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Database Observability

Grafana LabsOfficialPublished by Grafana Labs

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  • Cloud and DevOps
  • Apache-2.0
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From the author

Set up Grafana Cloud Database Observability for MySQL and PostgreSQL — enables `pg_stat_statements` / Performance Schema, creates a least-privilege monitoring user, configures the `database_observability.postgres` / `database_observability.mysql` Alloy components, ships query samples + visual explain plans + RED metrics + schema details to Grafana Cloud, and correlates slow queries with application traces via `db.statement` / `db.system` OTel attributes. Use when monitoring database performance, diagnosing slow queries, setting up DB observability for RDS / Aurora / Cloud SQL / Azure Database / self-managed instances, correlating DB metrics with APM, or alerting on query latency — even when the user says "my database is slow", "find the slow queries", or "monitor RDS" without saying "observability".
— the description by Grafana Labs

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a comprehensive guide to setting up Grafana Cloud Database Observability for MySQL and PostgreSQL databases. It covers the necessary database-level configurations, such as enabling pg_stat_statements or Performance Schema, creating least-privilege monitoring users, and configuring Alloy components to ship metrics, logs, and query samples. By integrating these components, users can monitor database performance, analyze slow queries with visual explain plans, and correlate database activity with application traces using OpenTelemetry attributes.

What it does

  • Enables database-level performance tracking via pg_stat_statements or Performance Schema.
  • Configures Alloy components to collect and forward query samples, metrics, and schema details.
  • Provides automated dashboards for query performance, including RED metrics and latency analysis.
  • Enables trace correlation between application spans and database queries using OTel attributes.
  • Includes pre-defined alert rules for query latency, error rates, and connection pool saturation.

When to use it

  • When you need to diagnose slow database queries in MySQL or PostgreSQL environments.
  • When setting up monitoring for RDS, Aurora, Cloud SQL, Azure Database, or self-managed instances.
  • When you want to correlate database performance metrics with application-level traces.
  • When you need to implement alerting for database connection saturation or high query error rates.

Advantages

  • Provides deep visibility into query performance without requiring application code changes.
  • Supports a wide range of managed and self-managed database environments.
  • Facilitates rapid troubleshooting by linking application traces directly to slow SQL queries.
  • Includes ready-to-use configurations for metrics, logs, and alerting.

Usage examples

“How do I set up database observability for my PostgreSQL instance?”
The agent provides the steps to enable pg_stat_statements, create a monitoring user, and configure the Alloy component for PostgreSQL.
“My database is slow, how can I find the problematic queries?”
The agent guides you through configuring query samples and explain plans, which populate the Query Performance dashboard to identify high-latency queries.
“How do I link my application traces to database queries?”
The agent explains how to ensure your application emits OTel attributes like db.statement and db.system to enable drill-down from traces to database query samples.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/grafana/database-observability/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip database-observability.zip -d .agents/skills

Documentation by Grafana Labs

Grafana Cloud Database Observability

Docs: https://grafana.com/docs/grafana-cloud/monitor-applications/database-observability/

Query-level insights (RED metrics, query samples, explain plans) for MySQL and PostgreSQL without application code changes. GA since April 2026.

Common Workflows

PostgreSQL setup
-- 1. Enable pg_stat_statements (requires restart)
--    In postgresql.conf:  shared_preload_libraries = 'pg_stat_statements'
--    Then restart PostgreSQL.

-- 2. Create a least-privilege monitoring user
CREATE USER grafana_monitoring WITH PASSWORD 'secret';
GRANT pg_monitor TO grafana_monitoring;
GRANT CONNECT ON DATABASE mydb TO grafana_monitoring;

-- 3. Enable the extension on each monitored DB
CREATE EXTENSION IF NOT EXISTS pg_stat_statements;

-- 4. Verify
SELECT count(*) FROM pg_stat_statements;
-- Should return >= 0 (not an error). If "relation does not exist": step 3 didn't run.
  1. Add the Alloy config block from references/alloy-config.md § PostgreSQL

  2. Restart Alloy and wait ~60s for first scrape

  3. Verify in Grafana Cloud → Database Observability — the instance should appear with non-empty db_query_total{db_instance="mydb"} series. If empty after 2 minutes: check Alloy logs for connection errors, then re-verify pg_stat_statements permissions.

MySQL setup
-- 1. Create least-privilege monitoring user
CREATE USER 'grafana_monitoring'@'%' IDENTIFIED BY 'secret';
GRANT SELECT, PROCESS, REPLICATION CLIENT ON *.* TO 'grafana_monitoring'@'%';
GRANT SELECT ON performance_schema.* TO 'grafana_monitoring'@'%';
FLUSH PRIVILEGES;

-- 2. Verify performance_schema is enabled (default in MySQL 8+)
SELECT @@performance_schema;
-- Returns 1 → enabled. 0 → set performance_schema=ON in my.cnf + restart.
  1. Add the Alloy config block from references/alloy-config.md § MySQL

  2. Restart Alloy, wait ~60s, verify the instance appears in Grafana Cloud → Database Observability

Supported databases

DatabaseVariants
MySQLSelf-managed, RDS MySQL, Aurora MySQL, Cloud SQL MySQL, Azure Database for MySQL
PostgreSQLSelf-managed, RDS PostgreSQL, Aurora PostgreSQL, Cloud SQL PostgreSQL, Azure Database for PostgreSQL

For managed databases (RDS / Cloud SQL / Azure), pg_stat_statements is preinstalled but disabled — enable it via the parameter group / flag, then re-create the extension at the DB level.

What you get

  • Query Performance dashboard (auto-provisioned): top queries by total time / call count / mean latency, sampled query texts with parameters, visual explain plans, RED metrics per query digest

  • APM correlation: drill from a slow service-latency span to the specific slow SQL via db.statement / db.system / db.name OTel attributes — see references/queries-alerts.md § Trace correlation

  • Alerting on query latency, error rate, and connection-pool saturation — full alert YAML in references/queries-alerts.md § Alert rules

References

  • references/alloy-config.md — full Alloy database_observability.postgres / database_observability.mysql config blocks + collector reference (pg_stat_statements, query_samples, schema_details, explain_plans)

  • references/queries-alerts.md — key PromQL queries, alert rule YAML, and trace-correlation setup

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

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Attribution and licence

Published by
Grafana Labs
Publisher
Grafana Labs (Official)
Source
grafana/skills · skills/grafana-cloud/database-observability · commit 8fb72a5
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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