Vector Index Build Strategies

Overview

Index build strategies determine how vector indexes are constructed and maintained, with trade-offs between build time, search quality, and resource usage.

Build Approaches

Batch Build

• Build index from scratch with all data
• Optimized index quality
• Requires downtime or dual-indexing
• Best for periodic rebuilds

Incremental Build

• Add vectors to existing index
• No downtime
• May degrade index quality over time
• Periodic rebuilds recommended

Online Indexing

• Index updates in real-time
• Immediate searchability
• Higher resource overhead
• Complex coordination

Optimization Techniques

GPU Acceleration

# Milvus with GPU
index_params = {
    "index_type": "GPU_IVF_PQ",
    "metric_type": "L2",
    "params": {"nlist": 1024}
}

• 10-100x faster build
• Requires GPU resources
• Not all algorithms supported

Parallel Construction

• Distribute build across cores
• Linear speedup with cores
• Memory intensive

Sampling for Parameters

• Use subset to determine optimal parameters
• Much faster than full-data optimization
• Representative sample is key

Best Practices

1. Initial Build: Use batch with optimization
2. Updates: Incremental up to threshold
3. Rebuild: Periodic (weekly/monthly)
4. Monitoring: Track index quality metrics
5. Testing: Validate search quality after builds

Resource Planning

CPU: 8-16 cores for parallel build Memory: 2-3x vector data size GPU: Optional, 10-100x speedup Time: Hours for billions of vectors

Pricing

Resource costs during build; varies by strategy and scale.