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A new application version often causes many changes to the database, which involves adding, changing or dropping database objects. It’s essential to monitor such changes, since these new or modified objects can cause performance issues.
Throughput metrics, such as replication latency, are crucial for creating comparative baselines you can use to quickly identify anomalies. By closely following these metrics, you can identify their normal value. Afterward, you’ll be able to compare this baseline value with current metrics, allowing you to identify suspicious deviations. To determine this baseline, you can’t take the value of a single or few readings. With so few samples, you wouldn’t have a way to know for sure they’re not outliers. Instead, take multiple readings over an extended time (about four weeks.).
This practice is two-fold. It consists of verifying the database is online and checking the consumption of resources. How is CPU usage? How is memory usage? Is the disk close to being full? If any of these metrics are looking bad, your database monitoring tools should alert the person responsible for that area.
Collecting and monitoring database logs is essential if you want to practice proactive monitoring. These logs contain critical information you don’t typically find in general performance metrics, such as the time it took for each query in the database to run.