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

2. set the maxdop for parelellism set 3. compatibility as per the version of SQL server 4.Try to optimize a query by creating proper indexing on where sorting columns to retrieve data faster to avoid more logical reads . 5.Avoid Table scan for big tables . 6.Regularly update the statistics of the tables which are using a most 7.Try to avoid parameter shifting with proper analysis and use best solutions eg option(with recompile) 8.Identify most resource consuming query or procedure and try to analyse the plan 9. Open a actual plan and set some execution related settings and try to minimize the lookup operation . 10.Read the memory grant allocation form the plan and try to minimize with cluster index properly.

- Check if the query is utilizing indexes efficiently. Look for full table scans or index scans that can be avoided by creating or optimizing indexes -Analyze how joins and subqueries are performed. Ensure that join conditions are selective and that the smallest possible result set is generated early in the query execution process - Sometimes, rewriting a query using different SQL constructs or restructuring the logic can improve performance

To start optimizing your database, focus on analyzing your queries. Long-running queries can be a major performance bottleneck. Using tools like the EXPLAIN statement, which is available in most database management systems (DBMS), you can get a detailed view of how your queries are executed. Look for full table scans, which can be inefficient on large tables, and aim to optimize them by adding indexes to columns used in WHERE clauses. Ensure that your indexes are designed to cover the queries you run most often, as this will reduce the amount of data the DBMS has to scan, resulting in faster query execution.

- If EXPLAIN output indicates that WHERE clause filters lots of rows, but instead of Index scans you see Seq scan then it maybe useful to create index. But, first verify if that column has high selectivity from database statictics. For example, pg_stats for PostgreSQL. - Update database statistics and adjust its frequency for specific table. Sometimes, it can be that statistics is not updated on time, so it affects query planner.

- Analyze the WHERE clauses and JOIN conditions used in the identified queries. Determine which columns are frequently used for filtering, sorting, or joining tables - Index columns that are frequently used in WHERE clauses, especially those that filter data effectively (i.e., columns with high selectivity). Columns used in JOIN conditions should also be considered for indexing. - If queries frequently use combinations of columns in WHERE clauses or JOIN conditions, consider creating composite indexes (indexes on multiple columns) - If a query retrieves only a few columns from a table, consider creating a covering index that includes those columns

- Increase the capacity of your database server by upgrading hardware components such as CPU, memory, and storage - Use connection pooling to manage database connections efficiently -Implement caching mechanisms to reduce the number of queries hitting the database - Improve the efficiency of queries by optimizing SQL queries, ensuring proper indexing, and minimizing unnecessary operations - DistribuIncrease the capacity of your database server by upgrading hardware components such as CPU, memory, and storage -Distribute incoming traffic and queries evenly across multiple database servers or replicas

- Implement a regular backup strategy to protect data against loss or corruption. Schedule automated backups at appropriate intervals (e.g., daily, weekly) and verify backup integrity periodically - Update database statistics regularly to provide the query optimizer with accurate information about data distribution and query patterns - Implement data purging and archiving strategies to remove obsolete or outdated data from the database - Regularly review database security measures, including user permissions, access controls, and encryption settings

- Monitor CPU utilization to ensure the database server - Track memory consumption to prevent excessive swapping and ensure efficient data caching - Monitor read and write operations to identify bottlenecks in disk performance - Monitor read and write operations to identify bottlenecks in disk performance - Measure the time taken for queries to execute, focusing on identifying slow queries that may impact overall performance

Data, database architecture and SQL should work hand in hand in order to achieve and preserve data consistency and optimal performance. Please remember that best performing SQL is useless when working on not consistent data. In my opinion you start with data. Its consistency and usability. Architecture and SQL are just tools to preserve it. So the most important task is to understand the data and purpose of processing (gathering, accessing, changing, removing) it. Second is instrumenting the application. It means that you know how long each step of processing the data takes. And you know the SLA of each step. And you know which SQL is responsible for particular part of the application. And your employees are capable of solving the issue.