MySQL is one of the most known open-source database systems. This database system saves and organizes data in a useful and accessible manner. Large applications, when not optimized, may experience performance issues due to the sheer volume of data. This article proposes some tips that developers can use to enhance the performance and response speed of their MySQL database.
Balancing hardware resources
Balancing hardware resources is a must when it comes to high performing database systems. This would avoid having bottleneck issues. So, when constructing systems that are expected to be running as MySQL servers, system builders have to give extra attention to having an equilibrium between the system elements:
Storage: Think about the storage needs for a second. You can upgrade to solid-state drives (SSD) for a performance boost if you currently use conventional hard disk drives (HDD). To keep track of your disk input/output rates, use a program from the sysstat package like iotop or sar. And, consider upgrading to faster storage if disk utilization is significantly higher than the usage of other resources.
Processor: Typically, we use processors usage to gauge how quickly a system is. To see a breakdown of how your resources are being used, use the Linux top command. Pay attention to how much the traffic to the MySQL processes consume of the CPU resources. And, if the usage is over 90% then consider upgrading the system.
Memory: RAM can also have a tremendous effect on the functioning of the MySQL database. In fact, having high memory for cache purposes increases the performance of the server. Not having enough memory or if the RAM you do have isn’t optimized will eventually lead to the degradation of speed.
Having a shared hosting for the MySQL data can help us limit these bottle-necking effects. Since, major hosts optimize their systems for better reliability. Same thing is valid for virtual private servers (VPS).
Use InnoDB instead of MyISAM
Some MySQL databases use the older MyISAM database style. This database design is less effective. The more recent InnoDB contains built-in optimization mechanics and enables more sophisticated features.
InnoDB employs a clustered index and stores data in pages that are stored in sequential physical blocks. In fact, InnoDB moves values that are too big for a page to another location before indexing them. The physical hard drive can access the data more quickly thanks to this feature’s assistance in keeping pertinent data in the same location on the storage device.
Update the MySQL version
Using the most recent version is not always possible for older and legacy databases. However, whenever possible, you must verify the version of MySQL that is currently in use and upgrade to the most recent version.
Performance improvements are part of the ongoing development. Newer version of MySQL can render obsolete some common performance tweaks. In general, native MySQL performance enhancement is preferable to scripting and configuration files.
A query is a programmed request to the database to find data that matches a specific value. There are some query operators that, by definition, take a long time to execute. Some SQL performance tuning techniques facilitate the optimization of queries for faster execution.
One of the most important tasks in performance tuning is detecting queries with long execution times. Queries on large datasets are typically slow and take up database space. As a result, the tables are no longer available for other tasks.
Use Indexes when necessary
Generally database queries have the following structure:
SELECT elements WHERE condition
These queries require the evaluation, filtering, and retrieval of results. We can restructure tables by adding an additional set of indexes. To speed up the query, it can be directed at the index.
Specify Columns in SELECT Function
SELECT * is a common expression for analytical queries. Choosing more than you require causes unnecessary performance degradation and redundancy. If you define the columns you require, your query will avoid scanning irrelevant columns.
For example, instead of using:
SELECT * FROM table
Select only needed columns:
SELECT col1,col2,col3 FROM table
GROUP BY Instead of SELECT DISTINCT
When attempting to eliminate duplicate values, the SELECT DISTINCT query comes in handy. However, the statement demands a significant amount of processing power.
Avoid using SELECT DISTINCT whenever possible because it is inefficient and sometimes confusing. And use the faster alternative GROUP BY.
Bottle-necking database systems is a serious issue that can easily be avoided. In this article, we listed some of the indispensable tips and tricks that developers can use to enhance the performance of their MySQL-based database systems.