TaskInstancesBatchBody.md

TaskInstancesBatchBody

Task Instance body for get batch.

Properties

Name	Type	Description	Notes
dag_ids	List[str]		[optional]
dag_run_ids	List[str]		[optional]
duration_gt	float		[optional]
duration_gte	float		[optional]
duration_lt	float		[optional]
duration_lte	float		[optional]
end_date_gt	datetime		[optional]
end_date_gte	datetime		[optional]
end_date_lt	datetime		[optional]
end_date_lte	datetime		[optional]
executor	List[str]		[optional]
logical_date_gt	datetime		[optional]
logical_date_gte	datetime		[optional]
logical_date_lt	datetime		[optional]
logical_date_lte	datetime		[optional]
order_by	str		[optional]
page_limit	int		[optional] [default to 100]
page_offset	int		[optional] [default to 0]
pool	List[str]		[optional]
queue	List[str]		[optional]
run_after_gt	datetime		[optional]
run_after_gte	datetime		[optional]
run_after_lt	datetime		[optional]
run_after_lte	datetime		[optional]
start_date_gt	datetime		[optional]
start_date_gte	datetime		[optional]
start_date_lt	datetime		[optional]
start_date_lte	datetime		[optional]
state	List[Optional[TaskInstanceState]]		[optional]
task_ids	List[str]		[optional]

Example

from airflow_client.client.models.task_instances_batch_body import TaskInstancesBatchBody

# TODO update the JSON string below
json = "{}"
# create an instance of TaskInstancesBatchBody from a JSON string
task_instances_batch_body_instance = TaskInstancesBatchBody.from_json(json)
# print the JSON string representation of the object
print(TaskInstancesBatchBody.to_json())

# convert the object into a dict
task_instances_batch_body_dict = task_instances_batch_body_instance.to_dict()
# create an instance of TaskInstancesBatchBody from a dict
task_instances_batch_body_from_dict = TaskInstancesBatchBody.from_dict(task_instances_batch_body_dict)

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