Metrics

Airflow can be set up to send metrics to StatsD.

Setup

First you must install statsd requirement:

pip install 'apache-airflow[statsd]'

Add the following lines to your configuration file e.g. airflow.cfg

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

If you want to avoid sending all the available metrics to StatsD, you can configure an allow list of prefixes to send only the metrics that start with the elements of the list:

[metrics]
statsd_allow_list = scheduler,executor,dagrun

If you want to redirect metrics to different name, you can configure stat_name_handler option in [scheduler] section. It should point to a function that validates the statsd stat name, applies changes to the stat name if necessary, and returns the transformed stat name. The function may looks as follow:

def my_custom_stat_name_handler(stat_name: str) -> str:
    return stat_name.lower()[:32]

If you want to use a custom Statsd client instead of the default one provided by Airflow, the following key must be added to the configuration file alongside the module path of your custom Statsd client. This module must be available on your PYTHONPATH.

[metrics]
statsd_custom_client_path = x.y.customclient

See Modules Management for details on how Python and Airflow manage modules.

Note

For a detailed listing of configuration options regarding metrics, see the configuration reference documentation - [metrics].

Counters

Name

Description

<job_name>_start

Number of started <job_name> job, ex. SchedulerJob, LocalTaskJob

<job_name>_end

Number of ended <job_name> job, ex. SchedulerJob, LocalTaskJob

<job_name>_heartbeat_failure

Number of failed Heartbeats for a <job_name> job, ex. SchedulerJob, LocalTaskJob

operator_failures_<operator_name>

Operator <operator_name> failures

operator_successes_<operator_name>

Operator <operator_name> successes

ti_failures

Overall task instances failures

ti_successes

Overall task instances successes

previously_succeeded

Number of previously succeeded task instances

zombies_killed

Zombie tasks killed

scheduler_heartbeat

Scheduler heartbeats

dag_processing.processes

Number of currently running DAG parsing processes

dag_processing.manager_stalls

Number of stalled DagFileProcessorManager

dag_file_refresh_error

Number of failures loading any DAG files

scheduler.tasks.killed_externally

Number of tasks killed externally

scheduler.orphaned_tasks.cleared

Number of Orphaned tasks cleared by the Scheduler

scheduler.orphaned_tasks.adopted

Number of Orphaned tasks adopted by the Scheduler

scheduler.critical_section_busy

Count of times a scheduler process tried to get a lock on the critical section (needed to send tasks to the executor) and found it locked by another process.

sla_email_notification_failure

Number of failed SLA miss email notification attempts

ti.start.<dagid>.<taskid>

Number of started task in a given dag. Similar to <job_name>_start but for task

ti.finish.<dagid>.<taskid>.<state>

Number of completed task in a given dag. Similar to <job_name>_end but for task

dag.callback_exceptions

Number of exceptions raised from DAG callbacks. When this happens, it means DAG callback is not working.

celery.task_timeout_error

Number of AirflowTaskTimeout errors raised when publishing Task to Celery Broker.

task_removed_from_dag.<dagid>

Number of tasks removed for a given dag (i.e. task no longer exists in DAG)

task_restored_to_dag.<dagid>

Number of tasks restored for a given dag (i.e. task instance which was previously in REMOVED state in the DB is added to DAG file)

task_instance_created-<operator_name>

Number of tasks instances created for a given Operator

Gauges

Name

Description

dagbag_size

DAG bag size

dag_processing.import_errors

Number of errors from trying to parse DAG files

dag_processing.total_parse_time

Seconds taken to scan and import all DAG files once

dag_processing.last_runtime.<dag_file>

Seconds spent processing <dag_file> (in most recent iteration)

dag_processing.last_run.seconds_ago.<dag_file>

Seconds since <dag_file> was last processed

dag_processing.processor_timeouts

Number of file processors that have been killed due to taking too long

scheduler.tasks.without_dagrun

Number of tasks without DagRuns or with DagRuns not in Running state

scheduler.tasks.running

Number of tasks running in executor

scheduler.tasks.starving

Number of tasks that cannot be scheduled because of no open slot in pool

scheduler.tasks.executable

Number of tasks that are ready for execution (set to queued) with respect to pool limits, dag concurrency, executor state, and priority.

executor.open_slots

Number of open slots on executor

executor.queued_tasks

Number of queued tasks on executor

executor.running_tasks

Number of running tasks on executor

pool.open_slots.<pool_name>

Number of open slots in the pool

pool.queued_slots.<pool_name>

Number of queued slots in the pool

pool.running_slots.<pool_name>

Number of running slots in the pool

pool.starving_tasks.<pool_name>

Number of starving tasks in the pool

smart_sensor_operator.poked_tasks

Number of tasks poked by the smart sensor in the previous poking loop

smart_sensor_operator.poked_success

Number of newly succeeded tasks poked by the smart sensor in the previous poking loop

smart_sensor_operator.poked_exception

Number of exceptions in the previous smart sensor poking loop

smart_sensor_operator.exception_failures

Number of failures caused by exception in the previous smart sensor poking loop

smart_sensor_operator.infra_failures

Number of infrastructure failures in the previous smart sensor poking loop

Timers

Name

Description

dagrun.dependency-check.<dag_id>

Milliseconds taken to check DAG dependencies

dag.<dag_id>.<task_id>.duration

Milliseconds taken to finish a task

dag_processing.last_duration.<dag_file>

Milliseconds taken to load the given DAG file

dagrun.duration.success.<dag_id>

Milliseconds taken for a DagRun to reach success state

dagrun.duration.failed.<dag_id>

Milliseconds taken for a DagRun to reach failed state

dagrun.schedule_delay.<dag_id>

Milliseconds of delay between the scheduled DagRun start date and the actual DagRun start date

scheduler.critical_section_duration

Milliseconds spent in the critical section of scheduler loop – only a single scheduler can enter this loop at a time

dagrun.<dag_id>.first_task_scheduling_delay

Milliseconds elapsed between first task start_date and dagrun expected start

collect_db_dags

Milliseconds taken for fetching all Serialized Dags from DB

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