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Add STATUS.md with consistent project status format
Status: FUNCTIONAL - Pure OCaml Snappy compression.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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STATUS.md
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STATUS.md
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# snappy
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**Status: FUNCTIONAL**
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## Overview
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A pure OCaml implementation of Google's Snappy compression format. This is not C bindings - it is a complete reimplementation of the Snappy algorithm in OCaml, designed for minimal memory allocation during compression and decompression.
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## Current State
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The implementation is feature-complete with both compression and decompression working:
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- **Compression**: LZ77-style compression with hash table for match finding
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- **Decompression**: Full support for all Snappy tag types (literals, 1/2/4-byte offset copies)
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- **Varint encoding/decoding**: For length headers
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- **Overlap handling**: Proper byte-by-byte copy for RLE-style patterns
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- **Low-allocation API**: `compress_into`/`decompress_into` for writing to pre-allocated buffers
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- **Error handling**: Typed errors and exception variants
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Performance optimizations included:
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- Unsafe byte access in verified hot paths (`Bytes.unsafe_get`/`unsafe_set`)
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- OCaml compiler optimization flags (`-O3 -unbox-closures`)
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- Hash table-based match finding with 32KB window
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## Dependencies
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- ocaml (>= 4.14.0)
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- dune (>= 3.0)
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- alcotest (test only, >= 1.7.0)
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## TODO
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- [ ] Streaming API for processing large data without full buffering
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- [ ] Framing format support (Snappy framing for arbitrary-length streams)
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- [ ] Benchmarks comparing to C snappy bindings
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- [ ] Update placeholder author/maintainer info in dune-project
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## Build & Test
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```bash
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# Build
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dune build
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# Run tests
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dune test
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# Run only quick tests
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dune test --force
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# Install
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dune install
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```
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## Notes
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- Tests pass against standard Snappy test corpus (alice29.txt, html, urls, etc.)
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- Handles bad/malformed compressed data gracefully with proper error messages
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- Maximum copy offset is 32KB (standard Snappy limitation)
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- Compression ratio on repeated patterns is excellent (<10% for highly repetitive data)