Database / PineCone Database Interview questions II
The upsert operation in Pinecone inserts new vectors or updates existing ones if the vector ID already exists. This ensures that the latest vector and metadata are stored for each unique ID, supporting both insert and update semantics in a single API call.
The fetch operation retrieves vectors by their IDs, returning the exact vectors and metadata. The query operation performs a similarity search, returning the most similar vectors to a given query vector, optionally filtered by metadata.
A namespace in Pinecone is a logical partition within an index, allowing users to separate data for different applications, tenants, or use cases. Namespaces help manage access, isolation, and organization of vectors within the same index.
When a vector is deleted in Pinecone, it is removed from the index and is no longer retrievable via queries or fetches. Deletions are eventually consistent, and may temporarily affect recall until the index is fully updated.
To optimize query latency and throughput in Pinecone, choose the appropriate index type, tune the number of replicas, use metadata filtering efficiently, and batch queries when possible. Monitoring and scaling resources based on workload also help maintain performance.
