This module covers optimizing vector search performance in Azure Database for PostgreSQL Flexible Server with pgvector. You must understand how IVFFlat and HNSW indexes work, when to choose each, how to tune build-time and query-time parameters, how distance metrics interact with operator classes, and how to measure recall vs latency tradeoffs using EXPLAIN ANALYZE.
After completing this module, you should be able to:
- Compare IVFFlat and HNSW index algorithms and select the right one for a workload
- Configure index build parameters (lists, m, ef_construction) based on dataset size
- Tune query-time parameters (probes, ef_search) to balance recall and latency
- Match distance operators to the correct operator class for index-backed queries
- Use EXPLAIN ANALYZE to verify index usage and measure query performance
- Apply batch-build strategies for large-scale index creation
- Implement hybrid search combining vector similarity with full-text or metadata filters
This topic falls under "Implement vector search solutions" within the AI-200 data tier. Expect 3-4 questions covering:
- IVFFlat vs HNSW index selection based on workload characteristics
- Index parameter tuning (lists, probes, m, ef_construction, ef_search)
- Operator class selection for index creation
- EXPLAIN ANALYZE interpretation for vector queries
- Recall vs latency tradeoffs and when to accept approximate results
- maintenance_work_mem and max_parallel_maintenance_workers for index builds
| Resource | Link |
|---|---|
| Optimize performance using pgvector | https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/how-to-optimize-performance-pgvector |
| Use pgvector on Azure Database for PostgreSQL | https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/how-to-use-pgvector |
| pgvector indexing best practices | https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/how-to-optimize-performance-pgvector#indexing |
| File | Description |
|---|---|
| notes.md | IVFFlat vs HNSW algorithms, build tradeoffs, distance metrics, performance tuning |
| cheat-sheet.md | Index creation commands, parameter recommendations, tuning snippets |
| practice-questions.md | 10 exam-style practice questions with explanations |
| hands-on-lab.md | Labs measuring recall vs latency with different index configurations |
| code-examples/ | Python examples for index management and performance measurement |