Airflow

Agent Check

Overview

The Datadog Agent collects many metrics from Airflow, including those for:

• DAGs (Directed Acyclic Graphs): Number of DAG processes, DAG bag size, etc.
• Tasks: Task failures, successes, killed, etc.
• Pools: Open slots, used slots, etc.
• Executors: Open slots, queued tasks, running tasks, etc.

Metrics are collected through the Airflow StatsD plugin and sent to Datadog’s DogStatsD.

In addition to metrics, the Datadog Agent also sends service checks related to Airflow’s health.

Setup

Installation

All three steps below are needed for the Airflow integration to work properly. Before you begin, install the Datadog Agent version >=6.17 or >=7.17, which includes the StatsD/DogStatsD mapping feature.

Step 1: Configure Airflow to collect health metrics and service checks

Configure the Airflow check included in the Datadog Agent package to collect health metrics and service checks.

Edit the airflow.d/conf.yaml file, in the conf.d/ folder at the root of your Agent’s configuration directory to start collecting your Airflow service checks. See the sample airflow.d/conf.yaml for all available configuration options.

Step 2: Connect Airflow to DogStatsD (included in the Datadog Agent) by using Airflow statsd feature to collect metrics

1. Install the Airflow StatsD plugin.

   pip install 'apache-airflow[statsd]'

2. Update the Airflow configuration file airflow.cfg by adding the following configs:

   [scheduler]
   statsd_on = True
   statsd_host = localhost
   statsd_port = 8125
   statsd_prefix = airflow

3. Update the Datadog Agent main configuration file datadog.yaml by adding the following configs:

   # dogstatsd_mapper_cache_size: 1000  # default to 1000
   dogstatsd_mapper_profiles:
    - name: airflow
      prefix: "airflow."
      mappings:
        - match: "airflow.*_start"
          name: "airflow.job.start"
          tags:
            job_name: "$1"
        - match: "airflow.*_end"
          name: "airflow.job.end"
          tags:
            job_name: "$1"
        - match: "airflow.operator_failures_*"
          name: "airflow.operator_failures"
          tags:
            operator_name: "$1"
        - match: "airflow.operator_successes_*"
          name: "airflow.operator_successes"
          tags:
            operator_name: "$1"
        - match: 'airflow\.dag_processing\.last_runtime\.(.*)'
          match_type: "regex"
          name: "airflow.dag_processing.last_runtime"
          tags:
            dag_file: "$1"
        - match: 'airflow\.dag_processing\.last_run\.seconds_ago\.(.*)'
          match_type: "regex"
          name: "airflow.dag_processing.last_run.seconds_ago"
          tags:
            dag_file: "$1"
        - match: 'airflow\.dag\.loading-duration\.(.*)'
          match_type: "regex"
          name: "airflow.dag.loading_duration"
          tags:
            dag_file: "$1"
        - match: "airflow.pool.open_slots.*"
          name: "airflow.pool.open_slots"
          tags:
            pool_name: "$1"
        - match: "airflow.pool.used_slots.*"
          name: "airflow.pool.used_slots"
          tags:
            pool_name: "$1"
        - match: "airflow.pool.starving_tasks.*"
          name: "airflow.pool.starving_tasks"
          tags:
            pool_name: "$1"
        - match: 'airflow\.dagrun\.dependency-check\.(.*)'
          match_type: "regex"
          name: "airflow.dagrun.dependency_check"
          tags:
            dag_id: "$1"
        - match: 'airflow\.dag\.(.*)\.([^.]*)\.duration'
          match_type: "regex"
          name: "airflow.dag.task.duration"
          tags:
            dag_id: "$1"
            task_id: "$2"
        - match: 'airflow\.dag_processing\.last_duration\.(.*)'
          match_type: "regex"
          name: "airflow.dag_processing.last_duration"
          tags:
            dag_file: "$1"
        - match: 'airflow\.dagrun\.duration\.success\.(.*)'
          match_type: "regex"
          name: "airflow.dagrun.duration.success"
          tags:
            dag_id: "$1"
        - match: 'airflow\.dagrun\.duration\.failed\.(.*)'
          match_type: "regex"
          name: "airflow.dagrun.duration.failed"
          tags:
            dag_id: "$1"
        - match: 'airflow\.dagrun\.schedule_delay\.(.*)'
          match_type: "regex"
          name: "airflow.dagrun.schedule_delay"
          tags:
            dag_id: "$1"
        - match: 'airflow\.task_removed_from_dag\.(.*)'
          match_type: "regex"
          name: "airflow.dag.task_removed"
          tags:
            dag_id: "$1"
        - match: 'airflow\.task_restored_to_dag\.(.*)'
          match_type: "regex"
          name: "airflow.dag.task_restored"
          tags:
            dag_id: "$1"
        - match: "airflow.task_instance_created-*"
          name: "airflow.task.instance_created"
          tags:
            task_class: "$1"

Step 3: Restart Datadog Agent and Airflow

1. Restart the Agent.
2. Restart Airflow to start sending your Airflow metrics to the Agent DogStatsD endpoint.

Integration Service Checks

Use the default configuration of your airflow.d/conf.yaml file to activate the collection of your Airflow service checks. See the sample airflow.d/conf.yaml for all available configuration options.

Log collection

Available for Agent versions >6.0

1. Collecting logs is disabled by default in the Datadog Agent. Enable it in your datadog.yaml file:

   logs_enabled: true

2. Uncomment and edit this configuration block at the bottom of your airflow.d/conf.yaml:

Change the path and service parameter values and configure them for your environment.

a. Configuration for DAG processor manager and Scheduler logs:

 ```yaml
 logs:
   - type: file
     path: '<PATH_TO_AIRFLOW>/logs/dag_processor_manager/dag_processor_manager.log'
     source: airflow
     service: '<SERVICE_NAME>'
     log_processing_rules:
       - type: multi_line
         name: new_log_start_with_date
         pattern: \[\d{4}\-\d{2}\-\d{2}
   - type: file
     path: '<PATH_TO_AIRFLOW>/logs/scheduler/*/*.log'
     source: airflow
     service: '<SERVICE_NAME>'
     log_processing_rules:
       - type: multi_line
         name: new_log_start_with_date
         pattern: \[\d{4}\-\d{2}\-\d{2}
 ```

 Regular clean up is recommended for scheduler logs with daily log rotation.

b. Additional configuration for DAG tasks logs:

 ```yaml
 logs:
   - type: file
     path: '<PATH_TO_AIRFLOW>/logs/*/*/*/*.log'
     source: airflow
     service: '<SERVICE_NAME>'
     log_processing_rules:
       - type: multi_line
         name: new_log_start_with_date
         pattern: \[\d{4}\-\d{2}\-\d{2}
 ```

 Caveat: By default Airflow uses this log file template for tasks: `log_filename_template = {{ ti.dag_id }}/{{ ti.task_id }}/{{ ts }}/{{   try_number }}.log`. The number of log files will grow quickly if not cleaned regularly. This pattern is used by Airflow UI to display logs individually for each executed task.

 If you do not view logs in Airflow UI, Datadog recommends this configuration in `airflow.cfg`: `log_filename_template = dag_tasks.log`. Then log rotate this file and use this configuration:

 ```yaml
 logs:
   - type: file
     path: '<PATH_TO_AIRFLOW>/logs/dag_tasks.log'
     source: airflow
     service: '<SERVICE_NAME>'
     log_processing_rules:
       - type: multi_line
         name: new_log_start_with_date
         pattern: \[\d{4}\-\d{2}\-\d{2}
 ```

1. Restart the Agent.

Validation

Run the Agent’s status subcommand and look for airflow under the Checks section.

Data Collected

Metrics

Service Checks

airflow.can_connect:

Returns CRITICAL if unable to connect to Airflow. Returns OK otherwise.

airflow.healthy:

Returns CRITICAL if Airflow is not healthy. Returns OK otherwise.

Events

The Airflow check does not include any events.

Annexe

Airflow DatadogHook

In addition, Airflow DatadogHook can be used to interact with Datadog:

• Send Metric
• Query Metric
• Post Event

Troubleshooting

Need help? Contact Datadog support.