About Query Analytics (QAN)

The Query Analytics dashboard shows how queries are executed and where they spend their time. It helps you analyze database queries over time, optimize database performance, and find and remedy the source of problems.

Query Analytics supports MySQL, MongoDB and PostgreSQL with the following minimum requirements:

MySQL requirementsPostgreSQL requirementsMongoDB requirements

• MySQL 5.1 or later (if using the slow query log)
• MySQL 5.6.9 or later (if using Performance Schema)
• Percona Server 5.6+ (all Performance Schema and slow log features)
• MariaDB 5.2+ (for user statistics), 10.0+ (for Performance Schema)

Some limitations and tuning options apply only when using MySQL’s Performance Schema.
See Query Analytics with MySQL.

• PostgreSQL 11 or later
• pg_stat_monitor extension (recommended) or pg_stat_statements extension
• Appropriate shared_preload_libraries configuration
• Superuser privileges for PMM monitoring account

• MongoDB 6.0 or later (4.4+ may work with limited features)

Requirements for Profiler

• Profiling enabled for Query Analytics
• Appropriate user roles: clusterMonitor, read (local), and custom monitoring roles. For MongoDB 8.0+: Additional directShardOperations role required for sharded clusters

Requirements for Mongolog

• MongoDB configured to log slow operations to a file
• MongoDB server has write permissions to the log directory and file
• PMM agent has read permissions to the MongoDB log file
• Appropriate user roles: clusterMonitor, or custom monitoring roles (getCmdLineOpts privilege on { cluster: true })

Dashboard components

Query Analytics displays metrics in both visual and numeric form. Performance-related characteristics appear as plotted graphics with summaries.

Dashboard layout

The dashboard contains three panels:

• the Filters panel
• the Overview panel
• the Details panel

Data retrieval delays

Query Analytics data retrieval is not instantaneous because metrics are collected once per minute. When collection delays occur, no data is reported and gaps will appear in the sparkline.

Label-based access control

Query Analytics integrates with PMM’s label-based access control (LBAC) to enforce data security and user permissions.

When LBAC is enabled:

• users see only queries from databases and services permitted by their assigned roles
• filter dropdown options are dynamically restricted based on user permissions
• data visibility is controlled through Prometheus-style label selectors

QAN for PMM Server’s internal PostgreSQL

By default, Query Analytics (QAN) hides queries from PMM Server’s internal PostgreSQL database. This avoids clutter and keeps the focus on your monitored databases.

Enable QAN for PMM Server when you need to troubleshoot performance issues, check resource usage, or ensure that applications are not using the default postgres database instead of dedicated databases. This is particularly useful in High Availability (HA) deployments where monitoring system health is essential.

Enable QAN for PMM Server

To include PMM Server’s own queries in QAN, enable the feature in the settings:

1. Go to PMM Configuration > Settings > Advanced Settings.
2. Switch on the QAN for PMM Server option.
3. Open PMM Query Analytics (QAN) from the main menu and filter by the pmm-server-postgresql service to view queries.

When enabled, QAN displays queries related to PMM’s internal operations—such as inventory, settings, advisor checks, alerts, backups, and authentication.

These are usually lightweight, but unusual spikes in volume, latency, or unexpected queries may indicate performance issues or misuse of the database.

Warning

Do not use the default PostgreSQL database for application workloads. PMM monitors it for visibility, but applications should always run on dedicated databases.