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There are various techniques and methods to reduce the MySQL CPU usages where we have to make changes in the code and configuration file (.conf) or we can use any dynamic binary optimizer which can handle this problem and we do not have to make any manual changes in the system.
Here in this article, we are going to discuss Dynimize, which is a Dynamic Binary Optimizer, it can improve the MySQL performance in CPU intensive workloads. This article covers the benefits and working of Dynimize.
Most of the hosting servers use Linux as a bash terminal so we are considering Linux as a standard here while working with MySQL based applications where a lot of intensive work is done by the database system, there the top Linux command shows that MySQL is consuming a lot of CPU resources, and because of this CPU does not able to allocate proper resources to the other process of the application.
There are various methods you can use to deal with this problem but many of those related to the hardcore manual changes of the complete application and even after making those changes it's not guaranteed that the MySQL reduces the CUP usages.
There are various reasons why MySQL consumes so much CPU resources, this could happen because of the poorly written query, incorrectly configured database or having high traffic workload.
This happens because when we have a large number of well-indexed queries hitting the buffer pool in memory this leads to intense use of the database system and makes the CPU allocate all the resources to the Database itself.
The MySQL CPU usage is not a bug so it cannot be fixed, this is a problem which occurs for a limited period, and this article does not cover any technique which can fix this problem rather than here we have discussed how the Dynimize reduce this problem up to some extent.
Dynimize is a Dynamic Binary Optimizer which can instantly improve MySQL or MariaDB performance by some percentage. It acts as a machine code JIT compiler. Dynimize work on the principle of Java Just-in-time (JIT) compiler.
The Java just-in-time compiler combines the Java bytecode and profiling information to generate optimized machine code, similarly, the Dynimize combines the in-memory x86-64 machine code of regular Linux processes and live profiling data to generate current workload-optimized machine code.
At the run time of the process, the Dynimize optimize the in-memory machine code of the process based on the profiling information, which improves the performance of the processing workload.
In a nutshell, the Dynimize uses the in-memory machine code and profiling information of the processes and try to create an optimal machine for those processes without changing any statement or code of the actual application. The Dynimize only applied to MySQL and MariaDB.
Dynimize works transparently, you do not need any expert knowledge or hardcore manual configuration changes to use it, which mean you do not need to modify the application, migrate server or restart MySQL for applying Dynimize.
wget https://dynimize.com/install -O install wget https://dynimizecloud.com/install.sha256 -O install.sha256 sha256sum -c install.sha256; if [ $? -eq 0 ]; then sudo bash ./install; fi
It works as a Just-in-time compiler which uses the profile information of the process to optimize the program and rewrite and optimize in-memory machine code so the performance can be improved.
It runs at the background of all the processes and optimizes the instruction of the process which are running on the same system.
It optimizes the machine code at run time.
It does not make any changes in the program it just uses the information of the program process and its run time environment to optimize in-memory Instructions.
Every time the program runs, Dynimize specializes in the code. This cycle repeats for the lifetime of the program.
Dynimize is one of the best tools we have to optimize the machine code which results in reducing the MySQL and MariaDB CPU usage. It is supported by various cloud computing vendor services such as DigitalOcean AWS EC2, etc., and various workloads have shown improvement of up to 70%.
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