14b80377cd
* fix(server): optimize smart playlist role queries for large criteria (#5511) Role-based smart playlist criteria (artist, composer, etc.) now query the indexed media_file_artists join table instead of parsing JSON via json_tree() on every row. Multiple conditions for the same role within an OR group are merged into a single EXISTS subquery (batched at 200 to stay under SQLite's expression tree depth limit). A composite index (media_file_id, role) replaces the now-redundant single-column (media_file_id) index on media_file_artists. Benchmark (40k tracks, 500 patterns, 3 artists/track): - Merged join-table: 15ms (9.3x faster) - Merged json_tree: 30ms (4.6x faster) - Unmerged baseline: 137ms * refactor: simplify role condition SQL generation and benchmark Extract shared roleCondSQL/roleExistsSQL helpers to deduplicate the EXISTS template between roleCond and roleCondGroup. Use slices.Chunk for batching per project convention. Extract runBenchQuery helper to eliminate triplicated benchmark execution loop. * chore: raise roleCondBatchSize to 350 The empirical SQLite limit is 496 conditions per merged EXISTS subquery. Raising from 200 to 350 reduces the number of batches (e.g. 500 patterns now splits into 2 batches instead of 3). * fix(server): apply OR-merge optimization to tag conditions too Generalize mergeRoleConds into mergeJsonConds to also collapse multiple tag conditions for the same tag (e.g. genre) within OR groups. This gives the same ~5x speedup for tag-heavy smart playlists as the role optimization gives for artist-heavy ones. * refactor: benchmark uses real criteria pipeline instead of hand-built SQL The "Current" sub-benchmark now builds criteria.Criteria expressions and runs them through the actual newSmartPlaylistCriteria → Where() → ToSql() pipeline, validating the real production code path. The baseline still uses hand-built SQL representing the old json_tree approach. * fix: stabilize merged group ordering and close rows before error check Sort group keys in mergeJsonConds so the merged additions have deterministic order across runs, improving SQLite statement cache reuse. Move rows.Close() before rows.Err() in benchmark helper.
Navidrome Music Server 
Navidrome is an open source web-based music collection server and streamer. It gives you freedom to listen to your music collection from any browser or mobile device. It's like your personal Spotify!
Note: The master branch may be in an unstable or even broken state during development.
Please use releases instead of
the master branch in order to get a stable set of binaries.
Check out our Live Demo!
Any feedback is welcome! If you need/want a new feature, find a bug or think of any way to improve Navidrome, please file a GitHub issue or join the discussion in our Subreddit. If you want to contribute to the project in any other way (ui/backend dev, translations, themes), please join the chat in our Discord server.
Installation
See instructions on the project's website
Cloud Hosting
PikaPods has partnered with us to offer you an officially supported, cloud-hosted solution. A share of the revenue helps fund the development of Navidrome at no additional cost for you.
Features
- Handles very large music collections
- Streams virtually any audio format available
- Reads and uses all your beautifully curated metadata
- Great support for compilations (Various Artists albums) and box sets (multi-disc albums)
- Multi-user, each user has their own play counts, playlists, favourites, etc...
- Very low resource usage
- Multi-platform, runs on macOS, Linux and Windows. Docker images are also provided
- Ready to use binaries for all major platforms, including Raspberry Pi
- Automatically monitors your library for changes, importing new files and reloading new metadata
- Themeable, modern and responsive Web interface based on Material UI
- Compatible with all Subsonic/Madsonic/Airsonic clients
- Transcoding on the fly. Can be set per user/player. Opus encoding is supported
- Translated to various languages
Translations
Navidrome uses POEditor for translations, and we are always looking for more contributors
Documentation
All documentation can be found in the project's website: https://www.navidrome.org/docs. Here are some useful direct links:
Screenshots
