When there is a task running, Airflow will pop a notice saying the scheduler does not appear to be running and it kept showing until the task finished:
The scheduler does not appear to be running. Last heartbeat was received 5 minutes ago.
The DAGs list may not update, and new tasks will not be scheduled.
Actually, the scheduler process is running, as I have checked the process. After the task finished, the notice will disappear and everything back to normal.
My task is kind of heavy, may running for couple hours.
I think it is expected for Sequential Executor. Sequential Executor runs one thing at a time so it cannot run heartbeat and task at the same time.
Why do you need to use Sequential Executor / Sqlite? The advice to switch to other DB/Executor make perfect sense.
You have started airflow webserver and you haven't started your airflow scheduler.
Run airflow scheduler in background
airflow scheduler > /console/scheduler_log.log &
I had the same issue.
I switch to postgresql by updating airflow.cfg file > sql_alchemy_conn =postgresql+psycopg2://airflow#localhost:5432/airflow
and executor = LocalExecutor
This link may help how to set this up locally
https://medium.com/#taufiq_ibrahim/apache-airflow-installation-on-ubuntu-ddc087482c14
A quick fix could be to run the airflow scheduler separately. Perhaps not the best solution but it did work for me. To do so, run this command in the terminal:
airflow scheduler
I had a similar issue and have been trying to troubleshoot this for a while now.
I managed to fix it by setting this value in airflow.cfg:
scheduler_health_check_threshold = 240
PS: Based on a recent conversation in Airflow Slack Community, it could happen due to contention at the Database side. So, another workaround suggested was to scale up the database. In my case, this was not a viable solution.
EDIT:
This was last tested with Airflow Version 2.3.3
I have solved this issue by deleting airflow-scheduler.pid file.
then
airflow scheduler -D
Check the airflow-scheduler.err and airflow-scheduler.log files.
I got an error like this:
Traceback (most recent call last):
File "/home/myVM/venv/py_env/lib/python3.8/site-packages/lockfile/pidlockfile.py", ine 77, in acquire
write_pid_to_pidfile(self.path)
File "/home/myVM/venv/py_env/lib/python3.8/site-packages/lockfile/pidlockfile.py", line 161, in write_pid_to_pidfile
pidfile_fd = os.open(pidfile_path, open_flags, open_mode)
FileExistsError: [Errno 17] File exists: '/home/myVM/venv/py_env/airflow-scheduler.pid'
I removed the existing airflow-scheduler.pid file and started the scheduler again by airflow scheduler -D. It was working fine then.
Our problem is that the file "logs/scheduler.log" is too large, 1TB. After cleaning this file everything is fine.
I had the same issue while using sqlite. There was a special message in Airflow logs: ERROR - Cannot use more than 1 thread when using sqlite. Setting max_threads to 1. If you use only 1 thread, the scheduler will be unavailable while executing a dag.
So if use sqlite, try to switch to another database. If you don't, check max_threads value in your airflow.cfg.
On Composer page, click on your environment name, and it will open the Environment details, go to the PyPIPackages tab.
Click on Edit button, increase the any package version.
For example:
I increased the version of pymsql packages, and this restarted the airflow environment, it took a while for it to update. Once it is done, I'm no longer have this error.
You can also add a Python package, it will restart the airflow environment.
I've had the same issue after changing the airflow timezone. I then restarted the airflow-scheduler and it works. You can also check if the airflow-scheduler and airflow-worker are on different servers.
In simple words, using LocalExecutor and postgresql could fix this error.
Running Airflow locally, following the instruction, https://airflow.apache.org/docs/apache-airflow/stable/start/local.html.
It has the default config
executor = SequentialExecutor
sql_alchemy_conn = sqlite:////Users/yourusername/airflow/airflow.db
It will use SequentialExecutor and sqlite by default, and it will have this "The scheduler does not appear to be running." error.
To fix it, I followed Jarek Potiuk's advice. I changed the following config:
executor = LocalExecutor
sql_alchemy_conn = postgresql://postgres:masterpasswordforyourlocalpostgresql#localhost:5432
And then I rerun the "airflow db init"
airflow db init
airflow users create \
--username admin \
--firstname Peter \
--lastname Parker \
--role Admin \
--email spiderman#superhero.org
After the db inited. Run
airflow webserver --port 8080
airflow scheduler
This fixed the airflow scheduler error.
This happens to me when AIRFLOW_HOME is not set.
By setting AIRFLOW_HOME to the correct path, the indicated executor will be selected.
If it matters: somehow, the -D flag causes a lot of problems for me. The airflow webserver -D immediately crashes after starting, and airflow scheduler -D somehow does next to nothing for me.
Weirdly enough, it works without the detach flag. This means I can just run the program normally, and make it run in the background, with e.g. nohup airflow scheduler &.
After change executor from SequentialExecutor to LocalExecutor, it works!
in airflow.cfg:
executor = LocalExecutor
Related
In an Airflow DAG, I am trying to use a TimeDeltaTrigger:
from airflow.triggers.temporal import TimeDeltaTrigger
...
self.defer(trigger=TimeDeltaTrigger(timedelta(seconds=15)), method_name="execute")
But when my DAG runs, I get a warning in the GUI:
In the GUI, if I go to Browse -> Triggers I see one trigger, but it is not for TimeDeltaTrigger:
The documentation for Deferrable Operators (https://airflow.apache.org/docs/apache-airflow/stable/concepts/deferring.html) says:
Ensure your Airflow installation is running at least one triggerer process, as well as the normal scheduler
But it is not clear how to do this.
How can I configure my Airflow installation so that I can use a TimeDeltaTrigger?
triggerer is a process like scheduler, webserver, and worker. You need to start a process or container dedicated to running the triggerer to use deferrable operators.
To start a triggerer process, run airflow triggerer in your Airflow environment. You should see an output similar to the below image.
Triggerer Logs
I am trying to run a single task within a DAG on a GCP cloud composer airflow instance and mark all other tasks in the dag both upstream and downstream as successful. However, the following airflow command seems to not be working for me on cloud composer.
Does anyone know what is wrong with the followinggcloud cli command?
dag_id: "airflow_monitoring" <br>
task_id: "echo1" <br>
execution_date: "2020-07-03" <br>
gcloud composer environments run my-composer --location us-centra1 \
-- "airflow_monitoring" "echo1" "2020-07-03"
Thanks for your help.
If you aim just to correctly compose the above mentioned gcloud command, triggering the specific DAG, then after fixing some typos and propagating Airflow CLI sub-command parameters, I got this works:
gcloud composer environments run my-composer --location=us-central1 \
--project=<project-id> trigger_dag -- airflow_monitoring --run_id=echo1 --exec_date="2020-07-03"
I would also encourage you to check out the full Airflow CLI sub-command list.
In case you expect to get some different functional result, then feel free to expand the initial question, adding more essential content.
I'm running a custom backfill script that backfills a DAG serially. (If I run the backfill concurrently, I either run into a deadlock problem or a Serializable isolation problem.) As part of the process, I sometimes have failed DAGs mixed in with non-existing dates. To backfill that failed date, I need to clear it first. The problem comes in that a cleared DAG auto restarts and conflicts with the first date running in the backfill.
airflow clear dag_id -f -s 01-01-20 -e 01-12-20 -f
Because of the way it is built, I'll have to rewrite it from scratch and clear each backfill individually. If I can prevent a cleared DAG from rerunning, I would save me some time. Is there a way to do this in the cli?
You could try setting the max_active_runs argument to 1 when creating the DAG object. This will ensure that no more than one execution is active at a time, that way you can clear as many as you'd like and let Airflow handle the rest.
I deleted dag from airflow dag_bag and corresponding .pyc file as well. When I try to delete the same dag from airflow UI it is showing this error:
Dag id MY_DAG_ID is still in DagBag. Remove the DAG file first.
The airflow version I am using is 1.10.4
Even after restarting airflow I'm not able to delete from UI. I was using 1.10.3 previously, but I never faced this issue. I was able to delete from UI after deleting from dags folder.
When I click on that dag in UI it is showing :
DAG "MY_DAG_ID" seems to be missing.( this is expected as I deleted dag from folder)
Try stopping the scheduler and the webserver and then deleting the DAG from the command line:
airflow delete_dag 'MY_DAG_ID'
I had the same issues after I upgraded to 1.10.6. Here's what I did:
Before removing the DAG, make sure no instance is on running, retry status. Then Pause it
Delete on UI or using the command airflow delete_dag dag_id
Restart the scheduler and webserver
Try to execute airflow list_dags to see if it really got deleted.
If it doesn't work, try to upgrade to the latest version.
We installed Airflow 1.7, and used it for several months. I used PIP to uninstall ariflow 1.7 and install 1.9 (gory details are [here|airflow initdb failed: ImportError: No module named log.logging_mixin
Since then, I haven't had a single DAG run. I renamed and moved log files to match the 1.9 expectations, but still nothing happens.
I have a "run every 40 minutes" DAG, it hasn't run since 3/28. When I manually trigger it, no log file is created, nothing happens except I get a running DAG listed under "DAG Runs" (I do NOT get anything listed under "Recent Tasks", and "Last Run" does not get updated.
I have a "Run once" DAG that I created. I triggered it, same behavior.
I have also tried running the example_bash_operator DAG. Same behavior.
The Airflow documentation is a bit thin on all the requirements needed to run DAGs correctly.
Aside from the webserver, make sure the scheduler is running as well. Also check if the DAG is configured with a correct schedule and there is scheduling information in the "Task Instance Information" page.
See this answer for more "checkpoints": Airflow 1.9.0 is queuing but not launching tasks