Then we will need insert the current output from global status into the table we created and use it to store historical data. The data type I select for each column is the same as the types defined in performance_schema global_status table to ensure consistency (Below is the default definition of the table global_status in performance_schema). origin_metric_value will be copied directly from performance_schema.global_status when calling metric_name will be matched the VARIABLE_NAME in performance_schema.global_status The first step is to create a table used to storing the data, just like the sample below.
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Get the current output and save the data into a tableĪfter some time, get another output and do the subtraction with the data stored in Step #1 based on metric nameĪfter subtraction, update the value in Step #1 as the new benchmark for later use Since the returned value is not historical, the idea applied here is Understand the data to collect for monitoring !!!Īs mentioned, the metrics are returned by below SQL statement. Use KUSTO query to generate report based on those metrics to fulfill different purposes. Get an output of global status with change data capturedĪutomatically upload them in Azure Log Analytics workspace. Luckily, Azure Log Analytics can help us achieve it! Therefore, it will be very handy if we can record historical data and data changes, it will allow us to perform data analysis to understand what changed and draw some conclusions. There is no historical value to compare and draw any conclusions. This makes it hard to determine the change that reflects the server running status The value of each metric is an cumulative value since the server ran, which means all of those metric value will keep increasing until a restart is triggered. However, the challenges with the results of the earlier command is: With the help of performance_schema, you will learn more about your MySQL server runtime status.Īs many of you may already know, the following command in MySQL will return a long list of server metrics. However, at times, this information may not be sufficient enough to understand why CPU utilization is going high.
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This blog will illustrate how to automatically upload the output of performance_schema metrics to Azure Log Analytics and then use Log Analytics to generate different types of report for performance monitor/investigating purpose.Īzure DB for MySQL offers some metrics in Azure Portal that allows users to check the CPU, Memory, Connection activity etc. Further, leveraging the power of Azure Log Analytics, you can achieve even more! Using performance_schema, you can do a lot of magic work to understand your server slowness. Luckily, MySQL natively provides a system schema called performance_schema to log all the server runtime information. Moreover, this product supports all versions of the MySQL database server, from version 3.23 to version 5.4ĭevelopment and management of MySQL features never was this easy! Start working immediately with stored procedures, triggers, views, foreign keys, scheduled events, blob, etc. With DreamCoder you can create, edit, duplicate, export and delete objects, compile and run stored procedures, export and import data, generate reports, monitor the database activity, synchronize the database, build and execute queries, format code, manipulate blob, create users and execute scripts. In the DreamCoder for MySQL you will find different modules and utilities to manage and develop in MySQL.ĭreamCoder for MySQL provides an intuitive interface that allows you to perform database tasks quickly and easily. This product can increase the productivity and quality of development teams and database administrators (DBAs). DreamCoder for MySQL Enterprise Freeware is a tool for developing and managing MySQL databases.
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