MySQL is an extremely popular open-source database platform originally developed by Oracle. It currently is the second most popular database management system in the world, only trailing Oracle’s proprietary offering. If you aim to be a professional database administrator, knowledge of MySQL is almost a prerequisite. It is an important part of the multi-platform database environment found in the majority of IT departments.
A recent addition that has added to the complexity of managing a MySQL environment is the introduction of big data. Big data is characterized by the volume, velocity, and variety of information that is gathered and which needs to be processed. It can be used to provide an organization with the business intelligence (BI) it needs to gain a competitive advantage and better understanding of its customers.
The size of big data sets and its diversity of data formats can pose challenges to effectively using the information. These characteristics are what make big data useful in the first place. It is the convergence of large amounts of data from diverse sources that provide additional insight into business processes that are not apparent through traditional data processing.
Some examples of how big data can be beneficial to a business are:
- Studying customer engagement as it relates to how a company’s products and services compare with its competitors;
- Marketing analysis to fine-tune promotions for new offerings;
- Analyzing customer satisfaction to identify areas in service delivery that can be improved;
- Listening on social media to uncover trends and activity around specific sources that can be used to identify potential target audiences.
MySQL Limitations When Handling Big Data
MySQL was not designed with big data in mind. This does not mean that it cannot be used to process big data sets, but some factors must be considered when using MySQL databases in this way. Here are some MySQL limitations to keep in mind.
- The lack of a memory-centered search engine can result in high overhead and performance bottlenecks.
- Handling large data volumes requires techniques such as shading and splitting data over multiple nodes to get around the single-node architecture of MySQL.
- Processing volatile data can pose a problem in MySQL. This issue can be somewhat alleviated by proper data design.
- The analytical capabilities of MySQL are stressed by the complicated queries necessary to draw value from big data resources.
These limitations require that additional emphasis be put on monitoring and optimizing the MySQL databases that are used to process and organization’s big data assets. It can be the difference in your ability to produce value from big data.
Optimizing the Performance of Your MySQL Databases
Managing a MySQL environment that is used, at least in part, to process big data demands a focus on optimizing the performance of each instance. SQL Diagnostic Manager for MySQL offers a dedicated tool for MySQL monitoring that will help identify potential problems and allow you to take corrective action before your systems are negatively impacted. The tool helps teams cope with some of the limitations presented by MySQL when processing big data.
Some specific features of SQL Diagnostic Manager for MySQL that will assist with handling big data are:
- Real-time query monitoring to find and resolve issues before they impact end-users;
- Monitoring of long-running and locked queries that can result from the complexity of processing the volume of information in big data sets;
- Creating custom dashboards and charts that focus on the particular aspects of your MySQL systems and help identify trends and patterns in system performance;
- Employing over 600 built-in monitors that cover all areas of MySQL performance.
Neither big data nor MySQL is going away anytime soon. Getting them to play nicely together may require third-party tools and innovative techniques. SQL Diagnostic Manager for MySQL is one such tool that can be used to maintain the performance of your MySQL environment so it can help produce business value from big data.