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About Grapa

What is Grapa?

Grapa is a modern, grammar-based programming language and data processing platform designed for unlimited precision, true parallelism, and seamless Python integration. It is ideal for data engineers, educators, researchers, and anyone who needs high-performance ETL, language experimentation, or advanced scripting.

Core Innovations

🧪 Grapa was built around a fundamental question: "Could I build a language that can mutate its own grammar at runtime, with a storage engine to support fragmented, update-efficient columnar access?"

The result is a system with two groundbreaking innovations:

  1. Runtime Grammar Mutation — Syntax rules defined via mutable $RULE variables, enabling scoped language evolution and meta-programming at runtime. Grapa is a language for building languages, using executable runtime syntax-mutable grammar — the first of its kind.

  2. Weighted BTree Storage Engine — A novel index structure supporting fragmented column-store access with in-place updates. Nodes carry "weight" (byte length) instead of just count, enabling fast partial reads, inserts, and in-place updates across COL, ROW, and GROUP storage models.

Architecture & Design Philosophy

Cross-Platform Foundation: Built from the ground up to support both BigEndian and LittleEndian formats, ensuring true cross-platform compatibility across Windows, Mac, and Linux.

Selective Open Source Integration: While Grapa is entirely original in architecture and execution model, it selectively integrates established libraries (OpenSSL, PCRE2, BLST, FLTK) to avoid reinventing well-tested systems — allowing focus on core innovations.

Production-Ready: Used in production-grade ETL and AWS Lambda workflows, demonstrating real-world applicability of these research concepts.

Research Applications: - Language extensibility and meta-programming - Columnar storage and database systems - Compiler theory and grammar evolution - Parallel data processing architectures

Key Features

  • Unlimited Precision: Arbitrary-precision math for $INT, $FLOAT, $TIME
  • True Parallelism: Production-ready parallel ETL/data processing (map/reduce/filter, $thread, $net)
  • Unified File/Database API: Seamless navigation and processing of files and databases
  • Python Integration: Use GrapaPy for idiomatic, high-performance scripting and data workflows
  • Executable BNF & Mutable Grammar: Define and experiment with grammars at runtime
  • Education & Prototyping: Rapidly prototype languages, teach compiler concepts, and experiment with meta-programming
  • Cross-Platform: Windows, Mac, Linux, AWS, and more

Who Should Use Grapa?

  • Data Engineers & Scientists: For high-throughput ETL, analytics, and automation
  • Educators & Researchers: For teaching, language prototyping, and meta-programming
  • Python/CLI Power Users: For offloading heavy data processing or integrating with existing workflows
  • Anyone needing:
  • Unlimited precision math
  • Parallel/concurrent scripting
  • Unified file/database access
  • Custom language/grammar experimentation

Why/When to Use Grapa?

  • When you need to process large data sets with true parallelism
  • When you want to experiment with grammars, compilers, or language design
  • When Python’s GIL or precision limits are a bottleneck
  • When you want a scripting language that is both high-level and deeply extensible
  • When you need to unify file, database, and scripting workflows

High-Level Architecture & Feature Map

[A visual diagram will be added here in the future. For now, see the summary below.]

  • Core Language: Grammar-based, block-structured, unlimited precision
  • Execution Model: Dynamic code execution, execution trees, meta-programming
  • Parallelism: Built-in support for parallel map/filter/reduce, $thread, $net
  • Unified Path System: Navigate seamlessly between file systems and databases
  • Integration: Python (GrapaPy), CLI, extensible via modules
  • Cross-Platform: Windows, Mac, Linux, AWS

Documentation Structure (Onboarding Map)

Next Steps

Grapa: High-performance scripting for data, ETL, and automation.