How to optimize database performance: best practices and tips

Databases are often the main bottleneck in modern applications. MySQL and PostgreSQL remain the most popular RDBMS and offer many tools for database optimization. The key to improve database performance is a mix of good schema design, proper indexing, tuned configuration and ongoing monitoring. This article walks through how to optimize database performance in MySQL and PostgreSQL with practical concepts and best practices.

Introduction: Why Database Optimization Matters

In modern 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 when they are configured correctly.

But as the amount of information grows, so does the complexity of processing it, making database optimization and database performance optimization a key challenge for many organizations. Poor database performance quickly turns into slow user interfaces, timeouts, and scalability issues.

MySQL and PostgreSQL are among the most popular database management systems (DBMS) in the world. MySQL is 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 database optimisation and for optimizing your work with these and other databases so that your system always performs at the peak of its capabilities. This article is a practical guide on how to improve database performance and how to optimize database performance in real-world environments.

How DBMS Engines Work: The Foundation of Database Performance

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

Key mechanisms that directly affect database performance:

• 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 access time and helps increase database performance under load.
• Query Optimizer. This component analyzes database queries and determines the most efficient way to execute them, choosing execution plans based on statistics and available indexes.
• Transactions. DBMSs use the transaction mechanism to ensure data integrity and provide consistency control for simultaneous access by several users.

Understanding these core mechanisms is critical if you want to improve database performance in a structured, predictable way.

Why Database Performance Optimization Is Critical

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;
• improve overall user experience;
• extend the life of existing hardware.

In other words, database optimization is both a technical and a business task that directly influences the efficiency of your applications.

Internal Database Performance Optimization

Internal database performance optimization focuses on what happens inside the DBMS: schema design, indexes, and memory settings. These are the core levers when you want to increase database performance without immediately scaling hardware.

Indexes

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

Regularly review and clean up unused indexes. This simple step can improve database performance both for reads and writes.

Normalization and Schema Design

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 by keeping data consistent and structures predictable.

At the same time, for some analytical and reporting workloads, selective denormalization or materialized views may be used as part of your overall database optimisation strategy to speed up heavy read queries.

Memory and Cache Settings

Customizing memory settings, such as buffer and cache sizes, can have a significant impact on performance.

• In MySQL, particular attention should be paid to settings related to the InnoDB storage engine, especially innodb_buffer_pool_size which determines how much data and indexes can be cached in memory.
• In PostgreSQL, parameters such as shared_buffers work_memmaintenance_work_mem help control how much data is processed in memory during queries and maintenance operations.

For modern MySQL (8.0+), the legacy Query Cache has been removed, so database optimization should focus on InnoDB tuning, proper indexing, and query design rather than relying on a global query cache.

Query Optimization: How to Improve Database Performance with Better SQL

Query design is one of the most important aspects of database performance optimization. Even a well-tuned server cannot compensate for inefficient SQL.

Using Query Plan Explanation (EXPLAIN)

Use the EXPLAIN command to analyze how MySQL executes a query and determine which indexes are used or ignored. For PostgreSQL, EXPLAIN ANALYZE provides both the planned and actual execution details, which is extremely useful when you need to understand how to optimize database performance for specific queries.

Regularly reviewing query plans is a practical way to improve database performance without changing hardware.

Optimizing Joins and Subqueries

Whenever possible:

• avoid correlated subqueries that are executed once per row;
• rewrite them as regular joins where applicable;
• make sure join conditions are indexed on both sides.

It is also preferable to use explicit JOIN syntax instead of mixing multiple comma-separated tables, as it makes intent clearer and helps prevent accidental Cartesian products.

Working with MySQL: Practical 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 database performance, it is important to know how to optimize MySQL server correctly.

MySQL Performance Monitoring and Profiling

MySQL provides several tools that help you analyze query behavior and execution time:

• the slow query log to detect the most expensive queries;
• EXPLAIN to understand execution plans;
• Performance Schema and SHOW STATUS to monitor internal metrics.

These tools help you identify bottlenecks and decide where to focus your database optimisation efforts.

Keep an eye on server metrics such as CPU, RAM, and disk usage. High resource usage combined with slow queries usually indicates that it is time to review your indexing, query patterns or configuration.

Key MySQL Settings for Better Database Performance

For InnoDB-based workloads, start with:

• innodb_buffer_pool_size — often 60–70% of RAM on a dedicated database host;
• innodb_log_file_size and innodb_flush_log_at_trx_commit — affecting durability vs performance;
• connection limits and temporary table sizes.

Tuning these parameters based on real workload metrics is one of the most effective ways to increase database performance in MySQL.

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-running transactions can lead to the accumulation of "dead" rows (dead tuples), which requires automatic or manual intervention to clear them using VACUUM or tuned autovacuum settings.

Data Types and Extensions.
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/JSONB, hstore or PostGIS for geospatial data. The right choice of data type can have a significant impact on database performance.

Indexes.
As in other DBMSs, PostgreSQL indexes speed up data access. Particular attention should be paid to B-tree indexes for general use, as well as such optional index types as GiST, GIN and SP-GiST for full-text search, JSONB and geospatial data. Proper index design is one of the most effective ways to improve database performance in PostgreSQL.

PostgreSQL Query Optimization (External)

Use ANALYZE to collect statistics on tables so that the optimizer can correctly estimate query costs. Combine this with EXPLAIN ANALYZE to see how a specific query is actually executed and where time is spent.

When optimizing queries:

• Use LEFT JOIN only where it is really necessary.
• When possible, replace correlated subqueries with regular joins as a way to improve database performance.
• Avoid unnecessary SELECT * in frequently executed queries.

PostgreSQL Performance Optimization Tools and Configuration

To increase database performance in PostgreSQL, it is important to combine monitoring and configuration tuning.

• Monitoring tools. Many PostgreSQL monitoring tools such as pg_stat_statements or PgBadger can help you identify performance bottlenecks, frequently executed queries, and inefficient patterns.
• Server configuration. Special attention should be paid to configuration parameters such as shared_bufferswork_mem, and maintenance_work_mem to ensure maximum performance of your PostgreSQL server. In many production deployments, a connection pooler such as PgBouncer is also used to manage a large number of client connections efficiently.

General Tips and Best Practices for Database Optimization

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 as part of your overall database optimisation strategy.

A well-designed schema makes it much easier to improve database performance later, without painful refactoring.

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 before they affect users.

Monitoring and testing are continuous processes: they provide feedback on how to optimize database performance over time, not just once.

Security

Performance and security should go hand in hand:

• 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 database performance in your current system.

Summary: 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.

To effectively optimize your database performance:

• understand how your DBMS works internally — this knowledge will allow you to properly customize and fine-tune your system;
• design your data schema with future requirements in mind to prevent many performance issues before they appear;
• regularly review indexes, query plans, and configuration to increase database performance step by step;
• combine database optimization with monitoring, testing, and solid security practices.

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 performance optimization is an ongoing process. It requires attention, time, and resources, but investing in it promises significant improvements in 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 and real monitoring data.