FAQ¶

Scheduling / DAG file parsing¶

Why is task not getting scheduled?¶

There are very many reasons why your task might not be getting scheduled. Here are some of the common causes:

Does your script “compile”, can the Airflow engine parse it and find your DAG object? To test this, you can run airflow dags list and confirm that your DAG shows up in the list. You can also run airflow tasks list foo_dag_id --tree and confirm that your task shows up in the list as expected. If you use the CeleryExecutor, you may want to confirm that this works both where the scheduler runs as well as where the worker runs.
Does the file containing your DAG contain the string “airflow” and “DAG” somewhere in the contents? When searching the DAG directory, Airflow ignores files not containing “airflow” and “DAG” in order to prevent the DagBag parsing from importing all python files collocated with user’s DAGs.
Is your start_date set properly? For time-based DAGs, the task won’t be triggered until the the first schedule interval following the start date has passed.
Is your schedule argument set properly? The default is one day (datetime.timedelta(1)). You must specify a different schedule directly to the DAG object you instantiate, not as a default_param, as task instances do not override their parent DAG’s schedule.
Is your start_date beyond where you can see it in the UI? If you set your start_date to some time say 3 months ago, you won’t be able to see it in the main view in the UI, but you should be able to see it in the Menu -> Browse ->Task Instances.
Are the dependencies for the task met? The task instances directly upstream from the task need to be in a success state. Also, if you have set depends_on_past=True, the previous task instance needs to have succeeded or been skipped (except if it is the first run for that task). Also, if wait_for_downstream=True, make sure you understand what it means - all tasks immediately downstream of the previous task instance must have succeeded or been skipped. You can view how these properties are set from the Task Instance Details page for your task.
Are the DagRuns you need created and active? A DagRun represents a specific execution of an entire DAG and has a state (running, success, failed, …). The scheduler creates new DagRun as it moves forward, but never goes back in time to create new ones. The scheduler only evaluates running DagRuns to see what task instances it can trigger. Note that clearing tasks instances (from the UI or CLI) does set the state of a DagRun back to running. You can bulk view the list of DagRuns and alter states by clicking on the schedule tag for a DAG.
Is the concurrency parameter of your DAG reached? concurrency defines how many running task instances a DAG is allowed to have, beyond which point things get queued.
Is the max_active_runs parameter of your DAG reached? max_active_runs defines how many running concurrent instances of a DAG there are allowed to be.

You may also want to read about the Scheduler and make sure you fully understand how the scheduler cycle.

How to improve DAG performance?¶

There are some Airflow configuration to allow for a larger scheduling capacity and frequency:

DAGs have configurations that improves efficiency:

max_active_tasks: Overrides max_active_tasks_per_dag.
max_active_runs: Overrides max_active_runs_per_dag.

Operators or tasks also have configurations that improves efficiency and scheduling priority:

max_active_tis_per_dag: This parameter controls the number of concurrent running task instances across dag_runs per task.
pool: See Pools.
priority_weight: See Priority Weights.
queue: See Queues for CeleryExecutor deployments only.

How to reduce DAG scheduling latency / task delay?¶

Airflow 2.0 has low DAG scheduling latency out of the box (particularly when compared with Airflow 1.10.x), however, if you need more throughput you can start multiple schedulers.

How do I trigger tasks based on another task’s failure?¶

You can achieve this with Trigger Rules.

How to control DAG file parsing timeout for different DAG files?¶

(only valid for Airflow >= 2.3.0)

You can add a get_dagbag_import_timeout function in your airflow_local_settings.py which gets called right before a DAG file is parsed. You can return different timeout value based on the DAG file. When the return value is less than or equal to 0, it means no timeout during the DAG parsing.

airflow_local_settings.py¶

 def get_dagbag_import_timeout(dag_file_path: str) -> Union[int, float]:
     """
     This setting allows to dynamically control the DAG file parsing timeout.

     It is useful when there are a few DAG files requiring longer parsing times, while others do not.
     You can control them separately instead of having one value for all DAG files.

     If the return value is less than or equal to 0, it means no timeout during the DAG parsing.
     """
     if "slow" in dag_file_path:
         return 90
     if "no-timeout" in dag_file_path:
         return 0
     return conf.getfloat("core", "DAGBAG_IMPORT_TIMEOUT")

See Configuring local settings for details on how to configure local settings.

When there are a lot (>1000) of DAG files, how to speed up parsing of new files?¶

(only valid for Airflow >= 2.1.1)

Change the file_parsing_sort_mode to modified_time, raise the min_file_process_interval to 600 (10 minutes), 6000 (100 minutes) or a higher value.

The DAG parser will skip the min_file_process_interval check if a file is recently modified.

This might not work for case where the DAG is imported/created from a separate file. Example: dag_file.py that imports dag_loader.py where the actual logic of the DAG file is as shown below. In this case if dag_loader.py is updated but dag_file.py is not updated, the changes won’t be reflected until min_file_process_interval is reached since DAG Parser will look for modified time for dag_file.py file.

dag_file.py¶

 from dag_loader import create_dag

 globals()[dag.dag_id] = create_dag(dag_id, schedule, dag_number, default_args)

dag_loader.py¶

 from airflow import DAG
 from airflow.decorators import task

 import pendulum


 def create_dag(dag_id, schedule, dag_number, default_args):
     dag = DAG(
         dag_id,
         schedule=schedule,
         default_args=default_args,
         pendulum.datetime(2021, 9, 13, tz="UTC"),
     )

     with dag:

         @task()
         def hello_world():
             print("Hello World")
             print(f"This is DAG: {dag_number}")

         hello_world()

     return dag

DAG construction¶

What’s the deal with `start_date`?¶

start_date is partly legacy from the pre-DagRun era, but it is still relevant in many ways. When creating a new DAG, you probably want to set a global start_date for your tasks. This can be done by declaring your start_date directly in the DAG() object. A DAG’s first DagRun will be created based on the first complete data_interval after start_date. For example, for a DAG with start_date=datetime(2024, 1, 1) and schedule="0 0 3 * *", the first DAG run will be triggered at midnight on 2024-02-03 with data_interval_start=datetime(2024, 1, 3) and data_interval_end=datetime(2024, 2, 3). From that point on, the scheduler creates new DagRuns based on your schedule and the corresponding task instances run as your dependencies are met. When introducing new tasks to your DAG, you need to pay special attention to start_date, and may want to reactivate inactive DagRuns to get the new task onboarded properly.

We recommend against using dynamic values as start_date, especially datetime.now() as it can be quite confusing. The task is triggered once the period closes, and in theory an @hourly DAG would never get to an hour after now as now() moves along.

Previously, we also recommended using rounded start_date in relation to your DAG’s schedule. This meant an @hourly would be at 00:00 minutes:seconds, a @daily job at midnight, a @monthly job on the first of the month. This is no longer required. Airflow will now auto align the start_date and the schedule, by using the start_date as the moment to start looking.

You can use any sensor or a TimeDeltaSensor to delay the execution of tasks within the schedule interval. While schedule does allow specifying a datetime.timedelta object, we recommend using the macros or cron expressions instead, as it enforces this idea of rounded schedules.

When using depends_on_past=True, it’s important to pay special attention to start_date, as the past dependency is not enforced only on the specific schedule of the start_date specified for the task. It’s also important to watch DagRun activity status in time when introducing new depends_on_past=True, unless you are planning on running a backfill for the new task(s).

It is also important to note that the task’s start_date is ignored in backfills.

Using time zones¶

Creating a time zone aware datetime (e.g. DAG’s start_date) is quite simple. Just make sure to supply a time zone aware dates using pendulum. Don’t try to use standard library timezone as they are known to have limitations and we deliberately disallow using them in DAGs.

What does `execution_date` mean?¶

Execution date or execution_date is a historical name for what is called a logical date, and also usually the start of the data interval represented by a DAG run.

Airflow was developed as a solution for ETL needs. In the ETL world, you typically summarize data. So, if you want to summarize data for 2016-02-19, you would do it at 2016-02-20 midnight UTC, which would be right after all data for 2016-02-19 becomes available. This interval between midnights of 2016-02-19 and 2016-02-20 is called the data interval, and since it represents data in the date of 2016-02-19, this date is also called the run’s logical date, or the date that this DAG run is executed for, thus execution date.

For backward compatibility, a datetime value execution_date is still as Template variables with various formats in Jinja templated fields, and in Airflow’s Python API. It is also included in the context dictionary given to an Operator’s execute function.

class MyOperator(BaseOperator):
    def execute(self, context):
        logging.info(context["execution_date"])

However, you should always use data_interval_start or data_interval_end if possible, since those names are semantically more correct and less prone to misunderstandings.

Note that ds (the YYYY-MM-DD form of data_interval_start) refers to date *string*, not date *start* as may be confusing to some.

Tip

For more information on logical date, see Data Interval and Running DAGs.

How to create DAGs dynamically?¶

Airflow looks in your DAGS_FOLDER for modules that contain DAG objects in their global namespace and adds the objects it finds in the DagBag. Knowing this, all we need is a way to dynamically assign variable in the global namespace. This is easily done in python using the globals() function for the standard library, which behaves like a simple dictionary.

def create_dag(dag_id):
    """
    A function returning a DAG object.
    """

    return DAG(dag_id)


for i in range(10):
    dag_id = f"foo_{i}"
    globals()[dag_id] = DAG(dag_id)

    # or better, call a function that returns a DAG object!
    other_dag_id = f"bar_{i}"
    globals()[other_dag_id] = create_dag(other_dag_id)

Even though Airflow supports multiple DAG definition per python file, dynamically generated or otherwise, it is not recommended as Airflow would like better isolation between DAGs from a fault and deployment perspective and multiple DAGs in the same file goes against that.

Are top level Python code allowed?¶

While it is not recommended to write any code outside of defining Airflow constructs, Airflow does support any arbitrary python code as long as it does not break the DAG file processor or prolong file processing time past the dagbag_import_timeout value.

A common example is the violation of the time limit when building a dynamic DAG which usually requires querying data from another service like a database. At the same time, the requested service is being swamped with DAG file processors requests for data to process the file. These unintended interactions may cause the service to deteriorate and eventually cause DAG file processing to fail.

Refer to DAG writing best practices for more information.

Do Macros resolves in another Jinja template?¶

It is not possible to render Macros or any Jinja template within another Jinja template. This is commonly attempted in user_defined_macros.

dag = DAG(
    # ...
    user_defined_macros={"my_custom_macro": "day={{ ds }}"}
)

bo = BashOperator(task_id="my_task", bash_command="echo {{ my_custom_macro }}", dag=dag)

This will echo “day={{ ds }}” instead of “day=2020-01-01” for a DAG run with a data_interval_start of 2020-01-01 00:00:00.

bo = BashOperator(task_id="my_task", bash_command="echo day={{ ds }}", dag=dag)

By using the ds macros directly in the template_field, the rendered value results in “day=2020-01-01”.

Why `next_ds` or `prev_ds` might not contain expected values?¶

When scheduling DAG, the next_ds next_ds_nodash prev_ds prev_ds_nodash are calculated using logical_date and the DAG’s schedule (if applicable). If you set schedule as None or @once, the next_ds, next_ds_nodash, prev_ds, prev_ds_nodash values will be set to None.
When manually triggering DAG, the schedule will be ignored, and prev_ds == next_ds == ds.

Task execution interactions¶

What does `TemplateNotFound` mean?¶

TemplateNotFound errors are usually due to misalignment with user expectations when passing path to operator that trigger Jinja templating. A common occurrence is with BashOperator.

Another commonly missed fact is that the files are resolved relative to where the pipeline file lives. You can add other directories to the template_searchpath of the DAG object to allow for other non-relative location.

How to trigger tasks based on another task’s failure?¶

For tasks that are related through dependency, you can set the trigger_rule to TriggerRule.ALL_FAILED if the task execution depends on the failure of ALL its upstream tasks or TriggerRule.ONE_FAILED for just one of the upstream task.

import pendulum

from airflow.decorators import dag, task
from airflow.exceptions import AirflowException
from airflow.utils.trigger_rule import TriggerRule


@task()
def a_func():
    raise AirflowException


@task(
    trigger_rule=TriggerRule.ALL_FAILED,
)
def b_func():
    pass


@dag(schedule="@once", start_date=pendulum.datetime(2021, 1, 1, tz="UTC"))
def my_dag():
    a = a_func()
    b = b_func()

    a >> b


dag = my_dag()

See Trigger Rules for more information.

If the tasks are not related by dependency, you will need to build a custom Operator.

Airflow UI¶

Why did my task fail with no logs in the UI?¶

Logs are typically served when a task reaches a terminal state. Sometimes, a task’s normal lifecycle is disrupted, and the task’s worker is unable to write the task’s logs. This typically happens for one of two reasons:

Zombie tasks.
Tasks failed after getting stuck in queued (Airflow 2.6.0+). Tasks that are in queued for longer than scheduler.task_queued_timeout will be marked as failed, and there will be no task logs in the Airflow UI.

Setting retries for each task drastically reduces the chance that either of these problems impact a workflow.

How do I stop the sync perms happening multiple times per webserver?¶

Set the value of [fab] update_fab_perms configuration in airflow.cfg to False.

How to reduce the airflow UI page load time?¶

If your DAG takes long time to load, you could reduce the value of default_dag_run_display_number configuration in airflow.cfg to a smaller value. This configurable controls the number of DAG runs to show in UI with default value 25.

Why did the pause DAG toggle turn red?¶

If pausing or unpausing a DAG fails for any reason, the DAG toggle will revert to its previous state and turn red. If you observe this behavior, try pausing the DAG again, or check the console or server logs if the issue recurs.

MySQL and MySQL variant Databases¶

What does “MySQL Server has gone away” mean?¶

You may occasionally experience OperationalError with the message “MySQL Server has gone away”. This is due to the connection pool keeping connections open too long and you are given an old connection that has expired. To ensure a valid connection, you can set sql_alchemy_pool_recycle to ensure connections are invalidated after that many seconds and new ones are created.

Does Airflow support extended ASCII or unicode characters?¶

If you intend to use extended ASCII or Unicode characters in Airflow, you have to provide a proper connection string to the MySQL database since they define charset explicitly.

sql_alchemy_conn = mysql://airflow@localhost:3306/airflow?charset=utf8

You will experience UnicodeDecodeError thrown by WTForms templating and other Airflow modules like below.

'ascii' codec can't decode byte 0xae in position 506: ordinal not in range(128)

How to fix Exception: Global variable `explicit_defaults_for_timestamp` needs to be on (1)?¶

This means explicit_defaults_for_timestamp is disabled in your mysql server and you need to enable it by:

Set explicit_defaults_for_timestamp = 1 under the mysqld section in your my.cnf file.
Restart the Mysql server.

Does Airflow collect any telemetry data?¶

Airflow integrates Scarf to collect basic usage data during operation. This data assists Airflow maintainers in better understanding how Airflow is used. Insights gained from this data are helpful for prioritizing patches, minor releases, and security fixes. Additionally, this information supports key decisions related to the development road map.

Deployments can opt-out of data collection by setting the [usage_data_collection] enabled option to False, or the SCARF_ANALYTICS=false environment variable. Individual users can easily opt-out of analytics in various ways documented in the Scarf Do Not Track docs.

The telemetry data collected is limited to the following:

Airflow version
Python version
Operating system & machine architecture
Executor
Metadata DB type & its version