Optimize queries by eliminating unnecessary joins, indexing frequently accessed columns, and restructuring inefficient query patterns. Review and create indexes for columns used in WHERE clauses, JOIN conditions, and ORDER BY clauses. Avoid over-indexing, which can slow down write operations; Partition large tables into smaller, more manageable segments to distribute the workload.Ensure optimal use of database connections with connection pooling to limit overhead during peak traffic.

When your database is slow at certain times, especially peak times, it is important to check the processes that are running at the moment, whether it is possible to optimize the queries involved, using strategies such as index.

It is always about the SQL. Find the SQL statements that are not scaling. As in, when they work with more data, the slower they go. Indexing is important and the right indexes is key. More indexes doesn't mean better performance. With Exadata, fewer indexes might be better. Best thing is to make sure when a statement executes it touches each table as few times as possible, ideally being once. Cutting down on LIOs is really the gaol of any SQL optimization effort.

Index Optimization: Create or optimize indexes to speed up query performance. Avoid excessive indexing, as it can slow down write operations. Query Performance Tuning: Analyze slow queries using execution plans. Rewrite queries for efficiency. Database Configuration: Adjust database settings (e.g., memory allocation, cache size) to better handle load. Ensure the database server has adequate resources (CPU, RAM). Load Balancing: Distribute traffic across multiple database instances. Implement read replicas for read-heavy applications. Regular Maintenance: Perform routine tasks like vacuuming (for PostgreSQL) or defragmenting indexes. Monitor performance metrics regularly to identify bottlenecks.