Airflow progressive slowness

[llamageddon83]

Apache Airflow version

Other Airflow 2 version (please specify below)

What happened

We are running Airflow on EKS with version 2.5.3. Airflow has been experiencing progressive slowness over a period of 2-3 weeks where DAGs start getting queued without ever executing and leads us to restart the scheduler pod. After the pod restart, problem goes away for a few days and then starts to slowly creep back up.

The pods, the logs and the dashboards all look healthy, the UI shows that no tasks are currently running, and that there are no worker pods alive. The resource usage graphs (CPU, memory) also look what they should if no DAGs are actually executing.

During one such outage, we disabled all the DAGs and marked all the tasks as success just to see if scheduler is able to spin up new worker pods. Scheduler never recovered and we restarted the scheduler pod.

However, there is one dashboard that shows metrics named Executor running tasks and Executor open slots. We noticed that this dashboard was accurately representing the slowness behavior. Over a period of time, number of open slots would decrease and vice versa for running tasks. These two would never reset even when nothing is running during a long period of time which is every day between 10:00 PM to 8:00 AM.

These metrics are coming from base_exeuctor :

        Stats.gauge("executor.open_slots", open_slots)
        Stats.gauge("executor.queued_tasks", num_queued_tasks)
        Stats.gauge("executor.running_tasks", num_running_tasks)

and num_running_tasks is defined as num_running_tasks = len(self.running) in base_executor.

So we enabled some logs from KuberenetesExecutor under this method to see what was in self.running:

    def sync(self) -> None:
        """Synchronize task state."""
      ####
        if self.running:
            self.log.debug("self.running: %s", self.running)  #--> this log
       ###
        self.kube_scheduler.sync()

where self.running is defined as self.running: set[TaskInstanceKey] = set(). The log showed that somehow the tasks that have been completed successfully in the past still exist in self.running. For example, a snippet of the log outputted on the 28th is holding on to the tasks that have already been successfully completed on the 24th and 27th:

**time: Jul 28, 2023 @ 15:07:01.784**
self.running: {TaskInstanceKey(dag_id='flight_history.py', task_id='load_file', run_id=**'manual__2023-07-24T01:06:18+00:00'**, try_number=1, map_index=17), TaskInstanceKey(dag_id='emd_load.py', task_id='processing.emd', run_id='**scheduled__2023-07-25T07:30:00+00:00'**, try_number=1, map_index=-1), 

We validated that these tasks have been completed without any issue from the UI and Postgres DB (which we use as the metadata backend).

Once the scheduler pod is restarted, the problem goes away, the metrics in Grafana dashboard reset and tasks start executing.

What you think should happen instead

Airflow's scheduler is keeping a track of currently running tasks and their state in memory. And that state in some cases is not getting cleared. The tasks that have been completed should eventually be cleared from running set in KubernetesExecutor once the worker pod exits.

How to reproduce

Beats me. Our initial assumption was that that is a DAG implementation issue and some particular DAG is misbehaving. But this problem has occurred with all sorts of DAGs, happens for scheduled and manual runs, and is sporadic. Tt here is some edge scenario that causes this to happen. But we are unable to nail it down any further.

Operating System

Debian GNU/ Linux 11 (bullseye)

Versions of Apache Airflow Providers

aiofiles==23.1.0
aiohttp==3.8.4
airflow-dbt>=0.4.0
airflow-exporter==1.5.3
anytree==2.8.0
apache-airflow-providers-ftp==2.0.1
apache-airflow-providers-http>=2.0.3
apache-airflow-providers-microsoft-mssql==2.1.3
apache-airflow-providers-snowflake>=4.0.4
apache-airflow-providers-hashicorp==3.3.0
apache-airflow-providers-cncf-kubernetes==5.2.2
apache-airflow>=2.2.3
asgiref==3.5.0
Authlib==0.15.5
dbt-snowflake==1.5.2
flatdict==4.0.1
hvac==0.11.2
jsonschema>=4.17.3
pandas==1.3.5
psycopg2-binary==2.9.3
pyOpenSSL==23.1.1
pysftp==0.2.9
pysmbclient==0.1.5
python-gnupg==0.5.0
PyYAML~=5.4.1
requests~=2.26.0
smbprotocol==1.9.0
snowflake-connector-python== 3.0.4
snowflake-sqlalchemy==1.4.7
statsd==3.3.0
py7zr==0.20.5

Deployment

Official Apache Airflow Helm Chart

Deployment details

Airflow is deployed via helm charts on EKS in AWS. There are two scheduler pods with AIRFLOW__CORE__PARALLELISM set to 10.

Anything else

N/A

Are you willing to submit PR?

• Yes I am willing to submit a PR!

Code of Conduct

• I agree to follow this project's Code of Conduct

[ephraimbuddy]

We have had a minor release since 2.5.3, could you try this with Airflow 2.6.3?

[llamageddon83]

Hi @ephraimbuddy thank you for taking a look at this. Yes I will bump the version and see if the issue persists. It will take about 2-3 weeks to push the change through to production.

[llamageddon83]

Hi @ephraimbuddy. We pushed 2.6.3 to prod last week. And the progressive slowness has started to creep up again. Version bump didn't seem to remedy the issue.

[potiuk]

Is it possible that you do check which tasks are in "running" state for some time when it happens and maybe try to find some correlated logs from scheduler and task executtion - and ideally also find the logs from another task instance of the same task that was not found in "running" so that we could compare them and see the difference?

I preseume that there is some kind of race happenin that will somehow skip removal of the task even if it has been completed. - but seeing the logs and comparing them could narrow down the searrch an might allow us to come up with a plausible hypothesis.

Question - do you run deferrable tasks / triggerer ? Maybe somehow there is a problem with deferrable code ? (wild guessing now after looking into some possible paths).