Designing a scalable vector database requires a combination of distributed architecture, efficient indexing, and optimized storage. The core challenge is balancing fast similarity searches with the ability to handle growing data volumes and query loads. To achieve this, the system must distribute data and computation across multiple nodes while minimizing latency and maintaining accuracy.
First, the architecture should be distributed by design. Data is partitioned (sharded) across nodes to spread storage and processing. A common approach is to use a combination of hash-based or range-based sharding, where vectors are grouped by similarity or randomly distributed. For example, using a hash function to assign vectors to shards ensures even distribution, while range-based partitioning based on vector clusters can improve query efficiency. Replication is also critical for fault tolerance: each shard is copied to multiple nodes to prevent data loss and enable read scalability. Tools like Apache Cassandra or custom solutions using Raft/Paxos consensus protocols can manage replication. Indexing strategies, such as Hierarchical Navigable Small World (HNSW) graphs or Inverted File (IVF) indexes, are applied per shard to accelerate nearest-neighbor searches. These indexes reduce the number of vectors compared during queries by organizing data into manageable subsets.
Second, storage optimization is essential. Vector databases often use columnar storage formats like Parquet or specialized binary formats to compress high-dimensional data. For instance, quantizing floating-point vectors to 8-bit integers can reduce storage by 75% with minimal accuracy loss. Metadata (e.g., timestamps or labels) is stored alongside vectors, often in a separate database like RocksDB or a relational system, to enable hybrid queries. To handle real-time updates, a write-ahead log (WAL) ensures durability while allowing asynchronous index updates. Partitioning data by time or usage patterns (e.g., separating hot and cold data) can further optimize performance. Systems like Milvus use this approach, storing frequently accessed vectors in memory and archiving older data to disk.
Finally, query processing must scale horizontally. A coordinator node routes incoming queries to relevant shards and aggregates results. Load balancers distribute requests to prevent hotspots, while caching layers (e.g., Redis) store frequently accessed vectors to reduce latency. For example, a k-nearest-neighbor (k-NN) query might fan out to multiple shards, retrieve top candidates from each, and merge them globally. Monitoring tools track query latency, node health, and resource usage to trigger auto-scaling—adding nodes during peak traffic or rebalancing shards. Open-source frameworks like FAISS or ScaNN can be integrated into this pipeline to accelerate vector comparisons. By combining these techniques, the database maintains low-latency responses even as data grows into billions of vectors.