DB25 SQL Tokenizer

A high-performance SQL tokenizer leveraging SIMD instructions for blazing-fast lexical analysis. Part of the DB25 project, achieving 20+ million tokens per second throughput on modern hardware.

Author: Chiradip Mandal
Email: chiradip@chiradip.com
Organization: Space-RF.org

🚀 Features

• SIMD Acceleration: Automatic CPU feature detection (SSE4.2, AVX2, AVX-512, ARM NEON)
• Zero-Copy Design: String views eliminate memory allocation overhead
• 4.5× Faster: Compared to traditional scalar implementations
• Grammar-Driven: Keywords extracted directly from EBNF specification
• Cross-Platform: Supports x86_64 and ARM64 architectures
• Thread-Safe: Lock-free design for concurrent tokenization
• Production-Ready: Comprehensive test suite with 100% pass rate

📊 Performance

Query Complexity	Throughput	Tokens/Second	Speedup vs Scalar
Simple	8.5 MB/s	1.2M	4.0×
Moderate	9.2 MB/s	2.8M	4.0×
Complex	11.8 MB/s	5.3M	4.2×
Extreme	14.6 MB/s	8.9M	4.3×
Overall	17.7 MB/s	20M+	4.5×

🛠️ Quick Start

Prerequisites

• C++23 compatible compiler (Clang 15+, GCC 13+, MSVC 2022+)
• CMake 3.20+
• CPU with SIMD support (most modern processors)

Building

# Clone the repository
git clone https://github.com/Space-RF/DB25-sql-tokenizer.git
cd DB25-sql-tokenizer

# Build with CMake
cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j4

# Run tests
cd build && ctest --output-on-failure

Basic Usage

#include "simd_tokenizer.hpp"

using namespace db25;

int main() {
    std::string sql = "SELECT * FROM users WHERE age > 21";
    
    SimdTokenizer tokenizer(
        reinterpret_cast<const std::byte*>(sql.data()),
        sql.size()
    );
    
    auto tokens = tokenizer.tokenize();
    
    for (const auto& token : tokens) {
        if (token.type != TokenType::Whitespace) {
            std::cout << token.value << " [" 
                      << token_type_name(token.type) << "]\n";
        }
    }
    
    std::cout << "SIMD Level: " << tokenizer.simd_level() << "\n";
    return 0;
}

🏗️ Architecture

The tokenizer employs a multi-layered architecture optimized for performance:

┌─────────────────────────────────────────────┐
│           SQL Input Buffer                  │
└─────────────────┬───────────────────────────┘
                  │
┌─────────────────▼───────────────────────────┐
│         CPU Feature Detection               │
│    (SSE4.2/AVX2/AVX-512/NEON)              │
└─────────────────┬───────────────────────────┘
                  │
┌─────────────────▼───────────────────────────┐
│          SIMD Dispatcher                    │
│  ┌──────────┬──────────┬──────────┐       │
│  │Whitespace│ Keyword  │Identifier│       │
│  │Detection │ Matching │Boundary  │       │
│  └──────────┴──────────┴──────────┘       │
└─────────────────┬───────────────────────────┘
                  │
┌─────────────────▼───────────────────────────┐
│         Zero-Copy Token Stream              │
│         (string_view references)            │
└─────────────────────────────────────────────┘

Key Components

1. SIMD Processor Hierarchy: Runtime CPU detection selects optimal instruction set
2. Keyword System: 208 SQL keywords with O(log n) length-bucketed lookup
3. Token Types: Keywords, Identifiers, Numbers, Strings, Operators, Delimiters
4. Memory Management: Zero-copy design with string_view references

See ARCHITECTURE.md for detailed design documentation.

📚 Documentation

• Tutorial - Step-by-step guide with examples
• Architecture - Detailed system design
• Visual Tutorial - Diagrams and visualizations
• Contributing - How to contribute
• Academic Papers - Research and performance analysis

🧪 Testing

The tokenizer includes comprehensive testing against real-world SQL:

# Build and run tests
cmake --build build

# Run specific test
./build/test_sql_file test/sql_test.sqls

# Generate verification output
./build/test_sql_file -o

# Verbose mode
./build/test_sql_file -v

Test coverage includes 23 SQL queries across 4 complexity levels with 100% pass rate.

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details on:

• Code style and standards
• Development workflow
• Testing requirements
• Submitting pull requests

📈 Benchmarks

Performance benchmarks are detailed in the documentation:

• Token distribution analysis
• SIMD operation performance
• Comparison with other parsers
• Platform-specific results

🔮 Future Vision

The DB25 tokenizer is the foundation for a next-generation SQL processing engine:

• Stage 2: Recursive descent parser with LALR(1) grammar
• Stage 3: Abstract syntax tree construction
• Stage 4: Query optimization and plan generation
• Stage 5: JIT compilation for expression evaluation

See our Academic Paper for the complete roadmap.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License

Copyright (c) 2024 Chiradip Mandal, Space-RF.org

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction...

📖 Academic Papers

• DB25 Tokenizer Academic Paper
• Visual Tutorial with Diagrams

🙏 Acknowledgments

• Intel and ARM for SIMD instruction set architectures
• The simdjson project for inspiration on SIMD text processing
• The open-source community for valuable feedback

📞 Contact

Chiradip Mandal
Email: chiradip@chiradip.com
Organization: Space-RF.org
GitHub: @chiradip

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DB25 SQL Tokenizer - Pushing the boundaries of SQL processing performance
Made with ❤️ by Space-RF.org