Templates reference¶

Variables, macros and filters can be used in templates (see the Jinja Templating section)

The following come for free out of the box with Airflow. Additional custom macros can be added globally through Plugins, or at a DAG level through the DAG.user_defined_macros argument.

Variables¶

The Airflow engine passes a few variables by default that are accessible in all templates

Variable	Type	Description
`{{ data_interval_start }}`	pendulum.DateTime	Start of the data interval. Added in version 2.2.
`{{ data_interval_end }}`	pendulum.DateTime	End of the data interval. Added in version 2.2.
`{{ logical_date }}`	pendulum.DateTime	A date-time that logically identifies the current DAG run. This value does not contain any semantics, but is simply a value for identification. Use `data_interval_start` and `data_interval_end` instead if you want a value that has real-world semantics, such as to get a slice of rows from the database based on timestamps.
`{{ ds }}`	str	The DAG run’s logical date as `YYYY-MM-DD`. Same as `{{ logical_date \| ds }}`.
`{{ ds_nodash }}`	str	Same as `{{ logical_date \| ds_nodash }}`.
`{{ exception }}`	None \| str \| Exception KeyboardInterrupt	Error occurred while running task instance.
`{{ ts }}`	str	Same as `{{ logical_date \| ts }}`. Example: `2018-01-01T00:00:00+00:00`.
`{{ ts_nodash_with_tz }}`	str	Same as `{{ logical_date \| ts_nodash_with_tz }}`. Example: `20180101T000000+0000`.
`{{ ts_nodash }}`	str	Same as `{{ logical_date \| ts_nodash }}`. Example: `20180101T000000`.
`{{ prev_data_interval_start_success }}`	pendulum.DateTime \| `None`	Start of the data interval of the prior successful `DagRun`. Added in version 2.2.
`{{ prev_data_interval_end_success }}`	pendulum.DateTime \| `None`	End of the data interval of the prior successful `DagRun`. Added in version 2.2.
`{{ prev_start_date_success }}`	pendulum.DateTime \| `None`	Start date from prior successful `DagRun` (if available).
`{{ prev_end_date_success }}`	pendulum.DateTime \| `None`	End date from prior successful `DagRun` (if available).
`{{ inlets }}`	list	List of inlets declared on the task.
`{{ outlets }}`	list	List of outlets declared on the task.
`{{ dag }}`	DAG	The currently running `DAG`. You can read more about DAGs in DAGs.
`{{ task }}`	BaseOperator	The currently running `BaseOperator`. You can read more about Tasks in Operators
`{{ macros }}`		A reference to the macros package. See Macros below.
`{{ task_instance }}`	TaskInstance	The currently running `TaskInstance`.
`{{ ti }}`	TaskInstance	Same as `{{ task_instance }}`.
`{{ params }}`	dict[str, Any]	The user-defined params. This can be overridden by the mapping passed to `trigger_dag -c` if `dag_run_conf_overrides_params` is enabled in `airflow.cfg`.
`{{ var.value }}`		Airflow variables. See Airflow Variables in Templates below.
`{{ var.json }}`		Airflow variables. See Airflow Variables in Templates below.
`{{ conn }}`		Airflow connections. See Airflow Connections in Templates below.
`{{ task_instance_key_str }}`	str	A unique, human-readable key to the task instance. The format is `{dag_id}__{task_id}__{ds_nodash}`.
`{{ conf }}`	AirflowConfigParser	The full configuration object representing the content of your `airflow.cfg`. See `airflow.configuration.conf`.
`{{ run_id }}`	str	The currently running `DagRun` run ID.
`{{ dag_run }}`	DagRun	The currently running `DagRun`.
`{{ test_mode }}`	bool	Whether the task instance was run by the `airflow test` CLI.
`{{ map_index_template }}`	None \| str	Template used to render the expanded task instance of a mapped task. Setting this value will be reflected in the rendered result.
`{{ expanded_ti_count }}`	int \| `None`	Number of task instances that a mapped task was expanded into. If the current task is not mapped, this should be `None`. Added in version 2.5.
`{{ triggering_dataset_events }}`	dict[str, list[DatasetEvent]]	If in a Dataset Scheduled DAG, a map of Dataset URI to a list of triggering `DatasetEvent` (there may be more than one, if there are multiple Datasets with different frequencies). Read more here Datasets. Added in version 2.4.

Note

The DAG run’s logical date, and values derived from it, such as ds and ts, should not be considered unique in a DAG. Use run_id instead.

Accessing Airflow context variables from TaskFlow tasks¶

While @task decorated tasks don’t support rendering jinja templates passed as arguments, all of the variables listed above can be accessed directly from tasks. The following code block is an example of accessing a task_instance object from its task:

from airflow.models.taskinstance import TaskInstance
from airflow.models.dagrun import DagRun


@task
def print_ti_info(task_instance: TaskInstance | None = None, dag_run: DagRun | None = None):
    print(f"Run ID: {task_instance.run_id}")  # Run ID: scheduled__2023-08-09T00:00:00+00:00
    print(f"Duration: {task_instance.duration}")  # Duration: 0.972019
    print(f"DAG Run queued at: {dag_run.queued_at}")  # 2023-08-10 00:00:01+02:20

Deprecated variables¶

The following variables are deprecated. They are kept for backward compatibility, but you should convert existing code to use other variables instead.

Deprecated Variable	Description
`{{ execution_date }}`	the execution date (logical date), same as `logical_date`
`{{ next_execution_date }}`	the logical date of the next scheduled run (if applicable); you may be able to use `data_interval_end` instead
`{{ next_ds }}`	the next execution date as `YYYY-MM-DD` if exists, else `None`
`{{ next_ds_nodash }}`	the next execution date as `YYYYMMDD` if exists, else `None`
`{{ prev_execution_date }}`	the logical date of the previous scheduled run (if applicable)
`{{ prev_ds }}`	the previous execution date as `YYYY-MM-DD` if exists, else `None`
`{{ prev_ds_nodash }}`	the previous execution date as `YYYYMMDD` if exists, else `None`
`{{ yesterday_ds }}`	the day before the execution date as `YYYY-MM-DD`
`{{ yesterday_ds_nodash }}`	the day before the execution date as `YYYYMMDD`
`{{ tomorrow_ds }}`	the day after the execution date as `YYYY-MM-DD`
`{{ tomorrow_ds_nodash }}`	the day after the execution date as `YYYYMMDD`
`{{ prev_execution_date_success }}`	execution date from prior successful DAG run; you may be able to use `prev_data_interval_start_success` instead if the timetable/schedule you use for the DAG defines `data_interval_start` compatible with the legacy `execution_date`.

Note that you can access the object’s attributes and methods with simple dot notation. Here are some examples of what is possible: {{ task.owner }}, {{ task.task_id }}, {{ ti.hostname }}, … Refer to the models documentation for more information on the objects’ attributes and methods.

Airflow Variables in Templates¶

The var template variable allows you to access Airflow Variables. You can access them as either plain-text or JSON. If you use JSON, you are also able to walk nested structures, such as dictionaries like: {{ var.json.my_dict_var.key1 }}.

It is also possible to fetch a variable by string if needed (for example your variable key contains dots) with {{ var.value.get('my.var', 'fallback') }} or {{ var.json.get('my.dict.var', {'key1': 'val1'}) }}. Defaults can be supplied in case the variable does not exist.

Airflow Connections in Templates¶

Similarly, Airflow Connections data can be accessed via the conn template variable. For example, you could use expressions in your templates like {{ conn.my_conn_id.login }}, {{ conn.my_conn_id.password }}, etc.

Just like with var it’s possible to fetch a connection by string (e.g. {{ conn.get('my_conn_id_'+index).host }} ) or provide defaults (e.g {{ conn.get('my_conn_id', {"host": "host1", "login": "user1"}).host }}).

Additionally, the extras field of a connection can be fetched as a Python Dictionary with the extra_dejson field, e.g. conn.my_aws_conn_id.extra_dejson.region_name would fetch region_name out of extras. This way, defaults in extras can be provided as well (e.g. {{ conn.my_aws_conn_id.extra_dejson.get('region_name', 'Europe (Frankfurt)') }}).

Filters¶

Airflow defines some Jinja filters that can be used to format values.

For example, using {{ logical_date | ds }} will output the logical_date in the YYYY-MM-DD format.

Filter	Operates on	Description
`ds`	datetime	Format the datetime as `YYYY-MM-DD`
`ds_nodash`	datetime	Format the datetime as `YYYYMMDD`
`ts`	datetime	Same as `.isoformat()`, Example: `2018-01-01T00:00:00+00:00`
`ts_nodash`	datetime	Same as `ts` filter without `-`, `:` or TimeZone info. Example: `20180101T000000`
`ts_nodash_with_tz`	datetime	As `ts` filter without `-` or `:`. Example `20180101T000000+0000`

Macros¶

Macros are a way to expose objects to your templates and live under the macros namespace in your templates.

A few commonly used libraries and methods are made available.

Variable	Description
`macros.datetime`	The standard lib’s `datetime.datetime`
`macros.timedelta`	The standard lib’s `datetime.timedelta`
`macros.dateutil`	A reference to the `dateutil` package
`macros.time`	The standard lib’s `time`
`macros.uuid`	The standard lib’s `uuid`
`macros.random`	The standard lib’s `random.random`

Some airflow specific macros are also defined:

airflow.macros.datetime_diff_for_humans(dt, since=None)[source]¶

Return a human-readable/approximate difference between datetimes.

When only one datetime is provided, the comparison will be based on now.

Parameters

dt (Any) – The datetime to display the diff for
since (DateTime | None) – When to display the date from. If None then the diff is between dt and now.

airflow.macros.ds_add(ds, days)[source]¶

Add or subtract days from a YYYY-MM-DD.

Parameters

ds (str) – anchor date in YYYY-MM-DD format to add to
days (int) – number of days to add to the ds, you can use negative values

>>> ds_add("2015-01-01", 5)
'2015-01-06'
>>> ds_add("2015-01-06", -5)
'2015-01-01'

airflow.macros.ds_format(ds, input_format, output_format)[source]¶

Output datetime string in a given format.

Parameters

ds (str) – input string which contains a date
input_format (str) – input string format. E.g. %Y-%m-%d
output_format (str) – output string format E.g. %Y-%m-%d

>>> ds_format("2015-01-01", "%Y-%m-%d", "%m-%d-%y")
'01-01-15'
>>> ds_format("1/5/2015", "%m/%d/%Y", "%Y-%m-%d")
'2015-01-05'

airflow.macros.random() → x in the interval [0, 1).¶