The #799 uniqueness-guard added a source_id_conflict(self.db, ...) call to the
MusicBrainz worker's artist path. Two TestWorkerAliasEnrichment fixtures build
the worker via __new__ and set only .database, not .db, so the new call raised
AttributeError and the artist was marked 'error'. Mirror the third fixture
(which already sets worker.db). Production always sets self.db in __init__ —
test-only gap exposed by the new code path.
Closes#586. Follow-up to #442 — Cyrillic / kanji canonical names
weren't bridging cross-script comparisons. Reporter case: "Dmitry
Yablonsky" tracks quarantined as audio mismatch with file identified
as "Русская филармония, Дмитрий Яблонский" (4% artist sim) even
though the Cyrillic spelling is just the Russian transliteration.
Codex diagnosed three layered bugs in the alias resolution chain.
This fixes all three.
Bug 1 — fetch_artist_aliases ignores canonical name + sort-name
`core/musicbrainz_service.py:fetch_artist_aliases` only read
`data['aliases']`. For artists where MB's canonical `name` IS the
cross-script form (and the Latin spelling lives only in aliases —
or vice versa), the missing direction never made it into the
returned list. Fix: include both `data['name']` and `data['sort-name']`
alongside the explicit alias entries (deduped, also pulls each
alias entry's sort-name when present).
Bug 2 — lookup_artist_aliases ran search in strict mode only
Strict mode queries `artist:"..."` only and skips MB's alias and
sortname indexes. Cross-script searches found nothing under strict
because the user's Latin input never matches a Cyrillic canonical
name in the artist index. Fix: lifted the search-and-score logic
to a private helper `_search_and_score_artists(name, strict=)` and
fall back to non-strict when strict returns empty OR all results
fail the trust gate. Non-strict (bare query) hits all indexes.
Bug 3 — trust gate weighted local similarity 70%
Combined score = local_sim * 0.7 + mb_score/100 * 0.3. Cross-script
pairs have local sim ~0 → combined ~0.30 → below the 0.85 threshold
→ cached as empty even when MB's own confidence was 100. Fix: added
an MB-only escape — when MB score is >= 95 AND the result is
unambiguous (top result's MB score leads the runner-up by >= 5),
accept regardless of local similarity. The existing combined-score
path stays intact for same-script matches (#442 Hiroyuki Sawano
case still passes via that path).
12 new tests pin every layer:
- fetch_artist_aliases canonical-name inclusion + dedup against
alias entries + missing-canonical handling + exception path
- strict-then-non-strict fallback (empty-strict + low-strict-score)
- trust gate MB-only escape + low-confidence rejection + ambiguity
rejection (two artists same MB score) + same-script regression
- end-to-end reporter scenario with the real `artist_names_match`
helper proving the bridge works for "Русская филармония, Дмитрий
Яблонский" vs expected "Dmitry Yablonsky"
Existing alias tests in `test_artist_alias_service.py` updated to
reflect: canonical name now appears in `fetch_artist_aliases`
output, lookup makes 2 search calls (strict + non-strict fallback)
on first cache miss instead of 1.
Full suite: 3065 passed.
Cin pre-review pass on the false-positive risk. Three tightenings:
# 1. Bumped MB-search trust threshold from 0.6 → 0.85
`MusicBrainzService.lookup_artist_aliases` previously trusted any
MB search match scoring ≥ 0.6 combined (name-similarity + MB
relevance). For distinctive cross-script artists the user-reported
case targets (Hiroyuki Sawano, Сергей Лазарев, etc.) real matches
score ~1.0 — well above 0.85. The 0.6 floor was loose enough to
let in moderate matches for ambiguous names, risking aliases for
the wrong artist getting cached + applied.
Bumped to 0.85. Tighter without rejecting any of the legit
cross-script cases the PR is for.
# 2. Ambiguity gate — skip when results within 0.1 of best
When MB search returns multiple results all scoring high (within
0.1 of the best), the artist name is ambiguous — common name with
multiple distinct artists ("John Smith" returning 10 different
John Smiths). Pulling aliases for any one of them risks the wrong
artist's data bridging incorrectly to a file's tag.
Added explicit ambiguity detection: when 2+ results within 0.1,
skip alias lookup entirely + cache empty. Matches Cin's
"explicit > implicit" — the prior code just picked the highest
score blindly.
# 3. Diagnostic log when alias rescues a comparison
When the alias path triggers a PASS that direct similarity would
have FAILed, emit an INFO log: `Artist alias rescued comparison:
expected='X' vs actual='Y' (direct sim=0.00, alias 'Z' →
score=1.00)`.
Lets future bug reports trace which alias triggered which decision.
Doesn't change behavior — visibility only. Logs ONLY the rescue
case, not happy-path direct matches (no log spam).
# Tests added (5)
`test_artist_alias_service.py` (+3):
- `test_moderate_confidence_match_now_skipped_strict_threshold`
- `test_ambiguous_results_skipped`
- `test_unambiguous_high_confidence_match_succeeds`
`test_acoustid_verification_aliases.py` (+3):
- `test_alias_rescue_emits_info_log` — direct-fail + alias-pass
emits INFO log
- `test_no_log_when_direct_match_succeeds` — happy path quiet
- `test_no_log_when_alias_doesnt_help` — failed path also quiet
# Test infrastructure note
Logging tests use a directly-attached `ListHandler` on
`soulsync.acoustid.verification` (the actual logger name —
dot-separated by `get_logger`), NOT pytest's caplog. Same pattern
as the prior watchdog-test fix — caplog is intermittently flaky
in full-suite runs for soulsync namespace loggers. An owned
handler sidesteps both issues.
# Verification
- 85/85 matching tests pass (+5 from prior commit)
- 2543 full suite passes (+6 from prior, +85 PR-total)
- Ruff clean
- Reporter's Japanese + Russian regression tests still pass —
legit cross-script case (sim ≈ 1.0) clears the new 0.85
threshold easily
Two perf gaps that would have failed Cin's review:
# Gap #1: alias lookup fired unconditionally
Pre-fix in this commit, `_resolve_expected_artist_aliases` ran at
the top of every `verify_audio_file` call regardless of whether
the direct artist match would have passed. For users whose library
is mostly same-script (95% of cases), every successful verification
was paying for a wasted DB query (and possibly a wasted MB API
call for un-enriched artists).
Restructured the helper to accept a callable provider instead of a
pre-resolved list. Provider invoked LAZILY only when direct
similarity falls below `ARTIST_MATCH_THRESHOLD`. Verifier passes a
memoising thunk that resolves once across the 3 comparison sites
within one verification.
`_alias_aware_artist_sim` now accepts `aliases` as either:
- iterable of strings (used eagerly — backward compat with tests
that already know the aliases)
- callable returning the iterable (resolved on first need within
a verification)
Happy path (direct match passes): zero DB queries, zero MB calls.
Cross-script case: one resolution shared across 3 sites — same as
the prior contract.
# Gap #2: existing-MBID artists never got alias backfill
Worker's `_process_item` artist branch had an `existing_id` short-
circuit (line 296) that updated MBID status but skipped alias
fetch. Result: every user with an already-enriched library had
MBIDs but NULL aliases on day-one of this PR. Live MB lookup at
verify-time covered them, but at the cost of N live calls for N
artists across the library.
Added one-time backfill: when existing-MBID is found AND
`artists.aliases` for that row is empty, fetch + persist aliases.
Subsequent re-scan cycles short-circuit on the populated column —
no repeated MB calls.
New helper `_artist_aliases_empty(artist_id)` does the cheap NULL
check via direct SQL. Best-effort: defensively returns True on
errors so backfill happens (a redundant MB call is cheaper than
missing the backfill entirely).
# Tests added (9)
`test_acoustid_verification_aliases.py` (+6):
- `TestLazyAliasResolution` (3): no lookup when direct match passes,
lookup fires only when direct fails, lookup memoised across the
3 sites within one verification.
- `TestAliasProviderCallable` (3): iterable passed directly,
callable resolves lazily, callable returning empty falls back to
direct sim.
`test_artist_alias_service.py` (+3):
- `test_existing_mbid_path_backfills_aliases_when_column_empty`
- `test_existing_mbid_path_skips_backfill_when_aliases_already_set`
- `test_existing_mbid_backfill_failure_does_not_break_match`
# Verification
- 79/79 matching tests pass (+9 from prior commit)
- 2537 full suite passes (+9, +79 PR-total)
- Ruff clean
- Backward compat: every prior-commit test still passes (the
iterable-shape API still works alongside the new callable shape)
Previous commit only populated `artists.aliases` for artists the MB
worker had enriched. But the AcoustID verifier (next commit) needs
aliases for ANY expected artist — including:
- Artists not yet in the user's library (first download)
- Artists in the library where MB enrichment hasn't run yet
- Artists where MB enrichment ran but found no MBID (NULL aliases)
This commit adds a multi-tier resolution helper that fills those
gaps without thrashing the MB API.
# Multi-tier resolution
`lookup_artist_aliases(artist_name) -> list[str]`:
1. **Library DB** (fast path): existing `get_artist_aliases` lookup
by name. No network. Most common path once the worker has
enriched everything.
2. **Cache** (existing `musicbrainz_cache` table, entity_type=
`artist_aliases`): a prior live lookup for this name. Empty
cache hit is respected (don't re-query when MB previously had
nothing).
3. **Live MB**: search artist by name → pick highest-confidence
match (combined name-similarity + MB relevance) → fetch aliases
for that MBID → cache the result.
Always returns a list (possibly empty), never raises. Empty result
on any tier means "no alternate spellings found, fall back to
direct match" — identical to the pre-fix behaviour.
# Threshold gate
Live lookup only trusts the MB search result when combined
similarity score >= 0.6. Below that, we'd be guessing at the wrong
artist — searching `John Smith` returns multiple John Smiths and
pulling aliases for one of them could mismatch. Cache the empty
result so we don't keep re-searching the same low-confidence name.
# Performance contract
Critical for the verifier path: 100 quarantine candidates with the
same expected artist must NOT trigger 100 MB API calls. Cache hit
on second + subsequent calls per unique artist name. Verified by
test pinning the call counts.
# Tests added (8)
- Tier 1 library DB hit — no MB API call fired
- Tier 3 live MB lookup → search → fetch → returns aliases
- Tier 2 cache hit on second call — no re-query
- Empty input → empty return + no API call
- Network failure on search → empty + cached so we don't retry
- No search results → empty + cached
- Low-confidence match (sim < 0.6) skipped — defends against
picking the wrong artist
- Library row exists but aliases NULL → falls through to live
lookup (defends against the half-enriched state)
# Verification
- 31/31 service tests pass (8 new + 23 prior)
- Ruff clean
Issue #442 — MusicBrainz exposes alternate-spelling aliases (Japanese
kanji `澤野弘之` for `Hiroyuki Sawano`, Cyrillic `Сергей Лазарев` for
`Sergey Lazarev`, etc.) on every artist record. SoulSync's MB
enrichment worker had access to this data via `get_artist(mbid,
includes=['aliases'])` but wasn't reading or persisting it.
This commit wires the alias fetch into the worker's existing
artist-match path, persists to the new `artists.aliases` column
added in the prior commit, and adds a verifier-friendly read-by-
name lookup so the AcoustID verifier (next commit) can resolve
aliases without an MB round-trip when the artist is in the library.
# New service methods
- `fetch_artist_aliases(mbid) -> list[str]` — calls
`mb_client.get_artist(mbid, includes=['aliases'])`, parses the
alias array, dedupes case-insensitively. Returns empty list on
any failure (missing key, network error, malformed response) so
transient MB outages never trigger stricter quarantine decisions
than the pre-fix behaviour. Empty mbid → no API call.
- `update_artist_aliases(artist_id, aliases)` — persists as JSON
array to `artists.aliases`. Idempotent — overwrites prior value.
Empty list clears the column. None artist_id is a no-op.
- `get_artist_aliases(artist_name) -> list[str]` — reads back by
artist NAME (not id), case-insensitive. Used by the verifier
where the expected artist comes from track metadata — there's no
library row id at quarantine time. Returns empty list for unknown
artists, missing data, or corrupt JSON (defensive against legacy
rows).
# Worker integration
`MusicBrainzWorker._process_item` artist branch:
- After `update_artist_mbid` succeeds, fetch aliases for the matched
MBID and persist via `update_artist_aliases`.
- Best-effort: alias fetch wrapped in try/except, failure logs at
debug level, doesn't regress the match outcome.
- No alias call when the artist didn't match an MBID (nothing to
enrich).
# Tests (23)
- `fetch_artist_aliases`: extracts names from MB response,
case-insensitive dedup, skips empty/null entries, missing-key
fallback, network failure → empty, empty mbid no API call,
verifies `inc=aliases` request param.
- `update_artist_aliases`: persists as JSON, idempotent overwrite,
empty list clears column, None id is no-op.
- `get_artist_aliases`: returns aliases for known artist,
case-insensitive lookup, empty for unknown artist / no-aliases
row, handles corrupt JSON + non-list shape gracefully.
- Worker integration: matched artist triggers fetch + persist,
no alias call when not matched, alias-fetch failure doesn't
break the match outcome.
# Verification
- 23/23 new tests pass
- Ruff clean
BoulderBadgeDad