How Do We Use PMM For DB Cluster Monitoring – Real-Life Use Case
Valery Panov··Updated We would like to share our experience of using PMM for DB cluster monitoring. This is a great tool with useful dashboards that help us improve the availability and performance of our infrastructure.
We perform weekly reviews of our main cluster using these dashboards:
- PMM Query analytics
The main concept on this screen is ‘Load’ which is the average query execution time multiplied by queries per sec, essentially this metric indicates how much MySQL resources are consumed. E.g. query that was running for 5 minutes but just a single run won’t make it to the top.
Usually, we look at the top 10 queries.
You can click on each query digest and see more details about it.
It helped us several times to detect inefficient queries and things when a query was executed a lot more often than it was anticipated.
- Nodes overview
Supplementary graph that just displays basic OS metrics – usually we look here to examine moments of the peak load in order to determine the performance bottleneck.
- MySQL instances overview
Same as (2) but holds MySQL-specific metrics.
- PMM Advisor checks
This may be rather handy to be aware of vulnerabilities in your version, configuration flaws etc. Warnings in the screenshot above are about minor Percona Server version (”Current version is 8.0.22, the latest available version is 8.0.29.”).
- Our last check is PMM Query errors, but the representation there was inconvenient for us so we’ve replaced it with our own implementation which collects MySQL query errors each 2 seconds and injects them into Clickhouse for further inspection. The results look this way
In the screenshot above we can see a lot of lock insert collisions (which is expected) and 15 DELETE deadlocks that are yet to be investigated.
Need help setting up database monitoring? Talk to our team.
Frequently Asked Questions
What PMM dashboards should you review weekly for database clusters?
We review five dashboards weekly: Query Analytics (top queries by Load), Nodes Overview (CPU, memory, disk IO), MySQL Instances Overview (database-specific metrics), PMM Advisor Checks (configuration flaws and vulnerabilities), and Query Errors. This covers performance bottlenecks, resource constraints, configuration drift, and emerging issues before they become outages.
What is the Load metric in PMM Query Analytics?
Load represents Average Active Queries — query execution time multiplied by queries per second. It surfaces the queries consuming the most database resources overall, not just the slowest individual queries. A fast query running millions of times can have higher Load than a slow query running once. We use Load as the primary ranking metric when reviewing the top 10 queries weekly.
How do you detect inefficient queries using PMM?
Sort Query Analytics by Load to find resource-heavy queries. Click each query digest to see execution time, rows examined vs. rows sent, and lock time. High rows-examined-to-rows-sent ratios indicate missing indexes.
How do you monitor MySQL query errors with PMM?
PMM's built-in QAN can filter by query errors, but the default view may not suit all workflows. We replaced that view with a custom collector that captures MySQL query errors every 2 seconds and stores them in ClickHouse for deeper analysis. This lets us spot patterns like deadlocks and lock collisions that the standard PMM dashboard might aggregate away.
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