Many businesses find their data volumes increase rapidly and their workloads continuously evolve. As a result, database performance tuning has become crucial to maximizing system performance and resource use to ensure the system is operating at peak efficiency.
Database tuning and optimization require extensive expertise, an understanding of execution plans, and the ability to write SQL. It’s also highly time-consuming because there will likely be many SQL statements to fine-tune. Once you’ve determined which statements need tuning, you face the challenge of refining your tuning approach to suit each one.
Like all relational databases, MySQL can be complicated. Issues can cause it to halt without notice, putting your applications and business at risk. MySQL performance issues are usually caused by common mistakes, including subtle issues within a configuration trap or workload. To keep your MySQL server’s performance stable, consistent, and running at optimum speed, it’s important to eradicate these mistakes. Fortunately, many MySQL performance problems can be rectified by implementing MySQL DB optimization best practices.
Database optimization can help you identify bottlenecks, eliminate the guesswork associated with tuning queries, and target insufficient processes. I’ve compiled this guide to MySQL to help you achieve your database optimization goals. Optimize database performance and solve MySQL problems with these best practices and tools. For those who don’t have time to read the full guide, I recommend a high-quality MySQL-optimized DB performance tool like SolarWinds Database Performance Analyzer or SolarWinds Database Observability.
MySQL: Optimize database best practices
Profile your server workload
You must first profile the server’s workload to gain a robust understanding of how your server functions. This can reveal which queries are the most expensive, so they can be identified for further tuning efforts. Remember, time is the most important metric because completion speed should be considered a priority when a query is issued against the server.
Certain tools can assist you with profiling your workload by capturing queries the server executes and returning a table of tasks. The table is usually sorted by response time, in decreasing order, giving you instant visibility into the most time-consuming and expensive tasks. Tools profiling your workload can group queries of a similar nature, letting you view fast queries executed numerous times and slow queries.
Understand the key resources
Every database server relies on four key resources critical to its functionality:
- Memory
- CPU
- Disk
- Network
When one of these is overwhelmed, weak, or inconsistent, your server is going to experience performance issues and lags. MySQL database optimization starts with understanding these resources to better inform your choice of hardware and facilitate successful troubleshooting practices.
Hardware is extremely important. All the tuning in the world won’t make up for bad hardware, so choosing high-performing components from the start is fundamental. Many businesses choose servers with rapid CPUs and disks but without memory capacity. Adding memory can be a low-cost way of maximizing performance, especially on disk-bound workloads. While this might sound counterintuitive, disks are often overwhelmed because there isn’t enough memory to hold the server’s working data set.
MySQL will likely perform well with a rapid CPU because each query runs in an individual thread and cannot be parallelized across CPUs. When performing troubleshooting processes, check the use and performance of all four resources: CPU, disk, memory, and network. Carefully consider whether they’re performing poorly or whether they’re simply overloaded. This is a crucial distinction. Knowing the difference can help you rectify performance issues more quickly.
Curate baseline metrics
HData collection and analysis is an important part of rectifying database performance issues. Before you begin the MySQL-optimized database process, you should establish a rough idea of how much time the process will take. It’s also useful to gain an understanding of how long a query should run. This could be a second, 15 minutes, or even a full hour.
At this stage, gather the necessary baseline metrics. This should include rows examined and rows sent. Note how long the query currently takes to run. Ensure you’ve collected wait and thread states—such as sending data, calculating statistics, detecting system blocks, and writing to the network—before moving on to the optimization process. These wait states can give you valuable insight into where you should focus your optimization efforts.
Analyze the execution plan
Creating an execution plan is a critical aspect of your MySQL-optimized database mission because it contributes to the creation of a roadmap for overall query performance. MySQL provides you with multiple ways of selecting an execution plan, along with simple navigational structures for examining the query. You can view a tabular version of the plan by using explain, explain extended, or the optimizer trace.
Alternatively, you can access a graphical view of the plan and clearly understand which steps are the costliest by using MySQL Workbench. These execution plans provide steps from top to bottom, table names, key length, select type, potential keys to target, reference, and number of rows to read. Extra columns can also give you insight into how data will be accessed, sorted, and filtered.
Review the index and table
Once you’ve curated the metrics and analyzed your execution plan, you’ll need to review the query table and index information. This review will inform your optimization approach.
First, take the time to gain an understanding of table size and location. It’s also useful, at this stage, to review the constraints and keys to help you understand how the tables are related. Column size and makeup should also be considered, especially when the “where” clause is in use.
To view table size, input the following statement into the command line: mysqlshow –status <dbname>. The show index from <table_name> statement is also helpful because it’ll allow you to check the indices and their relative cardinality. This insight can drive your execution plan.
Be sure to identify whether the indices are multi-column and take note of the order of the columns within the index. If the left-leading column is referenced, MySQL will use the index. It won’t use the index under any other circumstances.
Avoid using MySQL as a queue
Queues, and access patterns resembling queues, can infiltrate your application without you noticing. If, for instance, you establish an item status, so a certain worker process can claim it before acting, then you have unintentionally created a queue. A common example of creating a queue would be marking emails as unsent, sending them, and then marking them as sent.
Queues should be avoided because they’re responsible for two issues. First, they prevent tasks from running in parallel because the workload has been serialized. Second, queues are often responsible for tables containing work in progress alongside historical data from jobs processed much earlier. Both issues create application latency and unnecessary MySQL load.
Be aware of scalability traps
Scalability is often thought of as a vague and obscure concept, but there are mathematical definitions attributed to scalability, which are represented as precise equations. These equations highlight why systems are so difficult to scale—and how they often don’t scale as well as they should.
The Universal Scalability Law can be applied to a system’s scalability characteristics. This law explains scalability issues about crosstalk and serialization costs. If parallel processes must stop for a serialized element, scalability is inherently limited. Similarly, if parallel processes need to communicate to coordinate their efforts, this is also an extreme limitation.
Avoiding serialization and crosstalk is key to optimizing your database because it’ll help your applications scale more successfully. In MySQL, this means avoiding exclusive locks on rows. Queues often scale poorly because of this.
Use response time analysis to identify MySQL bottlenecks
When your applications are slowing down, and your end-user experience is being affected, getting to the root cause of the issue quickly is critical. Traditional MySQL performance monitoring solutions focus on server health and track resource metrics. Response-time analysis tools, on the other hand, focus on time rather than resource metrics. This type of analysis is based on what the database engine and application are waiting for, which is captured in waits.
The response-time analysis is the most efficient and successful route to solving complex performance issues because it allows you to look at where time is being spent by the database engine. This goes one step further than identifying query execution times or slow queries. Response-time analysis tools correlate wait times with queries, resources, storage performance, response time, execution plans, and many other dimensions. This gives you a comprehensive understanding of what’s happening inside your database and the causes of database issues.
Common hurdles in MySQL database optimization
Optimizing MySQL databases offers numerous benefits, yet manual optimization poses significant challenges without dedicated IT tools. This formidable task demands expertise, especially for database administrators facing challenges such as query performance tuning. Identifying and improving slow queries becomes complex without query profiling tools, requiring administrators to sift through extensive logs. Index optimization adds another layer of complexity, necessitating a deep understanding of database structure and query performance. Security measures, crucial for safeguarding sensitive data, can be challenging to implement manually. Effective schema design, considering data normalization and relationships, becomes intricate without dedicated IT tools. Resource allocation issues, like CPU and memory sharing, can hinder optimal MySQL database utilization and create bottlenecks during manual optimization.
- Query performance tuning: Optimizing MySQL involves improving sluggish SQL queries, a complex process streamlined with query profiling and analysis tools. Database administrators are tasked with scrutinizing extensive logs and analyzing query execution plans to pinpoint performance bottlenecks effectively.
- Index optimization: In maintaining and establishing accurate indexes, administrators may make suboptimal choices. Undertaking this task requires administrators to possess a profound understanding of database structure and query performance.
- Security: Ensuring database security necessitates a comprehensive strategy to protect sensitive data and mitigate SQL vulnerability threats. This task becomes challenging when manually optimizing your database system.
- Schema design: Crafting an efficient database schema requires meticulous attention to data normalization and relationships. This endeavor can pose significant challenges without the support of a dedicated IT tool.
- Resource allocation: Efficiently distributing resources like CPU and memory poses a challenge during the optimization of MySQL database systems. Manual resource allocation frequently results in suboptimal resource utilization and significant bottlenecks as database demands evolve.
Navigating MySQL database optimization challenges
During manual MySQL database optimization, you’ll face challenges leading to bottlenecks and complications, making the process distinct and intricate. Effectively addressing these challenges requires developers and database administrators to utilize specialized IT tools designed to automate essential processes. Choosing a reliable tool is crucial; I highly recommend the SolarWinds Database Observability tool and the Database Performance Analyzer (DPA).
SolarWinds Database Observability transforms the landscape of MySQL database management by delivering unprecedented visibility and performance insights. This innovative solution revolutionizes the orchestration of intricate, open-source database ecosystems, simplifying oversight for even the most complex databases. SolarWinds Database Observability enhances operational efficiency and facilitates strategic decision-making by offering a sophisticated lens into the inner workings of databases.
SolarWinds Database Performance Analyzer (DPA) is engineered to provide users with comprehensive database performance analysis, streamlining the process of deciphering the myriad factors influencing database performance. This tool automatically identifies correlations, significantly reducing the labor-intensive nature of optimizing MySQL databases. Recognizing the critical importance of response time in pinpointing the root causes of issues, DPA continuously collects SQL statement data at a one-second interval. The collected data is then ranked based on response time, aiding in the identification of the SQL queries that require attention.
Operating around the clock, DPA monitors your database, compiling historical and real-time data points. This unique capability allows the tool to simultaneously analyze data spanning five years ago and five seconds ago. The results are presented in easily interpretable formats, such as bar charts, facilitating efficient comprehension of database performance trends.
Getting started with SolarWinds Observability SaaS (formerly known as SolarWinds Observability) tools
Database monitoring is a critical process that collects data about the performance, stability, and overall health of monitored databases. SolarWinds Observability SaaS (formerly known as SolarWinds Observability) supports various database types, including MySQL, MongoDB PostgreSQL, and SQL Server. This monitoring helps in identifying and preventing issues and improving database performance, responsiveness, and error rates.
Install the SolarWinds Observability SaaS (formerly known as SolarWinds Observability) Agent on your host server to implement database monitoring. This agent gathers detailed information about the database instances on the server and sends the data to SolarWinds Observability SaaS (formerly known as SolarWinds Observability). It operates efficiently with minimal impact on performance and captures data by inspecting network traffic or from database statistics tables.
SolarWinds Observability SaaS (formerly known as SolarWinds Observability) provides dashboards to display database metrics and allows the configuration of alerts to notify users when predefined thresholds are crossed. The data collected helps in identifying and addressing issues proactively.
The monitoring process in SolarWinds Observability SaaS (formerly known as SolarWinds Observability) offers various tools for analysis, including the Entity Explorer, Metrics Explorer, and Queries Explorer, to delve into database health, performance, and query details. These tools help users optimize and troubleshoot their databases effectively.
This post was written by Precious Ukpai. Precious is an Experienced Full Stack (MERN) JavaScript developer. He loves contributing to Open source software and also sharing his knowledge and experience to help people solve basic software development problems.