Mar 16, 2021. By Aleena Mathew
Organizations today rely on traditional database monitoring for the optimal performance of their business-critical applications. Databases centrally store all the vital information in an organization. A single point of failure can result in a significant loss. These losses can lead to substantial revenue losses. This is not an acceptable situation in any organization. So maintaining a database without any failure is a bedrock requirement. Moreover, the database admins need to track continuously and monitor database performance and resources to manage high performance. That's where database performance monitoring comes into action.
Database Performance Monitoring:
Database performance monitoring helps to analyze databases in real-time and determine any fault or performance issues in any application. This eventually helps in increasing the efficiency and fixing performance issues of the database. This method of analysis is possible in traditional systems. But, this scenario completely changes as we move to the digital era. As the IT trends evolve, more and more use of applications keeps on adding up and, it is a must to monitor every metric of this application. It won't be easy to track down what went wrong and where did it go wrong. The noise keeps on adding up. So this situation calls for more advanced solutions to solve the above problems. That is where AI comes into the picture.
AI-based Database Monitoring:
The application of AI is hitting wide-spread and, one such area is database performance monitoring. AI-based systems are capable of monitoring and observing the entire health of the database automatically. These models provide insights into every IT resource metric and automatically identify issues and predict them pre-hand. This AI-based observability helps in identifying any outliers in the system and alerting the appropriate operator.
AI-Based Observability for Database Monitoring:
Previously we have seen how efficiently AI can improve the observability of monitoring the database performance. Let's make it more specific with the help of an example. Here we depict how multi-variate KPI anomaly detection tasks place between CPU utilization and Query performance metric. Let's consider a scenario where an IT organization uses a MySQL database wherein most of the applications are connected. All of the query threads will be executed by the database. This means that threads are executed in a proper pipeline and, the database can scale well. However, at some point, the database will not be able to handle the incoming query thread. This situation leads to a scenario where the threads cached size starts to increase gradually. That is, all the query threads that were not executed by the database will be moved into the thread's cache. The cache size starts to increase, which will eventually impact the CPU utilization and affects system performance. Manually identifying such a scenario is a difficult task. AI-based models will automatically alert the above situation as an anomaly. That is, a multi-variate KPI anomaly detection happens here. The CPU utilization started to shoot up due to the query performance, which is an abnormal situation. Effectively at the point of occurrence, the IT operator can be alerted that such an anomaly took place and log the scenario as an incident ticket.
With the utilization of AI, database performance monitoring became an automated task. In this way, the system's efficiency can be improved by AI, and every application can be monitored intelligently.
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