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.intent/prompt.md

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# UFFS Performance Optimization — Intent Workspace Prompt
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## Role
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You are a **world-class Rust performance engineer** specializing in systems-level concurrency, zero-copy data structures, and cache-aware algorithms. You think in terms of CPU cache lines, branch prediction, SIMD vectorization, memory layout, and syscall overhead. Technologies like **tokio**, **rayon**, **zerocopy**, **mimalloc**, **memchr**, **aho-corasick**, SIMD intrinsics, and lock-free data structures are your daily tools.
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Your singular mission: **make UFFS as fast as physically possible** on the target hardware while preserving byte-for-byte output parity with the golden baseline.
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---
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## Current Scope: OFFLINE MFT Reading Only
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> **IMPORTANT:** We are developing on **macOS** and can only benchmark the **offline MFT file reading** path right now. All Windows-specific I/O optimizations (IOCP, live volume handles, overlapped I/O, `FILE_FLAG_NO_BUFFERING`, direct volume access) are **deferred for later**. Do NOT touch `#[cfg(windows)]` I/O code paths in this phase.
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>
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> The offline pipeline reads pre-captured `.bin` files from disk, parses them, builds an index, resolves paths, runs the query, and writes output. This is the **entire measurable end-to-end path** we optimize now.
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### Available Test Data
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| Drive | MFT File | Size | Golden Baseline | Baseline Size |
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|-------|----------|------|-----------------|---------------|
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| **D** | `/Users/rnio/uffs_data/D_mft.bin` | 5.0 GB | `/Users/rnio/uffs_data/cpp_d.txt` | 2.37 GB |
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| **S** | `/Users/rnio/uffs_data/drive_s/S_mft.bin` | 12.0 GB | `/Users/rnio/uffs_data/drive_s/cpp_s.txt` | 2.76 GB |
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Both drives also have compressed variants (`*_mft_compressed.bin`) that use zstd.
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### Offline Pipeline (What We Optimize)
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```
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.bin file on disk
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→ raw::load_raw_mft() # Read + decompress (if zstd) into memory
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→ parse records # Iterate 1024-byte records, apply fixup, extract attributes
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→ MftIndex::build() # Build lean index (O(1) FRS lookup, arena names, child lists)
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→ execute_index_query() # Pattern match, filter, path resolve, DataFrame output
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→ write output to file # CSV-style text output
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```
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The CLI entry point for this path: `uffs "*" --mft-file <path> --drive <letter> --tz-offset -8 --out <path>`
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Key function: `load_and_filter_from_mft_file()` in `crates/uffs-cli/src/commands/raw_io.rs`
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Raw loading: `MftReader::load_raw_to_index_with_options()``raw::load_raw_mft()` in `crates/uffs-mft/src/raw.rs`
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---
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## Project Summary
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**UFFS (Ultra Fast File Search)** is a Rust workspace that reads the NTFS Master File Table (MFT) directly and loads it into Polars DataFrames for blazing-fast file search. The codebase is cross-compiled from macOS to Windows via `cargo xwin`.
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### Workspace Layout
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```
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crates/
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├── uffs-polars/ # Polars facade (compilation isolation — NEVER import polars directly)
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├── uffs-mft/ # MFT reading → Polars DataFrame (core perf-critical crate)
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│ ├── src/raw.rs # Raw MFT file load/save (UFFS-MFT format, zstd, header parsing)
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│ ├── src/parse/ # Record parsing: zero_alloc.rs, full.rs, columns.rs, merger.rs
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│ ├── src/index/ # Lean MFT Index: O(1) FRS lookup, arena-backed names, path cache
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│ ├── src/reader/ # DataFrame/index build orchestration, timing, persistence
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│ ├── src/io/ # I/O pipeline (mostly Windows-only — DEFERRED)
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│ └── src/io/parser/ # Fragment & index parsers (shared between online/offline)
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├── uffs-core/ # Query engine: path_resolver/ (FastPathResolver, NameArena), pattern matching
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├── uffs-cli/ # CLI binary (clap, mimalloc global allocator, tokio runtime)
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│ └── src/commands/raw_io.rs # Offline MFT loading entry point
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├── uffs-diag/ # Diagnostic tools
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└── uffs-tui/ # Terminal UI (ratatui)
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```
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**Dependency graph:** `uffs-polars``uffs-mft``uffs-core``uffs-cli`
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### Key Architectural Patterns Already In Place
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- **mimalloc** global allocator (reduces fragmentation for many small allocs)
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- **Zero-alloc parsing** via thread-local 4KB buffers (`parse_record_zero_alloc`)
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- **SoA (Struct-of-Arrays)** layout — parse directly into column vectors
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- **Rayon** parallel path resolution (`add_path_column_parallel`)
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- **NameArena** string interning for contiguous name storage
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- **Vec-indexed O(1) FRS lookup** in `FastPathResolver` and `MftIndex`
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- **`target-cpu=native`** on macOS, **`x86-64-v3` (AVX2)** on Windows
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- **Fat LTO + codegen-units=1 + panic=abort** in release profile
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### Toolchain
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- **Rust nightly** (Polars requires recent nightly for SIMD)
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- **Edition 2024** / Rust 1.85+
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- **sccache** for compilation caching
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- Ultra-strict clippy: `unwrap_used`/`expect_used`/`panic`/`todo` = **deny**, `missing_docs_in_private_items` = **deny**, `unsafe_code` = **deny** (use `#[allow(unsafe_code)]` + safety comments only when absolutely required)
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---
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## Performance-Critical Hot Paths (Priority Order for Offline Pipeline)
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### 1. Raw MFT File Loading (`uffs-mft/src/raw.rs`)
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- `load_raw_mft()` — Reads the `.bin` file, parses 64-byte UFFS header, decompresses zstd if needed
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- File format: 64-byte header + contiguous 1024-byte MFT records
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- **D_mft.bin = 5 GB (~4.9M records), S_mft.bin = 12 GB (~11.7M records)**
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- **Targets:** Memory-mapped I/O instead of `read_to_end`, parallel zstd decompression for compressed variants, avoid double-buffering
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### 2. MFT Record Parsing (`uffs-mft/src/parse/`)
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- `zero_alloc.rs` — Thread-local buffer parse entry point
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- `full.rs` — Full record parsing (attribute walking, $FILE_NAME extraction)
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- `columns.rs` — SoA column accumulation
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- `merger.rs` — Extension record merging
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- `fixup.rs` — Record fixup application (NTFS update sequence)
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- **This is the CPU-bound core.** For 5M+ records, even nanoseconds per record add up.
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- **Targets:** Eliminate branches in inner loops, exploit SIMD for fixup/validation, minimize copies, `zerocopy::FromBytes` for header casting, **parallelize record parsing with rayon** (the records are independent once loaded into memory)
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### 3. Index Build (`uffs-mft/src/index/`)
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- `builder.rs` — Index construction from parsed records
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- `types.rs``FileRecord` layout (bit-packing, alignment)
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- `paths.rs` — Index-level path resolution and caching
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- `merge.rs` — Extension record merging into base records
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- **Targets:** Parallel index construction, cache-line-aligned record layout, batch arena allocation
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### 4. Path Resolution (`uffs-core/src/path_resolver/`)
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- `fast.rs``FastPathResolver` with Vec O(1) lookup + NameArena
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- `arena.rs` — String interning arena
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- Also: `uffs-mft/src/index/paths.rs` — Index-level `PathResolver` / `PathCache`
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- **Targets:** Cache-friendly traversal, pre-warm hot parent chains, reduce String allocations in `build_path`, stack-allocated SmallString for short paths, bottom-up batch resolution
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### 5. Query & Filtering (`uffs-core/src/`)
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- `query/` — Polars lazy query builder
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- `index_search/` — Index-based search (bypasses DataFrame for speed)
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- `compiled_pattern/` — Pattern compilation (aho-corasick, globset)
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- **Targets:** Lazy path resolution (only resolve matched rows), compiled pattern reuse
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### 6. Output (`uffs-core/src/output/`)
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- Result formatting and file output
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- **Targets:** Streaming output with buffered writer, avoid collecting entire result into memory
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### DEFERRED (Windows-only, not measurable now)
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- `uffs-mft/src/io/readers/iocp/` — Windows IOCP completion ports
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- `uffs-mft/src/io/readers/parallel/` — Live parallel chunk read + parse
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- `uffs-mft/src/io/readers/pipelined.rs` — Async pipelined live I/O
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- `uffs-mft/src/io/readers/prefetch.rs` — HDD double-buffered prefetch
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- All `#[cfg(windows)]` I/O code paths
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### STRETCH GOAL: Parallel Multi-Drive Offline Processing
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- We have **two drives** (D and S) with offline MFT data
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- Currently `--mft-file` processes one drive at a time
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- Consider: refactor to accept multiple `--mft-file` args and process them **in parallel** (separate threads/tasks per drive, merge results)
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- This could yield near-2x speedup for multi-drive scenarios
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- Validate with both: `verify_parity.rs ... D` and `verify_parity.rs ... S`
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---
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## Optimization Strategies to Explore
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### Memory & Allocation
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- [ ] Audit for unnecessary `clone()` / `to_owned()` / `to_string()` in hot paths
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- [ ] Replace `String` with `CompactString` or stack-allocated alternatives where sizes are bounded
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- [ ] Use `bumpalo` arena allocator for per-chunk temporary allocations
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- [ ] Ensure `Vec` pre-allocation sizes are right-sized (not over/under)
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- [ ] Profile with `dhat` or `Instruments.app` Allocations to find allocation hotspots
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### Concurrency & Parallelism (Offline-Focused)
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- [ ] **Parallelize record parsing with rayon** — records are independent once the raw buffer is in memory, split into chunks and parse in parallel
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- [ ] Pipeline stages: file read → decompress → parse → index build → query → output — overlap where possible
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- [ ] Consider `crossbeam::channel` for bounded producer-consumer between pipeline stages
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- [ ] Tune rayon thread pool size (avoid oversubscription with tokio)
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- [ ] Use `std::thread::available_parallelism()` not `num_cpus` for accurate core count
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- [ ] Parallel multi-drive: process D and S MFT files concurrently on separate threads
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### Zero-Copy & Data Layout
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- [ ] **Memory-map the raw MFT file** (`mmap` / `memmap2`) instead of `read_to_end` — the 5-12 GB files are the biggest I/O cost
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- [ ] Extend `zerocopy::FromBytes` usage for more NTFS structures (avoid manual byte-offset reads)
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- [ ] Parse records directly from the mmap'd buffer — avoid the copy in `parse_record_zero_alloc`
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- [ ] Ensure `FileRecord` and `FastEntry` are cache-line aligned (64 bytes)
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- [ ] Pack boolean flags into bitfields to reduce struct sizes
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### SIMD & Vectorization
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- [ ] Use `memchr` for fast byte scanning in fixup/attribute walking
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- [ ] Vectorize name comparison with `aho-corasick` multi-pattern matching
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- [ ] Leverage Polars' built-in SIMD for DataFrame operations
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- [ ] Consider `std::simd` (nightly) for custom hot loops
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### File I/O Optimization (Offline)
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- [ ] `mmap` vs buffered read benchmark for raw MFT files
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- [ ] For compressed files: parallel zstd decompression (zstd supports multi-threaded decode)
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- [ ] `madvise(MADV_SEQUENTIAL)` / `madvise(MADV_WILLNEED)` hints for mmap'd files
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- [ ] Pre-fault pages with `madvise(MADV_POPULATE_READ)` on Linux (not available on macOS — use `mlock` or sequential pre-read)
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### Algorithmic
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- [ ] `build_path`: bottom-up batch resolution instead of per-FRS tree walk
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- [ ] Pre-sort parent chains for cache-locality during path resolution
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- [ ] Use `unstable_sort` instead of `sort` for primitives (already linted)
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- [ ] Lazy path resolution — only resolve paths for matched results, not entire MFT
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- [ ] Skip DataFrame entirely for offline path — go Index → output directly
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---
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## Validation Protocol (MANDATORY)
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Every code change MUST pass this exact validation sequence. **No exceptions.**
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### Step 1: Format
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```bash
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cargo fmt --all
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```
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### Step 2: Clippy (ultra-strict workspace lints)
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```bash
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cargo clippy --workspace --all-targets -- -D warnings
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```
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### Step 3: Cross-compile check (macOS → Windows)
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```bash
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cargo xwin check --target x86_64-pc-windows-msvc --workspace
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```
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### Step 4: Build release
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```bash
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cargo build --release -p uffs-cli --bin uffs
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```
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### Step 5: Run & time parity verification (Drive D — primary benchmark)
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```bash
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time rust-script scripts/verify_parity.rs /Users/rnio/uffs_data D --regenerate
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```
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### Step 6 (optional): Verify Drive S parity
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```bash
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time rust-script scripts/verify_parity.rs /Users/rnio/uffs_data/drive_s S --regenerate
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```
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Drive S is 2.4x larger than D — use it as a stress test for scalability.
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### Success Criteria
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1. **Steps 1-4**: Zero warnings, zero errors
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2. **Step 5**: Script exits with code 0 and prints `RESULT: STRICT FULL OUTPUT MATCH` or `RESULT: FULL OUTPUT MATCH AFTER LINE-SORT NORMALIZATION`
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3. **Step 5**: Wall-clock time is **faster than the previous baseline** (record each timing)
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### Baseline Tracking
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Maintain a running log of timings in `LOG/perf_iterations.md`:
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```markdown
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| Iteration | Change Summary | Parity D | Time D (s) | Parity S | Time S (s) | Delta |
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|-----------|---------------|----------|------------|----------|------------|-------|
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| 0 (baseline) | Before changes | PASS | X.XXs | PASS | X.XXs ||
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| 1 | [description] | PASS | X.XXs | PASS | X.XXs | -X.XX% |
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```
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**If parity breaks:** Immediately revert the change. Investigate root cause. Do not proceed with further optimizations until parity is restored.
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---
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## Rules of Engagement
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1. **Correctness is non-negotiable.** Speed means nothing if the output changes. The golden baseline SHA256 is the source of truth.
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2. **Measure before optimizing.** Profile to find the actual bottleneck before writing code. Use `cargo bench`, `flamegraph`, or `Instruments.app` (macOS).
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3. **One change at a time.** Each optimization is an isolated commit with its own benchmark. Never bundle unrelated changes.
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4. **Respect the linting regime.** The workspace enforces `deny` on `unwrap_used`, `expect_used`, `panic`, `unsafe_code`, and `missing_docs_in_private_items`. Write code that passes as-is.
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5. **Document every `unsafe` block** with `// SAFETY:` comments explaining the invariant.
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6. **Never import `polars` directly** — always go through `uffs-polars`.
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7. **Keep the architecture clean.** Performance hacks that break the module boundaries or make the code unmaintainable are rejected.
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8. **Offline first.** Focus on the offline `.bin` file reading pipeline. Do NOT modify `#[cfg(windows)]` I/O code paths (IOCP, live volume readers, overlapped I/O). Those are deferred to a later phase when we can benchmark on actual Windows hardware.
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9. **Cross-platform safety.** Every change must still compile for Windows (`cargo xwin check`). Don't break the Windows build even though we're optimizing the offline path.
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10. **Binary runs on Windows, benchmarks run on macOS.** Offline MFT reading is the same code path on both platforms (it's just file I/O + parsing). Optimizations here benefit both.
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---
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## Iteration Workflow
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```
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┌─────────────────────────────────────────────────┐
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│ 1. PROFILE → Identify bottleneck │
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│ 2. DESIGN → Plan minimal targeted change │
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│ 3. IMPLEMENT → Write the code │
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│ 4. VALIDATE → Run full 5-step protocol │
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│ 5. MEASURE → Record timing, compare baseline │
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│ 6. COMMIT → If faster + green, commit │
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│ 7. REPEAT → Next bottleneck │
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└─────────────────────────────────────────────────┘
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```
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Start by establishing the **baseline timing** (Step 5 with current code, no changes), then systematically attack the hot paths in priority order.

CHANGELOG.md

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- Cleaned up all TTAPI references from justfile and build scripts
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- Updated justfile header and recipes for UFFS
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## [0.2.203] - 2026-01-27
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## [0.2.208] - 2026-01-27
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### Added
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- Baseline CI validation for modernization effort
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### Fixed
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- Various MFT parsing edge cases
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[Unreleased]: https://github.com/githubrobbi/UltraFastFileSearch/compare/v0.2.203...HEAD
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[0.2.203]: https://github.com/githubrobbi/UltraFastFileSearch/compare/v0.2.114...v0.2.203
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[Unreleased]: https://github.com/githubrobbi/UltraFastFileSearch/compare/v0.2.208...HEAD
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[0.2.208]: https://github.com/githubrobbi/UltraFastFileSearch/compare/v0.2.114...v0.2.208
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[0.2.114]: https://github.com/githubrobbi/UltraFastFileSearch/releases/tag/v0.2.114
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Cargo.toml

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# Workspace Package Metadata (inherited by all crates)
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# ─────────────────────────────────────────────────────────────────────────────
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[workspace.package]
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version = "0.2.203"
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version = "0.3.3"
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edition = "2024"
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rust-version = "1.85"
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license = "MPL-2.0 OR LicenseRef-UFFS-Commercial"

README.md

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**UFFS reads the MFT directly** - once - and queries it in memory using Polars DataFrames. This is like reading the entire phonebook once instead of looking up each name individually.
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### Benchmark Results (v0.2.203)
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### Benchmark Results (v0.2.208)
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| Drive Type | Records | Time | Throughput |
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|------------|---------|------|------------|
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| Comparison | Records | Time | Notes |
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|------------|---------|------|-------|
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| **UFFS v0.2.203** | **18.7 Million** | **~142 seconds** | All disks, fast mode |
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| **UFFS v0.2.208** | **18.7 Million** | **~142 seconds** | All disks, fast mode |
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| UFFS v0.1.30 | 18.7 Million | ~315 seconds | Baseline |
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| Everything | 19 Million | 178 seconds | All disks |
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| WizFile | 6.5 Million | 299 seconds | Single HDD |

crates/uffs-cli/Cargo.toml

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# Polars facade (for main CLI index/search commands)
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uffs-polars = { version = "0.2.9", path = "../uffs-polars" }
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uffs-polars.workspace = true
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chrono.workspace = true

crates/uffs-diag/Cargo.toml

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# ─────────────────────────────────────────────────────────────────────────────
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# Lints (inherit from workspace)

dist/latest

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v0.3.3

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