Apache Airflow OpenTelemetry Assets


Version	0.1.0 (View all)
Subscription level What's this?	Basic
Developed by What's this?	Elastic
Minimum Kibana version(s)	9.3.0

The Airflow OpenTelemetry integration v0.1.0 is in beta

To use beta integrations, go to the Integrations page in Kibana, scroll down, and toggle on the Display beta integrations option.

Apache Airflow is a platform for programmatically authoring, scheduling, and monitoring workflows expressed as Directed Acyclic Graphs (DAGs). These assets provide prebuilt dashboards, alert rules, and SLO templates for Airflow metrics collected via the OpenTelemetry StatsD receiver, covering scheduler health, task execution, DAG processing, and capacity utilization.

Compatibility

The Apache Airflow OpenTelemetry assets have been tested with OpenTelemetry Collector Contrib StatsD receiver v0.146.x.

Apache Airflow tested against:

Apache Airflow 2.6, 2.7, 2.8, 3.0

Requirements

You need Elasticsearch for storing and searching your data and Kibana for visualizing and managing it. You can use our hosted Elasticsearch Service on Elastic Cloud, which is recommended, or self-manage the Elastic Stack on your own hardware.

Setup

Prerequisites

Enable StatsD metrics in Airflow and point them at the OpenTelemetry Collector. In your airflow.cfg or via environment variables:

		[metrics]
statsd_on = True
statsd_host = <COLLECTOR_HOST>
statsd_port = 8125
statsd_prefix = airflow
		
	

Set statsd_host to the hostname or IP of the machine running the collector where the StatsD receiver listens (default port 8125). For tagged metrics (recommended for dashboards and alerts), enable Datadog or InfluxDB-style tags in Airflow:

		[metrics]
statsd_datadog_enabled = True
# or
statsd_influxdb_enabled = True
		
	

Install the StatsD extra if needed: pip install 'apache-airflow[statsd]'.

Configuration

Configure the OpenTelemetry Collector or Elastic Distribution of OpenTelemetry Collector (EDOT) to receive StatsD metrics from Airflow and export them to Elasticsearch. The example below shows a minimal pipeline.

Placeholder table:

Placeholder	Description	Example
`<ES_ENDPOINT>`	Elasticsearch URL	`https://my-deployment.es.us-central1.gcp.cloud.es.io:443`
`${env:ELASTICSEARCH_API_KEY}`	Elasticsearch API key for authentication (from environment)	Set `ELASTICSEARCH_API_KEY` in your environment

		receivers:
  statsd:
    endpoint: 0.0.0.0:8125
    transport: udp

processors:
  resource/dataset:
    attributes:
      - key: data_stream.dataset
        value: airflow
        action: upsert
  batch:
    timeout: 10s
    send_batch_size: 1024

exporters:
  elasticsearch/otel:
    endpoints: [<ES_ENDPOINT>]
    api_key: ${env:ELASTICSEARCH_API_KEY}
    mapping:
      mode: otel
    metrics_dynamic_index:
      enabled: true

service:
  pipelines:
    metrics:
      receivers: [statsd]
      processors: [resource/dataset, batch]
      exporters: [elasticsearch/otel]
		
	

Dashboard	Description
[Airflow OTel] Overview	Scheduler health, task outcomes, executor capacity, and DAG processing at a glance.
[Airflow OTel] Tasks & Errors	Task throughput, failure rate, operator breakdown, and duration metrics.
[Airflow OTel] Scheduler & Capacity	Scheduler loop duration, critical section contention, executor and pool utilization.
[Airflow OTel] DAG Processing	DAG file import errors, parse times, queue size, and processor timeouts.

Alert rules

Alert	Trigger	Severity
[Airflow OTel] Scheduler heartbeats critically low	No scheduler heartbeats received in the evaluation window.	Critical
[Airflow OTel] Executor open slots exhausted	Executor open slots are zero on at least one host.	Critical
[Airflow OTel] Scheduler job heartbeat failures	SchedulerJob heartbeat failure count is non-zero.	High
[Airflow OTel] Task failures above threshold	One or more task instance failures observed.	High
[Airflow OTel] Tasks killed externally	Tasks killed by external processes (OOM, eviction).	High
[Airflow OTel] DAG import errors present	DAG import errors are non-zero.	High
[Airflow OTel] DAG processor timeouts	DAG processor timeouts detected.	High
[Airflow OTel] DAG run schedule delay high	DAG run schedule delay exceeds 5 minutes.	Medium
[Airflow OTel] Scheduler loop duration excessive	Scheduler loop duration exceeds 60 seconds.	Medium
[Airflow OTel] Tasks starving due to pool limits	Starving tasks (blocked by pool limits) exceed threshold.	Medium
[Airflow OTel] DAG file processing queue large	DAG file processing queue size exceeds 50.	Medium

SLO templates

Note

SLO templates require Elastic Stack version 9.4.0 or later.

SLO	Target	Window	Description
[Airflow OTel] Task success rate 99.5% rolling 30 days	99.5%	30-day rolling	Proportion of task instances that complete successfully vs failures.
[Airflow OTel] DAG import errors zero 99.5% rolling 30 days	99.5%	30-day rolling	DAG files parse without import errors.
[Airflow OTel] Scheduler loop duration 99.5% rolling 30 days	99.5%	30-day rolling	Scheduler loop completes within acceptable duration.

Screenshots

This integration includes one or more Kibana dashboards that visualizes the data collected by the integration. The screenshots below illustrate how the ingested data is displayed.