Summary
In this module, you learned how to optimize Azure Database for PostgreSQL and pgvector for AI workloads. You explored PostgreSQL configuration parameters that affect vector query performance, including memory settings like shared_buffers and work_mem, and query planner settings optimized for SSD storage. You learned the differences between IVFFlat and HNSW vector indexes, understanding when each is appropriate based on dataset size, accuracy requirements, memory constraints, and build time tolerance. You configured index parameters like lists, probes, m, ef_construction, and ef_search to balance query speed against recall accuracy.
You discovered how data layout decisions affect filtered vector search performance. Structured columns with B-tree indexes provide efficient filtering for common predicates, while JSONB with GIN indexes offers flexibility for dynamic attributes. You learned to combine vector similarity with metadata filters effectively, using query patterns that let PostgreSQL optimize execution plans. For large datasets, you explored table partitioning strategies that improve both query performance and maintenance operations.
You also learned scaling strategies for high-volume vector workloads on Azure. Vertical scaling with Memory Optimized compute tiers keeps indexes cached in memory. Read replicas distribute query load for read-heavy workloads. Application-level caching with Azure Cache for Redis reduces database load for frequently requested data. Finally, you implemented connection optimization techniques including PgBouncer configuration in transaction mode and application-level connection pooling to maximize throughput while staying within connection limits.