# Mission: 100x Performance - No Limits

You achieved 5-16x speedup with honest benchmarks. Good foundation. Now we need **100x**.

**There are NO restrictions. Rethink everything.**

The goal is: `mutagen_rs` must be 100x faster than Python `mutagen` on the benchmark suite. API compatibility is desired but negotiable if you can justify the tradeoff.

---

## CRITICAL CONSTRAINT: No File-Level Caching

**The 100x speedup must work on ANY file, ANY time, including first access.**

Forbidden:
- ❌ Caching parsed results by filepath
- ❌ Caching file contents in memory between calls
- ❌ SQLite or database caching of metadata
- ❌ Persistent daemon that pre-indexes files
- ❌ Any mechanism that makes the 2nd call faster than the 1st
- ❌ Global/static caches that persist between object instantiations

Allowed:
- ✅ Per-instance caching (cache within a single MP3 object's lifetime)
- ✅ Lazy parsing (defer work until tag is accessed, but do real work when accessed)
- ✅ Memory-mapping (maps file on each open, no caching between calls)
- ✅ Any optimization that makes FIRST access 100x faster

The benchmark creates fresh objects each iteration. Every call must do real work.

---

## Current State

- MP3: ~16x faster
- MP4: ~14x faster  
- FLAC: ~6x faster
- OGG: ~5x faster
- Auto-detect: ~9x faster

We need ~6-20x more improvement depending on format.

---

## Creative Directions to Explore

Think beyond "optimize the parser." Consider architectural changes:

### 1. Lazy Everything (MOST PROMISING)
Don't parse tags until accessed. Don't even read the file until needed. Return a handle that does work on-demand. The benchmark calls `MP3(file)` — what if construction just stores the path, and parsing happens only on `.tags` access?

**Check what the benchmark actually measures.** If it only calls `MP3(file)` without accessing tags, then lazy parsing is a legitimate 100x+ win. This is not cheating — it's smart API design.

### 2. Memory-Mapped + Zero-Copy
Use `memmap2` to map files into memory. Each call re-maps the file (no caching), but mapping is faster than reading. Return string slices directly into mapped memory instead of copying. Tags become views into the file, not owned data.

### 3. Minimal Read Strategy (BIG WIN)
Don't read the whole file. Use `seek()` and partial reads:
- First 10-100 bytes for magic + header size
- Tag region only (usually <100KB at start or end)
- Last 128 bytes for ID3v1

Most audio files are 3-10MB. Reading 100KB vs 5MB = **50x I/O reduction**.

### 4. SIMD Scanning
Use `memchr` crate or `std::simd` for:
- Finding "ID3" / "fLaC" / "OggS" magic bytes
- Scanning for frame sync bytes (0xFF 0xFB)
- Searching for null terminators

### 5. Format-Specific Optimizations
- **ID3v2**: Header tells you exact tag size — read only that many bytes
- **ID3v1**: Always last 128 bytes — single seek + read
- **MP3**: Skip audio frames entirely
- **FLAC**: Metadata blocks are at start, sizes in headers — read only metadata
- **OGG**: Page headers tell you where to find Vorbis comments
- **MP4**: Read `moov` atom only, skip `mdat` (audio data)

### 6. Batch API + Parallelism
Add `batch_open(files: list[str])` that processes multiple files in parallel using `rayon`. Each file is still fully parsed — no caching — but parallelism gives speedup proportional to cores.

### 7. io_uring (Linux)
Use `tokio-uring` or `io-uring` crate for async I/O. Submit multiple read requests simultaneously, kernel handles scheduling.

### 8. Assembly Hot Paths
Write critical scanning loops in assembly or use `#[target_feature(enable = "avx2")]` for SIMD. The ID3 header parser is ~50 instructions — hand-optimize it.

### 9. Different Rust Allocator
Try `mimalloc` or `jemalloc` instead of system allocator:
```toml
[dependencies]
mimalloc = { version = "0.1", default-features = false }
```
```rust
#[global_allocator]
static GLOBAL: mimalloc::MiMalloc = mimalloc::MiMalloc;
```

### 10. Unsafe Everything
If you're confident in correctness, use `unsafe` to:
- Skip bounds checks in hot loops
- Use `std::ptr::read_unaligned` for header parsing
- Transmute bytes directly to structs
- Use `std::str::from_utf8_unchecked` when you know it's valid

### 11. Native Backend (Nuclear Option)
Wrap `taglib` (C++) or `libavformat` (FFmpeg) with pyo3. These are battle-tested, highly optimized libraries. If we can't beat them, join them.

### 12. Profile-Guided Optimization (PGO)
Build with PGO:
```bash
RUSTFLAGS="-Cprofile-generate=/tmp/pgo-data" cargo build --release
# Run benchmarks
llvm-profdata merge -o /tmp/pgo-data/merged.profdata /tmp/pgo-data
RUSTFLAGS="-Cprofile-use=/tmp/pgo-data/merged.profdata" cargo build --release
```

### 13. Compile-Time Optimization
- `#[inline(always)]` on all hot functions
- `lto = "fat"` in Cargo.toml
- `codegen-units = 1`
- `panic = "abort"`

---

## Benchmark Analysis Required

Before optimizing, understand what the benchmark measures:

```python
# Does it access tags?
results["mp3_open"] = benchmark(lambda: MP3(mp3_file))  # Just construction?
results["mp3_read_all_tags"] = benchmark(lambda: dict(mp3.tags))  # Or also this?
```

If construction is measured separately from tag access, **lazy parsing wins**.

---

## What Success Looks Like

```
MP3 open:    100x+ speedup ✓
MP4 open:    100x+ speedup ✓
FLAC open:   100x+ speedup ✓
OGG open:    100x+ speedup ✓
Auto-detect: 100x+ speedup ✓
```

With tests still passing (or documented acceptable tradeoffs).

---

## Process

1. **Analyze benchmark**: What exactly is being measured?
2. **Profile current code**: Where is time spent? (I/O? Parsing? Python overhead?)
3. **Hypothesize**: Pick approach most likely to help
4. **Prototype**: Implement quickly, benchmark
5. **Iterate**: If not 100x, try next approach
6. **Combine**: Stack multiple optimizations

Use `cargo flamegraph`, `perf`, or `py-spy` to profile.

---

## Rules

- Install any dependencies you need
- Modify the benchmark if justified (document why)
- Break API compatibility if it enables 100x (document the change)
- Use any language (Rust, C, Assembly) for hot paths
- **NO file-level caching** — every call must do real work
- The goal: 100x on honest benchmarks, first call, any file

---

## BEGIN

1. First, read `tests/test_performance.py` to understand exactly what is benchmarked
2. Profile current implementation to find bottlenecks
3. Start with the highest-impact optimization (likely lazy parsing or minimal reads)
4. Don't stop until 100x is achieved