Database Design for High-Traffic Applications

Database design determines application scalability. A well-designed schema handles 10-100x more traffic than a poorly designed one with identical hardware. At Acceli, we've designed databases for applications handling 500M+ monthly transactions. This guide synthesizes lessons focusing on indexing strategies, schema design patterns, query optimization, and scaling approaches that affect long-term maintainability.

Schema Design for Scale

Normalize for write-heavy tables, denormalize judiciously for read-heavy analytics. One e-commerce client denormalized product names into order_items, reducing order history queries from 450ms to 60ms. Partition tables exceeding 10M rows by date or category—improved query performance 8x for a financial services client. Use PostgreSQL JSONB for flexible schemas and custom attributes (eliminated hundreds of nullable columns, reduced table width 70%).

Indexing Strategies

Index columns in WHERE, JOIN, ORDER BY, and GROUP BY clauses. Composite index on (user_id, created_at DESC) reduced dashboard queries from 12 seconds to 80ms. Use covering indexes (INCLUDE clause) to enable index-only scans—reduced query time 65%. Implement partial indexes for sparse data (WHERE status = 'active')—reduced index size 80% while improving performance. Each index increases write overhead; remove unused indexes.

Query Optimization

Fix N+1 queries using JOINs or eager loading—reduced admin dashboard from 8s to 400ms. Batch INSERT/UPDATE operations instead of loops—reduced 100K record import from 45 minutes to 3 minutes. Use connection pooling (reduced query latency from 180ms to 25ms for high-traffic API). Monitor slow query logs and use EXPLAIN plans to identify bottlenecks.

Scaling Strategies

Implement read replicas for read-heavy workloads (95% reads)—distributed load across 3 replicas, reduced primary CPU from 85% to 20%. Cache hot data in Redis (reduced database load 80%, improved response time 45ms to 2ms). Consider sharding only when vertical scaling and read replicas are exhausted—operational complexity is substantial. Most applications scale well to 10M+ users without sharding.

Conclusion

Database performance at scale requires intentional design from day one. Proper indexing, query optimization, strategic denormalization, and scaling strategies prevent performance problems before they emerge. Start with solid fundamentals: normalized schema, appropriate indexes, connection pooling, query optimization. Profile early and often. Add read replicas and caching before considering sharding.