Files
Oxicloud/src/application/ports/face_ports.rs
T
Claude c930f865b0 perf: round 14 — faces narrow projection, auth per-request allocs, CalDAV emit buffers, frontend set churn
Benchmark-gated (benches/ROUND14.md); every change ships a BEFORE/AFTER
benchmark with an equivalence gate and is rolled back on regression (the
rule is encoded as a GATE FAIL exit / threshold expect).

Backend
- Q1 faces_for_file → narrow face_boxes_for_file(id, person_id, bbox) with the
  caller filter pushed into SQL: drops the 2 KiB embedding BYTEA + 6 unused
  columns per face. 15-face lightbox open 0.312→0.219 ms, 32 KB→840 B/req.
- A1 cookie auth uses the borrow-only extract_cookie_str (already backs CSRF)
  instead of extract_cookie_value's owned String: -1 alloc/cookie request.
- A2 compute_relevance ASCII case-fold fast path vs name.to_lowercase() per
  result row (Unicode fallback preserved): 1.40x, 12→3 allocs/page.
- A3 sub pre-parsed to Uuid at decode time (TokenClaims.sub_id) vs re-parsing
  the 36-char claim on every request incl. cache hits: 22.7→0.7 ns.
- A4 auth + NextCloud middlewares borrow request.headers() instead of taking
  axum's HeaderMap extractor (a full map clone): 2→0 allocs/authed request.
- A5 CalDAV getlastmodified via the stack rfc2822_utc (byte-identical to
  chrono) vs a per-event to_rfc2822() heap String: 5→0 allocs.
- A6 CalDAV per-event href + quoted etag written into reused page buffers vs a
  fresh format! pair per event: 3.48x, 240→6 allocs/40-event page.

Frontend
- F1 t() shares one frozen EMPTY_PARAMS for the no-interpolation call forms vs
  a throwaway {} per call: -1 alloc/call.
- F2 favorites favoriteIds is a persistent SvelteSet with per-page add (clear
  on reset) vs a brand-new set over the whole accumulated list each page:
  22.3x over a 40-page drain (O(N^2)→O(N)).

Verified: cargo check --all-targets, cargo clippy -D warnings, both bench
packs (GATE PASS), frontend npm run check + vitest (4/4). ROUND14.md also
records the investigated-but-deferred backlog (music N+1, contact vcard
over-fetch, CachedBlobBackend syscalls, ResourceList.sections builder, etc.).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01PymgCdK78NzUF3oRAQCJfN
2026-07-19 10:22:12 +00:00

114 lines
4.9 KiB
Rust

//! Ports for the People (faces) feature.
use async_trait::async_trait;
use uuid::Uuid;
use crate::common::errors::DomainError;
use crate::domain::entities::face::{DetectedFace, Face, FaceBox, Person};
/// Detects faces in an image and produces an aligned, L2-normalized embedding
/// for each. Takes raw encoded bytes (it decodes internally) so the
/// application layer stays decoupled from any image/ML crate.
///
/// The default implementation ([`NoopFaceAnalyzer`](crate::infrastructure::services::noop_face_analyzer::NoopFaceAnalyzer))
/// is a no-op that reports `is_ready() == false`; a real ONNX-backed
/// implementation is wired in when the operator provides models at runtime.
#[async_trait]
pub trait FaceAnalyzerPort: Send + Sync + 'static {
/// Whether a usable model is loaded. When false, indexing is skipped.
fn is_ready(&self) -> bool;
/// Detect and embed every face in `image_bytes` (an encoded JPEG/PNG/…).
async fn analyze(&self, image_bytes: &[u8]) -> Result<Vec<DetectedFace>, DomainError>;
}
/// Persistence for faces and persons. Every method is user-scoped; the
/// repository enforces `WHERE user_id = …` so callers only ever touch their
/// own biometric data.
#[async_trait]
pub trait FaceRepository: Send + Sync + 'static {
// ── faces ──────────────────────────────────────────────────────
async fn save_faces(&self, faces: &[Face]) -> Result<(), DomainError>;
/// Face boxes for a photo, caller-scoped — the lightbox tagging overlay
/// needs only `(id, person_id, bbox)`, so this narrow projection drops the
/// 2 KiB embedding BYTEA (+ det_score/quality/blob_hash/created_at) a full
/// `Face` fetch hydrates, and pushes the caller filter into SQL instead of
/// filtering in Rust. See benches/ROUND14.md §Q1.
async fn face_boxes_for_file(
&self,
file_id: Uuid,
user_id: Uuid,
) -> Result<Vec<FaceBox>, DomainError>;
async fn delete_faces_for_file(&self, file_id: Uuid) -> Result<(), DomainError>;
async fn faces_for_user(&self, user_id: Uuid) -> Result<Vec<Face>, DomainError>;
/// Faces previously computed for any file sharing this content hash —
/// lets indexing reuse results for deduplicated (identical) uploads.
async fn faces_for_blob(
&self,
user_id: Uuid,
blob_hash: &str,
) -> Result<Vec<Face>, DomainError>;
/// `(person_id, face_count)` per non-empty cluster — a grouped COUNT
/// instead of dragging every face row (each with a 2 KiB embedding
/// BYTEA) across the wire just to count them. See benches/PEOPLE-LIST.md.
async fn person_face_stats(&self, user_id: Uuid) -> Result<Vec<(Uuid, i64)>, DomainError>;
/// face id → file id for the given faces (cover-photo resolution).
async fn file_ids_for_faces(
&self,
user_id: Uuid,
face_ids: &[Uuid],
) -> Result<std::collections::HashMap<Uuid, Uuid>, DomainError>;
/// Reassign every face of `from` to `into` in one statement (merge).
async fn reassign_person_faces(
&self,
user_id: Uuid,
from: Uuid,
into: Uuid,
) -> Result<u64, DomainError>;
async fn assign_person(
&self,
face_id: Uuid,
person_id: Option<Uuid>,
) -> Result<(), DomainError>;
/// Batch variant of [`Self::assign_person`]: apply every
/// `(face_id, person_id)` pair in one statement. Reclustering an
/// F-face library used to issue F sequential UPDATE round-trips
/// (benches/ROUND11.md §Q5 — the ROUND10 `save_faces` UNNEST pattern).
async fn assign_person_batch(
&self,
assignments: &[(Uuid, Option<Uuid>)],
) -> Result<(), DomainError>;
// ── persons ────────────────────────────────────────────────────
async fn create_person(&self, person: &Person) -> Result<(), DomainError>;
async fn persons_for_user(&self, user_id: Uuid) -> Result<Vec<Person>, DomainError>;
async fn rename_person(
&self,
user_id: Uuid,
person_id: Uuid,
name: Option<String>,
) -> Result<(), DomainError>;
async fn set_person_cover(
&self,
person_id: Uuid,
cover_face_id: Uuid,
) -> Result<(), DomainError>;
async fn set_person_hidden(
&self,
user_id: Uuid,
person_id: Uuid,
hidden: bool,
) -> Result<(), DomainError>;
/// File ids that contain a face assigned to this person (most recent first).
async fn files_for_person(
&self,
user_id: Uuid,
person_id: Uuid,
) -> Result<Vec<Uuid>, DomainError>;
/// Hard-delete every face and person for a user (right to erasure /
/// disabling the feature).
async fn delete_all_for_user(&self, user_id: Uuid) -> Result<(), DomainError>;
}