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Query Exporter

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What is Query Exporter?

Query Exporter is a Prometheus exporter that allows you to collect metrics from database queries at specified time intervals. It uses SQLAlchemy to connect to different database engines, enabling you to monitor any database that supports SQL queries. With Query Exporter, you can:
  • Run SQL queries on multiple databases simultaneously
  • Export query results as Prometheus metrics (gauge, counter, summary, histogram, enum)
  • Schedule queries at specific intervals or using cron expressions
  • Track query execution with built-in monitoring metrics
  • Apply custom labels to databases and metrics for better organization

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Key features

Multi-database support

Connect to PostgreSQL, MySQL, Oracle, SQL Server, SQLite, and more through SQLAlchemy

Flexible scheduling

Run queries at fixed intervals or using cron-style schedules

Multiple metric types

Support for gauge, counter, summary, histogram, and enum metric types

Built-in monitoring

Track query latency, execution counts, errors, and timestamps automatically

Label support

Add custom labels to databases and derive labels from query results

Easy deployment

Available via pip, Docker, and Snap with minimal configuration required

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How it works

Query Exporter runs as a lightweight web service that:
  1. Loads your configuration file defining databases, metrics, and queries
  2. Connects to your databases using SQLAlchemy
  3. Executes queries on schedule (interval-based or cron-based)
  4. Exports results as Prometheus metrics on the /metrics endpoint (default port 9560)
  5. Provides built-in metrics for query health and performance

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Who should use Query Exporter?

Monitor database performance, connection counts, query statistics, and other operational metrics without custom exporters.
Export application-specific metrics stored in databases to Prometheus for unified monitoring and alerting.
Track business metrics, SLA compliance, and application health through database queries.
Monitor data pipeline health, row counts, data freshness, and processing metrics.

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Example use cases

  • Database monitoring: Export connection counts, table sizes, replication lag, lock statistics
  • Business metrics: Track user signups, order counts, revenue figures from application databases
  • Data quality: Monitor null counts, duplicate records, data freshness timestamps
  • Application health: Export error counts, processing queues, job statuses from application tables
  • Custom metrics: Any metric that can be queried with SQL

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Quick example

Here’s a minimal configuration that queries a SQLite database: 
databases:
  mydb:
    dsn: sqlite:///data.db

metrics:
  user_count:
    type: gauge
    description: Total number of users

queries:
  count_users:
    interval: 60
    databases: [mydb]
    metrics: [user_count]
    sql: SELECT COUNT(*) AS user_count FROM users
This configuration:
  • Connects to a SQLite database at data.db
  • Defines a gauge metric called user_count
  • Runs a query every 60 seconds to count users
  • Exports the result as user_count metric at /metrics

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Next steps

Get started

Follow our quickstart guide to run your first query

Installation

Install Query Exporter using pip, Docker, or Snap

Configuration

Learn about all configuration options

GitHub

View source code and contribute
Query Exporter is open source and available under the GPL-3.0 license. It’s actively maintained and used in production by many organizations.

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