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database-observability

Grafana Cloud Database Observability — query-level performance insights for MySQL and PostgreSQL.

Skill by Kilo-Org

IndexedApache-2.0★ 195
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About

Grafana Cloud Database Observability — query-level performance insights for MySQL and PostgreSQL. Covers setup with Grafana Alloy, query samples, visual explain plans, RED metrics, pg_stat_statements and Performance Schema integration, and correlation with application traces. Use when monitoring database performance, diagnosing slow queries, setting up database observability for MySQL or PostgreSQL (self-managed, RDS, Aurora, Azure, Cloud SQL), or correlating DB metrics with APM data.

Use cases: backend, cloud, data

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What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.

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  • codeload.github.com · found in install command

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  • ~/.claude/skills/database-observability

What's inside

Skill name
database-observability
Description
Grafana Cloud Database Observability — query-level performance insights for MySQL and PostgreSQL. Covers setup with Grafana Alloy, query samples, visual explain plans, RED metrics, pg_stat_statements and Performance Schema integration, and correlation with application traces. Use when monitoring database performance, diagnosing slow queries, setting up database observability for MySQL or PostgreSQL (self-managed, RDS, Aurora, Azure, Cloud SQL), or correlating DB metrics with APM data.
License
Apache-2.0
Metadata
category: observability
Instructions
163 lines in SKILL.md · view on GitHub

Files (2)

  • LICENSE
  • SKILL.md

SKILL.md (first lines)

# Grafana Cloud Database Observability

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

Provides query-level insights (RED metrics, query samples, explain plans) for MySQL and PostgreSQL
without application code changes. Generally Available as of April 2026.

## Supported Databases

| Database | Variants |
|----------|---------|
| **MySQL** | Self-managed, RDS MySQL, Aurora MySQL, Cloud SQL MySQL, Azure Database for MySQL |
| **PostgreSQL** | Self-managed, RDS PostgreSQL, Aurora PostgreSQL, Cloud SQL PostgreSQL, Azure Database for PostgreSQL |

## Prerequisites

### PostgreSQL

```sql
-- 1. Enable pg_stat_statements in postgresql.conf
-- shared_preload_libraries = 'pg_stat_statements'
-- Then restart PostgreSQL

-- 2. Create 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
CREATE EXTENSION IF NOT EXISTS pg_stat_statements;
```

### MySQL

```sql
-- Create monitoring user with least-privilege permissions
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;
```

## Alloy Configuration

### PostgreSQL

```alloy
database_observability.postgres "mydb" {
  data_source_name = "postgresql://grafana_monitoring:secret@localhost:5432/mydb?sslmode=disable"

  enable_collectors = ["pg_stat_statements", "query_samples", "schema_details"]

  forward_metrics_to = [prometheus.remote_write.cloud.receiver]
  forward_logs_to    = [loki.write.cloud.receiver]
}

prometheus.remote_write "cloud" {
  endpoint {
    url = sys.env("PROMETHEUS_URL")
    basic_auth {
      username = sys.env("PROMETHEUS_USER")
      password = sys.env("GRAFANA_CLOUD_API_KEY")
    }
  }
}

loki.write "cloud" {
  endpoint {
    url = sys.env("LOKI_URL")
    basic_auth {
      username = sys.env("LOKI_USER")
      password = sys.env("GRAFANA_CLOUD_API_KEY")
    }
  }
}
```

### MySQL

```alloy
database_observability.mysql "mydb" {
  data_source_name = "grafana_monitoring:secret@tcp(localhost:3306)/mydb"

  enable_collectors = ["query_samples", "explain_plans", "schema_details"]

  forward_metrics_to = [prometheus.remote_write.cloud.receiver]
  forward_logs_to    = [loki.write.cloud.receiver]
}
```

## Key Metrics

```promql
# Query rate by database
rate(db_query_total{db_instance="mydb"}[5m])

# P95 query latency
histogram_quantile(0.95, rate(db_query_duration_seconds_bucket[5m]))

# Error rate
rate(db_query_errors_total[5m]) / rate(db_query_total[5m])

# Slow queries (over 1 second)
count(db_query_duration_seconds > 1) by (db_query_digest)

# Active connections
db_connections_active{db_instance="mydb"}
```

## What You Get

**Query Performance Dashboard** (auto-provisioned):
- Top queries by total time, call count, mean latency
- Query samples with actual parameters and timing
- Visual explain plans showing index usage, scan types, costs
- RED metrics per query digest

**Correlation with APM:**
- Link slow DB queries to the application traces that triggered them

From Kilo-Org/kilo-marketplace, Apache-2.0.

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    Stars
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    Forks
    328
    Open issues
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    Last commit (this folder)
    Jun 24, 2026 (4 months ago)
    Last release
    Sep 28, 2026 (29 days ago)

    From GitHub (fetched Oct 24, 2026).

    Details

    License
    Apache-2.0
    First seen
    Oct 24, 2026
    Last checked
    Oct 24, 2026

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    Cite this page

    database-observability skill. Agent Mart. https://agentmart.dev/skills/kilo-org-database-observability (last updated 2026-10-24)