Network Communication Bottlenecks Network communication becomes a bottleneck when distributing data across nodes in a cluster, as queries or updates may require cross-node coordination. For example, a nearest-neighbor search might need to access multiple shards, increasing latency due to inter-node communication. To mitigate this, partition data strategically (e.g., using locality-sensitive hashing to group similar vectors on the same node) to minimize cross-node requests. Use efficient serialization formats like Protocol Buffers or Cap’n Proto instead of JSON to reduce payload size. Additionally, implement edge caching for frequently accessed vectors and optimize query routing (e.g., using a coordinator node to batch requests and reduce round trips).
Disk I/O Bottlenecks Disk I/O limits arise when the database must read/write large volumes of vector data from persistent storage, especially with high-dimensional vectors. For instance, loading 1 billion 768-dimensional vectors (stored as 32-bit floats) requires ~3 TB of disk space, leading to slow read times. Mitigate this by using SSDs for faster random access, adopting tiered storage (keep hot data in memory, cold data on disk), and employing columnar file formats like Parquet for compression and efficient reads. Precompute vector indexes (e.g., HNSW or IVF) to reduce disk seeks during queries. For writes, use buffering and asynchronous commits to batch disk operations.
CPU Bottlenecks Vector operations like similarity calculations (e.g., cosine similarity) are computationally intensive, especially for large datasets. A brute-force search across 1 million vectors with 1,000 dimensions requires ~1 billion floating-point operations per query. Mitigate this by using approximate nearest neighbor (ANN) algorithms like FAISS or ScaNN, which reduce computational complexity by trading minimal accuracy for speed. Parallelize operations across CPU cores (e.g., SIMD instructions for vectorized dot products) or offload computations to GPUs/TPUs. Optimize code for cache locality—for example, structuring data in arrays-of-structs (AoS) to structs-of-arrays (SoA) to improve vectorization.
Memory Bottlenecks Storing high-dimensional vectors in memory can exhaust available RAM. For example, 100 million 512-dimensional vectors (32-bit floats) occupy ~200 GB. Mitigate this by using quantization (e.g., 8-bit integers instead of 32-bit floats) to reduce memory footprint by 4x, or apply product quantization for higher compression ratios. Implement memory-mapped files to lazily load data from disk without fully loading it into RAM. Distribute data across nodes via sharding, ensuring each node handles a subset of the dataset. For in-memory databases, use LRU caches to evict rarely accessed vectors while retaining hot data.