Your database is starting to slow down. What can you do to speed it up?

To speed up a database: 1. Optimize queries 2. Index frequently used columns 3. Adjust database configuration 4. Partition large tables 5. Upgrade hardware 6. Implement caching 7. Utilize query optimization tools 8. Perform regular maintenance 9. Scale horizontally 10. Enable compression 11. Use connection pooling 12. Cache query results 13. Shard the database 14. Upgrade database software

Limit autogrowth of the database: This prevents the database from automatically increasing in size, which can cause performance issues during growth operations. Disable autoshrink/autoclose for the databases: Autoshrink and autoclose are features that can impact performance negatively. Move all databases to data disks, including system databases: Moving databases to dedicated data disks can improve performance by reducing I/O contention and allowing for better disk management and optimization. Move SQL Server error log and trace file directories to data disks: Storing error logs and trace files on dedicated data disks can prevent them from competing with database I/O operations and improve overall performance.

To speed up a slowing database, consider these steps: 1. Indexing: Review and optimize database indexes for frequently queried columns. 2. Query Optimization: Rewrite complex queries for better performance. 3. Hardware Upgrades: Increase RAM, use faster disks, or add CPUs to improve performance. 4. Database Configuration: Adjust database settings for better performance. 5. Caching: Use caching mechanisms to reduce the need for repeated database queries. 6. Regular Maintenance: Perform regular database maintenance tasks like vacuuming, reindexing, and updating statistics. 7. Database Sharding: Consider sharding to distribute data across multiple servers. 8. Database Tuning: Monitor database performance metrics and tune settings accordingly.

Bottleneck analysis is somehow a combination of three approaches: a) as already mentioned by other contributors, you should gather "pain reports" from users and/or application owners to better understand, where poor database performance could be the cause of such "pain". b) Use tools provided by your RDBMS! Later versions of Microsoft SQL Server, for instance, offer tools like the Query Store and the Management Data Warehouse to continuously gather statistics and detail about your database workloads. If used appropriately (before issues arise), you get invaluable insight into the workloads and will find it easy to identify runaway processes. c) if running on own (virtual) hardware, don't forget to monitor the whole environment.

Optimización de índices: Reevalúa y ajusta los índices existentes para garantizar que estén optimizados para las consultas más frecuentes. Refactorización de consultas: Analiza el plan de ejecución de consultas para identificar cuellos de botella y optimiza las consultas mediante la reescritura de cláusulas, la introducción de índices adicionales. Particionamiento de tablas: Considera la partición de tablas grandes en fragmentos más peque?os con el fin de distribuir la carga y facilitar la administración de datos. Actualización de hardware: Si el rendimiento sigue siendo un problema y se descarta la optimización de software, considera actualizar el hardware subyacente, como la CPU, la memoria o el almacenamiento

Tools and techniques for analyzing query performance, such as query execution plans, can pinpoint inefficiencies. Adjusting database configurations, cleaning up fragmented data, and revising schema designs can also enhance performance.

Recently I was reviewing a Stored Procedure that was quite slow. After some investigation I found the root cause in one of the queries. The developer ignored or forgot to use left join and he used "COALESCE(SELECT TOP 1 EmailAddress from CustomerEmail,'')" in select statement that introduced the slowness because of COALESCE and TOP. Try avoiding such queries which will later on create performance issues.

Poor (index) design often results from trying to fulfill multiple requirements with a single design, e.g. OLTP plus logging plus archiving plus "some" reports plus populating a data warehouse. Learn about modern database features, useful to decouple such different workloads, like e.g. Change Tracking or Change Data Capture (instead of triggers) for audit purposes decoupling from your OLTP workloads. Use any of the various features for data replication to process reporting workloads on another server than OLTP workloads. You can even offload backup workloads to different hardware. So many options... learn about them and start using.

Sure, upgrading your hardware can't be a bad idea, but don't take this step first and monitor your server to figure out, which upgrade provides the most "bang for the bucks". For SQL Server, there are decades old guidelines, still valid at least for "traditional" (as opposed to in-memory or streaming) workloads on how to monitor and rate data provided by the OS and the server and how to distinguish bottleneck symptoms from the root causes. So, don't listen to the hardware (or cloud) salesperson, but to your experienced administrator instead. If not available, go hire one ;)

Again, modern RDBMS provide plenty of tools to assist with that. SQL Server not only provides the already mentioned tools for monitoring, but also dynamic management data, including - among many other useful insights - data about missing indexes. If you want to take that a step further, set up a test environment, that sees all realistic (production like) workflows, including infrequent workflows like year-end processing and use either the modern tools like Management DWH and Query Store or the good old profiler to analyze which design changes might provide the best return in terms of performance for your efforts. Test any design changes on all (!!!) affected workloads; don't optimize a particular workload, possibly harming many others.

Plan and document everything. For new joiners or externals, temporarily aiming to assist your efforts, it might be difficult to understand your design and the constraints for changes (e.g. non-functional requirements) from just reviewing your database. Even the most sophisticated tools can merely help with that, so insist on creating and maintaining a meaningful documentation of your design from the beginning. The effort spent will pay back in multiples over your solution's lifetime.