Asopitech With · Music streaming
Spotify with Alopex DB
Spotify is a music streaming service for finding and listening to music and podcasts. How would we make it with Alopex DB?
Updated: Oct 12, 2026
What the service does
On a Spotify-like audio streaming service, listeners discover, manage and enjoy music, podcasts and audiobooks. The data is a catalog of tracks and artists, playlists, and each listener’s play history. Recommendations are built from that catalog and history.
Where the data lives
- SQL tables: tracks, artists, albums, playlists and the order of tracks in each playlist live in tables.
- Vectors: each track’s embedding sits in a vector column. “Tracks like this one” is a nearest-neighbor search through an HNSW index, with conditions on genre or release year added in the same SQL.
- Columnar tables: play history is loaded in batches into a columnar table through HTTP ingest. Each listener’s favorite artists and listening by time of day are aggregated in SQL.
The app creates the track embeddings. A recommendation is then one SQL query: favorite artists from the aggregation become the filter, and vectors find the nearest tracks.
Deployment
An Alopex DB server runs alongside the app backend, which connects over HTTP or gRPC. The catalog, history and vectors are in the same database, so nothing is copied into another system to make recommendations.
Why Alopex DB fits
Recommendations combine what is similar (vectors) with what is popular (aggregation). Alopex DB handles vector search and columnar aggregation in SQL on the same server.
Alopex DB serves similar-track vectors and listening trends in columnar tables from one server. Why not try it for recommendations? The quickstart is the starting point.