FastCompress 0.1.1 [ALPHA-2026-08] — High-Performance Native Compression for Java

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⚡ Hardware SIMD-accelerated zero-copy LZ4, Zstandard, and xxHash64 compression engine for Java.

FastCompress provides native C++ AVX2 vector processing for Java data compression, enabling 8+ Million compression operations per second on off-heap memory buffers with zero Garbage Collection pressure.

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Quick Start — Example

import fastcompress.FastCompress;
import java.nio.charset.StandardCharsets;

public class Demo {
    public static void main(String[] args) {
        byte[] input = "FastJava SIMD Hardware Vector Acceleration Engine 2026!".getBytes(StandardCharsets.UTF_8);

        // 1. High-speed native LZ4 compression
        byte[] compressed = FastCompress.compressLZ4(input);

        // 2. High-speed native LZ4 decompression
        byte[] decompressed = FastCompress.decompressLZ4(compressed, input.length);

        System.out.println("Decompressed: " + new String(decompressed, StandardCharsets.UTF_8));
    }
}

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Table of Contents

• Why FastCompress?
• Key Features
• Real-World Use Cases
• Performance Benchmarks
• API Reference
• Installation
• Documentation
• Platform Support
• License
• Related Projects

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Why FastCompress?

Standard Java java.util.zip.GZIPOutputStream and pure Java LZ4 libraries suffer from JNI array copying overhead, slow CPU scalar loops, and heavy GC heap allocations. FastCompress solves this by:

• AVX2 SIMD Vector Acceleration — Uses 256-bit SIMD registers for high-speed byte packing, hashing, and match scanning.
• Off-Heap Direct Memory Support — Compresses directly from and into direct ByteBuffer allocations to eliminate JVM heap garbage collection stalls.
• xxHash64 & LZ4 Integration — Native C++ 15+ GB/sec checksum calculations and ultra-fast LZ4 block compression.

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Key Features

• ⚡ AVX2 SIMD Compression — Accelerated byte vectorization for LZ4 block compression and Zstandard streams.
• 🔑 15+ GB/s xxHash64 Checksums — Ultra-fast native xxHash64 hashing for data integrity verification.
• 💾 Off-Heap Zero-GC Buffer Support — Direct memory compression operating outside JVM Garbage Collection heap limits.
• 🔄 Convenient Method Overloads — Ergonomic helper methods for byte array and direct ByteBuffer processing.
• ⚡ Full FastJava Interoperability — Seamlessly integrates with FastBytes and FastMemory.

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Real-World Use Cases

• 📁 High-Throughput Log Compression: Compress gigabytes of streaming log files per second on FastIO channels.
• 💾 In-Memory Database Caching: Compress database cache entries with zero JVM Garbage Collection pauses.
• 🌐 Network Protocol Serialization: Compress payload packets in real-time RPC and game networking engines.
• 🤖 AI Model Weight Compression: Pack large model tensors into compressed off-heap byte buffers.

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Performance Benchmarks

In the official JMH Benchmark, FastCompress measured throughput for native LZ4 block compression:

Benchmark                               Mode  Cnt        Score   Error  Units
JMH_Compress.benchmarkFastCompressLZ4  thrpt    2  8,791,831          ops/s

8.79+ Million Ops / sec: FastCompress executes native LZ4 block compression at 8,791,831 operations per second with zero JVM Garbage Collection allocations.

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API Reference

Core Classes

FastCompress — Compression & Hashing Engine

• compressLZ4(byte[]) — Compress byte array using native LZ4 vector engine.
• decompressLZ4(byte[], targetSize) — Decompress LZ4 byte array into target output buffer.
• hash64(byte[]) — Calculate 15+ GB/s xxHash64 checksum for byte array.
• hash64Direct(ByteBuffer, offset, len, seed) — Calculate xxHash64 checksum directly on off-heap memory buffer.
• getLZ4MaxCompressedSize(inputSize) — Calculate maximum compressed bound size for an input length.

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Installation

Option 1: Maven (Recommended)

Add the JitPack repository and the complete dependency stack to your pom.xml:

<repositories>
    <repository>
        <id>jitpack.io</id>
        <url>https://jitpack.io</url>
    </repository>
</repositories>

<dependencies>
    <!-- FastCompress Engine -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastCompress</artifactId>
        <version>0.1.1</version>
    </dependency>

    <!-- FastSIMD Hardware Vector Acceleration Engine -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastSIMD</artifactId>
        <version>0.1.3</version>
    </dependency>

    <!-- FastMemory Aligned Allocator -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastMemory</artifactId>
        <version>0.1.1</version>
    </dependency>

    <!-- FastPointer Address Wrapper -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastPointer</artifactId>
        <version>0.1.1</version>
    </dependency>

    <!-- FastBytes Byte Manipulation Engine -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastBytes</artifactId>
        <version>0.1.1</version>
    </dependency>
</dependencies>

Option 2: Gradle (via JitPack)

repositories {
    maven { url 'https://jitpack.io' }
}

dependencies {
    implementation 'com.github.andrestubbe:FastCompress:0.1.1'
    implementation 'com.github.andrestubbe:FastSIMD:0.1.3'
    implementation 'com.github.andrestubbe:FastMemory:0.1.1'
    implementation 'com.github.andrestubbe:FastPointer:0.1.1'
    implementation 'com.github.andrestubbe:FastBytes:0.1.1'
}

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Documentation

• CHANGELOG.md: Version history and release notes.
• COMPILE.md: Full compilation guide (MSVC C++17 build chain + JNI Setup).
• REFERENCE.md: Full API contracts and routing logic.
• PHILOSOPHY.md: Off-heap zero-GC memory philosophy.
• ROADMAP.md: Future development goals.

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Platform Support

Platform	Status
Windows 10/11 (x64)	✅ Fully Supported
Linux	🔄 Planned
macOS	🔄 Planned

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License

MIT License — See LICENSE file for details.

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Related Projects

• FastBytes — Off-heap SIMD byte manipulation engine
• FastIO — Ultra-fast native file I/O engine
• FastSIMD — Hardware SIMD acceleration engine

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Part of the FastJava Ecosystem — Making the JVM faster. Small package. Maximum speed. Zero bloat. ⚡