To establish a MySQL database performance baseline, first clarify key indicators, collect data and observe trends, establish a benchmark model, and update dynamically. 1. Determine monitoring indicators, including CPU usage, memory usage, disk I/O, number of connections, number of slow queries, QPS/TPS, and collect them using tools such as SHOW STATUS or PMM. 2. Continue to collect data from different time periods, retain it for at least one week, and establish a reasonable baseline based on business peak periods and special periods. 3. Extract typical performance, compare data in the same time period using mean, peak, and percentile, and define anomaly thresholds. 4. Update the baseline regularly or after system changes, and use automation tools to adapt to environmental changes to ensure that the baseline always reflects the real operating status.

The core of establishing a MySQL database performance baseline is to clarify the performance of key indicators in the "normal" state, so that problems can be quickly identified and located when abnormalities occur. This baseline is not static, but should be dynamically adjusted based on actual business load, data volume growth and system environment changes.

1. Identify key indicators that need to be monitored

The first step in establishing a performance baseline is to determine which metrics truly reflect the health of the database. These metrics should cover resource usage, query efficiency, and system response capabilities.

• CPU Usage : High CPU usage may mean complex queries or missing indexes.
• Memory usage : Focus on InnoDB buffer pool hit rate, which is one of the key factors affecting performance.
• Disk I/O : View read and write delays, especially random I/O situations.
• Number of connections : Keeping a large number of connections for a long time may be due to improper configuration or problem with application logic.
• Number of slow queries : Regularly analyze slow queries logs to understand the performance bottlenecks.
• QPS/TPS : the number of queries per second and transaction processing capabilities, reflecting the database load level.

It is recommended to use SHOW STATUS , SHOW ENGINE INNODB STATUS or tools such as MySQL Enterprise Monitor , Percona Monitoring and Management (PMM) to collect this data.

2. Collect data and observe trends

It is not enough to have indicators alone, and it must be continuously collected and observed its changing trends over different time periods. For example, the data difference between business peaks and troughs will be very large and cannot be generalized.

You can do this:

• Record the above indicators every few minutes and keep historical data for at least one week.
• Visualizing these data with graphical tools such as Grafana makes it easier to see the pattern.
• Pay attention to the performance of special periods such as holidays and promotions, which may significantly change the baseline.

For example: The QPS of an e-commerce system is usually around 500, but it may soar to 3,000 during the big promotion period. At this time, you cannot use the usual standards to measure whether it is abnormal. You have to set up a temporary baseline for the peak period alone.

3. Establish a comparable benchmark model

With historical data, the next step is to extract "typical" performance from it as a benchmark. This usually includes information such as average, peak, fluctuation range, etc.

Some practical practices:

• Compare data for the same time period, such as 10 am every day to compare the same moment of the previous day.
• Percentiles (such as P95) are used to evaluate performance in most cases.
• Define an "exception threshold", for example, if an indicator exceeds 2 times the historical average, the alarm will be triggered.

For example, if the number of slow queries in daily life is less than 10 per day, and suddenly 100 per day will reach 100, then you should pay attention.

4. Dynamic update baseline

The database environment is not static. With the growth of data volume, SQL changes, hardware upgrades and other factors, the original baseline may no longer be applicable.

therefore:

• Reassess the baseline every quarter or after each major change.
• If the system changes structurally (such as changing from a stand-alone machine to a master-slave architecture), data needs to be re-collected.
• Automation tools can help you automatically learn new behavior patterns, such as the Prometheus ML plug-in to make trend predictions.

Basically that's it. Establishing a MySQL performance baseline is not complicated, but details are easily overlooked, especially when choosing data acquisition frequency and historical cycles. As long as you persist for a while, you can establish a performance reference system that suits you.

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