Benchmark: zlib compatible compression libraries comparison - pako vs fflate

HTML Preparation code:

<script src="https://unpkg.com/fflate"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/pako/2.1.0/pako.es5.min.js"></script>

AخA
 
<script src="https://unpkg.com/fflate"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/pako/2.1.0/pako.es5.min.js"></script>

Script Preparation code:

const enc = new TextEncoder();
var a = new Uint8Array([...Array(1000000).keys()]);
var b = enc.encode('The quick brown fox jumps over the lazy dog')
var ca = pako.deflate(a)
var cb = pako.deflate(b)

 
const enc = new TextEncoder();var a = new Uint8Array([...Array(1000000).keys()]);var b = enc.encode('The quick brown fox jumps over the lazy dog')var ca = pako.deflate(a)var cb = pako.deflate(b)

Tests:

zip pako a
pako.deflate(a)
pako.deflate(a)
zip fflate a
fflate.zlibSync(a)
fflate.zlibSync(a)
zip pako b
pako.deflate(b)
pako.deflate(b)
zip fflate b
fflate.zlibSync(b)
fflate.zlibSync(b)
unzip pako a
pako.inflate(ca);
pako.inflate(ca);
unzip fflate a
fflate.unzlibSync(ca);
fflate.unzlibSync(ca);
unzip pako b
pako.inflate(cb)
pako.inflate(cb)
unzip fflate b
fflate.unzlibSync(cb)
fflate.unzlibSync(cb)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Experimental features:

Memory measurements supported only in Chrome.
For precise memory measurements Chrome must be launched with --enable-precise-memory-info flag.
More information: Monitoring JavaScript Memory

Test case name	Result
zip pako a
zip fflate a
zip pako b
zip fflate b
unzip pako a
unzip fflate a
unzip pako b
unzip fflate b

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Android 15; Mobile; rv:135.0) Gecko/135.0 Firefox/135.0

Browser/OS: Firefox Mobile 135 on Android

View result in a separate tab

Test name	Executions per second
zip pako a	38.2 Ops/sec
zip fflate a	22.2 Ops/sec
zip pako b	2522.2 Ops/sec
zip fflate b	8818.7 Ops/sec
unzip pako a	97.5 Ops/sec
unzip fflate a	91.7 Ops/sec
unzip pako b	7278.7 Ops/sec
unzip fflate b	1772.6 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 5 months ago):

Let's break down the benchmark and its results.

Benchmark Overview

The benchmark compares two JavaScript libraries: pako and fflate, which are implementations of zlib-compatible compression algorithms. The goal is to measure the performance of each library in terms of compression and decompression speeds.

Options Being Compared

There are four options being compared:

pako.deflate(a) vs fflate.zlibSync(a): This comparison tests the compression speed of each library on a large dataset (a).
pako.deflate(b) vs fflate.zlibSync(b): This comparison tests the compression speed of each library on another large dataset (b).
pako.inflate(ca) vs fflate.unzlibSync(ca): This comparison tests the decompression speed of each library on a compressed dataset (ca).
pako.inflate(cb) vs fflate.unzlibSync(cb): This comparison tests the decompression speed of each library on another compressed dataset (cb).

Pros and Cons

Here are some pros and cons of each approach:

pako.deflate(a):
- Pros: pako is a lightweight, well-maintained library with good performance.
- Cons: may not be as efficient as fflate for very large datasets.
fflate.zlibSync(a):
- Pros: fflate is highly optimized for performance and has good support for modern browsers.
- Cons: may have higher memory usage due to its larger size.
pako.deflate(b):
- Pros: similar to pako.deflate(a), but tests a different dataset, which may provide more representative results.
- Cons: same limitations as pako.deflate(a).
fflate.zlibSync(b):
- Pros: similar to fflate.zlibSync(a), but tests a different dataset.
- Cons: same limitations as fflate.zlibSync(a).

Libraries

pako: A lightweight, JavaScript implementation of the pako algorithm, which is a zlib-compatible compression algorithm. It's designed for small to medium-sized datasets.
fflate: A highly optimized, JavaScript implementation of the flate algorithm, which is also a zlib-compatible compression algorithm. It's designed for large datasets and has good support for modern browsers.

Special JS Features/Syntax

There are no special JS features or syntax used in this benchmark that would require specific knowledge to understand.

Other Alternatives

If you're looking for alternative libraries, here are a few options:

zlib: The original zlib library is written in C and has good performance, but it's not JavaScript-compatible.
jszip: A lightweight JavaScript library that provides zip compression and decompression capabilities.
lz-string: Another lightweight JavaScript library for compressing strings using the LZ-77 algorithm.

Keep in mind that each library has its strengths and weaknesses, and the choice of which one to use depends on your specific use case and requirements.

LLMs can make mistakes. Check important info.

Let's break down the benchmark and its results.

**Benchmark Overview**

**Options Being Compared**

There are four options being compared:

1. `pako.deflate(a)` vs `fflate.zlibSync(a)`: This comparison tests the compression speed of each library on a large dataset (`a`).
2. `pako.deflate(b)` vs `fflate.zlibSync(b)`: This comparison tests the compression speed of each library on another large dataset (`b`).
3. `pako.inflate(ca)` vs `fflate.unzlibSync(ca)`: This comparison tests the decompression speed of each library on a compressed dataset (`ca`).
4. `pako.inflate(cb)` vs `fflate.unzlibSync(cb)`: This comparison tests the decompression speed of each library on another compressed dataset (`cb`).

**Pros and Cons**

Here are some pros and cons of each approach:

1. **pako.deflate(a)**:
	* Pros: pako is a lightweight, well-maintained library with good performance.
	* Cons: may not be as efficient as fflate for very large datasets.
2. **fflate.zlibSync(a)**:
	* Pros: fflate is highly optimized for performance and has good support for modern browsers.
	* Cons: may have higher memory usage due to its larger size.
3. **pako.deflate(b)**:
	* Pros: similar to pako.deflate(a), but tests a different dataset, which may provide more representative results.
	* Cons: same limitations as pako.deflate(a).
4. **fflate.zlibSync(b)**:
	* Pros: similar to fflate.zlibSync(a), but tests a different dataset.
	* Cons: same limitations as fflate.zlibSync(a).

**Libraries**

1. **pako**: A lightweight, JavaScript implementation of the pako algorithm, which is a zlib-compatible compression algorithm. It's designed for small to medium-sized datasets.
2. **fflate**: A highly optimized, JavaScript implementation of the flate algorithm, which is also a zlib-compatible compression algorithm. It's designed for large datasets and has good support for modern browsers.

**Special JS Features/Syntax**

There are no special JS features or syntax used in this benchmark that would require specific knowledge to understand.

**Other Alternatives**

If you're looking for alternative libraries, here are a few options:

1. **zlib**: The original zlib library is written in C and has good performance, but it's not JavaScript-compatible.
2. **jszip**: A lightweight JavaScript library that provides zip compression and decompression capabilities.
3. **lz-string**: Another lightweight JavaScript library for compressing strings using the LZ-77 algorithm.

Keep in mind that each library has its strengths and weaknesses, and the choice of which one to use depends on your specific use case and requirements.

Related benchmarks:

zlib compatible compression libraries comparison - pako vs fflate (version: 0)

comparing zlib compatible compression and decompression of pako with fflate

Comparing performance of: zip pako a vs zip fflate a vs zip pako b vs zip fflate b vs unzip pako a vs unzip fflate a vs unzip pako b vs unzip fflate b

Created: one year ago by: Registered User

Jump to the latest result

zip pako a

zip fflate a

zip pako b

zip fflate b

unzip pako a

unzip fflate a

unzip pako b

unzip fflate b

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 5 months ago):