What are the most common performance tuning mistakes and how can you avoid them?

1 Mistake 1: Not measuring baseline performance

One of the biggest mistakes you can make when tuning your database is not measuring the baseline performance of your system before you make any changes. Without a baseline, you won't be able to tell if your changes have improved or worsened the performance, or if they have had no effect at all. You also won't be able to compare different scenarios and identify the best solution. To measure your baseline performance, you need to use tools such as performance counters, query execution plans, and dynamic management views. You also need to collect data from different workloads and time periods, and use a consistent methodology for testing and analysis.

4 Mistake 4: Not using stored procedures

Not using stored procedures can be a mistake that can have an effect on your database performance. Stored procedures are precompiled and reusable blocks of code that you store in the database, and they can be executed with one call. They can offer various advantages for your performance tuning, such as reducing network traffic and latency, improving execution plan reuse and caching, and enhancing security and maintainability. Stored procedures can reduce the amount of data sent and received over the network, allowing the database engine to reuse and cache the execution plans for the stored procedures, as well as controlling access and permissions. In addition, stored procedures can centralize and standardize the logic and code of your system.

5 Mistake 5: Not monitoring and reviewing your performance

The last mistake we will discuss in this article is not monitoring and reviewing your performance. Performance tuning is an ongoing process that requires attention and evaluation. You need to monitor and review your performance regularly to identify and address any issues or bottlenecks, as well as to ensure the system meets your performance goals and expectations. To do this, you should use performance monitoring tools and alerts to collect and analyze performance data and metrics, such as CPU, memory, disk, network, transactions, errors, wait statistics, etc. Additionally, you should use tools for performance testing and benchmarking to simulate and measure the performance of the system under different workloads and scenarios. Moreover, you should use best practices and guidelines to optimize and improve your performance. Lastly, you should document and communicate your performance tuning efforts and results, while seeking feedback from peers and experts.