To plan capacity for a vector database cluster anticipating growth, start by analyzing current and projected data volume, query patterns, and performance requirements. Break this into three areas: index size scaling, query load distribution, and maintaining performance buffers. Use these insights to model resource needs and design a scalable architecture.
First, estimate storage and memory for index growth. Vector indexes (e.g., HNSW, IVF) scale with data size and dimensionality. For example, a 1M-vector dataset with 768-dimensional float32 vectors requires ~3GB of raw storage (1M × 768 × 4 bytes). However, indexes add overhead: HNSW may require 10–20x the raw data size in memory for fast search, while disk-based indexes like DiskANN use less RAM but need fast SSDs. Project future data volume (e.g., 5x growth in 12 months) and multiply by the index-specific multiplier. If using replication (e.g., 3 copies for fault tolerance), include this in total storage calculations. For cloud deployments, select instance types with sufficient RAM-to-vCPU ratios to hold hot indexes in memory.
Second, model query throughput and latency requirements. Measure baseline queries per second (QPS) and average latency, then simulate projected loads. For example, if QPS is expected to grow from 500 to 2,000, test how adding nodes affects throughput. Use sharding to distribute data—if the dataset is split across 4 shards, each node handles a subset of queries. However, ensure top-K cross-shard aggregation doesn’t become a bottleneck. For mixed read/write workloads, separate query and indexing nodes. Allocate 20–30% extra compute capacity to handle peak loads without exceeding 70% CPU utilization. Load test with tools like Locust to validate scaling assumptions.
Third, implement observability and automation. Deploy monitoring for metrics like cache hit rate, node memory pressure, and query queue depth. Set alerts for thresholds like 75% memory usage or 90th percentile latency exceeding 150ms. Use auto-scaling groups to add nodes when metrics breach thresholds, but ensure the cluster can rebalance data without downtime. Pre-provision empty nodes in Kubernetes clusters for faster scaling. Periodically review capacity models against actual growth rates—if data ingestion accelerates, adjust scaling policies. For on-premises deployments, maintain a hardware buffer (e.g., 2 unused nodes) to absorb unplanned surges.
For example, a cluster handling 10M vectors might start with 3 nodes (64GB RAM, 8 vCPUs each). If projections show 50M vectors and 5x QPS in a year, plan for 8 nodes with 128GB RAM each, using auto-scaling to add nodes as RAM usage crosses 60%. Test index rebuild times—if inserting 1M vectors takes 2 hours, ensure the cluster can parallelize ingestion across nodes to avoid bottlenecks.