FastTokenize 0.1.1 [ALPHA-2026-09-30] — Ultra-Fast Code & Syntax Tokenizer for Java

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⚡ Minimal, deterministic, zero-dependency tokenization engine for code analysis, syntax highlighting, and LLM text pipelines. Operates in $O(n)$ time with zero-allocation byte-array output.

FastTokenize is a high-performance, zero-dependency Java tokenization library and part of the FastJava ecosystem. It provides dedicated scanners for 10+ programming languages and formats, outputting structured token streams and zero-allocation byte arrays for FastTerminal and real-time TUI text applications.

Watch Demo (YouTube) | Watch JMH Benchmark (YouTube)

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Quick Start

1. Minimal Java Tokenization & Style Generation

import fasttokenize.FastTokenize;
import fasttokenize.Language;
import fasttokenize.Token;
import fasttokenize.TokenType;
import java.util.List;

public class Demo {
    public static void main(String[] args) {
        String code = """
            public class HelloWorld {
                public static void main(String[] args) {
                    System.out.println("Hello, FastTokenize!");
                }
            }
            """;

        // 1. Structured Token Stream
        List<Token> tokens = FastTokenize.tokenize(Language.JAVA, code);
        for (Token token : tokens) {
            System.out.printf("%-12s: '%s'%n", token.getType(), token.getText());
        }

        // 2. Zero-Allocation Style Byte-Array (1 byte per character offset)
        byte[] styleIds = FastTokenize.tokenizeStyles(Language.JAVA, code);
        System.out.println("Total styled character offsets: " + styleIds.length);

        // 3. Auto-detect language from filename
        List<Token> cppTokens = FastTokenize.tokenizeForFile("main.cpp", "#include <iostream>");
        System.out.println("C++ Token Count: " + cppTokens.size());
    }
}

2. Interactive Terminal Highlighting Demo

Launch the interactive Tokyo Night syntax demo:

.\run-demo.bat

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Table of Contents

• Why FastTokenize?
• Quick Start
• Key Features
• Real-World Use Cases
• Performance Benchmarks
• Supported Languages
• API Quick Reference
• Technical Demos & Benchmarks
• Installation
• Documentation
• Platform Support
• License
• Related Projects

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Why FastTokenize?

Traditional syntax highlighters and code tokenizers are poorly suited for real-time terminal editors and low-latency text pipelines:

1. Regex Backtracking & CPU Spikes: Conventional highlighters (like RSyntaxTextArea) run multiple regex passes per line, triggering catastrophic backtracking and CPU spikes on dense files.
2. Heavy AST Heap Bloat: Full language parsers like Tree-sitter or ANTLR instantiate deep syntax trees, creating heavy JVM heap churn and GC pauses during fast scrolling.
3. Slow Line Re-Tokenization: Interactive terminal editors require sub-millisecond per-line token styling; heavy parsers require multi-pass passes that stall the render loop.
4. Complex Native & Grammar Dependencies: Solutions like Tree-sitter require platform-specific compiled C libraries for each grammar, complicating deployment.

FastTokenize resolves these issues with dedicated deterministic scanners and compact byte outputs:

• $O(n)$ Single-Pass Scanning: Scans source code in microseconds with zero backtracking and deterministic linear complexity.
• Zero-Allocation Style Byte Streams: Generates compact byte[] style IDs matching character offsets, directly consumable by FastTerminal and TUIs.
• Microsecond Tokenization Latency: Processes files in ~5.4 µs (>183,000 tokenizations/sec) to maintain effortless 60+ FPS viewport rendering.
• Zero Dependencies: Lightweight standalone JAR (<50 KB) with dedicated scanners for 10+ languages and zero native binary requirements.

Feature	Regex Highlighters (RSyntaxTextArea)	Full AST Parsers (Tree-sitter / ANTLR)	FastTokenize
Parsing Model	Multi-pass Regex matching	Full LALR / GLR syntax tree	$O(n)$ Single-pass deterministic scanner
Tokenization Speed	150–800 µs / line	2–10 ms (Full tree parse)	~5.4 µs / file (>183,000 ops/s)
Heap Memory Overhead	Regex Matcher & String churn	Deep AST node trees on heap	Zero-allocation byte[] style stream
Terminal / TUI Synergy	❌ Complex token conversion	⚠️ Requires tree traversal	✅ Direct cell-by-cell styling for FastTerminal
Dependency Footprint	Java regex engine	Heavy C libraries / Grammar JARs	Zero dependencies (< 50 KB pure Java)
60 FPS Viewport Sync	⚠️ GC frame drops on fast scroll	❌ Stalls real-time render loops	✅ Flawless 60+ FPS terminal sync

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Key Features

• 🚀 Ultra-Fast $O(n)$ Tokenization — Process large source files in microseconds with minimal CPU usage.
• 🎨 10+ Supported Languages — Dedicated scanners for Java, C/C++, Python, C#, JS/TS, JSON, CSS, XML/HTML, and Markdown.
• 🖌️ Direct Terminal Style Integration — Native byte-array style output for zero-copy terminal rendering.
• ⚡ Native AVX2 Acceleration — Optional native C++/AVX2 SIMD scanner on Windows with 100% pure Java fallback for Linux/macOS.
• 📂 Comprehensive Test Corpus — Fully validated against a complete language spectrum in docs/samples.

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Real-World Use Cases

• 🧭 CreamCLI Next-Gen Terminal Editor: Power 60+ FPS zero-latency syntax highlighting and code line rendering in CreamCLI without JVM Garbage Collection stalls.
• 🤖 FastAI & LLM Code Prompting: Tokenize and filter code snippets into structured Token Streams before feeding them to local or cloud LLM models.
• 🔍 High-Speed Code Search & Indexing: Extract method identifiers, classes, and annotations for instant indexing in FastFileContentIndex while skipping comments and strings.
• 📄 Terminal File Previews: Generate instant colored ANSI / TUI previews for large source code files in FastTerminal and TUI dashboards.

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Performance Benchmarks

FastTokenize is built for high-throughput code tokenization and zero-copy terminal rendering. In the official JMH Benchmark, the system measured throughput across 180+ byte source snippets:

Benchmark                                         Mode  Cnt       Score        Error  Units
Benchmark.benchmarkCppTokenization               thrpt    5  183690.846 ± 259902.107  ops/s
Benchmark.benchmarkJavaStyleByteStream           thrpt    5   72195.332 ±  30698.835  ops/s
Benchmark.benchmarkJavaTokenization              thrpt    5   85806.426 ±  48010.356  ops/s

183,000 Tokenizations per Second: FastTokenize parses source code files and outputs zero-allocation style byte arrays in ~5.4 microseconds per file.

Run the benchmarks locally:

.\run-benchmark.bat

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Supported Languages

Language / Format	Extensions	Dedicated Scanner	Key Constructs Handled
Java / Kotlin	.java, .kt	JavaScanner	Javadoc, Annotations (@Override), Generics, Literals
C / C++	.c, .cpp, .h, .hpp	CppScanner	Preprocessor (#include, #define), Intrinsics, Namespaces
Python	.py, .pyw, .pyi	PythonScanner	Triple Quotes ("""), Decorators (@property), Raw Strings
C# (.NET)	.cs, .csx	CSharpScanner	Verbatim (@""), Interpolation ($""), Attributes, Async
JS / TS / JSX	.js, .ts, .jsx, .tsx	CppScanner	Template Literals, React Components, Arrow Functions
JSON	.json	JsonScanner	Property Keys ("key":) vs Values, Exponents, Booleans
CSS / SCSS	.css, .scss, .less	CssScanner	At-Rules (@media), Selectors (.class, #id), Variables
XML / HTML	.xml, .html, .svg	XmlScanner	Directives (<?xml?>), Tags, Attributes, Comments (<!-- -->)
Markdown	.md, .markdown	MarkdownScanner	Headers (#), Fenced Code Blocks (```), Links

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API Quick Reference

Method / Signature	Return Type	Description	Docs
FastTokenize.tokenize(Language lang, CharSequence text)	List<Token>	Scans text and produces an immutable token stream.	Wiki
FastTokenize.tokenizeForFile(String filename, CharSequence text)	List<Token>	Automatically resolves language from file extension and tokenizes.	Wiki
FastTokenize.tokenizeStyles(Language lang, CharSequence text)	byte[]	Generates a 1-to-1 byte array of TokenType IDs matching character offsets.	Wiki
Language.fromFilename(String filename)	Language	Resolves target language enum by examining file extension.	Wiki
token.getType() / token.getText()	TokenType / CharSequence	Queries token classification type and underlying character slice.	Wiki

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Technical Demos & Benchmarks

Case	Java Example	Launcher	Description
Interactive Terminal Highlighting	Demo.java	run-demo.bat	Renders highlighted Java/C++/Python files in 24-bit Tokyo Night terminal colors.
JMH Microbenchmark Suite	Benchmark.java	run-benchmark.bat	Formal OpenJDK JMH throughput measurements for tokens and style byte streams.

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Installation

FastJava modules are distributed via JitPack.

Option 1: Maven (Recommended via JitPack)

Add the JitPack repository and dependencies to your pom.xml:

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

<dependencies>
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastTokenize</artifactId>
        <version>0.1.1</version>
    </dependency>
    <!-- Hardware acceleration & native JNI dependencies -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastCore</artifactId>
        <version>0.1.0</version>
    </dependency>
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastSIMD</artifactId>
        <version>0.1.3</version>
    </dependency>
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastPointer</artifactId>
        <version>0.1.1</version>
    </dependency>
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastMemory</artifactId>
        <version>0.1.1</version>
    </dependency>
</dependencies>

Option 2: Gradle (via JitPack)

Add this to your build.gradle:

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

dependencies {
    implementation 'com.github.andrestubbe:FastTokenize:0.1.1'
    implementation 'com.github.andrestubbe:FastCore:0.1.0'
    implementation 'com.github.andrestubbe:FastSIMD:0.1.3'
    implementation 'com.github.andrestubbe:FastPointer:0.1.1'
    implementation 'com.github.andrestubbe:FastMemory:0.1.1'
}

Option 3: Direct Download (No Build Tool)

Download the latest pre-compiled JARs directly:

1. ⚡ FastTokenize-0.1.1.jar (The Core Tokenizer)
2. ⚙️ FastCore-0.1.0.jar (Native JNI Loader)
3. 🚀 FastSIMD-0.1.3.jar (Hardware Vector Acceleration)

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Documentation

• REFERENCE.md: Full API contracts, token data models, and TokenType enum mappings.
• PHILOSOPHY.md: Rationale for $O(n)$ single-pass scanning and zero-allocation byte streams.
• ROADMAP.md: Future milestones, BPE/Tiktoken LLM tokenization, and Tree-sitter mapping.
• CHANGELOG.md: Version history, release notes, and migration guides.
• COMPILE.md: Compilation guide for C++/AVX2 native libraries and Java sources.
• Language Test Corpus: Reference test files across all supported languages.

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Platform Support

Platform	Architecture	Status	Notes
Windows 10/11	x64	✅ Fully Supported	Native C++/AVX2 SIMD acceleration (fasttokenize.dll)
Linux	x64, ARM64	✅ Fully Supported	100% Pure Java fallback engine
macOS	Apple Silicon, x64	✅ Fully Supported	100% Pure Java fallback engine

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License

MIT License — See LICENSE file for details.

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Related Projects

• Cream-CLI — Next-generation command-line workspace
• FastTerminal — High-performance double-buffered TUI terminal engine
• FastFileContentIndex — High-throughput source code and text content indexing
• FastFileIndex — Native mmap file indexing engine
• FastCore — Unified JNI loader and platform abstraction

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