airflow_pydantic.TaskArgs¶

pydantic model airflow_pydantic.TaskArgs[source]¶

Bases: BaseModel

Show JSON schema

{
   "title": "TaskArgs",
   "type": "object",
   "properties": {
      "owner": {
         "anyOf": [
            {
               "type": "string"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "the owner of the task. Using a meaningful description (e.g. user/person/team/role name) to clarify ownership is recommended.",
         "title": "Owner"
      },
      "email": {
         "anyOf": [
            {
               "items": {
                  "type": "string"
               },
               "type": "array"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "the 'to' email address(es) used in email alerts",
         "title": "Email"
      },
      "email_on_failure": {
         "anyOf": [
            {
               "type": "boolean"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Indicates whether email alerts should be sent when a task failed",
         "title": "Email On Failure"
      },
      "email_on_retry": {
         "anyOf": [
            {
               "type": "boolean"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Indicates whether email alerts should be sent when a task is retried",
         "title": "Email On Retry"
      },
      "retries": {
         "anyOf": [
            {
               "type": "integer"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "the number of retries that should be performed before failing the task",
         "title": "Retries"
      },
      "retry_delay": {
         "anyOf": [
            {
               "format": "duration",
               "type": "string"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "delay between retries",
         "title": "Retry Delay"
      },
      "start_date": {
         "anyOf": [
            {
               "format": "date-time",
               "type": "string"
            },
            {
               "maxItems": 2,
               "minItems": 2,
               "prefixItems": [
                  {
                     "format": "date-time",
                     "type": "string"
                  },
                  {
                     "type": "string"
                  }
               ],
               "type": "array"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "The start_date for the task, determines the execution_date for the first task instance. The best practice is to have the start_date rounded to your DAG\u2019s schedule_interval. Daily jobs have their start_date some day at 00:00:00, hourly jobs have their start_date at 00:00 of a specific hour. Note that Airflow simply looks at the latest execution_date and adds the schedule_interval to determine the next execution_date. It is also very important to note that different tasks\u2019 dependencies need to line up in time. If task A depends on task B and their start_date are offset in a way that their execution_date don\u2019t line up, A\u2019s dependencies will never be met. If you are looking to delay a task, for example running a daily task at 2AM, look into the TimeSensor and TimeDeltaSensor. We advise against using dynamic start_date and recommend using fixed ones. Read the FAQ entry about start_date for more information.",
         "title": "Start Date"
      },
      "end_date": {
         "anyOf": [
            {
               "format": "date-time",
               "type": "string"
            },
            {
               "maxItems": 2,
               "minItems": 2,
               "prefixItems": [
                  {
                     "format": "date-time",
                     "type": "string"
                  },
                  {
                     "type": "string"
                  }
               ],
               "type": "array"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "if specified, the scheduler won\u2019t go beyond this date",
         "title": "End Date"
      },
      "depends_on_past": {
         "anyOf": [
            {
               "type": "boolean"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "when set to true, task instances will run sequentially and only if the previous instance has succeeded or has been skipped. The task instance for the start_date is allowed to run.",
         "title": "Depends On Past"
      },
      "queue": {
         "anyOf": [
            {
               "type": "string"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "which queue to target when running this job. Not all executors implement queue management, the CeleryExecutor does support targeting specific queues.",
         "title": "Queue"
      },
      "pool": {
         "anyOf": [
            {
               "type": "string"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "the slot pool this task should run in, slot pools are a way to limit concurrency for certain tasks",
         "title": "Pool"
      },
      "pool_slots": {
         "anyOf": [
            {
               "type": "integer"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "the number of pool slots this task should use (>= 1) Values less than 1 are not allowed",
         "title": "Pool Slots"
      },
      "do_xcom_push": {
         "anyOf": [
            {
               "type": "boolean"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "if True, an XCom is pushed containing the Operator\u2019s result",
         "title": "Do Xcom Push"
      },
      "task_display_name": {
         "anyOf": [
            {
               "type": "string"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "The display name of the task which appears on the UI.",
         "title": "Task Display Name"
      }
   }
}

Fields:

depends_on_past (bool | None)
do_xcom_push (bool | None)
email (List[str] | None)
email_on_failure (bool | None)
email_on_retry (bool | None)
end_date (datetime.datetime | Tuple[datetime.datetime, str] | None)
owner (str | None)
pool (str | None)
pool_slots (int | None)
queue (str | None)
retries (int | None)
retry_delay (datetime.timedelta | None)
start_date (datetime.datetime | Tuple[datetime.datetime, str] | None)
task_display_name (str | None)

field owner: str | None = None¶: the owner of the task. Using a meaningful description (e.g. user/person/team/role name) to clarify ownership is recommended.

field email: List[str] | None = None¶: the ‘to’ email address(es) used in email alerts

field email_on_failure: bool | None = None¶: Indicates whether email alerts should be sent when a task failed

field email_on_retry: bool | None = None¶: Indicates whether email alerts should be sent when a task is retried

field retries: int | None = None¶: the number of retries that should be performed before failing the task

field retry_delay: timedelta | None = None¶: delay between retries

field start_date: Annotated[datetime | Tuple[datetime, str], AfterValidator(func=_datetime_or_datetime_and_timezone)] | None = None¶: The start_date for the task, determines the execution_date for the first task instance. The best practice is to have the start_date rounded to your DAG’s schedule_interval. Daily jobs have their start_date some day at 00:00:00, hourly jobs have their start_date at 00:00 of a specific hour. Note that Airflow simply looks at the latest execution_date and adds the schedule_interval to determine the next execution_date. It is also very important to note that different tasks’ dependencies need to line up in time. If task A depends on task B and their start_date are offset in a way that their execution_date don’t line up, A’s dependencies will never be met. If you are looking to delay a task, for example running a daily task at 2AM, look into the TimeSensor and TimeDeltaSensor. We advise against using dynamic start_date and recommend using fixed ones. Read the FAQ entry about start_date for more information.

field end_date: Annotated[datetime | Tuple[datetime, str], AfterValidator(func=_datetime_or_datetime_and_timezone)] | None = None¶: if specified, the scheduler won’t go beyond this date

field depends_on_past: bool | None = None¶: when set to true, task instances will run sequentially and only if the previous instance has succeeded or has been skipped. The task instance for the start_date is allowed to run.

field queue: str | None = None¶: which queue to target when running this job. Not all executors implement queue management, the CeleryExecutor does support targeting specific queues.

field pool: str | None = None¶: the slot pool this task should run in, slot pools are a way to limit concurrency for certain tasks

field pool_slots: int | None = None¶: the number of pool slots this task should use (>= 1) Values less than 1 are not allowed

field do_xcom_push: bool | None = None¶: if True, an XCom is pushed containing the Operator’s result

field task_display_name: str | None = None¶: The display name of the task which appears on the UI.