I am trying to write a test case where I:
instantiate a collection of (Python)Operators (patching some of their dependencies with unittest.mock.patch)
arrange those Operators in a DAG
run that DAG
make assertions about the calls to various mocked downstreams
I see from here that running a DAG is not so simple as calling dag.run - I should instantiate a local_client and call trigger_dag on that. However, the resultant code constructs its own DagBag, and does not accept any parameter that allows me to pass in my manually-constructed DAG - so I cannot see how to run this DAG with local_client.
I see a couple of options here:
I could declare my testing DAG in a separate folder, as specified by DagModel.get_current(dag_id).fileloc, so that my DAG will get picked up by trigger_dag and so run - but this seems pretty indirect, and also I doubt that I'd be able to cleanly reference the injected mocks from my test code.
I could directly call api.common.experimental.trigger_dag._trigger_dag, which has a dag_bag argument. Both the experimental in the name, and the underscored-prefixed method name, suggest that this would be A Bad Idea.
Related
Recently I'm developing an airflow pipeline that will be running for multi tenants. This DAG will be triggered via API, and separated by batches, which is controlled by a metadabase in SQL following some business rules.
Each batch has a batch_id in order to controll the batches, and it is passed to conf DAG via API. The batch id has the timestamp of creation combined with tenant and filetype. Example: tenant1_20221120123323 ... tenant2_20221120123323. These batches can contain two filetypes ( for example purpouses) and for each filetype a DAG is triggered (DAG1 for filetype 1 and DAG2 for filetype 2) and then from the file perspective, it is combined with the filetype in some stages tenant1_20221120123323_filetype1, tenant1_20221120123323_filetype2 ...
For illustrate this, imagine that the first dag the following pipeline process_data_on_spark >> check_new_files_on_statingstorage >> [filetype2_exists, write_new_data_to_warehouse] filetype2_exists >> read_data_from_filetype2 >> merge_filetype2_filetype2 >> write_new_data_to_warehouse . Where the filetype2_exists is a BranchPythonOperator, that verify if DAG_2 was triggered, and if it was, it will merge the resulted data form DAG2 with the DAG1 before execute write_new_data_to_warehouse.
Based on this DAG model, there will be one DAG run for each tenant. So, the DAG can have multiple DAG runs running in parallel if we trigger more than one DAG run (one per tenant). Here is my first question:
Is a good practice work with multiple DAG runs in the same DAG instead of working with Dynamic DAGs ? In this case, I would end withprocess_data_on_spark _tenant1,
process_data_on_spark _tenant2, ...process_data_on_spark _tenantN. It worth mention that the number of tenants can reach hundreads.
Now, considering that the filetype2 can or not be present in the batch, and, considering that I would use the model mentioned above (on single DAG with multiples DAG run runnning in parallel - one for each tenant). The only idead that I have for check if DAG2 was triggered for the current batch (ie., filetype2 was present in the batch) was modify the DAG_run_id to include the batch_id, combined with the filetype:
The default dag_run_id: manual__2022-11-19T00:00:00+00:00
The new dag_run_id: manual__tenant1_20221120123323_filetype2__2022-11-19T00:00:00+00:00
And from then, I would be able to query the airflow metadatabse and check if there was an dag_run_id that contains the current batch_id and the filetype2 running, and, with a sensor, wait for the dag_status be success. Then, I could run the read_data_from_filetype2 task. Otherwise, if there is no dag_run_id with batch_id and filetype2 registed in airflow metadatabase, I can follow the write_new_data_to_warehouse directly.
Here's the other question:
Is a good practice to modify dag_run_id and use it combined with airflow metadatabase to control pipelines?
Considering this scenario, It would be better to create dynamic DAGs, even if there would be result in hundeads DAGs or working with dag_run_id and airflow_metadabase and keep parallel DAG runs in one single DAG?
Or, there would be a better approach for this problem?
Thank You.
I have a function which dynamically creates a sub task, where I am reading value from xcom_pull but there I am getting the error:
File "/home/airflow/gcs/recon_nik_v6.py", line 168, in create_audit_task
my_dict=kwargs["ti"].xcom_pull(task_ids='accept_input_from_cli', key='my_ip_dict')
KeyError: 'ti'
If I use the same my_dict=kwargs["ti"].xcom_pull(task_ids='accept_input_from_cli', key='my_ip_dict') code in another function then it works, but in this dynamic part it's not working.
Ssimilarly to your other questions (and explained in slack several times). This is not how Airflow works.
XCom pull and task instances are only available when DAG Run is being executed. When you create your DAG structure (i.e. dynamically generate DAGs) you cannot use them.
Only Task Instances when executing tasks can access them and this is already long after the DAGs have been parsed and DAG structure established.
So what you try to do is simply impossible.
I read the API reference and couldnt find anything on it, is that possible?
Currently, there is no such feature that does it out-of-the-box but you can write some custom code in your DAG to get around this. For example, use PythonOperator (you can use MySQL operator if your metadata db is mysql) to get status of the last X runs for the dag.
use BranchPythonOperator to see if the number is more than X, if it is then use a BashOperator to run airflow pause dag cli.
You can also just make this a 2-step task by adding logic of PythonOperator in BranchPythonOperator. This is just an idea, you can use a different logic.
I am using Airflow 1.9.0 with a custom SFTPOperator. I have code in my DAGs that poll an SFTP site to find new files. If any are found, then I create custom task id's for the dynamically created task and retrieve/delete the files.
directory_list = sftp_handler('sftp-site', None, '/', None, SFTPToS3Operation.LIST)
for file_path in directory_list:
... SFTP code that GET's the remote files
That part works fine. It seems both the airflow webserver and airflow scheduler are iterating through all the DAGs once a second and actually running the code that retrieves the directory_list. This means I'm hitting the SFTP site ~2 x a second to authenticate and pull a list of files. I'd like to have some conditional code that only executes if the DAG is actually being run.
When an SFTP site uses password authentication, the # of times I connect really isn't an issue. One site requires key authentication and if there are too many authentication failures in a short timespan, the account is locked. During my testing, this seems to happen occasionally for reasons I'm still trying to track down.
However, if I were authenticating only when the DAG was scheduled to execute, or executing manually, this would not be an issue. It also seems wasteful to spend so much time connecting to an SFTP site when it's not scheduled to do so.
I've seen a post that can check to see if a task is executing, but that's not ideal as I'd have to create a long-running task, using up resources I shouldn't require, just to perform that test. Any thoughts on how to accomplish this?
You have a very good use case for Airflow (SFTP to _____ batch jobs), but Airflow is not meant for dynamic DAGs as you are attempting to use them.
Top-Level DAG Code and the Scheduler Loop
As you noticed, any top-level code in a DAG is executed with each scheduler loop. Or put another way, every time the scheduler loop processes the files in your DAG directory it is interpreting all the code in your DAG files. Anything not in a task or operator is interpreted/executed immediately. This puts undue strain on the scheduler as well as any external systems you are making calls to.
Dynamic DAGs and the Airflow UI
Airflow does not handle dynamic DAGs through the UI well. This is mostly the result of the Airflow DAG state not being stored in the database. DAG views and history are rendered based on what exist in the interpreted DAG file at any given moment. I personally hope to see this change in the future with some form of DAG versioning.
In a dynamic DAG you can both add and remove tasks from a DAG.
Adding Tasks Dynamically
When adding tasks for a DAG run will make it appear (in the UI) that all DAG
runs before when that task never ran that task all. The will have a None state
and the DAG run will be set to success or failed depending on the outcome
of the DAG run.
Removing Tasks Dynamically
If your dynamic DAG ever removes tasks you will lose the ability to review history of the DAG. For example, if you run a DAG with task_x in the first 20 DAG runs but remove it after that, it will fail to show up in the UI until it is added back into the DAG.
Idempotency and Airflow
Airflow works best when the DAG runs are idempotent. This means that re-running any DAG Run should have the same affect no matter when you run it or how many times you run it. Dynamic DAGs in Airflow break idempotency by adding and removing tasks to previous DAG runs so that the results of re-running are not the same.
Solution Options
You have at least two options moving forward
1.) Continue to build your SFTP DAG dynamically, but create another DAG that writes the available SFTP files to a local file (if not using distributed executor) or an Airflow Variable (this will result in more reads to the Airflow DB) and build your DAG dynamically from that.
2.) Overload the SFTPOperator to take a list of files so that every file that exist is processed within a single task run. This will make the DAGs idempotent and you will maintain accurate history through the logs.
I apologize for the extended explanation, but you're touching on one of the rough spots of Airflow and I felt it was appropriate to give an overview of the problem at hand.
Is it possible to pass parameters to Airflow's jobs through UI?
AFAIK, 'params' argument in DAG is defined in python code, therefore it can't be changed at runtime.
Depending on what you're trying to do, you might be able to leverage Airflow Variables. These can be defined or edited in the UI under the Admin tab. Then your DAG code can read the value of the variable and pass the value to the DAG(s) it creates.
Note, however, that although Variables let you decouple values from code, all runs of a DAG will read the same value for the variable. If you want runs to be passed different values, your best bet is probably to use airflow templating macros and differentiate macros with the run_id macro or similar
Two ways to change your DAG behavior:
Use Airflow variables like mentioned by Bryan in his answer.
Use Airflow JSON Conf to pass JSON data to a single DAG run. JSON can be passed either from
UI - manual trigger from tree view
UI - create new DAG run from browse > DAG runs > create new record
or from
CLI
airflow trigger_dag 'MY_DAG' -r 'test-run-1' --conf '{"exec_date":"2021-09-14"}'
Within the DAG this JSON can be accessed using jinja templates or in the operator callable function context param.
def do_some_task(**context):
print(context['dag_run'].conf['exec_date'])
task1 = PythonOperator(
task_id='task1_id',
provide_context=True,
python_callable=do_some_task,
dag=dag,
)
#access in templates
task2 = BashOperator(
task_id="task2_id",
bash_command="{{ dag_run.conf['exec_date'] }}",
dag=dag,
)
Note that the JSON conf will not be present during scheduled runs. The best use case for JSON conf is to override the default DAG behavior. Hence set meaningful defaults in the DAG code so that during scheduled runs JSON conf is not used.