bvandeusen
199fec2058
Milestone #160 Opt 5. A collaborative candidate arm: tracks by artists co-played across the instance with the seed's artist. Minstrel is a single shared-library, multi-user server (no per-user library ACL — verified: no owner/share/group model), so the "household" is the whole instance's user set; the rule #47 scoping is satisfied by the shared-library boundary. Single-user servers produce no edges. - No migration: source='user_cooccurrence' was pre-whitelisted in the 0009 similarity CHECK from day one. - internal/db/queries/coplay.sql: Delete + Insert artist co-play edges. Score = Jaccard of the two artists' distinct-player sets (controls for globally-popular artists); >= 2 co-players AND Jaccard >= floor kept (the floor also self-limits hub artists). Completed plays, 365d window. - internal/coplay: periodic worker (6h) that atomic-replaces the user_cooccurrence edge set from play_events — pure local SQL, no external calls. Wired in main.go alongside the similarity worker. - LoadRadioCandidatesV2: new coplay_artists arm (source='user_cooccurrence', seed-artist based, 0.5 damp like similar_artists) + $11 limit; CandidateSourceLimits.UserCoplay (default 20, For-You 40). - Integration tests: perfect-overlap Jaccard=1.0 edge + single-user empty-set gate. Device axis and AcousticBrainz (Opt 4) are separately tracked; this closes the milestone-#160 sequential options. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
530 lines
20 KiB
SQL
530 lines
20 KiB
SQL
-- name: LoadRadioCandidates :many
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-- Returns all tracks except the seed and any played by the user within
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-- the last $3 hours, joined with the stats needed for scoring:
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-- is_liked — boolean from general_likes for this user
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-- last_played_at — max(play_events.started_at) for this user/track
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-- play_count — count of play_events for this user/track
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-- skip_count — play_events where was_skipped=true
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SELECT
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sqlc.embed(t),
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(l.user_id IS NOT NULL)::bool AS is_liked,
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pe.last_played_at::timestamptz AS last_played_at,
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pe.play_count,
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pe.skip_count,
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al.release_date AS release_date
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FROM tracks t
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JOIN albums al ON al.id = t.album_id
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LEFT JOIN general_likes l ON l.user_id = $1 AND l.track_id = t.id
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LEFT JOIN LATERAL (
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SELECT
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max(started_at) AS last_played_at,
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count(*) AS play_count,
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count(*) FILTER (WHERE was_skipped) AS skip_count
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FROM play_events
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WHERE user_id = $1 AND track_id = t.id
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) pe ON true
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WHERE t.id <> $2
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AND NOT EXISTS (
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SELECT 1 FROM play_events
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WHERE user_id = $1 AND track_id = t.id
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AND started_at > now() - $3 * interval '1 hour'
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)
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AND NOT EXISTS (
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SELECT 1 FROM lidarr_quarantine q
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WHERE q.user_id = $1 AND q.track_id = t.id
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);
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-- name: LoadRadioCandidatesV2 :many
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-- M4c: similarity-driven candidate pool. 6-way UNION:
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-- $1 user_id, $2 seed_track_id, $3 recently_played_hours,
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-- $4 exclude (uuid[]), $5 lb_similar K, $6 similar_artists K,
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-- $7 tag_overlap K, $8 likes_overlap K, $9 random_fill K,
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-- $10 taste_overlap K (#796 phase 2b — tracks by the user's top
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-- positively-weighted taste-profile artists, so taste-relevant tracks
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-- enter the pool even when the similarity/random arms miss them; scored
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-- in Go via TasteMatch, so sim_score here is 0 pool-inclusion),
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-- $11 coplay_artists K (#1533 — tracks by artists co-played across the
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-- instance with the seed's artist; source='user_cooccurrence').
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-- Returns same shape as LoadRadioCandidates plus similarity_score column.
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WITH
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seed_artist AS (
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SELECT artist_id
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FROM tracks
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WHERE tracks.id = $2
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),
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seed_tags AS (
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SELECT trim(g) AS tag
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FROM tracks t
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LEFT JOIN LATERAL regexp_split_to_table(coalesce(t.genre, ''), '[;,]') AS g(tag) ON true
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WHERE t.id = $2 AND trim(g) <> ''
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),
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excluded_ids AS (
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SELECT unnest($4::uuid[]) AS id
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UNION ALL
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SELECT pe.track_id AS id
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FROM play_events pe
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WHERE pe.user_id = $1 AND pe.started_at > now() - $3 * interval '1 hour'
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UNION ALL
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SELECT q.track_id AS id
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FROM lidarr_quarantine q
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WHERE q.user_id = $1
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),
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lb_similar AS (
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SELECT ts.track_b_id AS track_id, ts.score AS sim_score
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FROM track_similarity ts
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WHERE ts.track_a_id = $2
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AND ts.source = 'listenbrainz'
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AND ts.track_b_id NOT IN (SELECT id FROM excluded_ids)
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ORDER BY ts.score DESC
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LIMIT $5
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),
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similar_artists AS (
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SELECT t.id AS track_id, asim.score * 0.5 AS sim_score
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FROM artist_similarity asim
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JOIN tracks t ON t.artist_id = asim.artist_b_id
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JOIN seed_artist sa ON asim.artist_a_id = sa.artist_id
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WHERE asim.source = 'listenbrainz'
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AND t.id NOT IN (SELECT id FROM excluded_ids)
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ORDER BY asim.score DESC, random()
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LIMIT $6
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),
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tag_overlap AS (
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SELECT t.id AS track_id,
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(count(DISTINCT trim(g))::float8
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/ GREATEST((SELECT count(*) FROM seed_tags), 1)) AS sim_score
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FROM tracks t
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LEFT JOIN LATERAL regexp_split_to_table(coalesce(t.genre, ''), '[;,]') AS g_split(g) ON true
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WHERE trim(g_split.g) IN (SELECT tag FROM seed_tags)
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AND t.id NOT IN (SELECT id FROM excluded_ids)
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AND t.id <> $2
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GROUP BY t.id
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HAVING count(DISTINCT trim(g_split.g)) > 0
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ORDER BY sim_score DESC
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LIMIT $7
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),
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likes_overlap AS (
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SELECT gl.track_id, 0.6::float8 AS sim_score
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FROM general_likes gl
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WHERE gl.user_id = $1
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AND gl.track_id NOT IN (SELECT id FROM excluded_ids)
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AND EXISTS (
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SELECT 1 FROM tracks t
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LEFT JOIN LATERAL regexp_split_to_table(coalesce(t.genre, ''), '[;,]') AS g_overlap(g) ON true
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WHERE t.id = gl.track_id
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AND trim(g_overlap.g) IN (SELECT tag FROM seed_tags)
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)
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ORDER BY random()
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LIMIT $8
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),
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taste_overlap AS (
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SELECT t.id AS track_id, 0.0::float8 AS sim_score
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FROM taste_profile_artists tpa
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JOIN tracks t ON t.artist_id = tpa.artist_id
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WHERE tpa.user_id = $1
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AND tpa.weight > 0
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AND t.id NOT IN (SELECT id FROM excluded_ids)
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AND t.id <> $2
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ORDER BY tpa.weight DESC, t.id
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LIMIT $10
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),
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coplay_artists AS (
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-- Household co-play (#1533): tracks by artists co-played across the
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-- instance with the seed's artist (source='user_cooccurrence', built by
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-- the coplay worker). Mirrors similar_artists but from local co-occurrence
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-- instead of ListenBrainz; empty on single-user servers. Same 0.5 damp as
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-- similar_artists since it's artist-level.
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SELECT t.id AS track_id, asim.score * 0.5 AS sim_score
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FROM artist_similarity asim
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JOIN tracks t ON t.artist_id = asim.artist_b_id
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JOIN seed_artist sa ON asim.artist_a_id = sa.artist_id
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WHERE asim.source = 'user_cooccurrence'
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AND t.id NOT IN (SELECT id FROM excluded_ids)
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AND t.id <> $2
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ORDER BY asim.score DESC, random()
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LIMIT $11
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),
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random_fill AS (
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SELECT t.id AS track_id, 0.0::float8 AS sim_score
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FROM tracks t
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WHERE t.id NOT IN (SELECT id FROM excluded_ids)
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AND t.id <> $2
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AND t.id NOT IN (
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SELECT track_id FROM lb_similar
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UNION SELECT track_id FROM similar_artists
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UNION SELECT track_id FROM tag_overlap
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UNION SELECT track_id FROM likes_overlap
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UNION SELECT track_id FROM taste_overlap
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UNION SELECT track_id FROM coplay_artists
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)
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ORDER BY random()
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LIMIT $9
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)
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SELECT
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sqlc.embed(t),
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(l.user_id IS NOT NULL)::bool AS is_liked,
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pe.last_played_at::timestamptz AS last_played_at,
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pe.play_count,
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pe.skip_count,
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al.release_date AS release_date,
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COALESCE(max(u.sim_score), 0.0) AS similarity_score
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FROM (
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SELECT track_id, sim_score FROM lb_similar
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UNION ALL SELECT track_id, sim_score FROM similar_artists
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UNION ALL SELECT track_id, sim_score FROM tag_overlap
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UNION ALL SELECT track_id, sim_score FROM likes_overlap
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UNION ALL SELECT track_id, sim_score FROM taste_overlap
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UNION ALL SELECT track_id, sim_score FROM coplay_artists
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UNION ALL SELECT track_id, sim_score FROM random_fill
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) u
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JOIN tracks t ON t.id = u.track_id
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JOIN albums al ON al.id = t.album_id
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LEFT JOIN general_likes l ON l.user_id = $1 AND l.track_id = t.id
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LEFT JOIN LATERAL (
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SELECT max(started_at) AS last_played_at,
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count(*) AS play_count,
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count(*) FILTER (WHERE was_skipped) AS skip_count
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FROM play_events
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WHERE user_id = $1 AND track_id = t.id
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) pe ON true
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GROUP BY t.id, t.title, t.album_id, t.artist_id, t.duration_ms, t.file_path,
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t.file_format, t.file_size, t.bitrate, t.track_number, t.disc_number,
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t.mbid, t.genre, t.added_at, t.updated_at,
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l.user_id, pe.last_played_at, pe.play_count, pe.skip_count,
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al.release_date;
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-- name: ListArtistContextPlayCountsForUser :many
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-- Per-artist completed-play counts split by whether each play falls in the
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-- CURRENT daypart × weekday-type cell, in the user's local timezone (#1531).
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-- Feeds the context-affinity scoring term: an artist whose plays concentrate
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-- in the current cell (vs the user's overall baseline, computed Go-side from
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-- these rows) gets boosted right now. Skips excluded; a 365-day window bounds
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-- cost. Daypart buckets: night [22,5) morning [5,12) afternoon [12,17)
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-- evening [17,22). Weekend = ISO days 6–7 (Sat/Sun).
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WITH tz AS (
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SELECT COALESCE(NULLIF(u.timezone, ''), 'UTC') AS zone
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FROM users u WHERE u.id = $1
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),
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now_cell AS (
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SELECT
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CASE
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WHEN EXTRACT(hour FROM now() AT TIME ZONE tz.zone) < 5 THEN 3
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WHEN EXTRACT(hour FROM now() AT TIME ZONE tz.zone) < 12 THEN 0
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WHEN EXTRACT(hour FROM now() AT TIME ZONE tz.zone) < 17 THEN 1
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WHEN EXTRACT(hour FROM now() AT TIME ZONE tz.zone) < 22 THEN 2
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ELSE 3
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END AS daypart,
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(EXTRACT(isodow FROM now() AT TIME ZONE tz.zone) >= 6) AS is_weekend
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FROM tz
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),
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plays AS (
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SELECT t.artist_id,
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CASE
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WHEN EXTRACT(hour FROM pe.started_at AT TIME ZONE tz.zone) < 5 THEN 3
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WHEN EXTRACT(hour FROM pe.started_at AT TIME ZONE tz.zone) < 12 THEN 0
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WHEN EXTRACT(hour FROM pe.started_at AT TIME ZONE tz.zone) < 17 THEN 1
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WHEN EXTRACT(hour FROM pe.started_at AT TIME ZONE tz.zone) < 22 THEN 2
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ELSE 3
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END AS daypart,
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(EXTRACT(isodow FROM pe.started_at AT TIME ZONE tz.zone) >= 6) AS is_weekend
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FROM play_events pe
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JOIN tracks t ON t.id = pe.track_id
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CROSS JOIN tz
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WHERE pe.user_id = $1
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AND pe.was_skipped = false
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AND pe.started_at > now() - interval '365 days'
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)
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SELECT p.artist_id,
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count(*) AS total_plays,
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count(*) FILTER (
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WHERE p.daypart = (SELECT daypart FROM now_cell)
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AND p.is_weekend = (SELECT is_weekend FROM now_cell)
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) AS cell_plays
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FROM plays p
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GROUP BY p.artist_id;
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-- name: SuggestArtistsForUser :many
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-- M5c: per-user artist suggestions ranked by signal x similarity. The
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-- seeds CTE collects the user's likes (x5) plus recency-decayed plays
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-- (exp(-age_days / $2)). The contributions CTE joins those seeds against
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-- artist_similarity_unmatched and filters out candidates already in
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-- library or already in a non-terminal lidarr_request. The outer SELECT
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-- aggregates per candidate, returning the top-3 contributing seeds for
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-- attribution. $1=user_id, $2=half_life_days, $3=limit.
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WITH seeds AS (
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SELECT a.id AS artist_id,
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5.0 * (CASE WHEN gla.artist_id IS NOT NULL THEN 1 ELSE 0 END)
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+ COALESCE(SUM(EXP(- EXTRACT(epoch FROM now() - pe.started_at) / ($2::float8 * 86400.0))), 0)
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AS signal,
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(gla.artist_id IS NOT NULL) AS is_liked,
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COUNT(pe.id)::bigint AS play_count
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FROM artists a
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LEFT JOIN general_likes_artists gla ON gla.artist_id = a.id AND gla.user_id = $1
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LEFT JOIN tracks t ON t.artist_id = a.id
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LEFT JOIN play_events pe ON pe.track_id = t.id AND pe.user_id = $1
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WHERE gla.artist_id IS NOT NULL OR pe.id IS NOT NULL
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GROUP BY a.id, gla.artist_id
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),
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contributions AS (
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SELECT u.candidate_mbid,
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u.candidate_name,
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seeds.artist_id AS seed_id,
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seeds.is_liked,
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seeds.play_count,
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seeds.signal * u.score AS contribution
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FROM artist_similarity_unmatched u
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JOIN seeds ON seeds.artist_id = u.seed_artist_id
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WHERE NOT EXISTS (SELECT 1 FROM artists WHERE mbid = u.candidate_mbid)
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AND NOT EXISTS (
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SELECT 1 FROM lidarr_requests r
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WHERE r.user_id = $1
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AND r.lidarr_artist_mbid = u.candidate_mbid
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AND r.status NOT IN ('rejected', 'failed')
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)
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)
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SELECT candidate_mbid,
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candidate_name,
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SUM(contribution)::float8 AS total_score,
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((array_agg(seed_id ORDER BY contribution DESC))[1:3])::uuid[] AS top_seed_ids,
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((array_agg(contribution ORDER BY contribution DESC))[1:3])::float8[] AS top_contributions,
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((array_agg(is_liked ORDER BY contribution DESC))[1:3])::boolean[] AS top_is_liked,
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((array_agg(play_count ORDER BY contribution DESC))[1:3])::bigint[] AS top_play_counts
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FROM contributions
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GROUP BY candidate_mbid, candidate_name
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ORDER BY total_score DESC
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LIMIT $3;
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-- name: ListMostPlayedTracksForUser :many
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-- M6a: top-N tracks by completed-play count for the user. was_skipped
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-- excludes skips so a user spamming next doesn't fabricate a top track.
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-- Quarantined tracks (per-user soft-hide from M5b) are filtered out.
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--
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-- Aggregate play_events first (uses play_events_user_track_idx) and
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-- join through tracks/albums/artists only for the survivors. Earlier
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-- shape did the join across every play_event row before grouping —
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-- O(plays) instead of O(distinct tracks).
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WITH plays AS (
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SELECT track_id, count(*) AS cnt
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FROM play_events
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WHERE user_id = $1 AND was_skipped = false
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GROUP BY track_id
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)
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SELECT sqlc.embed(t),
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albums.title AS album_title,
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artists.name AS artist_name
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FROM plays p
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JOIN tracks t ON t.id = p.track_id
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JOIN albums ON albums.id = t.album_id
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JOIN artists ON artists.id = t.artist_id
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WHERE NOT EXISTS (
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SELECT 1 FROM lidarr_quarantine q
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WHERE q.user_id = $1 AND q.track_id = t.id
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)
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ORDER BY p.cnt DESC, t.id
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LIMIT $2;
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-- name: ListMostPlayedTracksForArtist :many
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-- Top tracks for one artist by this user's completed-play count (skips
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-- excluded, quarantine filtered). Same projection as
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-- ListMostPlayedTracksForUser plus an artist_id filter, so the handler
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-- reuses trackRefFrom(row.Track, row.AlbumTitle, row.ArtistName).
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WITH plays AS (
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SELECT track_id, count(*) AS cnt
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FROM play_events
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WHERE user_id = sqlc.arg(user_id) AND was_skipped = false
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GROUP BY track_id
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)
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SELECT sqlc.embed(t),
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albums.title AS album_title,
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artists.name AS artist_name
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FROM plays p
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JOIN tracks t ON t.id = p.track_id
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JOIN albums ON albums.id = t.album_id
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JOIN artists ON artists.id = t.artist_id
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WHERE t.artist_id = sqlc.arg(artist_id)
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AND NOT EXISTS (
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SELECT 1 FROM lidarr_quarantine q
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WHERE q.user_id = sqlc.arg(user_id) AND q.track_id = t.id
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)
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ORDER BY p.cnt DESC, t.id
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LIMIT sqlc.arg(result_limit);
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-- name: ListLastPlayedArtistsForUser :many
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-- M6a: artists ranked by max(play_events.started_at) for the user, with
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-- a derived cover_album_id via a representative-album lateral join (most
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-- recent album that has cover_art_path set). album_count joined for the
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-- ArtistRef wire shape.
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--
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-- Earlier shape iterated `FROM artists a` and ran a LATERAL play_events
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-- subquery per artist — O(total_artists) plan even for users with a
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-- handful of plays. New shape aggregates the user's plays by artist
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-- via the play_events → tracks join up front (uses
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-- play_events_user_track_idx + tracks pkey lookups), then attaches
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-- the artist row and lateral cover/count subqueries only for the
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-- artists that actually appear. Distinct-artists set is small for a
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-- typical user, so cost is bounded by play history not library size.
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WITH user_plays AS (
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SELECT t.artist_id, max(pe.started_at) AS last_started
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FROM play_events pe
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JOIN tracks t ON t.id = pe.track_id
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WHERE pe.user_id = $1
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GROUP BY t.artist_id
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)
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SELECT sqlc.embed(a),
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cov.id AS cover_album_id,
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cnt.album_count::bigint AS album_count,
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up.last_started::timestamptz AS last_played_at
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FROM user_plays up
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JOIN artists a ON a.id = up.artist_id
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LEFT JOIN LATERAL (
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SELECT id FROM albums
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WHERE artist_id = a.id AND cover_art_path IS NOT NULL
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ORDER BY created_at DESC LIMIT 1
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) cov ON true
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LEFT JOIN LATERAL (
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SELECT count(*) AS album_count
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FROM albums WHERE artist_id = a.id
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) cnt ON true
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ORDER BY up.last_started DESC, a.id
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LIMIT $2;
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-- name: ListRediscoverAlbumsForUser :many
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-- Albums eligible for Rediscover via either signal:
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-- (a) explicit album-like (general_likes_albums) liked >30 days ago, OR
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-- (b) >=2 liked tracks from the album (general_likes), where the
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-- earliest such track-like is >30 days ago.
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-- AND not played in the last 14 days. The HAVING epoch sentinel keeps
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-- albums with NO play history eligible (treated as last-played in 1970).
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-- Ordering is daily-stable random (md5 of album+user+date hashes),
|
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-- so the row rotates at midnight local without re-running per request.
|
||
-- Final cap + diversity + cross-section dedup land in the Go layer
|
||
-- (internal/recommendation/home.go).
|
||
WITH liked_signal AS (
|
||
-- Path (a): explicit album-likes >30 days old.
|
||
SELECT al.id AS album_id, gla.liked_at AS like_at
|
||
FROM general_likes_albums gla
|
||
JOIN albums al ON al.id = gla.album_id
|
||
WHERE gla.user_id = $1
|
||
AND gla.liked_at < now() - interval '30 days'
|
||
UNION ALL
|
||
-- Path (b): >=2 liked tracks per album, earliest like >30 days old.
|
||
SELECT t.album_id, MIN(gl.liked_at) AS like_at
|
||
FROM general_likes gl
|
||
JOIN tracks t ON t.id = gl.track_id
|
||
WHERE gl.user_id = $1
|
||
GROUP BY t.album_id
|
||
HAVING COUNT(*) >= 2
|
||
AND MIN(gl.liked_at) < now() - interval '30 days'
|
||
),
|
||
eligible AS (
|
||
SELECT ls.album_id,
|
||
MIN(ls.like_at) AS like_at,
|
||
max(pe.started_at) AS last_played
|
||
FROM liked_signal ls
|
||
LEFT JOIN tracks t ON t.album_id = ls.album_id
|
||
LEFT JOIN play_events pe ON pe.track_id = t.id AND pe.user_id = $1
|
||
GROUP BY ls.album_id
|
||
HAVING COALESCE(max(pe.started_at), '1970-01-01'::timestamptz)
|
||
< now() - interval '14 days'
|
||
)
|
||
SELECT sqlc.embed(albums), artists.name AS artist_name
|
||
FROM eligible e
|
||
JOIN albums ON albums.id = e.album_id
|
||
JOIN artists ON artists.id = albums.artist_id
|
||
ORDER BY md5(albums.id::text || current_date::text)
|
||
LIMIT $2;
|
||
|
||
-- name: ListRediscoverAlbumsFallbackForUser :many
|
||
-- Daily-stable random sample of liked albums for the user. The Go
|
||
-- service uses this to top up the rediscover row when the primary
|
||
-- eligibility query returns fewer than the inner limit. Same daily
|
||
-- hash ordering as the primary so the fallback rows stay stable
|
||
-- through the day instead of reshuffling on every refresh. Hash
|
||
-- omits user_id so sqlc can keep the user_id parameter UUID-typed
|
||
-- (a $1::text cast would force the param to string); eligibility
|
||
-- already differs per user, so output sets differ regardless.
|
||
SELECT sqlc.embed(albums), artists.name AS artist_name
|
||
FROM general_likes_albums gla
|
||
JOIN albums ON albums.id = gla.album_id
|
||
JOIN artists ON artists.id = albums.artist_id
|
||
WHERE gla.user_id = $1
|
||
ORDER BY md5(albums.id::text || current_date::text)
|
||
LIMIT $2;
|
||
|
||
-- name: ListRediscoverArtistsForUser :many
|
||
-- Artists eligible for Rediscover via either signal:
|
||
-- (a) explicit artist-like (general_likes_artists) liked >30 days ago, OR
|
||
-- (b) >=3 liked tracks from the artist (general_likes), where the
|
||
-- earliest such track-like is >30 days ago.
|
||
-- AND none of their tracks played in the last 14 days. Higher track
|
||
-- threshold than the album path (3 vs 2) because artists span many
|
||
-- releases, so the bar is higher to avoid surfacing one-hit-wonder
|
||
-- affinities. Ordering is daily-stable random (md5 of artist+user+date
|
||
-- hashes). cover_album_id derived from a representative album (most
|
||
-- recently created album with cover_art_path set).
|
||
WITH liked_signal AS (
|
||
SELECT a.id AS artist_id, gla.liked_at AS like_at
|
||
FROM general_likes_artists gla
|
||
JOIN artists a ON a.id = gla.artist_id
|
||
WHERE gla.user_id = $1
|
||
AND gla.liked_at < now() - interval '30 days'
|
||
UNION ALL
|
||
SELECT t.artist_id, MIN(gl.liked_at) AS like_at
|
||
FROM general_likes gl
|
||
JOIN tracks t ON t.id = gl.track_id
|
||
WHERE gl.user_id = $1
|
||
GROUP BY t.artist_id
|
||
HAVING COUNT(*) >= 3
|
||
AND MIN(gl.liked_at) < now() - interval '30 days'
|
||
),
|
||
eligible AS (
|
||
SELECT ls.artist_id,
|
||
MIN(ls.like_at) AS like_at,
|
||
max(pe.started_at) AS last_played
|
||
FROM liked_signal ls
|
||
LEFT JOIN tracks t ON t.artist_id = ls.artist_id
|
||
LEFT JOIN play_events pe ON pe.track_id = t.id AND pe.user_id = $1
|
||
GROUP BY ls.artist_id
|
||
HAVING COALESCE(max(pe.started_at), '1970-01-01'::timestamptz)
|
||
< now() - interval '14 days'
|
||
)
|
||
SELECT sqlc.embed(artists),
|
||
cov.id AS cover_album_id,
|
||
cnt.album_count::bigint AS album_count
|
||
FROM eligible e
|
||
JOIN artists ON artists.id = e.artist_id
|
||
LEFT JOIN LATERAL (
|
||
SELECT id FROM albums
|
||
WHERE artist_id = artists.id AND cover_art_path IS NOT NULL
|
||
ORDER BY created_at DESC LIMIT 1
|
||
) cov ON true
|
||
LEFT JOIN LATERAL (
|
||
SELECT count(*) AS album_count
|
||
FROM albums WHERE artist_id = artists.id
|
||
) cnt ON true
|
||
ORDER BY md5(artists.id::text || current_date::text)
|
||
LIMIT $2;
|
||
|
||
-- name: ListRediscoverArtistsFallbackForUser :many
|
||
-- Daily-stable random sample of liked artists, paired with the Go
|
||
-- top-up logic in HomeData when the primary eligibility query returns
|
||
-- fewer than the inner limit. Daily hash ordering matches the primary
|
||
-- (omitting user_id from the hash for the same sqlc-typing reason).
|
||
SELECT sqlc.embed(artists),
|
||
cov.id AS cover_album_id,
|
||
cnt.album_count::bigint AS album_count
|
||
FROM general_likes_artists gla
|
||
JOIN artists ON artists.id = gla.artist_id
|
||
LEFT JOIN LATERAL (
|
||
SELECT id FROM albums
|
||
WHERE artist_id = artists.id AND cover_art_path IS NOT NULL
|
||
ORDER BY created_at DESC LIMIT 1
|
||
) cov ON true
|
||
LEFT JOIN LATERAL (
|
||
SELECT count(*) AS album_count
|
||
FROM albums WHERE artist_id = artists.id
|
||
) cnt ON true
|
||
WHERE gla.user_id = $1
|
||
ORDER BY md5(artists.id::text || current_date::text)
|
||
LIMIT $2;
|