Database / Qdrant Vector DB Interview questions
What are the key features of Qdrant?
Qdrant combines a set of capabilities focused specifically on production-grade vector search, several of which distinguish it from simpler embedded or prototype-focused vector stores.
| Feature | What it Provides |
| HNSW indexing | Fast approximate nearest neighbor search over high-dimensional vectors |
| Payload filtering | Structured metadata filters combined directly with vector search |
| Quantization | Scalar, binary, and product quantization to reduce memory footprint |
| Distributed deployment | Sharding and replication for horizontal scale and fault tolerance |
| Hybrid search | Combining dense and sparse vectors via the Query API's fusion methods |
| Snapshots | Point-in-time backups of collections for recovery and migration |
Taken together, these features are aimed at taking an application from an initial prototype (a single Qdrant instance on a laptop) through to a production deployment handling millions or billions of vectors across a replicated, sharded cluster, without switching to a different underlying system along the way.
What does quantization primarily help reduce in Qdrant?
What enables horizontal scale and fault tolerance in Qdrant?
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