Here's how you can optimize database performance and query optimization using problem solving skills.

To enhance database performance, index frequently queried columns, including primary and foreign keys, and columns often used in WHERE clauses and joins. Use composite indexes for columns frequently queried together, ensuring the most selective column is first. Covering indexes, which include all columns needed by a query, can also boost performance by reducing table accesses. However, avoid over-indexing, as excessive indexes can degrade performance during insert, update, and delete operations. Regularly review and optimize your indexes based on query patterns and performance analysis.

Indexes in databases offer several benefits: Improved Query Performance: Indexes allow the database to quickly locate rows in tables based on the values of indexed columns. Faster Data Retrieval: With indexes, data retrieval becomes faster as the database engine can use efficient algorithms like binary search or B-tree traversal to locate rows, reducing the need for full table scans. Enhanced Sorting and Grouping: Indexes can speed up sorting and grouping operations as they provide a pre-sorted view of the data based on indexed columns. Query Optimization: Indexes provide the query optimizer with additional information about the data distribution and access patterns, allowing it to generate more efficient query execution plans.

Indexes in a databases system is of importance when you want fast data retrieval and manipulation. It’s easier to locate data when indexed and easy to manipulate indexed data. These creates a pretty good database system and well responsive one at that .

Limit the Number of Columns Returned: Only select the columns that you actually need in your query rather than selecting all columns using SELECT *. This reduces the amount of data transferred between the database server and the client application, improving query performance. Avoid Select Distinct and Subqueries. Optimize JOINs: Ensure that JOIN operations are efficient by joining tables on indexed columns whenever possible. Use appropriate JOIN types (e.g., INNER JOIN, LEFT JOIN) based on your requirements, and avoid unnecessary JOINs.

Query refinement helps in locating or creating a more accurate data selection and retrieval. This not only gives relative search but a more accurate data storage and retrieval.

You have to be modareted on your database normalization. Use good sense. Test and Benchmark: Test the performance of your queries using realistic datasets and workload scenarios. Benchmark different versions of your queries after making optimizations to ensure that they meet performance expectations. By following these best practices and continuously refining your database queries, you can improve the performance, scalability, and maintainability of your database applications.

Implementing an effective database cache strategy is crucial for improving the performance and scalability of applications that rely heavily on database interactions.

Many of the database tuning contributions I have read on LinkedIn focus on proactive tasks (adding indexes, scalability, etc). However, the DBA will likely spend more time troubleshooting production problems during their guardianship. For transient SQL problems, one can employ Oracle’s Active Session History (ASH) that tracks dbtime for all active sessions and is initially stored in the SGA and retrievable through EM and G/V$ views for a short time. For persistent problems, one can employ ADDM where statistical information is stored initially in memory and written to the sysaux tablespace each hour and retrievable with EM and ADDM provided scripts for a few weeks. The primary goal of monitoring this session information is to reduce dbtime.