Good database design is foundational to application scalability and performance. Here are proven patterns for different scenarios.
Normalization vs Denormalization: Normalize to reduce redundancy and ensure data integrity. Denormalize for read-heavy workloads where performance matters more than storage efficiency.
Indexing Strategy: Create indexes for frequently queried columns, but avoid over-indexing. Each index adds write overhead. Use composite indexes for multi-column queries.
Partitioning: Split large tables into smaller, more manageable pieces. Range partitioning by date is common for time-series data.
Caching Layer: Implement Redis or Memcached as a cache layer to reduce database load. Cache query results, computed values, and session data.
Connection Pooling: Database connections are expensive. Use connection pooling to reuse connections and limit concurrent database access.
Backup and Recovery: Implement automated backups, test restore procedures regularly, and maintain point-in-time recovery capabilities.
The right patterns depend on your specific access patterns and consistency requirements.