ListenBrainz Server is a full-stack music listening history platform operated by the MetaBrainz Foundation. In production it ingests scrobbled listening events ('listens') through its own REST API, a Last.fm/AudioScrobbler-compatible API, and background importers for Spotify, Last.fm, Libre.fm, Maloja, PanoScrobbler, Spinitron, Funkwhale, Navidrome and Audioscrobbler. Listens are validated, deduplicated, stored in TimescaleDB hypertables (with an in-progress migration to a newer listens store), and dispatched in real time via Socket.IO websockets to subscribed clients. On top of this core it provides love/hate feedback, pinned recordings, JSPF playlists, radio generation, follow/social-feed features, user data export/import, and public data dumps. The deployment is a polyglot microservice-style monorepo: Flask WSGI apps serve the HTML shell and REST APIs (server.py, api_server.py, api_compat.py, plus a datasethoster-based labs API), a React 18 + TypeScript SPA implements the UI, and a fleet of runit-managed workers consume RabbitMQ queues — timescale writer, Spark request reader, MBID mapping writer, metadata cache crawlers for Spotify/Apple/SoundCloud/Internet Archive, ClickHouse query workers, and a background task processor. Data stores include PostgreSQL, TimescaleDB, ClickHouse, Redis, CouchDB, RabbitMQ, HDFS, and Garage (S3-compatible object storage). Although classified as an application, the repository has a substantial data-engineering character: Apache Spark jobs (listenbrainz_spark) compute user/sitewide listening statistics, collaborative-filtering recording recommendations, similar-user/similar-recording datasets, Year-in-Music reports, and popularity scores against incremental dumps and MLHD datasets; results land in CouchDB-backed stats tables and Postgres similarity tables. A newer ClickHouse query-worker pipeline is gradually absorbing parts of this workload, and a dedicated mbid_mapping project builds canonical MusicBrainz ID mappings and metadata caches. Strengths include the incremental Spark stats engine abstraction (pluggable SQL query providers, message creators, and range selectors), declarative COPY-based dump models, well-structured external-service importer base classes, and an extensive integration/unit test matrix across Python, frontend, and Spark. Weaknesses are accumulation and drift: duplicate importer and test base classes, copy-pasted frontend chart components (2022/2023 cover-art collages, per-year Year-in-Music pages), Pydantic v1 usage with a v2 migration TODO, hardcoded production IDs (SPARK user ID, Troi bot IDs), a legacy nginx:1.13.5 prod Docker image, and multiple overlapping DB access patterns. The repository is actively developed — schema migrations and release tags extend through October 2026.
YesPlayMusic is a third-party desktop and web client for Netease Music (music.163.com), built with Vue 2, Electron, and a self-hosted API server. It reproduces the official client's features (playback, playlists, lyrics, personal FM, scrobbling) while adding cross-platform support (macOS, Windows, Linux, Docker, PWA) and integration with UnblockNeteaseMusic for region-restricted tracks. The architecture is a monolith: a Vuex-driven SPA communicates with Netease's unofficial API via a local proxy, with an Electron shell providing native features (system tray, media keys, MPRIS, Discord Rich Presence). The codebase is in maintenance mode, with the active development shifting to a 2.0 alpha. It suffers from outdated dependencies (Node 14, Vue 2, Electron 13) and several hardcoded workarounds, but remains a functional and feature-rich player for Netease Music users.