Metrics Configuration

Airflow can be set up to send metrics to StatsD or OpenTelemetry.

Setup - StatsD

To use StatsD you must first install the required packages:

pip install 'apache-airflow[statsd]'

then 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 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.

Setup - OpenTelemetry

To use OpenTelemetry you must first install the required packages:

pip install 'apache-airflow[otel]'

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

[metrics]
otel_on = True
otel_host = localhost
otel_port = 8889
otel_prefix = airflow
otel_interval_milliseconds = 30000  # The interval between exports, defaults to 60000
otel_ssl_active = False

Enable Https

To establish an HTTPS connection to the OpenTelemetry collector You need to configure the SSL certificate and key within the OpenTelemetry collector’s config.yml file.

receivers:
  otlp:
    protocols:
      http:
        endpoint: 0.0.0.0:4318
        tls:
          cert_file: "/path/to/cert/cert.crt"
          key_file: "/path/to/key/key.pem"

Allow/Block Lists

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

[metrics]
metrics_allow_list = scheduler,executor,dagrun,pool,triggerer,celery
[metrics]
metrics_block_list = scheduler,executor,dagrun,pool,triggerer,celery

Rename Metrics

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

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

Other Configuration Options

Note

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

Metric Descriptions

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

local_task_job.task_exit.<job_id>.<dag_id>.<task_id>.<return_code>

Number of LocalTaskJob terminations with a <return_code> while running a task <task_id> of a DAG <dag_id>.

local_task_job.task_exit

Number of LocalTaskJob terminations with a <return_code> while running a task <task_id> of a DAG <dag_id>. Metric with job_id, dag_id, task_id and return_code tagging.

operator_failures_<operator_name>

Operator <operator_name> failures

operator_failures

Operator <operator_name> failures. Metric with operator_name tagging.

operator_successes_<operator_name>

Operator <operator_name> successes

operator_successes

Operator <operator_name> successes. Metric with operator_name tagging.

ti_failures

Overall task instances failures. Metric with dag_id and task_id tagging.

ti_successes

Overall task instances successes. Metric with dag_id and task_id tagging.

previously_succeeded

Number of previously succeeded task instances. Metric with dag_id and task_id tagging.

zombies_killed

Zombie tasks killed. Metric with dag_id and task_id tagging.

scheduler_heartbeat

Scheduler heartbeats

dag_processing.processes

Relative number of currently running DAG parsing processes (ie this delta is negative when, since the last metric was sent, processes have completed). Metric with file_path and action tagging.

dag_processing.processor_timeouts

Number of file processors that have been killed due to taking too long. Metric with file_path tagging.

dag_processing.sla_callback_count

Number of SLA callbacks received

dag_processing.other_callback_count

Number of non-SLA callbacks received

dag_processing.file_path_queue_update_count

Number of times we’ve scanned the filesystem and queued all existing dags

dag_file_processor_timeouts

(DEPRECATED) same behavior as dag_processing.processor_timeouts

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. Metric with dag_id and task_id tagging.

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. Metric with dag_id and task_id tagging.

sla_callback_notification_failure

Number of failed SLA miss callback notification attempts. Metric with dag_id and func_name tagging.

sla_email_notification_failure

Number of failed SLA miss email notification attempts. Metric with dag_id tagging.

ti.start.<dag_id>.<task_id>

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

ti.start

Number of started task in a given dag. Similar to <job_name>_start but for task. Metric with dag_id and task_id tagging.

ti.finish.<dag_id>.<task_id>.<state>

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

ti.finish

Number of completed task in a given dag. Similar to <job_name>_end but for task Metric with dag_id and task_id tagging.

dag.callback_exceptions

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

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_removed_from_dag

Number of tasks removed for a given dag (i.e. task no longer exists in DAG). Metric with dag_id and run_type tagging.

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_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). Metric with dag_id and run_type tagging.

task_instance_created_<operator_name>

Number of tasks instances created for a given Operator

task_instance_created

Number of tasks instances created for a given Operator. Metric with dag_id and run_type tagging.

triggerer_heartbeat

Triggerer heartbeats

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

dataset.updates

Number of updated datasets

dataset.orphaned

Number of datasets marked as orphans because they are no longer referenced in DAG schedule parameters or task outlets

dataset.triggered_dagruns

Number of DAG runs triggered by a dataset update

Gauges

Name

Description

dagbag_size

Number of DAGs found when the scheduler ran a scan based on its 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 dag_processing.file_path_queue_size DAG files

dag_processing.file_path_queue_size

Number of DAG files to be considered for the next scan

dag_processing.last_run.seconds_ago.<dag_file>

Seconds since <dag_file> was last processed

dag_processing.last_num_of_db_queries.<dag_file>

Number of queries to Airflow database during parsing per <dag_file>

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.<executor_class_name>

Number of open slots on a specific executor. Only emitted when multiple executors are configured.

executor.open_slots

Number of open slots on executor

executor.queued_tasks.<executor_class_name>

Number of queued tasks on on a specific executor. Only emitted when multiple executors are configured.

executor.queued_tasks

Number of queued tasks on executor

executor.running_tasks.<executor_class_name>

Number of running tasks on on a specific executor. Only emitted when multiple executors are configured.

executor.running_tasks

Number of running tasks on executor

pool.open_slots.<pool_name>

Number of open slots in the pool

pool.open_slots

Number of open slots in the pool. Metric with pool_name tagging.

pool.queued_slots.<pool_name>

Number of queued slots in the pool

pool.queued_slots

Number of queued slots in the pool. Metric with pool_name tagging.

pool.running_slots.<pool_name>

Number of running slots in the pool

pool.running_slots

Number of running slots in the pool. Metric with pool_name tagging.

pool.deferred_slots.<pool_name>

Number of deferred slots in the pool

pool.deferred_slots

Number of deferred slots in the pool. Metric with pool_name tagging.

pool.scheduled_slots.<pool_name>

Number of scheduled slots in the pool

pool.scheduled_slots

Number of scheduled slots in the pool. Metric with pool_name tagging.

pool.starving_tasks.<pool_name>

Number of starving tasks in the pool

pool.starving_tasks

Number of starving tasks in the pool. Metric with pool_name tagging.

task.cpu_usage_percent.<dag_id>.<task_id>

Percentage of CPU used by a task

task.mem_usage_percent.<dag_id>.<task_id>

Percentage of memory used by a task

triggers.running.<hostname>

Number of triggers currently running for a triggerer (described by hostname)

triggers.running

Number of triggers currently running for a triggerer (described by hostname). Metric with hostname tagging.

Timers

Name

Description

dagrun.dependency-check.<dag_id>

Milliseconds taken to check DAG dependencies

dagrun.dependency-check

Milliseconds taken to check DAG dependencies. Metric with dag_id tagging.

dag.<dag_id>.<task_id>.duration

Milliseconds taken to run a task

task.duration

Milliseconds taken to run a task. Metric with dag_id and task-id tagging.

dag.<dag_id>.<task_id>.scheduled_duration

Milliseconds a task spends in the Scheduled state, before being Queued

task.scheduled_duration

Milliseconds a task spends in the Scheduled state, before being Queued. Metric with dag_id and task_id tagging.

dag.<dag_id>.<task_id>.queued_duration

Milliseconds a task spends in the Queued state, before being Running

task.queued_duration

Milliseconds a task spends in the Queued state, before being Running. Metric with dag_id and task_id tagging.

dag_processing.last_duration.<dag_file>

Milliseconds taken to load the given DAG file

dag_processing.last_duration

Milliseconds taken to load the given DAG file. Metric with file_name tagging.

dagrun.duration.success.<dag_id>

Milliseconds taken for a DagRun to reach success state

dagrun.duration.success

Milliseconds taken for a DagRun to reach success state. Metric with dag_id and run_type tagging.

dagrun.duration.failed.<dag_id>

Milliseconds taken for a DagRun to reach failed state

dagrun.duration.failed

Milliseconds taken for a DagRun to reach failed state. Metric with dag_id and run_type tagging.

dagrun.schedule_delay.<dag_id>

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

dagrun.schedule_delay

Milliseconds of delay between the scheduled DagRun start date and the actual DagRun start date. Metric with dag_id tagging.

scheduler.critical_section_duration

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

scheduler.critical_section_query_duration

Milliseconds spent running the critical section task instance query

scheduler.scheduler_loop_duration

Milliseconds spent running one scheduler loop

dagrun.<dag_id>.first_task_scheduling_delay

Milliseconds elapsed between first task start_date and dagrun expected start

dagrun.first_task_scheduling_delay

Milliseconds elapsed between first task start_date and dagrun expected start. Metric with dag_id and run_type tagging.

collect_db_dags

Milliseconds taken for fetching all Serialized Dags from DB

kubernetes_executor.clear_not_launched_queued_tasks.duration

Milliseconds taken for clearing not launched queued tasks in Kubernetes Executor

kubernetes_executor.adopt_task_instances.duration

Milliseconds taken to adopt the task instances in Kubernetes Executor

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