Design Review Areas
Databases are the foundation of most AI systems. Feature stores, vector databases, knowledge bases, and training data pipelines all depend on sound database design. But the access patterns AI systems create differ dramatically from typical OLTP workloads. AI pipelines need bulk reads across millions of rows. Embedding storage requires vector indexing. Feature computation demands denormalized access patterns. A database designed for transactional web traffic may perform poorly under AI workloads without targeted optimization.
Indexing Strategy
We audit your indexing against actual query patterns from AI workloads. This includes B-tree indexes for exact lookups, GIN indexes for JSONB and full-text search, GiST or HNSW indexes for vector similarity (pgvector), partial indexes for filtered queries, and composite indexes for multi-column WHERE clauses. Missing indexes on foreign keys alone can cause 10x to 100x slowdowns on JOIN operations.
Query Optimization
AI data pipelines often generate expensive queries: large batch SELECTs, complex JOINs across denormalized tables, window functions for feature computation, and recursive CTEs for graph traversal. We profile your heaviest queries with EXPLAIN ANALYZE, identify sequential scans on large tables, optimize sort operations, and restructure queries that cause unnecessary memory pressure.
Connection Pooling
AI workloads create unique connection patterns: burst connections during batch processing, long-held connections during streaming inference, and concurrent connections from multiple pipeline stages. We review your pooling configuration (PgBouncer, pgpool, application-level pools), pool sizing formulas, idle timeout policies, and connection limits per role to prevent exhaustion under AI load.
Schema Evolution
AI projects iterate rapidly, and schemas must evolve without breaking existing queries or causing downtime. We assess your migration strategy, evaluate ALTER TABLE safety (avoiding full table rewrites), review column addition patterns, and establish schema versioning practices that support continuous deployment of AI feature updates.
Review Process
Profile
Analyze current query patterns
Assess
Evaluate schema and indexes
Optimize
Apply targeted improvements
Validate
Benchmark before and after
Profile
Analyze current query patterns
Assess
Evaluate schema and indexes
Optimize
Apply targeted improvements
Validate
Benchmark before and after
Database Design Review Layers
AI-Specific Database Patterns
We evaluate your database for patterns that specifically support AI workloads. These include vector storage and similarity search using pgvector extensions, materialized views for pre-computed features that AI models consume, partitioned tables for time-series data that enables efficient range queries, JSONB columns for semi-structured AI metadata, and generated columns for computed features that stay in sync with source data.
For organizations using PostgreSQL, we assess advanced features including Row Level Security for multi-tenant AI applications, advisory locks for coordinating distributed AI workers, SKIP LOCKED patterns for job queues that feed AI pipelines, and LISTEN/NOTIFY for event-driven AI trigger architectures.
Database performance is not about throwing hardware at the problem. A properly indexed query on a small instance outperforms an unindexed query on the largest available machine. Our review focuses on design improvements that multiply performance at zeroinfrastructure cost.
Review Deliverables
The database design review produces a schema assessment report with specific optimization recommendations, an index analysis covering missing, duplicate, and unused indexes, a query profile of your top 20 most expensive queries with rewrite suggestions, connection management recommendations with configuration changes, and a migration safety checklist for future schema changes.
Who This Is For
Database design reviews are valuable for engineering teams building AI features on existing databases, organizations experiencing performance issues as AI workloads scale, database administrators preparing for AI integration projects, and startups designing database schemas for AI-first products. We work with PostgreSQL, MySQL, MongoDB, and cloud-managed databases including Amazon RDS, Cloud SQL, Azure Database, Supabase, and PlanetScale.
Contact us at ben@oakenai.tech