages to snappy

snappy is a fast compression and decompression library designed to provide high-speed data processing with reasonable compression ratios. Developed by Google, snappy is particularly well-suited for applications where speed is more critical than achieving the highest possible compression. It is often used in scenarios like data storage, network communication, and real-time data processing where performance is paramount.

While snappy is an excellent choice for fast compression, there are several alternatives in the ecosystem that offer different features and performance characteristics. Here are a few notable alternatives:

  • lz4 is a high-performance compression algorithm that focuses on speed, similar to snappy. It provides very fast compression and decompression speeds while achieving reasonable compression ratios. LZ4 is particularly effective for scenarios where low latency is essential, such as in real-time applications or when dealing with large datasets that need to be processed quickly. If your application prioritizes speed over compression efficiency, lz4 is a strong alternative to consider.

  • pako is a JavaScript implementation of the zlib compression library, which is widely used for data compression. Pako supports both deflate and inflate operations, making it suitable for compressing and decompressing data in various formats. While it may not be as fast as snappy or lz4, pako offers a good balance between compression speed and compression ratio, making it a versatile choice for many applications, especially those that need compatibility with zlib.

  • zlib is a widely used compression library that provides a range of compression algorithms, including deflate and gzip. It is known for its reliability and effectiveness in compressing data, although it may not be as fast as snappy or lz4. Zlib is a great option for applications that require robust compression capabilities and are less concerned with speed. It is commonly used in server-side applications and data transmission scenarios where data integrity and compression efficiency are critical.

To see how snappy compares with lz4, pako, and zlib, check out the comparison: Comparing lz4 vs pako vs snappy vs zlib.

Similar Npm Packages to zlib

zlib is a built-in Node.js module that provides compression and decompression functionalities using the DEFLATE algorithm. It is widely used for compressing data to reduce the size of files or network transmissions, making it an essential tool for optimizing performance in web applications and services. While zlib is a powerful and efficient option, there are several alternatives available that cater to different use cases. Here are a few notable alternatives:

  • gzip-js is a pure JavaScript implementation of the Gzip compression algorithm. It is designed to work in both Node.js and browser environments, making it a versatile choice for developers looking to compress data without relying on native modules. gzip-js is particularly useful for projects that require a lightweight solution and want to avoid the overhead of native dependencies. However, it may not be as performant as native implementations like zlib for large datasets.
  • node-gzip is a simple and straightforward library for Gzip compression in Node.js. It provides an easy-to-use API for compressing and decompressing data, making it a great option for developers who want to leverage Gzip without dealing with the complexities of the zlib module. node-gzip is particularly useful for applications that need to handle Gzip data but prefer a more user-friendly interface compared to the native zlib module.
  • pako is a high-performance JavaScript library for data compression and decompression, supporting both Gzip and DEFLATE formats. It is designed to work in both Node.js and browser environments, making it a flexible choice for various applications. pako is known for its speed and efficiency, making it suitable for applications that require fast compression and decompression, especially when handling large amounts of data.

To see how zlib compares with gzip-js, node-gzip, and pako, check out the comparison: Comparing gzip-js vs node-gzip vs pako vs zlib.

Similar Npm Packages to lz4

lz4 is a high-performance compression library that is designed to provide fast compression and decompression speeds while maintaining a reasonable compression ratio. It is particularly well-suited for scenarios where speed is more critical than achieving the highest possible compression, such as in real-time applications or when dealing with large datasets. LZ4 is widely used in various applications, including databases, file systems, and network protocols, due to its efficiency and speed.

While LZ4 is a powerful option for compression, there are several alternatives available, each with its own strengths and use cases:

  • brotli is a compression algorithm developed by Google that focuses on achieving a high compression ratio while still maintaining decent performance. It is particularly effective for web assets, making it a popular choice for compressing HTML, CSS, and JavaScript files. Brotli is often used in conjunction with HTTP/2 to improve loading times for web applications.
  • compression is a middleware for Node.js that allows you to compress HTTP responses using various algorithms, including Gzip and Brotli. It is commonly used in web applications to reduce the size of responses sent to clients, improving load times and reducing bandwidth usage. The compression middleware is easy to integrate into Express applications and can be configured to use different compression algorithms based on client support.
  • gzip-js is a pure JavaScript implementation of the Gzip compression algorithm. It is lightweight and can be used in both Node.js and browser environments. Gzip is a widely used compression method that balances compression ratio and speed, making it suitable for various applications, especially for web content.
  • lz-string is a lightweight compression library that focuses on compressing strings. It is particularly useful for compressing data that needs to be stored or transmitted as strings, such as in local storage or URL parameters. LZ-String provides a simple API for compressing and decompressing strings, making it easy to integrate into applications that require string manipulation.
  • pako is a fast zlib port to JavaScript, providing Gzip and Deflate compression. It is designed for performance and is suitable for both Node.js and browser environments. Pako is often used in applications that require efficient compression and decompression of binary data, such as in WebSocket communications or when handling large data files.
  • snappy is a compression algorithm developed by Google that prioritizes speed over compression ratio. It is designed for scenarios where fast compression and decompression are essential, such as in database systems or real-time data processing. Snappy is often used in conjunction with other data processing frameworks to provide quick data serialization and deserialization.
  • zlib is a widely used compression library that provides support for Gzip and Deflate compression algorithms. It is available in both Node.js and browser environments and is known for its balance between compression speed and ratio. Zlib is commonly used in web applications and server-side applications to compress data for storage or transmission.

To explore how these compression libraries compare, check out the comparison link: Comparing brotli vs compression vs gzip-js vs lz-string vs lz4 vs pako vs snappy vs zlib.

README for pako

pako

CI NPM version

zlib port to javascript, very fast!

Why pako is cool:

  • Results are binary equal to well known zlib (now contains ported zlib v1.2.8).
  • Almost as fast in modern JS engines as C implementation (see benchmarks).
  • Works in browsers, you can browserify any separate component.

This project was done to understand how fast JS can be and is it necessary to develop native C modules for CPU-intensive tasks. Enjoy the result!

Benchmarks:

node v12.16.3 (zlib 1.2.9), 1mb input sample:

deflate-imaya x 4.75 ops/sec ±4.93% (15 runs sampled)
deflate-pako x 10.38 ops/sec ±0.37% (29 runs sampled)
deflate-zlib x 17.74 ops/sec ±0.77% (46 runs sampled)
gzip-pako x 8.86 ops/sec ±1.41% (29 runs sampled)
inflate-imaya x 107 ops/sec ±0.69% (77 runs sampled)
inflate-pako x 131 ops/sec ±1.74% (82 runs sampled)
inflate-zlib x 258 ops/sec ±0.66% (88 runs sampled)
ungzip-pako x 115 ops/sec ±1.92% (80 runs sampled)

node v14.15.0 (google's zlib), 1mb output sample:

deflate-imaya x 4.93 ops/sec ±3.09% (16 runs sampled)
deflate-pako x 10.22 ops/sec ±0.33% (29 runs sampled)
deflate-zlib x 18.48 ops/sec ±0.24% (48 runs sampled)
gzip-pako x 10.16 ops/sec ±0.25% (28 runs sampled)
inflate-imaya x 110 ops/sec ±0.41% (77 runs sampled)
inflate-pako x 134 ops/sec ±0.66% (83 runs sampled)
inflate-zlib x 402 ops/sec ±0.74% (87 runs sampled)
ungzip-pako x 113 ops/sec ±0.62% (80 runs sampled)

zlib's test is partially affected by marshalling (that make sense for inflate only). You can change deflate level to 0 in benchmark source, to investigate details. For deflate level 6 results can be considered as correct.

Install:

npm install pako

Examples / API

Full docs - http://nodeca.github.io/pako/

const pako = require('pako');

// Deflate
//
const input = new Uint8Array();
//... fill input data here
const output = pako.deflate(input);

// Inflate (simple wrapper can throw exception on broken stream)
//
const compressed = new Uint8Array();
//... fill data to uncompress here
try {
  const result = pako.inflate(compressed);
  // ... continue processing
} catch (err) {
  console.log(err);
}

//
// Alternate interface for chunking & without exceptions
//

const deflator = new pako.Deflate();

deflator.push(chunk1, false);
deflator.push(chunk2); // second param is false by default.
...
deflator.push(chunk_last, true); // `true` says this chunk is last

if (deflator.err) {
  console.log(deflator.msg);
}

const output = deflator.result;


const inflator = new pako.Inflate();

inflator.push(chunk1);
inflator.push(chunk2);
...
inflator.push(chunk_last); // no second param because end is auto-detected

if (inflator.err) {
  console.log(inflator.msg);
}

const output = inflator.result;

Sometime you can wish to work with strings. For example, to send stringified objects to server. Pako's deflate detects input data type, and automatically recode strings to utf-8 prior to compress. Inflate has special option, to say compressed data has utf-8 encoding and should be recoded to javascript's utf-16.

const pako = require('pako');

const test = { my: 'super', puper: [456, 567], awesome: 'pako' };

const compressed = pako.deflate(JSON.stringify(test));

const restored = JSON.parse(pako.inflate(compressed, { to: 'string' }));

Notes

Pako does not contain some specific zlib functions:

  • deflate - methods deflateCopy, deflateBound, deflateParams, deflatePending, deflatePrime, deflateTune.
  • inflate - methods inflateCopy, inflateMark, inflatePrime, inflateGetDictionary, inflateSync, inflateSyncPoint, inflateUndermine.
  • High level inflate/deflate wrappers (classes) may not support some flush modes.

pako for enterprise

Available as part of the Tidelift Subscription

The maintainers of pako and thousands of other packages are working with Tidelift to deliver commercial support and maintenance for the open source dependencies you use to build your applications. Save time, reduce risk, and improve code health, while paying the maintainers of the exact dependencies you use. Learn more.

Authors

Personal thanks to:

  • Vyacheslav Egorov (@mraleph) for his awesome tutorials about optimising JS code for v8, IRHydra tool and his advices.
  • David Duponchel (@dduponchel) for help with testing.

Original implementation (in C):

  • zlib by Jean-loup Gailly and Mark Adler.

License

  • MIT - all files, except /lib/zlib folder
  • ZLIB - /lib/zlib content
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