Indexing the Data Lake for Online Point Queries
Spotify is addressing the challenge of performing low-latency point queries on massive data lakes by implementing Random Access Parquet (RAP). This solution allows for efficient data retrieval without the overhead of traditional distributed SQL engines, supporting both online services and AI agents.
Why it matters
Optimizing data access is critical for the performance of modern AI agents and real-time personalization features in large-scale data environments.
Companies like Spotify need vast quantities of data accessible at low latency for online services and, increasingly, AI Agents acting on behalf of users. Online services — such as portals and personalization features that use or display a user's listening history — need to look up and paginate through per-user data at interactive speeds. AI Agents answering questions like, "what was I listening to last summer?" need to retrieve a user's data quickly so they can reason over it — filtering, aggregating, or even running SQL locally — to build context for LLM prompts. Both patterns share the same underlying primitive: fast point queries by key over datasets that are often far too large to economically keep resident in a key-value (KV) store like Bigtable or DynamoDB.
Get smarter about the news
Sign up free for a feed built around what you actually care about, Dive Deeper research on any story, and the full text of every article.
Create free accountAlready have an account? Sign in