metrics.rst

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 [metrics] 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 - config: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.processor_timeouts	Number of file processors that have been killed due to taking too long
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_missed	Number of SLA misses
sla_callback_notification_failure	Number of failed SLA miss callback notification attempts
sla_email_notification_failure	Number of failed SLA miss email notification attempts
ti.start.<dag_id>.<task_id>	Number of started task in a given dag. Similar to <job_name>_start but for task
ti.finish.<dag_id>.<task_id>.<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.
celery.execute_command.failure	Number of non-zero exit code from Celery task.
task_removed_from_dag.<dag_id>	Number of tasks removed for a given dag (i.e. task no longer exists in DAG)
task_restored_to_dag.<dag_id>	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
triggers.blocked_main_thread	Number of triggers that blocked the main thread (likely due to not being fully asynchronous)
triggers.failed	Number of triggers that errored before they could fire an event
triggers.succeeded	Number of triggers that have fired at least one event

Gauges

Name	Description
dagbag_size	Number of DAGs found when the scheduler ran a scan based on it's configuration
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_run.seconds_ago.<dag_file>	Seconds since <dag_file> was last processed
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
triggers.running	Number of triggers currently running (per triggerer)

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>	Seconds 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>	Seconds 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	Seconds elapsed between first task start_date and dagrun expected start
collect_db_dags	Milliseconds taken for fetching all Serialized Dags from DB