Files
Oxicloud/src/infrastructure/repositories/pg/face_pg_repository.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

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//! PostgreSQL repository for the People (faces) feature.
//!
//! Embeddings are stored as `BYTEA` (512 × little-endian `f32`); there is no
//! pgvector dependency. Similarity search / clustering is done in-app over the
//! decoded vectors (see `PeopleService`).
use std::sync::Arc;
use async_trait::async_trait;
use chrono::{DateTime, Utc};
use sqlx::PgPool;
use uuid::Uuid;
use crate::application::ports::face_ports::FaceRepository;
use crate::common::errors::DomainError;
use crate::domain::entities::face::{BoundingBox, Face, FaceBox, Person};
/// Row shape for `faces.faces` selects (avoids `clippy::type_complexity`).
type FaceRow = (
Uuid, // id
Uuid, // file_id
Uuid, // user_id
Option<Uuid>, // person_id
Vec<f32>, // bbox (REAL[])
f32, // det_score
Option<f32>, // quality
Vec<u8>, // embedding (BYTEA)
Option<String>, // blob_hash
DateTime<Utc>, // created_at
);
type PersonRow = (
Uuid, // id
Uuid, // user_id
Option<String>, // display_name
Option<Uuid>, // cover_face_id
bool, // is_hidden
DateTime<Utc>, // created_at
);
fn embedding_to_bytes(e: &[f32]) -> Vec<u8> {
let mut out = Vec::with_capacity(e.len() * 4);
for v in e {
out.extend_from_slice(&v.to_le_bytes());
}
out
}
fn bytes_to_embedding(b: &[u8]) -> Vec<f32> {
b.chunks_exact(4)
.map(|c| f32::from_le_bytes([c[0], c[1], c[2], c[3]]))
.collect()
}
fn row_to_face(r: FaceRow) -> Face {
let (
id,
file_id,
user_id,
person_id,
bbox,
det_score,
quality,
embedding,
blob_hash,
created_at,
) = r;
Face {
id,
file_id,
user_id,
person_id,
bbox: BoundingBox::from_slice(&bbox),
det_score,
quality,
embedding: bytes_to_embedding(&embedding),
blob_hash,
created_at,
}
}
fn row_to_person(r: PersonRow) -> Person {
let (id, user_id, display_name, cover_face_id, is_hidden, created_at) = r;
Person {
id,
user_id,
display_name,
cover_face_id,
is_hidden,
created_at,
}
}
fn db_err(ctx: &'static str, e: sqlx::Error) -> DomainError {
DomainError::internal_error("FacePg", format!("{ctx}: {e}"))
}
const FACE_COLS: &str =
"id, file_id, user_id, person_id, bbox, det_score, quality, embedding, blob_hash, created_at";
const PERSON_COLS: &str = "id, user_id, display_name, cover_face_id, is_hidden, created_at";
pub struct FacePgRepository {
pool: Arc<PgPool>,
}
impl FacePgRepository {
pub fn new(pool: Arc<PgPool>) -> Self {
Self { pool }
}
}
#[async_trait]
impl FaceRepository for FacePgRepository {
async fn save_faces(&self, faces: &[Face]) -> Result<(), DomainError> {
if faces.is_empty() {
return Ok(());
}
// One multi-row INSERT over parallel UNNEST arrays instead of one
// round-trip per face — a group photo yields many faces per indexed
// image. The `bbox` float4[] can't ride an array-of-arrays through
// unnest (PG flattens), so its 4 components travel as 4 parallel
// arrays and are reassembled server-side. A single statement is
// atomic on its own; the per-row transaction wrapper is gone.
let n = faces.len();
let mut ids = Vec::with_capacity(n);
let mut file_ids = Vec::with_capacity(n);
let mut user_ids = Vec::with_capacity(n);
let mut person_ids: Vec<Option<Uuid>> = Vec::with_capacity(n);
let (mut bx, mut by, mut bw, mut bh) = (
Vec::with_capacity(n),
Vec::with_capacity(n),
Vec::with_capacity(n),
Vec::with_capacity(n),
);
let mut det_scores = Vec::with_capacity(n);
let mut qualities: Vec<Option<f32>> = Vec::with_capacity(n);
let mut embeddings = Vec::with_capacity(n);
let mut blob_hashes: Vec<Option<&str>> = Vec::with_capacity(n);
for f in faces {
ids.push(f.id);
file_ids.push(f.file_id);
user_ids.push(f.user_id);
person_ids.push(f.person_id);
bx.push(f.bbox.x);
by.push(f.bbox.y);
bw.push(f.bbox.w);
bh.push(f.bbox.h);
det_scores.push(f.det_score);
qualities.push(f.quality);
embeddings.push(embedding_to_bytes(&f.embedding));
blob_hashes.push(f.blob_hash.as_deref());
}
sqlx::query(
r#"
INSERT INTO faces.faces
(id, file_id, user_id, person_id, bbox, det_score, quality, embedding, blob_hash)
SELECT t.id, t.file_id, t.user_id, t.person_id,
ARRAY[t.bx, t.by, t.bw, t.bh]::real[],
t.det_score, t.quality, t.embedding, t.blob_hash
FROM unnest($1::uuid[], $2::uuid[], $3::uuid[], $4::uuid[],
$5::real[], $6::real[], $7::real[], $8::real[],
$9::real[], $10::real[], $11::bytea[], $12::text[])
AS t(id, file_id, user_id, person_id,
bx, by, bw, bh, det_score, quality, embedding, blob_hash)
"#,
)
.bind(&ids)
.bind(&file_ids)
.bind(&user_ids)
.bind(&person_ids)
.bind(&bx)
.bind(&by)
.bind(&bw)
.bind(&bh)
.bind(&det_scores)
.bind(&qualities)
.bind(&embeddings)
.bind(&blob_hashes)
.execute(self.pool.as_ref())
.await
.map_err(|e| db_err("save_faces", e))?;
Ok(())
}
async fn face_boxes_for_file(
&self,
file_id: Uuid,
user_id: Uuid,
) -> Result<Vec<FaceBox>, DomainError> {
// Narrow projection: the lightbox needs only (id, person_id, bbox), so
// the 2 KiB embedding BYTEA + 6 unused columns stay in the DB and the
// caller filter runs in SQL (idx_faces_file drives it) rather than in
// Rust after a full-row fetch. See benches/ROUND14.md §Q1.
let rows: Vec<(Uuid, Option<Uuid>, Vec<f32>)> = sqlx::query_as(
"SELECT id, person_id, bbox FROM faces.faces WHERE file_id = $1 AND user_id = $2",
)
.bind(file_id)
.bind(user_id)
.fetch_all(self.pool.as_ref())
.await
.map_err(|e| db_err("face_boxes_for_file", e))?;
Ok(rows
.into_iter()
.map(|(id, person_id, bbox)| FaceBox {
id,
person_id,
bbox: BoundingBox::from_slice(&bbox),
})
.collect())
}
async fn delete_faces_for_file(&self, file_id: Uuid) -> Result<(), DomainError> {
sqlx::query("DELETE FROM faces.faces WHERE file_id = $1")
.bind(file_id)
.execute(self.pool.as_ref())
.await
.map_err(|e| db_err("delete_faces_for_file", e))?;
Ok(())
}
async fn faces_for_user(&self, user_id: Uuid) -> Result<Vec<Face>, DomainError> {
let sql = format!("SELECT {FACE_COLS} FROM faces.faces WHERE user_id = $1");
let rows: Vec<FaceRow> = sqlx::query_as(&sql)
.bind(user_id)
.fetch_all(self.pool.as_ref())
.await
.map_err(|e| db_err("faces_for_user", e))?;
Ok(rows.into_iter().map(row_to_face).collect())
}
async fn faces_for_blob(
&self,
user_id: Uuid,
blob_hash: &str,
) -> Result<Vec<Face>, DomainError> {
let sql =
format!("SELECT {FACE_COLS} FROM faces.faces WHERE user_id = $1 AND blob_hash = $2");
let rows: Vec<FaceRow> = sqlx::query_as(&sql)
.bind(user_id)
.bind(blob_hash)
.fetch_all(self.pool.as_ref())
.await
.map_err(|e| db_err("faces_for_blob", e))?;
Ok(rows.into_iter().map(row_to_face).collect())
}
async fn person_face_stats(&self, user_id: Uuid) -> Result<Vec<(Uuid, i64)>, DomainError> {
// Grouped COUNT — the People tab only needs per-person counts, so
// this replaces a full faces_for_user scan that shipped a 2 KiB
// embedding BYTEA per row (benches/PEOPLE-LIST.md).
let rows: Vec<(Uuid, i64)> = sqlx::query_as(
"SELECT person_id, COUNT(*) FROM faces.faces
WHERE user_id = $1 AND person_id IS NOT NULL
GROUP BY person_id",
)
.bind(user_id)
.fetch_all(self.pool.as_ref())
.await
.map_err(|e| db_err("person_face_stats", e))?;
Ok(rows)
}
async fn file_ids_for_faces(
&self,
user_id: Uuid,
face_ids: &[Uuid],
) -> Result<std::collections::HashMap<Uuid, Uuid>, DomainError> {
if face_ids.is_empty() {
return Ok(std::collections::HashMap::new());
}
let rows: Vec<(Uuid, Uuid)> = sqlx::query_as(
"SELECT id, file_id FROM faces.faces WHERE user_id = $1 AND id = ANY($2)",
)
.bind(user_id)
.bind(face_ids)
.fetch_all(self.pool.as_ref())
.await
.map_err(|e| db_err("file_ids_for_faces", e))?;
Ok(rows.into_iter().collect())
}
async fn reassign_person_faces(
&self,
user_id: Uuid,
from: Uuid,
into: Uuid,
) -> Result<u64, DomainError> {
let result = sqlx::query(
"UPDATE faces.faces SET person_id = $3
WHERE user_id = $1 AND person_id = $2",
)
.bind(user_id)
.bind(from)
.bind(into)
.execute(self.pool.as_ref())
.await
.map_err(|e| db_err("reassign_person_faces", e))?;
Ok(result.rows_affected())
}
async fn assign_person(
&self,
face_id: Uuid,
person_id: Option<Uuid>,
) -> Result<(), DomainError> {
sqlx::query("UPDATE faces.faces SET person_id = $2 WHERE id = $1")
.bind(face_id)
.bind(person_id)
.execute(self.pool.as_ref())
.await
.map_err(|e| db_err("assign_person", e))?;
Ok(())
}
async fn assign_person_batch(
&self,
assignments: &[(Uuid, Option<Uuid>)],
) -> Result<(), DomainError> {
if assignments.is_empty() {
return Ok(());
}
let (face_ids, person_ids): (Vec<Uuid>, Vec<Option<Uuid>>) =
assignments.iter().cloned().unzip();
sqlx::query(
"UPDATE faces.faces f SET person_id = u.pid
FROM (SELECT unnest($1::uuid[]) AS fid, unnest($2::uuid[]) AS pid) u
WHERE f.id = u.fid",
)
.bind(&face_ids)
.bind(&person_ids)
.execute(self.pool.as_ref())
.await
.map_err(|e| db_err("assign_person_batch", e))?;
Ok(())
}
async fn create_person(&self, person: &Person) -> Result<(), DomainError> {
sqlx::query(
r#"
INSERT INTO faces.persons (id, user_id, display_name, cover_face_id, is_hidden)
VALUES ($1, $2, $3, $4, $5)
"#,
)
.bind(person.id)
.bind(person.user_id)
.bind(person.display_name.as_deref())
.bind(person.cover_face_id)
.bind(person.is_hidden)
.execute(self.pool.as_ref())
.await
.map_err(|e| db_err("create_person", e))?;
Ok(())
}
async fn persons_for_user(&self, user_id: Uuid) -> Result<Vec<Person>, DomainError> {
let sql = format!(
"SELECT {PERSON_COLS} FROM faces.persons WHERE user_id = $1 ORDER BY created_at"
);
let rows: Vec<PersonRow> = sqlx::query_as(&sql)
.bind(user_id)
.fetch_all(self.pool.as_ref())
.await
.map_err(|e| db_err("persons_for_user", e))?;
Ok(rows.into_iter().map(row_to_person).collect())
}
async fn rename_person(
&self,
user_id: Uuid,
person_id: Uuid,
name: Option<String>,
) -> Result<(), DomainError> {
sqlx::query(
"UPDATE faces.persons SET display_name = $3, updated_at = now() WHERE id = $2 AND user_id = $1",
)
.bind(user_id)
.bind(person_id)
.bind(name)
.execute(self.pool.as_ref())
.await
.map_err(|e| db_err("rename_person", e))?;
Ok(())
}
async fn set_person_cover(
&self,
person_id: Uuid,
cover_face_id: Uuid,
) -> Result<(), DomainError> {
sqlx::query(
"UPDATE faces.persons SET cover_face_id = $2, updated_at = now() WHERE id = $1",
)
.bind(person_id)
.bind(cover_face_id)
.execute(self.pool.as_ref())
.await
.map_err(|e| db_err("set_person_cover", e))?;
Ok(())
}
async fn set_person_hidden(
&self,
user_id: Uuid,
person_id: Uuid,
hidden: bool,
) -> Result<(), DomainError> {
sqlx::query(
"UPDATE faces.persons SET is_hidden = $3, updated_at = now() WHERE id = $2 AND user_id = $1",
)
.bind(user_id)
.bind(person_id)
.bind(hidden)
.execute(self.pool.as_ref())
.await
.map_err(|e| db_err("set_person_hidden", e))?;
Ok(())
}
async fn files_for_person(
&self,
user_id: Uuid,
person_id: Uuid,
) -> Result<Vec<Uuid>, DomainError> {
let rows: Vec<(Uuid,)> = sqlx::query_as(
r#"
SELECT file_id
FROM faces.faces
WHERE user_id = $1 AND person_id = $2
GROUP BY file_id
ORDER BY max(created_at) DESC
"#,
)
.bind(user_id)
.bind(person_id)
.fetch_all(self.pool.as_ref())
.await
.map_err(|e| db_err("files_for_person", e))?;
Ok(rows.into_iter().map(|(id,)| id).collect())
}
async fn delete_all_for_user(&self, user_id: Uuid) -> Result<(), DomainError> {
let mut tx = self.pool.begin().await.map_err(|e| db_err("begin", e))?;
sqlx::query("DELETE FROM faces.faces WHERE user_id = $1")
.bind(user_id)
.execute(&mut *tx)
.await
.map_err(|e| db_err("delete_all_faces", e))?;
sqlx::query("DELETE FROM faces.persons WHERE user_id = $1")
.bind(user_id)
.execute(&mut *tx)
.await
.map_err(|e| db_err("delete_all_persons", e))?;
tx.commit().await.map_err(|e| db_err("commit", e))?;
Ok(())
}
}