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High performance (de)compression in an 8kB package

Why fflate?

fflate (short for fast flate) is the fastest, smallest, and most versatile pure JavaScript compression and decompression library in existence, handily beating pako, tiny-inflate, and UZIP.js in performance benchmarks while being multiple times more lightweight. Its compression ratios are often better than even the original Zlib C library. It includes support for DEFLATE, GZIP, and Zlib data. Data compressed by fflate can be decompressed by other tools, and vice versa.

In addition to the base decompression and compression APIs, fflate supports high-speed ZIP compression and decompression for an extra 3 kB. In fact, the compressor, in synchronous mode, compresses both more quickly and with a higher compression ratio than most compression software (even Info-ZIP, a C program), and in asynchronous mode it can utilize multiple threads to achieve over 3x the performance of any other utility.

pako tiny-inflate UZIP.js fflate
Decompression performance 1x Up to 40% slower Up to 40% faster Up to 40% faster
Compression performance 1x N/A Up to 5% faster Up to 50% faster
Base bundle size (minified) 45.6kB 3kB (inflate only) 14.2kB 8kB (3kB for inflate only)
Compression support
Thread/Worker safe
ZIP support
Streaming support
GZIP/Zlib support
Doesn't hang on error
Uses ES Modules


Install fflate:

npm i fflate # or yarn add fflate, or pnpm add fflate


// I will assume that you use the following for the rest of this guide
import * as fflate from 'fflate';

// However, you should import ONLY what you need to minimize bloat.
// So, if you just need GZIP compression support:
import { gzipSync } from 'fflate';
// Woo! You just saved 20 kB off your bundle with one line.

If your environment doesn't support ES Modules (e.g. Node.js):

// Try to avoid this when using fflate in the browser, as it will import
// all of fflate's components, even those that you aren't using.
const fflate = require('fflate');

And use:

// This is an ArrayBuffer of data
const massiveFileBuf = await fetch('/aMassiveFile').then(
  res => res.arrayBuffer()
// To use fflate, you need a Uint8Array
const massiveFile = new Uint8Array(massiveFileBuf);
// Note that Node.js Buffers work just fine as well:
// const massiveFile = require('fs').readFileSync('aMassiveFile.txt');

// Higher level means lower performance but better compression
// The level ranges from 0 (no compression) to 9 (max compression)
// The default level is 6
const notSoMassive = fflate.zlibSync(massiveFile, { level: 9 });
const massiveAgain = fflate.unzlibSync(notSoMassive);
const gzipped = fflate.gzipSync(massiveFile, {
  // GZIP-specific: the filename to use when decompressed
  filename: 'aMassiveFile.txt',
  // GZIP-specific: the modification time. Can be a Date, date string,
  // or Unix timestamp
  mtime: '9/1/16 2:00 PM'

fflate can autodetect a compressed file's format as well:

const compressed = new Uint8Array(
  await fetch('/GZIPorZLIBorDEFLATE').then(res => res.arrayBuffer())
// Above example with Node.js Buffers:
// Buffer.from('H4sIAAAAAAAAE8tIzcnJBwCGphA2BQAAAA==', 'base64');

const decompressed = fflate.decompressSync(compressed);

Using strings is easy with fflate's string conversion API:

const buf = fflate.strToU8('Hello world!');

// The default compression method is gzip
// Increasing mem may increase performance at the cost of memory
// The mem ranges from 0 to 12, where 4 is the default
const compressed = fflate.compressSync(buf, { level: 6, mem: 8 });

// When you need to decompress:
const decompressed = fflate.decompressSync(compressed);
const origText = fflate.strFromU8(decompressed);
console.log(origText); // Hello world!

If you need to use an (albeit inefficient) binary string, you can set the second argument to true.

const buf = fflate.strToU8('Hello world!');

// The second argument, latin1, is a boolean that indicates that the data
// is not Unicode but rather should be encoded and decoded as Latin-1.
const compressedString = fflate.strFromU8(
const decompressed = fflate.decompressSync(
  fflate.strToU8(compressedString, true)
const origText = fflate.strFromU8(decompressed);
console.log(origText); // Hello world!

You can use streams as well to incrementally add data to be compressed or decompressed:

// This example uses synchronous streams, but for the best experience
// you'll definitely want to use asynchronous streams.

let outStr = '';
const gzipStream = new fflate.Gzip({ level: 9 }, (chunk, isLast) => {
  // accumulate in an inefficient binary string (just an example)
  outStr += fflate.strFromU8(chunk, true);

// You can also attach the data handler separately if you don't want to
// do so in the constructor.
gzipStream.ondata = (chunk, final) => { ... }

// Since this is synchronous, all errors will be thrown by stream.push()


// You should mark the last chunk by using true in the second argument
// In addition to being necessary for the stream to work properly, this
// will also set the isLast parameter in the handler to true.
gzipStream.push(lastChunk, true);

console.log(outStr); // The compressed binary string is now available

// The options parameter for compression streams is optional; you can
// provide one parameter (the handler) or none at all if you set
// deflateStream.ondata later.
const deflateStream = new fflate.Deflate((chunk, final) => {
  console.log(chunk, final);

const inflateStream = new fflate.Inflate();
inflateStream.ondata = (decompressedChunk, final) => {
  // print out a string of the compressed data

// Decompress streams auto-detect the compression method, as the
// non-streaming decompress() method does.
const dcmpStrm = new fflate.Decompress((chunk, final) => {
    'This chunk was encoded in either GZIP, Zlib, or DEFLATE',

You can create multi-file ZIP archives easily as well:

// Note that the asynchronous version (see below) runs in parallel and
// is *much* (up to 3x) faster for larger archives.
const zipped = fflate.zipSync({
  // Directories can be nested structures, as in an actual filesystem
  'dir1': {
    'nested': {
      // You can use Unicode in filenames
      '你好.txt': std('Hey there!')
    // You can also manually write out a directory path
    'other/tmp.txt': new Uint8Array([97, 98, 99, 100])
  // You can also provide compression options
  'myImageData.bmp': [aMassiveFile, { level: 9, mem: 12 }],
  'superTinyFile.bin': [new Uint8Array([0]), { level: 0 }]
}, {
  // These options are the defaults for all files, but file-specific
  // options take precedence.
  level: 1

// If you write the zipped data to and unzip, the folder
// structure will be outputted as:

// (original file)
// dir1
// |-> nested
// |   |-> 你好.txt
// |-> other
// |   |-> tmp.txt
// myImageData.bmp
// superTinyFile.bin

// When decompressing, folders are not nested; all filepaths are fully
// written out in the keys. For example, the return value may be:
// { 'nested/directory/a2.txt': Uint8Array(2) [97, 97] })
const decompressed = fflate.unzipSync(zipped);

As you may have guessed, there is an asynchronous version of every method as well. Unlike most libraries, this will cause the compression or decompression run in a separate thread entirely and automatically by using Web (or Node) Workers. This means that the processing will not block the main thread at all.

Note that there is a significant initial overhead to using workers of about 70ms, so it's best to avoid the asynchronous API unless necessary. However, if you're compressing multiple large files at once, or the synchronous API causes the main thread to hang for too long, the callback APIs are an order of magnitude better.

import { gzip, zlib, AsyncGzip, zip } from 'fflate';

// Workers will work in almost any browser (even IE11!)
// However, they fail below Node v12 without the --experimental-worker
// CLI flag, and will fail entirely on Node below v10.

// All of the async APIs use a node-style callback as so:
const terminate = gzip(aMassiveFile, (err, data) => {
  if (err) {
    // The compressed data was likely corrupt, so we have to handle
    // the error.
  // Use data however you like

if (needToCancel) {
  // The return value of any of the asynchronous APIs is a function that,
  // when called, will immediately cancel the operation. The callback
  // will not be called.

// If you wish to provide options, use the second argument.

// The consume option will render the data inside aMassiveFile unusable,
// but can dramatically improve performance and reduce memory usage.
zlib(aMassiveFile, { consume: true, level: 9 }, (err, data) => {
  // Use the data

// Asynchronous streams are similar to synchronous streams, but the
// handler has the error that occurred (if any) as the first parameter,
// and they don't block the main thread.

// Additionally, any buffers that are pushed in will be consumed and
// rendered unusable; if you need to use a buffer you push in, you
// should clone it first.
const gzs = new AsyncGzip({ level: 9, mem: 12, filename: 'hello.txt' });
let wasCallbackCalled = false;
gzs.ondata = (err, chunk, final) => {
  // Note the new err parameter
  if (err) {
    // Note that after this occurs, the stream becomes corrupt and must
    // be discarded. You can't continue pushing chunks and expect it to
    // work.
  wasCallbackCalled = true;

// Since the stream is asynchronous, the callback will not be called
// immediately. If such behavior is absolutely necessary (it shouldn't
// be), use synchronous streams.
console.log(wasCallbackCalled) // false

// This is way faster than zipSync because the compression of multiple
// files runs in parallel. In fact, the fact that it's parallelized
// makes it faster than most standalone ZIP CLIs. The effect is most
// significant for multiple large files; less so for many small ones.
zip({ f1: aMassiveFile, 'f2.txt': anotherMassiveFile }, (err, data) => {
  // Save the ZIP file

See the documentation for more detailed information about the API.

Bundle size estimates

Since fflate uses ES Modules, this table should give you a general idea of fflate's bundle size for the features you need. The maximum bundle size that is possible with fflate is about 22kB if you use every single feature, but feature parity with pako is only around 10kB (as opposed to 45kB from pako). If your bundle size increases dramatically after adding fflate, please create an issue.

Feature Bundle size (minified) Nearest competitor
Decompression 3kB tiny-inflate
Compression 5kB UZIP.js, 184% larger
Async decompression 4kB (1kB + raw decompression) N/A
Async compression 5kB (1kB + raw compression) N/A
ZIP decompression 5kB (2kB + raw decompression) UZIP.js, 184% larger
ZIP compression 7kB (2kB + raw compression) UZIP.js, 103% larger
GZIP/Zlib decompression 4kB (1kB + raw decompression) pako, 1040% larger
GZIP/Zlib compression 5kB (1kB + raw compression) pako, 812% larger
Streaming decompression 4kB (1kB + raw decompression) pako, 1040% larger
Streaming compression 5kB (1kB + raw compression) pako, 812% larger

What makes fflate so fast?

Many JavaScript compression/decompression libraries exist. However, the most popular one, pako, is merely a clone of Zlib rewritten nearly line-for-line in JavaScript. Although it is by no means poorly made, pako doesn't recognize the many differences between JavaScript and C, and therefore is suboptimal for performance. Moreover, even when minified, the library is 45 kB; it may not seem like much, but for anyone concerned with optimizing bundle size (especially library authors), it's more weight than necessary.

Note that there exist some small libraries like tiny-inflate for solely decompression, and with a minified size of 3 kB, it can be appealing; however, its performance is lackluster, typically 40% worse than pako in my tests.

UZIP.js is both faster (by up to 40%) and smaller (14 kB minified) than pako, and it contains a variety of innovations that make it excellent for both performance and compression ratio. However, the developer made a variety of tiny mistakes and inefficient design choices that make it imperfect. Moreover, it does not support GZIP or Zlib data directly; one must remove the headers manually to use UZIP.js.

So what makes fflate different? It takes the brilliant innovations of UZIP.js and optimizes them while adding direct support for GZIP and Zlib data. And unlike all of the above libraries, it uses ES Modules to allow for partial builds through tree shaking, meaning that it can rival even tiny-inflate in size while maintaining excellent performance. The end result is a library that, in total, weighs 8kB minified for the core build (3kB for decompression only and 5kB for compression only), is about 15% faster than UZIP.js or up to 60% faster than pako, and achieves the same or better compression ratio than the rest.

Before you decide that fflate is the end-all compression library, you should note that JavaScript simply cannot rival the performance of a compiled language. If you're willing to have 160 kB of extra weight and much less browser support, you can achieve more performance than fflate with a WASM build of Zlib like wasm-flate. And if you're only using Node.js, just use the native Zlib bindings that offer the best performance. Though note that even against these compiled libraries, fflate is only around 30% slower in decompression and 10% slower in compression, and can still achieve better compression ratios!

Browser support

fflate makes heavy use of typed arrays (Uint8Array, Uint16Array, etc.). Typed arrays can be polyfilled at the cost of performance, but the most recent browser that doesn't support them is from 2011, so I wouldn't bother.

The asynchronous APIs also use Worker, which is not supported in a few browsers (however, the vast majority of browsers that support typed arrays support Worker).

Other than that, fflate is completely ES3, meaning you probably won't even need a bundler to use it.


You can validate the performance of fflate with npm/yarn/pnpm test. It validates that the module is working as expected, ensures the outputs are no more than 5% larger than competitors at max compression, and outputs performance metrics to test/results.

Note that the time it takes for the CLI to show the completion of each test is not representative of the time each package took, so please check the JSON output if you want accurate masurements.




High performance (de)compression in an 8kB package





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