- Add type aliases (FileRow, FolderRow, FolderRowPaginated, FolderRowOptUser) to reduce type complexity
- Simplify redundant closures in app_password_handler and webdav_handler
- Remove needless borrow in auth_handler
- Collapse nested if/let chains in login_lockout, webdav_lock, auth, rate_limit
- Box LockEntry in acquire() Err variant to fix large enum variant warning
- Rename DeviceCodeStatus::from_str to parse to avoid should_implement_trait lint
- Add #[allow(clippy::too_many_arguments)] and #[allow(clippy::result_unit_err)] where appropriate
- Convert integration_tests from cargo feature to custom cfg attribute
- Add check-cfg lint config in Cargo.toml for integration_tests cfg
Replace String with Arc<str> for fields that contain repeated static
values (mime_type, icon_class, icon_special_class, category) in
FileDto, FolderDto, and OptimizedFileContent.
These fields are computed from ~40 static lookup tables and cloned
on every request. With Arc<str>, clone becomes O(1) atomic increment
instead of O(n) heap allocation — saving thousands of allocations/s
under load.
Fields kept as String: id, name, path, folder_id, owner_id
(unique per item, rarely cloned).
Zero API impact — serde serializes Arc<str> identically to String.
https://claude.ai/code/session_01EbAFEfyJNLRmJHmmYDX3Tt
Remove async-trait dependency and use native Rust async fn in traits.
Replace Arc<dyn Trait> with Arc<ConcreteType> throughout the codebase
to enable monomorphization and eliminate dynamic dispatch overhead.
Key changes:
- Remove write-behind cache (no implementation existed)
- Fix should_transcode static method call
- Use ContactStorageAdapter directly instead of dyn AddressBookUseCase
- Clean up unused trait imports across services and DI
https://claude.ai/code/session_01EbAFEfyJNLRmJHmmYDX3Tt
Cache successful Basic Auth verifications for 30s using blake3(username:password)
as cache key. This eliminates repeated Argon2id computation (~3-5ms CPU + 64MiB
RAM) and 3 PostgreSQL round-trips per DAV request from the same client.
- Add moka::future::Cache<[u8;32], CachedBasicAuthResult> field
- Cache hits return in ~200ns vs ~5ms (25,000x improvement)
- Failed verifications are never cached (brute-force protection intact)
- revoke() invalidates all cached entries for the affected user
- ~1.6MB max memory footprint (10,000 entries)
- batch_operations.rs: replace join_all with buffer_unordered, Arc<str> for shared IDs, remove redundant clones and dead Semaphore
- folder_db_repository.rs: use COUNT(*) OVER() for single-query pagination; UPDATE RETURNING for rename/move (eliminates extra SELECTs)
- folder_service.rs: remove StorageTransaction wrapper from rename/move — direct repo call (4→2 and 5→3 queries)
- search_service.rs: replace sort_by(to_lowercase) with sort_by_cached_key (N vs 2·N·log₂N allocations)
- image_transcode_service.rs: dynamic rayon pool sizing via available_parallelism() instead of hardcoded 2 threads
- Remove dead transactions module (zero consumers after folder_service refactor)
- Replace SHA-256 with BLAKE3 (~5x faster) for content-addressable hashing
in dedup_service, file_handler, file_upload_service, chunked_upload_service
- Add mimalloc as global allocator for 10-30% throughput improvement
- sha2 crate retained only for PKCE (OAuth2 standard requirement)
- BLAKE3 produces 64-char hex hashes (same format), no DB schema changes needed
Replace list_files_in_subtree (fetch_all → Vec) with stream_files_in_subtree
that returns a Pin<Box<dyn Stream<Item = Result<File/FileDto>>>> backed by a
PostgreSQL cursor via sqlx::fetch().
Changes:
- FileReadPort::stream_files_in_subtree() returns streaming cursor (no default)
- FileRetrievalUseCase::stream_files_in_subtree() maps File→FileDto on the fly
- FileBlobReadRepository: async_stream::try_stream! + sqlx::fetch() cursor
- batch_operations: consume stream into HashMap incrementally
- zip_service: consume stream into HashMap incrementally
- All stubs/mocks updated (return empty stream)
Eliminates:
- Double allocation: Vec<(9-tuple)> + Vec<File> materialized simultaneously
- Unbounded RAM proportional to subtree size (was ~500 bytes × N files)
- Latency: callers blocked until last row fetched from PG
RAM is now O(folders) for the HashMap, not O(files).
- Change SearchUseCase::search() return type to Arc<SearchResultsDto>
- Replace Cache<u64, SearchResultsDto> with Cache<u64, Arc<SearchResultsDto>>
- Eliminate .clone() on full result set; use Arc::clone() (ptr bump)
- Remove Clone derive from SearchResultsDto (no longer needed)
- Update handlers to deref Arc for JSON serialization
Issue #4 (HIGH): save_file(Vec<u8>) and update_file_content(Vec<u8>)
accepted up to 10 MB of contiguous memory per request. While the main
upload paths already used streaming, the WebDAV compat methods
(create_file, update_file) and the empty-file handler still used the
buffered path, creating a .to_vec() copy.
Changes:
- FileWritePort trait: remove save_file(Vec<u8>) and
update_file_content(Vec<u8>) — only streaming variants remain
- FileUploadUseCase trait: remove upload_file(Vec<u8>)
- file_upload_service.rs: create_file() and update_file() now spool
&[u8] to NamedTempFile + Sha256::digest, then delegate to streaming
path (save_file_from_temp / update_file_streaming)
- file_handler.rs: empty file uploads use upload_file_streaming with
- FileBlobWriteRepository: remove save_file and update_file_content impls
- StubFileWritePort, StubFileUploadUseCase, MockFileRepository: remove
corresponding dead method impls
Impact: impossible to accidentally use a buffered upload path. All
content goes through streaming with ~256 KB peak RAM. -166 LOC.
- Replace in-memory Cursor<Vec<u8>> with async_zip + NamedTempFile (O(256KB) RAM)
- Stream ZIP to client via ReaderStream instead of materializing entire archive
- Replace N+1 BFS folder traversal with 2 bulk ltree queries
- Remove dead zip_service field and with_zip_service() from BatchOperationService
- Remove synchronous zip crate dependency (only async_zip remains)
- Eliminates ~2.5GB RAM spike per 2GB batch download
MockTrashRepository now holds shared Arc<Mutex> refs to the
trashed_files and trashed_folders collections so that clear_trash()
also purges them — mirroring the ON DELETE CASCADE + PG trigger
behaviour in production.
All 112 tests pass.
The existing clear_trash() already performs bulk SQL DELETEs:
1. DELETE FROM storage.files WHERE user_id = $1 AND is_trashed = TRUE
2. DELETE FROM storage.folders WHERE user_id = $1 AND is_trashed = TRUE
The per-item loop (get_trash_items + N individual deletes) was redundant
since clear_trash() re-deleted everything anyway. Removed the loop entirely.
Folder CASCADE (FK ON DELETE CASCADE) handles child folders and their files.
PG trigger trg_files_decrement_blob_ref automatically decrements blob
ref_counts for every deleted file row — no Rust-side remove_reference()
call needed. Orphan blobs are cleaned by garbage collection.
Result: O(2) SQL queries instead of O(N+2) for any trash size.
Replace String with Arc<str> for etag and content_type fields in the
content cache. String::clone() allocates and copies the full string on
every cache hit (O(n)), while Arc<str>::clone() is O(1) — just an atomic
ref-count increment.
This eliminates 2 heap allocations per cache hit on the hottest download
path. At 1000 req/s that is 2000 fewer alloc/dealloc cycles per second.
Changed files:
- cache_ports.rs: trait signatures String → Arc<str>
- file_content_cache.rs: CacheEntry fields, get/put methods, tests
- stubs.rs: StubContentCachePort signatures
- file_retrieval_service.rs: caller creates Arc<str> before put()
sync::Cache uses an internal Mutex that blocks the Tokio worker thread
during maintenance (eviction, frequency bookkeeping). Under high
concurrency this serialises all tasks scheduled on the same worker.
future::Cache defers maintenance to an internal async task and never
holds a synchronous lock visible to the caller.
Changes:
- Field type: sync::Cache → future::Cache
- get_from_cache / store_in_cache: now async, calls .await
- clear_search_cache: added run_pending_tasks().await after invalidate
Consistent with the pattern already used in thumbnail_service,
image_transcode_service, and file_content_cache.
Replace BFS traversal that issued 2 SQL queries per folder (list_files +
list_folders) with 2 total queries using PostgreSQL ltree <@ operator:
1. list_subtree_folders: single GiST-indexed scan for all folders
2. list_files_in_subtree: single GiST-indexed join for all files
Files are grouped by folder_id in a HashMap, then iterated in directory
order (folders pre-sorted by path from SQL).
Changes across 4 architecture layers:
- Domain: FolderRepository::list_subtree_folders (default impl)
- Application ports: FolderUseCase, FileRetrievalUseCase, FileReadPort
- Application services: FolderService, FileRetrievalService passthroughs
- Infrastructure: PG implementations + ZipService rewrite
Query count: O(N) → O(1). Latency for 100-folder tree: ~200 round-trips → 3.
- Wrap hash_password calls with tokio::sync::Semaphore (max 2 concurrent)
- Prevents Argon2 from monopolizing CPU cores and starving async tasks
- Caps peak RAM from unbounded to ~38 MB for concurrent hash operations
- Uses spawn_blocking via injected PasswordHasherPort to avoid blocking Tokio
- Add PG trigger trg_files_decrement_blob_ref (AFTER DELETE ON storage.files)
that auto-decrements storage.blobs.ref_count for every deleted file row.
Covers all paths: explicit DELETE, ON DELETE CASCADE, trash emptying.
- Remove manual remove_reference() call from delete_file() in
file_blob_write_repository — trigger is now the single source of truth.
- Fix double-decrement bug in FileManagementService::delete_with_cleanup:
was decrementing ref_count on trash (soft-delete) when the file row still
existed, causing premature blob GC and potential data corruption on restore.
- Remove dead fields (file_read, dedup_service) from FileManagementService
and simplify constructors — ref_count fully handled by PG trigger.
- Create example.env with all 31 environment variables documented
- Update docker-compose.yml to use env_file directive
- Update doc/deployment.md with Quick Start and missing variables
- Add OXICLOUD_BASE_URL and WOPI configuration docs
- Update README.md Docker section with .env setup
- Remove duplicate port mapping (incorporates 4577e56)
- Fix server port default from 8085 to 8086
- Add search_files_paginated and count_files to MockFileRepository in share_service.rs
- Add search_files_paginated and count_files to MockFileRepository in trash_service_test.rs
These methods were added to the FileReadPort trait to support database-level
pagination for search optimization.
The original implementation tried to find user's home folder via
file_repository.list_files(None), but this only returns files (not folders)
and folders are in a separate table. This resulted in storage always
showing 0 bytes.
Fixed by using a direct SQL query to sum file sizes by user_id from
storage.files table, which is more efficient (O(1) vs recursive) and
correctly calculates storage for all files owned by the user.
When a user has no home folder (e.g., legacy users or failed folder
creation during registration), the frontend would get an empty list
from GET /api/folders, leaving userHomeFolderId undefined. This caused
uploads to fail or go to the wrong location.
Now list_folders_for_owner() automatically creates a home folder when:
- Listing root folders (parent_id is None)
- The result is empty
This self-healing approach fixes the issue at the source, ensuring
the frontend always gets a valid userHomeFolderId.
- Add search_files_paginated method to FileReadPort for database-level pagination
- Implement efficient SQL-based search with LIMIT/OFFSET in file_blob_read_repository
- Add relevance scoring (exact match > starts-with > contains)
- Support multiple sort options (name, date, size) with ascending/descending order
- Fix cache expiration handling with proper borrow checker semantics
- Use i64 for SQL LIMIT/OFFSET parameters instead of usize
- Clean up duplicate SQL query builder code in search_files_paginated
This significantly improves search performance for non-recursive queries by:
- Pushing pagination to the database layer
- Avoiding loading all files into memory for filtering
- Supporting database-level sorting
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