I have a dynamic dag that creates different tasks (and flows) on each run.
The issue is that the Graph or Tree View always shows only the last run flow (and tasks).
So if I want to see the logs from let's say 2 executions ago, I have to go to the log files themselves (because its tasks are not present at all in the last run).
Is there a way to see the actual dag run (that is not based on last dynamic dag run) view somewhere - so that I can see tasks that do not exist anymore?
Thanks.
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.
While assigning two different DAGs for different tasks its showing "Tried to set relationships between tasks in more than one DAG"
Task can not be assigned to more than one DAG.
You can however create a code that generate tasks from configuration file thus using the same configuration file to be used in daily DAG & weekly DAG.
Another option is to use one 1 daily dag that uses also DayOfWeekSensor.
In that way you can execute your daily tasks as usual and have more tasks that will run only once a week.
I have a DAG that, whenever there are files detected by FileSensor, generates tasks for each file to (1) move the file to a staging area, (2) trigger a separate DAG to process the file.
FileSensor -> Move(File1) -> TriggerDAG(File1) -> Done
|-> Move(File2) -> TriggerDAG(File2) -^
In the DAG definition file, the middle tasks are generated by iterating over the directory that FileSensor is watching, a bit like this:
# def generate_move_task(f: Path) -> BashOperator
# def generate_dag_trigger(f: Path) -> TriggerDagRunOperator
with dag:
for filepath in Path(WATCH_DIR).glob(*):
sensor_task >> generate_move_task(filepath) >> generate_dag_trigger(filepath)
The Move task moves the files that lead to the task generation, so the next DAG run won't have FileSensor re-trigger either Move or TriggerDAG tasks for this file. In fact, the scheduler won't generate the tasks for this file at all, since after all files go through Move, the input directory has no contents to iterate over anymore..
This gives rise to two problems:
After execution, the task logs and renderings are no longer available. The Graph View only shows the DAG as it is now (empty), not as it was at runtime. (The Tree View shows that the tasks' run and state, but clicking on the "square" and picking any details leads to an Airflow error.)
The downstream tasks can be memory-holed due to a race condition. The first task is to move the originating file to a staging area. If that takes longer than the scheduler polling period, the scheduler no longer collects the downstream TriggerDAG(File1) task, which means that task is not scheduled to be executed even though the upstream task ran successfully. It's as if the downstream task never existed.
The race condition issue is solved by changing the task sequence to Copy(File1) -> TriggerDAG(File1) -> Remove(File1), but the broader problem remains: is there a way to persist dynamically generated tasks, or at least a way to consistently access them through the Airflow interface?
While it isn't clear, i'm assuming that downstream DAG(s) that you trigger via your orchestrator DAG are NOT dynamically generated for each file (like your Move & TriggerDAG tasks); in other words, unlike your Move tasks that keep appearing and disappearing (based on files), the downstream DAGs are static and stay there always
You've already built a relatively complex workflow that does advanced stuff like generating tasks dynamically and triggering external DAGs. I think with slight modification to your DAGs structure, you can get rid of your troubles (which also are quite advanced IMO)
Relocate the Move task(s) from your upstream orchestrator DAG to the downstream (per-file) process DAG(s)
Make the upstream orchestrator DAG do two things
Sense / wait for files to appear
For each file, trigger the downstream processing DAG (which in effect you are already doing).
For the orchestrator DAG, you can do it either ways
have a single task that does file sensing + triggering downstream DAGs for each file
have two tasks (I'd prefer this)
first task senses files and when they appear, publishes their list in an XCOM
second task reads that XCOM and foreach file, triggers it's corresponding DAG
but whatever way you choose, you'll have to replicate the relevant bits of code from
FileSensor (to be able to sense file and then publish their names in XCOM) and
TriggerDagRunOperator (so as to be able to trigger multiple DAGs with single task)
here's a diagram depicting the two tasks approach
The short answer to the title question is, as of Airflow 1.10.11, no, this doesn't seem possible as stated. To render DAG/task details, the Airflow webserver always consults the DAGs and tasks as they are currently defined and collected to DagBag. If the definition changes or disappears, tough luck. The dashboard just shows the log entries in the table; it doesn't probe the logs for prior logic (nor does it seem to store much of it other than the headline).
y2k-shubham provides an excellent solution to the unspoken question of "how can I write DAGs/tasks so that the transient metadata are accessible". The subtext of his solution: convert the transient metadata into something Airflow stores per task run, but keep the tasks themselves fixed. XCom is the solution he uses here, and it does shows up in the task instance details / logs.
Will Airflow implement persistent interface access to fleeting one-time tasks whose definition disappears from the DagBag? It's possible but unlikely, for two reasons:
It would require the webserver to probe the historical logs instead of just the current DagBag when rendering the dashboard, which would require extra infrastructure to keep the web interface snappy, and could make the display very confusing.
As y2k-shubham notes in a comment to another question of mine, fleeting and changing tasks/DAGs are an Airflow anti-pattern. I'd imagine that would make this a tough sell as the next feature.
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.
I created some DAGs, ran them and stopped them in the middle of their execution (with the OFF button).
The UI still shows 'Running tasks' for those stopped DAGs though.
I tried to set 'clear' to those tasks and not they are in blue, in 'shutdown state'.
I am wondering if those tasks are counted in the total of running tasks, and blocking other tasks from starting (with my current configuration, only 32 tasks can run in parallel). Is there a way to clean completely the DAGs that I don't need anymore and to make sure the tasks are not blocking anything and making Airflow slower?
Thanks!
You can delete all of the dag data from the dag_run and task_instances tables in the meta database.
You can also do this through the Airflow Webserver UI by navigating to
Browse -> DAG Runs
& Browse -> Task Instances
And deleting all the records relevant to the dag id.
One note though is that the task and DAG status fields on the main page may take a while to reflect the changes.