What is Index Optimization

How It Works

Index optimization involves selecting the right index type, tuning its parameters, and maintaining it over time. For vector indices, this includes choosing between HNSW, IVF, or quantized variants, then tuning parameters like M (connections), ef (search width), and nprobe (clusters to search). For keyword indices, optimization covers analyzer configuration, field mapping, and segment merge policies.

Technical Details

Vector index optimization targets three dimensions: recall (accuracy), latency (speed), and memory (footprint). HNSW offers high recall and speed but high memory. IVF-PQ offers low memory but lower recall. Scalar quantization (SQ8) provides a middle ground. Keyword index optimization includes adjusting refresh intervals, merge policies, and replica counts. Benchmarking frameworks (ANN-benchmarks) help compare configurations systematically.

Best Practices

• Benchmark with representative queries and data before choosing index configuration
• Set recall targets first, then optimize for speed and memory within those constraints
• Use different index configurations for different collections based on their query patterns
• Schedule index maintenance (compaction, segment merging) during low-traffic periods

Common Pitfalls

• Optimizing for synthetic benchmarks that do not represent production query patterns
• Setting index parameters once and never re-evaluating as data grows or changes
• Over-optimizing for latency at the expense of recall without measuring the impact
• Not accounting for index build time and memory overhead during updates

Advanced Tips

• Use adaptive index parameters that change based on collection size and query volume
• Implement separate index configurations for different modalities based on their characteristics
• Apply index warm-up strategies to preload frequently accessed data into memory
• Build automated index tuning that adjusts parameters based on production query metrics