Usage of Astronomer Enterprise Edition (EE) requires a subscription.


Note: This feature will be available in the Astronomer EE 0.3 release (July 2018).


This document covers the current scope of monitoring features provided by Apache Airflow and the Astronomer platform.

The monitoring system has three primary concerns:

  1. metrics gathering
  2. metrics storage
  3. visualization


  • StatsD
  • StatsD Exporter
  • Prometheus
  • Grafana

Getting Started

You might be wondering how monitoring compares in vanilla Airflow vs in the Astronomer platform.


Airflow is instrumented with StatsD, which provides a stats aggregation daemon. StatsD is a push-based monitoring framework. While StatsD support is built-in to Airflow, the details are not yet well documented. Specifically, most of the instrumentation in Airflow today is around the task scheduler.

The Airflow StatsD integration works by sending data to StatsD which is picked up by the StatsD Exporter. The statsd PyPI package provides the Python client used in Airflow to gather metrics and push them to the exporter.

The StatsD Exporter (statsd_exporter) bridges the gap between StatsD and Prometheus by translating StatsD metrics into Prometheus metrics via configured mapping rules.


Astronomer’s monitoring picks up on the other side where Prometheus pulls the data from the StatsD Exporter, which is then made available for visualization in Grafana.

The Astronomer Monitoring stack provides Helm charts for Prometheus and Grafana as well as custom dashboards for Airflow. We provide Prometheus and Grafana as top-level instances independent of Airflow deployments.

Prometheus provides a pull-based monitoring framework for apps, services, hosts, etc.

Grafana provides support for dashboards and visualizations built on top of Prometheus data.


As a user, you have full access to Prometheus/Grafana and can customize it to suit your needs. You can, for example, create new dashboards, as well as add new instrumentation metrics to Airflow itself.

Known Bugs

Note: There is currently a known bug in Airflow where the number of DAGs reported to Airflow by scheduler subprocesses can be inaccurate - AIRFLOW-774.