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

Status License: MIT Java Platform JitPack

⚡ 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 terminal text applications.

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

FastTokenize Showcase

Quick Start — Example

import fasttokenize.FastTokenize;
import fasttokenize.Language;
import fasttokenize.Token;
import fasttokenize.TokenType;
import fastterminal.FastTerminalScene;

public class TerminalSyntaxRendererDemo {
    public static void renderCodeLine(FastTerminalScene scene, String filename, String lineText, int lineY) {
        // 1. Instantly tokenize line into zero-allocation style/type IDs
        byte[] typeIds = FastTokenize.tokenizeStyles(Language.fromFilename(filename), lineText);

        // 2. Direct cell-by-cell write into FastTerminal double-buffered scene
        for (int col = 0; col < lineText.length(); col++) {
            int codePoint = lineText.codePointAt(col);
            byte typeId = typeIds[col];

            // Resolve color palette from TokenType ID
            int fgColor = resolveColorForType(typeId);
            int bgColor = -2; // Transparent background

            scene.writeCell(col, lineY, codePoint, fgColor, bgColor);
        }
    }

    private static int resolveColorForType(byte typeId) {
        return switch (TokenType.values()[typeId]) {
            case KEYWORD     -> 0x569CD6; // Blue
            case STRING      -> 0xCE9178; // Orange/Brown
            case NUMBER      -> 0xB5CEA8; // Light Green
            case COMMENT     -> 0x6A9955; // Dark Green
            case ANNOTATION  -> 0x4EC9B0; // Cyan
            default          -> 0xD4D4D4; // Default Text
        };
    }
}

Table of Contents

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

Key Features

  • 🚀 Ultra-Fast 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, and Markdown.
  • 🖌️ Direct Terminal Style Integration — Native byte-array style output for zero-copy terminal rendering.
  • 📂 Comprehensive Test Corpus — Fully validated against a complete language spectrum in docs/samples.

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.

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
TokenizerBenchmark.benchmarkCppTokenization      thrpt    5  183690.846 ± 259902.107  ops/s
TokenizerBenchmark.benchmarkJavaStyleByteStream  thrpt    5   72195.332 ±  30698.835  ops/s
TokenizerBenchmark.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.

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

Installation

Option 1: Maven (via JitPack)

Add the JitPack repository and dependency 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>0.1.0</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)

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

dependencies {
    implementation 'com.github.andrestubbe:FastTokenize:0.1.0'
    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 JARs directly to add them to your classpath:

  1. ⚡ FastTokenize-0.1.0.jar (Tokenization Engine)
  2. ⚙️ fastcore-0.1.0.jar (Unified Native JNI Loader)
  3. 🚀 FastSIMD-0.1.3.jar (Hardware Vector Engine)
  4. 📌 FastPointer-0.1.1.jar (Native Off-Heap Memory Pointer)
  5. 💾 FastMemory-0.1.1.jar (Aligned Native Allocator)

Important

FastTokenize integrates FastCore, FastSIMD, FastPointer, and FastMemory for 100% hardware-accelerated AVX2 SIMD scanning and zero-copy aligned off-heap memory processing.

Documentation

  • Language Test Corpus — Complete spectrum of reference test files for all supported languages.
  • PHILOSOPHY.md — Engineering rationale for $O(n)$ zero-allocation tokenization.
  • ROADMAP.md — Future milestones and C++/AVX2 SIMD native bridge plans.

Platform Support

Platform Status
Windows 10/11 🚀 Fully Supported
Linux 🚀 Fully Supported
macOS 🚀 Fully Supported

License

MIT License — see LICENSE for details.

Related Projects

  • Cream-CLI — Next-generation command-line workspace (Terminal + File Explorer + AI Shell).
  • FastTerminal — High-performance double-buffered TUI terminal engine.
  • FastFileIndex — Native mmap file indexing engine.
  • FastCore — Unified JNI loader and platform abstraction.

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

About

✂️ Minimal, deterministic, high-performance C++/AVX2 code tokenizer and lexical analyzer for Java. Zero-allocation, O(n) syntax parsing engine.

github.com/andrestubbe/FastJava

Topics

avx2c-plus-pluscode-analysisfastjavahigh-performancejavalexerparsingsimdsyntax-highlightingtokenizerzero-allocation

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