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.
Related
The current problem that I am facing is that I have documents in a MongoDB collection which each need to be processed and updated by tasks which need to run in an acyclic dependency graph. If a task upstream fails to process a document, then none of the dependent tasks may process that document, as that document has not been updated with the prerequisite information.
If I were to use Airflow, this leaves me with two solutions:
Trigger a DAG for each document, and pass in the document ID with --conf. The problem with this is that this is not the intended way for Airflow to be used; I would never be running a scheduled process, and based on how documents appear in the collection, I would be making 1440 Dagruns per day.
Run a DAG every period for processing all documents created in the collection for that period. This follows how Airflow is expected to work, but the problem is that if a task fails to process a single document, none of the dependent tasks may process any of the other documents. Also, if a document takes longer than other documents do to be processed by a task, those other documents are waiting on that single document to continue down the DAG.
Is there a better method than Airflow? Or is there a better way to handle this in Airflow than the two methods I currently see?
From the knowledge I gained in my attempt to answer this question, I've come to the conclusion that Airflow is just not the tool for the job.
Airflow is designed for scheduled, idempotent DAGs. A DagRun must also have a unique execution_date; this means running the same DAG at the exact same start time (in the case that we receive two documents at the same time is quite literally impossible. Of course, we can schedule the next DagRun immediately in succession, but this limitation should demonstrate that any attempt to use Airflow in this fashion will always be, to an extent, a hack.
The most viable solution I've found is to instead use Prefect, which was developed with the intention of overcoming some of the limitations of Airflow:
"Prefect assumes that flows can be run at any time, for any reason."
Prefect's equivalent of a DAG is a Flow; one key advantage of a flow that we may take advantage of is the ease of parametriziation. Then, with some threads, we're able to have a Flow run for each element in a stream. Here is an example streaming ETL pipeline:
import time
from prefect import task, Flow, Parameter
from threading import Thread
def stream():
for x in range(10):
yield x
time.sleep(1)
#task
def extract(x):
# If 'x' referenced a document, in this step we could load that document
return x
#task
def transform(x):
return x * 2
#task
def load(y):
print("Received y: {}".format(y))
with Flow("ETL") as flow:
x_param = Parameter('x')
e = extract(x_param)
t = transform(e)
l = load(t)
for x in stream():
thread = Thread(target=flow.run, kwargs={"x": x})
thread.start()
You could change trigger_rule from "all_success" to "all_done"
https://github.com/apache/airflow/blob/62b21d747582d9d2b7cdcc34a326a8a060e2a8dd/airflow/example_dags/example_latest_only_with_trigger.py#L40
And also could create a branch that processes failed documents with trigger_rule set to "one_failed" to move processes those failed documents somehow differently (e.g. move to a "failed" folder and send a notification)
I would be making 1440 Dagruns per day.
With a good Airflow architecture, this is quite possible.
Choking points might be
executor - use Celery Executor instead of Local Executor for example
backend database - monitor and tune as necessary (indexes, proper storage etc)
webserver - well, for thousands of dagruns, tasks etc.. perhaps only use webeserver for dev/qa environments, and not for production where you have higher rate of task/dagruns submissions. You could use cli etc instead.
Another approach is scaling out by running multiple Airflow instances - partition documents let's say to ten buckets, and assign each partition's documents to just one Airflow instance.
I'd process the heavier tasks in parallel and feed successful operations downstream. As far as I know, you can't feed successes asynchronously to downstream tasks, so you would still need to wait for every thread to finish until moving downstream but, this would still be well more acceptable than spawning 1 dag for each record, something in these lines:
Task 1: read mongo filtering by some timestamp (remember idempotence) and feed tasks (i.e. via xcom);
Task 2: do stuff in paralell via PythonOperator, or even better via K8sPod, i.e:
def thread_fun(ret):
while not job_queue.empty():
job = job_queue.get()
try:
ret.append(stuff_done(job))
except:
pass
job_queue.task_done()
return ret
# Create workers and queue
threads = []
ret = [] # a mutable object
job_queue = Queue(maxsize=0)
for thr_nr in appropriate_thread_nr:
worker = threading.Thread(
target=thread_fun,
args=([ret])
)
worker.setDaemon(True)
threads.append(worker)
# Populate queue with jobs
for row in xcom_pull(task_ids=upstream_task):
job_queue.put(row)
# Start threads
for thr in threads:
thr.start()
# Wait to finish their jobs
for thr in threads:
thr.join()
xcom_push(ret)
Task 3: Do more stuff coming from previous task, and so on
We have built a system that queries MongoDB for a list, and generates a python file per item containing one DAG (note: having each dag have its own python file helps Airflow scheduler efficiency, with it's current design) - the generator DAG runs hourly, right before the scheduled hourly run of all the generated DAGs.
I have an Airflow DAG that runs once daily at a specific time. The DAG runs a bunch of SQL scripts to create and load tables in a database, and the very last task updates permissions so that users can access the tables. Currently the permissions task requires that all previous SQL tasks have completed, so this means that none of the tables' permissions are updated if any of the table tasks fail.
To fix this I'd like to create another permissions task (i.e., a backup task) that runs at a preset time regardless of the status of any of the previous tasks (doesn't hurt to update permissions multiple times). If I don't specify a time different from the DAG's time, then because the new task has no dependencies, the task will try updating permissions before any of the tables have been updated. Is there a setting for me to pass a cron string to a specific task? Or is there an option to pass a timedelta on top of the task's DAG time? I need to run the task some amount of time after the DAG time.
If your permissions task can run no matter what the result of the upstream tasks, I think the best option is simply to change the trigger_rule of your permissions task to all_done (default is all_success).
If you need to do some specific stuffs when there is a failure, you could consider creating a secondary DAG which first step is a sensor that waits for the main DAG to complete with State.FAILED, then run your permissions task.
Have a look at ExternalTaskSensor when you want to establish a dependency between DAGs.
I haven't checked but you might also need to use soft_fail on the sensor to prevent the secondary DAG to show up as failed when the main DAG completes successfully.
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.
I am new to airflow, I feel like I may be missing some convention or concept.
Context: I have files being periodically dropped into an S3 bucket. My pipeline will need to grab new files and process them.
Basically: How do I avoid re-processing?
It is not unlikely that some part of the pipeline will change in the future and I will want to re-process files. But on a day-to-day basis I don't want to re-process files. Additionally there will likely be other pipelines in the future which would need to start from the beginning and process all the files for a different output.
I have plenty of scrappy ways of preserving state (a local json file, or checking the existence of output files) - but I'm wondering if there's a convention in airflow. What makes the most sense to me at the moment is to re-use the postgres that exists for airflow (maybe bad form?), add another DB and start creating tables in there where I list input files if they have been processed for workflow X, workflow Y, etc.
How would you do this?
Here is how I have solved a similar problem with a 4 task DAG.
Write a custom S3Sensor that extends BaseSensorOperator.
This sensor uses the boto3 library, watches a specific folder in the bucket.
If any files are put into this bucket, it posts all the file paths to Xcom.
This Sensor is the first operator in the dag.
The next operator in the dag is a python operator that reads the list from the previous tasks Xcom.
It moves all the files to another folder in the same bucket, again listing the new paths to Xcom.
The next operator processes each of these files.
The next operator triggers this same dag again (so we start back at the custom s3 file sensor because this dag retriggers itself).
The dag needs to not have any schedule_interval, and needs to be triggered once manually. It will then watch the bucket until forever, or until something breaks.