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Airflow Integration

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Airflow Integration

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Version

0.10.0 [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
9.0.0 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)

0.10.0

Enhancement (View pull request)
Add support for Kibana 9.0.0.

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0.9.1

Bug fix (View pull request)
Update links to getting started docs

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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.

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0.8.0

Enhancement (View pull request)
Add global filter on data_stream.dataset to improve performance.

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0.7.0

Enhancement (View pull request)
Update documentation.

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0.6.0

Enhancement (View pull request)
Update to Kibana 8.11 to support enhanced statsd implementation, and add system test cases.

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0.5.1

Bug fix (View pull request)
Add dimension field for container.id which was previously missed during package-spec v3 migration

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0.5.0

Enhancement (View pull request)
Update the package format_version to 3.0.0.

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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.

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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.

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0.3.0

Enhancement (View pull request)
Revert metrics field definition to the format used before introducing metric_type.

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0.2.0

Enhancement (View pull request)
Add metric_type mapping for the fields of statsd datastream.

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0.1.0

Enhancement (View pull request)
Rename ownership from obs-service-integrations to obs-infraobs-integrations

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0.0.5

Bug fix (View pull request)
Modifed the dimension field mapping to support public cloud deployment.

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0.0.4

Enhancement (View pull request)
Added dimensions fields to enable TSDB.

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0.0.3

Enhancement (View pull request)
Added categories and/or subcategories.

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0.0.2

Enhancement (View pull request)
add dashboards

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0.0.1

Enhancement (View pull request)
initial release

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