Apart from the points mentioned, I would say follow the CQRS (Command Query Responsibility Segregation) design pattern to have Read/Write optimization --Read Replicas: Set up replicas to handle read-heavy workloads. --Write Optimization: Implement write-ahead logging or asynchronous writes to reduce blocking. Also, caching should be implemented to reduce the burden on DB. -- Query Caching: Cache frequent query results in memory to reduce database load. -- Application-Level Caching: Use tools like Redis or Memcached to store frequently accessed data.

Below additional strategies, can effectively optimize database for traffic spikes. Monitoring: Use tools like DataDog to track metrics such as query latency, I/O throughput, CPU, and memory usage in real time. Profiling: Analyze query execution plans to identify and optimize slow queries. Vertical Scaling: Upgrade server hardware (CPU, RAM, storage) to manage increased loads. Read Replicas: Offload read-intensive tasks to replicas to reduce the primary server's load. Connection Pooling: Reuse database connections to lower the overhead of establishing new ones. Routine Maintenance: Perform tasks like vacuuming tables, rebuilding indexes, and analyzing query statistics regularly to ensure optimal performance, especially during traffic spikes

Database performance optimization starts with monitoring to find bottlenecks. These could be slow queries, inefficient indexes, or resource limitations. Once identified, focus on: * Query optimization: Rewrite queries, use parameters, and cache results. * Indexing: Create and maintain effective indexes. * Database tuning: Adjust settings like buffer pool size and connection limits. * Data storage: Consider partitioning and compression. * Concurrency: Minimize locks and improve connection pooling. Continuous monitoring and ongoing optimization are key to maintaining a high-performing database.

Quick Optimization Techniques 1. Query Optimization • Create strategic indexes • Rewrite inefficient queries • Eliminate unnecessary joins 2. Infrastructure Improvements • Upgrade to SSD/NVMe storage • Implement connection pooling • Allocate sufficient memory 3. Scalability Solutions • Use database sharding • Create read replicas • Implement load balancing 4. Advanced Performance Methods • Enable query caching • Use batch processing • Leverage stored procedures Key Focus Areas • Monitor database performance • Track resource utilization • Proactively adjust configurations

To optimize a database during peak load, start with vertical scaling to increase server resources, using caching layers for frequently accessed data, and leveraging read replicas to offload workloads. Prioritize critical queries and optimize connection pooling. Medium-term strategies include query optimization, and table partitioning to reduce scan times. Tuning database configurations can also improve performance. For long-term solutions, consider revising the schema to balance normalization, migrating to distributed databases for scalability, and archiving historical data. Monitoring tools could help proactively detect issues, while automation enables scaling. Always test optimizations in staging environments, and monitor the impact.