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Andrew Monsoon
August 23, 2023
Updated August 23, 2023

How to optimize database performance: best practices and tips

How to optimize database performance: best practices and tips

In the era of information technology, databases have become an integral part of our daily lives. They are the foundation on which most modern applications from online shopping to social networking are built. Databases store, process, and make available huge amounts of data, making this process fast and efficient. But as the amount of information grows, so does the complexity of processing it, making database optimization a key challenge for many organizations.

MySQL and PostgreSQL are the most popular database management systems (DBMS) in the world. MySQL is an open-source software known for its reliability, ease of use, and high performance. PostgreSQL is a powerful open-source DBMS that offers an advanced set of features and capabilities for building complex applications.

Today, we'll look at best practices and tips for optimizing your work with these and other databases so that your system always performs at the peak of its capabilities.

To effectively optimize database performance, it's important to understand how RDBMSs are structured in depth. This will allow you to better assess system needs and apply appropriate optimization techniques.

DBMS operating principles and mechanisms

The basis of any DBMS is the so-called "database engine". It is responsible for query processing, data interaction, and storage. The engine uses various algorithms and data structures to efficiently search, modify and store information.

  • Indexing. One of the main mechanisms that speed up access to data. An index is a data structure that allows the DBMS to quickly find rows in a table without having to look through each row.
  • Buffering. Most DBMSs use a memory buffer to temporarily store frequently used data, which reduces the access time.
  • Query Optimizer. This component analyzes database queries and determines the most efficient way to execute them.
  • Transactions. DBMSs use the transaction mechanism to ensure data integrity and provide consistency control means for simultaneous access of several users.

The importance of optimizing database performance

As data volumes grow and query complexity increases, database performance can degrade. An unoptimized DBMS can become a bottleneck in system performance, causing delays and loss of productivity.

Optimizing the DBMS not only speeds up query response times, but can also reduce infrastructure maintenance costs, increase system reliability, and improve overall user experience.

Internal database (database) performance optimization

Indexes significantly speed up the extraction of data from the database. However, redundant indexes can slow down the insertion, update, and deletion processes. It is important to create indexes only for those columns that often participate in query conditions.

  • Normalization is the process of organizing data in a database in a way that reduces data duplication and avoids unwanted anomalies. Proper normalization can improve performance and facilitate scaling.

Customizing memory settings, such as buffer and cache sizes, can have a significant impact on performance. Particular attention should be paid to the InnoDB buffer and query cache size.

Query Optimization

  • Using query plan explanation (EXPLAIN): Use the EXPLAIN command to analyze how MySQL executes a query and determine which indexes are used or ignored.
  • Optimize joins and subqueries: Avoid correlated subqueries where possible and use joins. It is also preferable to use JOIN instead of multiple joins.

Working with MySQL database: performance optimization

MySQL is one of the most popular database management systems and is used in a variety of applications. However, to achieve maximum performance, it is important to know how to properly optimize MySQL server.

MySQL provides a profiler that helps you analyze the execution time of different query steps, which can help you identify bottlenecks. Keep an eye on server metrics such as CPU, RAM, and disk usage. MySQL's monitoring and optimization tools such as "Performance Schema" or "SHOW STATUS" can help identify potential problems.

PostgreSQL Optimization: The path to efficient database performance

PostgreSQL is a powerful and flexible open source database management system. However, even such an advanced DBMS requires regular optimization to ensure high database performance and responsiveness. Let's get to know the key aspects of PostgreSQL optimization.

PostgreSQL performance optimization (internal)

  • Transaction Management. Transactions in PostgreSQL are managed using MVCC (multiversion concurrency control), which allows multiple transactions to run safely in parallel. However, long transactions can lead to the accumulation of "dead" rows, which requires automatic or manual intervention to clear them.

Working with a PostgreSQL database involves interacting with a variety of data types, from simple numeric and string data to specialized ones such as JSON, hstore, or PostGIS for geospatial data. The right choice can have a significant impact on database performance.

As well as in other DBMSs, PostgreSQL indexes speed up data access. Particular attention should be paid to B-tree indexes, as well as such optional indexes as GiST, GIN and SP-GiST.

PostgreSQL Query Optimization (external)

Use ANALYZE to determine the cost of a query. Use the ANALYZE command to get statistics on a table, allowing the optimizer to correctly estimate the cost of a query and choose the best execution plan

Optimize JOINs and subqueries. Use LEFT JOIN only where it is really necessary. When possible, replace correlated subqueries with regular JOINs as a way to improve database performance.

PostgreSQL Performance Optimization

  • Utilize monitoring tools: many PostgreSQL monitoring tools such as pg_stat_statements or PgBadger can help you identify performance bottlenecks.
  • Server configuration: special attention should be paid to configuration parameters such as shared_buffers, work_mem, or maintenance_work_mem to ensure maximum performance of your PostgreSQL server.

Tips and best practices

Database optimization is not just a matter of choosing the right tool or setting parameters. It's also a matter of long-term planning, strategy, and continuous learning. Now let's take a look at tips and best practices that will help you maximize the performance and security of your database.

Data schema design

Before you begin development, it's important to do some careful planning. Identify the key entities, their relationships, and potential scaling requirements. Systems change, and what works today may require changes tomorrow. Design data schemas with potential growth and change in mind.

Regular testing and monitoring

Use tools such as Query Profilers or Performance Monitors to understand which queries or operations are bottlenecks. Run regular load tests to ensure your system can handle real-world workloads and identify potential problems.


  • Encryption. Data, especially sensitive data, should be encrypted. This applies to data on disk as well as data transmitted over the network.
  • Data backup. Back up your database regularly and store it in a secure location. This ensures that you can restore your data in the event of a disaster.
  • Access Control. Provide access control to your database. Use a role-based access model and minimize the number of accounts with full permissions.

Training and skill development

Technology is changing rapidly. Continuous training and learning new approaches and technologies ensures that you are always one step ahead. Even if you prefer one DBMS, learning about alternative technologies can give you new ideas and insight on how to improve your current system.

Recommendations for optimizing database performance

Working with databases is not just a technical task, but an art that requires knowledge, experience, and continuous learning, from understanding the principles of DBMSs to specific recommendations for optimizing popular systems like MySQL and PostgreSQL.

Optimizing a database: key points

To effectively optimize your database, it's important to understand how your DBMS works. This knowledge will allow you to properly customize and fine-tune your system. Properly designing your data schema for future requirements can prevent many future performance issues.

Regular database performance optimization, testing, and monitoring are necessary to identify bottlenecks and problems and to evaluate the effectiveness of changes made.

Data protection should be a priority. This includes encryption, backups, and strict access controls.

The technological world is constantly evolving. New tools, approaches, and techniques emerge regularly and it is important to stay up to date with the latest news and practices to maintain high database performance.

Database optimization is an ongoing process. It requires attention, time, and resources, but investing in it promises significant improvements in database performance, reliability, and security of your data. Don't be afraid to experiment and adopt new practices, but always do so thoughtfully and based on proven knowledge.

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