Database optimization ensures applications perform efficiently as data volumes grow. Understanding indexing, query optimization, caching strategies, and database scaling techniques is essential for maintaining responsive systems.

Key Optimization Techniques

• Indexing Strategies: Create indexes on frequently queried columns to accelerate lookups and joins.

• Query Optimization: Analyze execution plans and rewrite inefficient queries for better performance.

• Connection Pooling: Reuse database connections to reduce overhead and latency.

• Caching Layers: Store frequently accessed data in Redis or Memcached.

• Partitioning: Divide large tables into smaller, manageable segments.

Scaling Approaches

Vertical scaling increases server resources for immediate gains, horizontal scaling distributes data across multiple servers through sharding, read replicas handle read-heavy workloads, and database clustering provides high availability and fault tolerance.

Monitoring and Analysis

Track slow queries and identify bottlenecks, monitor connection counts and wait times, analyze cache hit rates and memory usage, measure query response times and throughput, and set alerts for performance degradation.

Best Practices

Normalize data appropriately to balance redundancy and performance, use prepared statements to prevent SQL injection and improve caching, implement database migrations for schema changes, regularly update statistics for query optimizer, and archive historical data to maintain table sizes.