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Airflow Integration

Version	0.9.0 [beta] This functionality is in beta and is subject to change. The design and code is less mature than official GA features and is being provided as-is with no warranties. Beta features are not subject to the support SLA of official GA features. (View all)
Compatible Kibana version(s)	8.13.0 or higher
Supported Serverless project types What’s this?	Security Observability
Subscription level What’s this?	Basic
Level of support What’s this?	Elastic

Overview

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Airflow is an open-source platform for programmatically authoring, scheduling, and monitoring workflows. It allows users to define workflows as Directed Acyclic Graphs (DAGs) of tasks, which are then executed by the Airflow scheduler on an array of workers while following the specified dependencies.

Use the Airflow integration to:

Collect detailed metrics from Airflow using StatsD to gain insights into system performance.
Create informative visualizations to track usage trends, measure key metrics, and derive actionable business insights.
Monitor your workflows' performance and status in real-time.

Data streams

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The Airflow integration gathers metric data.

Metrics provide insight into the statistics of Airflow. The Metric data stream collected by the Airflow integration is statsd, enabling users to monitor and troubleshoot the performance of the Airflow instance.

Data stream:

statsd: Collects metrics related to scheduler activities, pool usage, task execution details, executor performance, and worker states in Airflow.

Note:

Users can monitor and view metrics within the ingested documents for Airflow in the metrics-* index pattern from Discover.

Compatibility

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The Airflow module is tested with Airflow 2.4.0. It should work with versions 2.0.0 and later.

Prerequisites

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Users require Elasticsearch to store and search user data, and Kibana to visualize and manage it. They can utilize the hosted Elasticsearch Service on Elastic Cloud, which is recommended, or self-manage the Elastic Stack on their own hardware.

To ingest data from Airflow, users must have StatsD to receive the same.

Setup

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For step-by-step instructions on how to set up an integration, see the Getting started guide.

Steps to Setup Airflow

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Be sure to follow the official Airflow Installation Guide for the correct installation of Airflow.

Include the following lines in the user’s Airflow configuration file (e.g. airflow.cfg). Leave statsd_prefix empty and replace %HOST% with the address where the Agent is running:

[metrics]
statsd_on = True
statsd_host = %HOST%
statsd_port = 8125
statsd_prefix =

Validation

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Once the integration is set up, you can click on the Assets tab in the Airflow integration to see a list of available dashboards. Choose the dashboard that corresponds to your configured data stream. The dashboard should be populated with the required data.

Troubleshooting

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Check if the StatsD server is receiving data from Airflow by examining the logs for potential errors.
Make sure the %HOST% placeholder in the Airflow configuration file is replaced with the correct address of the machine where the StatsD server is running.
If Airflow metrics are not being emitted, confirm that the [metrics] section in the airflow.cfg file is properly configured as per the instructions above.

Metrics reference

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Statsd

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This is the statsd data stream, which collects metrics related to scheduler activities, pool usage, task execution details, executor performance, and worker states in Airflow.

Example

An example event for statsd looks as following:

{
    "@timestamp": "2024-06-18T07:24:40.220Z",
    "agent": {
        "ephemeral_id": "82e52250-5f2d-4fad-9f19-b88a209229db",
        "id": "97400795-188c-4140-a1ee-0002078c785d",
        "name": "docker-fleet-agent",
        "type": "metricbeat",
        "version": "8.13.0"
    },
    "airflow": {
        "scheduler_critical_section_duration": {
            "count": 1,
            "max": 7,
            "mean": 7,
            "mean_rate": 0.25568506211340597,
            "median": 7,
            "min": 7,
            "stddev": 0
        }
    },
    "data_stream": {
        "dataset": "airflow.statsd",
        "namespace": "ep",
        "type": "metrics"
    },
    "ecs": {
        "version": "8.11.0"
    },
    "elastic_agent": {
        "id": "97400795-188c-4140-a1ee-0002078c785d",
        "snapshot": false,
        "version": "8.13.0"
    },
    "event": {
        "agent_id_status": "verified",
        "dataset": "airflow.statsd",
        "ingested": "2024-06-18T07:24:50Z",
        "module": "statsd"
    },
    "host": {
        "architecture": "x86_64",
        "containerized": true,
        "hostname": "docker-fleet-agent",
        "id": "8259e024976a406e8a54cdbffeb84fec",
        "ip": [
            "192.168.245.7"
        ],
        "mac": [
            "02-42-C0-A8-F5-07"
        ],
        "name": "docker-fleet-agent",
        "os": {
            "codename": "focal",
            "family": "debian",
            "kernel": "3.10.0-1160.102.1.el7.x86_64",
            "name": "Ubuntu",
            "platform": "ubuntu",
            "type": "linux",
            "version": "20.04.6 LTS (Focal Fossa)"
        }
    },
    "metricset": {
        "name": "server"
    },
    "service": {
        "type": "statsd"
    }
}

ECS Field Reference

Please refer to the following document for detailed information on ECS fields.

Exported fields

Field	Description	Type	Metric Type
@timestamp	Event timestamp.	date
agent.id		keyword
airflow.*.count	Airflow counters	object	counter
airflow.*.max	Airflow max timers metric	object
airflow.*.mean	Airflow mean timers metric	object
airflow.*.mean_rate	Airflow mean rate timers metric	object
airflow.*.median	Airflow median timers metric	object
airflow.*.min	Airflow min timers metric	object
airflow.*.stddev	Airflow standard deviation timers metric	object
airflow.*.value	Airflow gauges	object	gauge
airflow.dag_file	Airflow dag file metadata	keyword
airflow.dag_id	Airflow dag id metadata	keyword
airflow.job_name	Airflow job name metadata	keyword
airflow.operator_name	Airflow operator name metadata	keyword
airflow.pool_name	Airflow pool name metadata	keyword
airflow.scheduler_heartbeat.count	Airflow scheduler heartbeat	double
airflow.status	Airflow status metadata	keyword
airflow.task_id	Airflow task id metadata	keyword
cloud.account.id	The cloud account or organization id used to identify different entities in a multi-tenant environment. Examples: AWS account id, Google Cloud ORG Id, or other unique identifier.	keyword
cloud.availability_zone	Availability zone in which this host is running.	keyword
cloud.image.id	Image ID for the cloud instance.	keyword
cloud.instance.id	Instance ID of the host machine.	keyword
cloud.provider	Name of the cloud provider. Example values are aws, azure, gcp, or digitalocean.	keyword
cloud.region	Region in which this host is running.	keyword
container.id	Unique container id.	keyword
data_stream.dataset	Data stream dataset.	constant_keyword
data_stream.namespace	Data stream namespace.	constant_keyword
data_stream.type	Data stream type.	constant_keyword
event.dataset	Event dataset	constant_keyword
event.module	Event module	constant_keyword
host.containerized	If the host is a container.	boolean
host.name	Name of the host. It can contain what `hostname` returns on Unix systems, the fully qualified domain name, or a name specified by the user. The sender decides which value to use.	keyword
host.os.build	OS build information.	keyword
host.os.codename	OS codename, if any.	keyword
service.address	Service address	keyword

Changelog

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Changelog

Version	Details	Kibana version(s)
0.9.0	Enhancement (View pull request) ECS version updated to 8.11.0. Update the kibana constraint to ^8.13.0. Modified the field definitions to remove ECS fields made redundant by the ecs@mappings component template.	—
0.8.0	Enhancement (View pull request) Add global filter on data_stream.dataset to improve performance.	—
0.7.0	Enhancement (View pull request) Update documentation.	—
0.6.0	Enhancement (View pull request) Update to Kibana 8.11 to support enhanced statsd implementation, and add system test cases.	—
0.5.1	Bug fix (View pull request) Add dimension field for container.id which was previously missed during package-spec v3 migration	—
0.5.0	Enhancement (View pull request) Update the package format_version to 3.0.0.	—
0.4.0	Enhancement (View pull request) Enable time series data streams for the metrics datasets. This dramatically reduces storage for metrics and is expected to progressively improve query performance. For more details, see https://www.elastic.co/guide/en/elasticsearch/reference/current/tsds.html.	—
0.3.1	Bug fix (View pull request) Remove metric_type mapping for airflow.scheduler.heartbeat field and adjust the dashboard to visualize this field using last_value.	—
0.3.0	Enhancement (View pull request) Revert metrics field definition to the format used before introducing metric_type.	—
0.2.0	Enhancement (View pull request) Add metric_type mapping for the fields of `statsd` datastream.	—
0.1.0	Enhancement (View pull request) Rename ownership from obs-service-integrations to obs-infraobs-integrations	—
0.0.5	Bug fix (View pull request) Modifed the dimension field mapping to support public cloud deployment.	—
0.0.4	Enhancement (View pull request) Added dimensions fields to enable TSDB.	—
0.0.3	Enhancement (View pull request) Added categories and/or subcategories.	—
0.0.2	Enhancement (View pull request) add dashboards	—
0.0.1	Enhancement (View pull request) initial release	—

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