Round 3 of benchmark-gated optimizations (benches/ROUND3.md; every change
gated by a before/after benchmark — an AFTER that did not beat its BEFORE
was to be rolled back; none needed it. Equivalence gates assert identical
row sequences / byte-identical output on every behavior-preserving rewrite):
DB hot paths (local PG16, EXPLAIN-verified):
- Web-UI listing (list_resources_paged): cursor pushed INSIDE the
folders/files UNION-ALL branches as sargable row-value comparisons with
per-branch ORDER/LIMIT + two partial expression indexes
(folder_id, LOWER(name), id). 20k-entry folder: 26.6 -> 1.3 ms/page
(19.5x); other sort modes at parity or better. New migration
20260918000000. [benches/LISTING-KEYSET.md section in ROUND3]
- Photos timeline (list_media_files): per-drive CROSS JOIN LATERAL top-N
on the timeline index, joins moved above the top-N. 50k-photo library:
97.4 -> 1.6 ms/page (55.7x). The old "LIMIT stops the scan early"
comment was refuted by EXPLAIN.
- PROPFIND sub-folders (both DAV surfaces): keyset list_folders_batch off
idx_folders_unique_name replaces COUNT(*) OVER() + LIMIT/OFFSET
(5k dirs: 79.7 -> 17.9 ms full walk, 4.5x).
Concurrency:
- Basic-auth cache single-flight (moka try_get_with): 8 concurrent DAV
connections at TTL expiry paid 8 Argon2id runs (2.6 s CPU + 8x64 MiB);
now 1 (300 ms). Failed verifications remain uncached.
- CachedBlobBackend per-hash single-flight + unique tmp names: 16
concurrent cold readers = 16 full remote downloads racing truncating
writes on ONE deterministic .tmp (corruptible cache); now 1 download
(16x less egress, 2.8x wall on a shared link) and torn files can never
be renamed into the cache.
I/O and allocations:
- Chunk-assembly reads 64K -> 512K buffers (2.3x, 8x fewer syscalls);
chunk-spool writes via BufWriter 512K (5.6x, 32x fewer syscalls).
- S3/Azure put_blob_from_bytes_unsynced overrides: dedup settle no longer
pays a HEAD probe per new chunk (2 RTT -> 1, 1.8x); Azure stops copying
every chunk (Bytes -> Body, -0.44 ms - 4 MiB alloc per 4 MiB chunk).
- Entity->DTO mapping: Arc<str> interning of closed-set display fields +
common MIMEs, 1-alloc etag/size formatting, FolderDto moves instead of
clones. File row: 11 -> 4 allocs; folder row: 11.8 -> 1 (2.1x faster).
- CardDAV REPORT: deleted dead per-contact vCard pre-generation and the
O(N^2) uid scan whose result was discarded (5k contacts: 55.7 -> 5.7 ms,
9.8x); byte-identical XML asserted.
- Search-results cache: byte weigher + 32 MiB budget
(OXICLOUD_SEARCH_CACHE_MAX_BYTES) replaces the 1000-ENTRY cap that let
~300 MiB of enriched rows sit in RSS; read latency parity.
- Dropped aws-config + aws-smithy-types (zero references; -82 dep-graph
nodes, three SDK stacks gone from every build). tokio "process" is now
an explicit feature (was enabled transitively by aws-config).
Frontend:
- Cached Intl.DateTimeFormat keyed by (locale, options) in formatDate and
4 sibling callsites: 20k dates 2612 -> 51 ms (51.6x); vitest gate
asserts output identity across locales and a 3x floor.
Validation: cargo fmt + clippy --all-features --all-targets -D warnings
clean; 518 unit + 548 integration-cfg tests green; new-shape endpoints
smoke-tested end-to-end over HTTP (all 5 listing sort modes with cursor
walks, WebDAV PROPFIND Depth-1, photos timeline, Basic-auth DAV login);
frontend npm run check clean, new vitest gates green.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EBsU2qEzny3A8WQUEuMNCr
read_blob_stream / read_blob_range_stream reassembled a CDC file by fetching
its chunks with `buffered(1)` — strictly sequential, so the next chunk's
backend fetch (a file `open` locally; a full request round-trip on S3/Azure)
only started after the current chunk was fully drained.
A benchmark of the exact pipeline (stream::iter(chunks).map(get).buffered(K)
.try_flatten()) showed a blind `buffered(4)` is the WRONG fix: on a local
disk it is neutral on a warm page cache and ~37% SLOWER cold, because
concurrent opens turn one sequential read into several competing random-I/O
streams over content-addressed (scattered) chunk files. The win is entirely
on remote backends, where per-chunk request latency dominates and overlapping
fetches hide it (≈ linear in K).
So the read-ahead depth is now a backend hint, not a constant:
- BlobStorageBackend::read_prefetch() default 1 (sequential; safe for local).
- S3 / Azure override to 8 (overlap GETs to hide TTFB).
- cached / encrypted / retry / migration delegate to the backend that serves
the bytes.
- Both CDC read paths use `self.backend.read_prefetch().max(1)`.
Net: local backend unchanged (no regression); remote reassembly ~4-8x faster.
Ordered `buffered` (not buffer_unordered) keeps chunks in sequence.
Bench (per-chunk fetch-latency model): buffered(1)->(4)/(8) = x3.9 / x7.8
@1ms, x4.0 / x8.1 @5ms, x4.0 / x8.0 @20ms. Local warm: 230ms@1 vs 227ms@4
(noise); local cold: 425ms@1 vs 585ms@4 (why local stays at 1).
https://claude.ai/code/session_01DCszkkU11LYxMEUWr4setK
Claude