Stage two. The scrobble history says what was played by name; Lidarr says what
is owned, and where the files are. Neither is useful for curation until the two
are tied together, and the quality of that join is what decides whether the
later cull can be trusted at all.
The index is a wholesale rebuild of every artist, album and track Lidarr holds,
including file paths and the date each file landed -- the latter for the age
floor a cull will need. It is rebuilt rather than reconciled because Lidarr is
the authority and a deletion there has to disappear here, not linger as a
library entry with no file behind it. Every call is a GET; nothing is written
back.
Matching runs at the level of the distinct artist/track pair rather than the
individual play, because a verdict is a property of the name pair and there are
three plays for every one of them. Two tiers: a MusicBrainz recording id, which
Last.fm supplies per scrobble and Lidarr exposes as ForeignRecordingId, gives an
exact join; everything else falls to a normalised name comparison. There is
deliberately no third tier. A near-miss guess is worse than an admitted one,
since the entire purpose of the resulting number is to state how far the
matching can be relied on.
Normalisation folds the ways the two sides habitually disagree: case, accents,
guest credits that Last.fm puts in the artist field, trailing version suffixes,
ampersands, and a leading article. Punctuation needs two opposing rules and both
are load-bearing -- apostrophes are deleted so "Don't" meets "Dont", while every
other mark becomes a space so "AC/DC", "AC-DC" and "AC DC" meet as well. It errs
towards collapsing too much: a false match makes a track look played, a missed
match makes it look abandoned, and only the second one loses music.
The coverage report deliberately does not lead with matched versus unmatched.
Most unmatched listening is music that was never in the library and says nothing
about the matcher. The figure that matters is unmatched listening by an artist
the library does hold: a track that was played, sitting next to a file it should
have matched. The worst fifteen are listed by play count.
The schema gains its tables additively and migrates a version 1 store in place,
because rebuilding a nine-year history costs several thousand API requests.
First stage of a curation tool for the music library that music-mirror
mirrors. Before anything can build playlists or decide what has gone cold,
there has to be a local, queryable record of what is actually played; an API
call per question does not scale to a library-sized analysis.
Ingest is in two halves. A catch-up fetches everything scrobbled since the
newest scrobble held, and a backfill walks the history backwards until it runs
out. Both take their bounds from the database rather than from a saved cursor,
so an interrupted run resumes from what it actually has, and both windows are
bounded at each end so paging cannot shift under the fetch while new scrobbles
arrive mid-run.
Scrobbles carry no identifier, so the primary key is timestamp, artist and
track. Two plays of one track in the same second collapse into a single row:
they are indistinguishable in the data, and a surrogate key would make
re-ingest non-idempotent, which is the worse trade.
Three API behaviours are handled explicitly because each fails silently:
the currently-playing track arrives with no timestamp and would be re-ingested
on every pass; a lone result is returned as a bare object rather than a
one-item list; and MBIDs are empty strings rather than absent when unknown,
which would later look like a usable join key.
Retries cover the rate limit and the transient backend errors with an
exponential backoff. An invalid or suspended key fails immediately.
The report exists to surface one number before the next stage is built: the
share of scrobbles carrying a MusicBrainz recording id. Lidarr exposes the same
identifier per track, so those can be joined exactly and the rest must go
through name matching. That percentage bounds how far the matcher can be
trusted.
No runtime dependencies, and the tests run against a fake transport that
reproduces the service's paging and response shapes, so they need neither
network nor credentials.
Emma Thorpe