Airflow scheduler connection in use12/3/2023 Alternative Approach – Automated OrchestrationĪlthough Airflow is a valuable tool, it can be challenging to troubleshoot. It could be related to the specific version of Airflow you are using, or there may be problems with your DAG code or the dependencies it uses. If you are still experiencing problems with the scheduler not triggering DAGs at the scheduled time, other issues may be at play. You may want to check the logs or try restarting the webserver and scheduler again to see if that resolves the issue. In that case, it could be due to a problem with the configuration or connectivity between the two. Suppose you are experiencing the issue where the DAG only executes once after restarting the webserver and scheduler. Airflow Webserver and Scheduler MisconfigurationĪnother possible issue could be with the configuration of the Airflow webserver and scheduler. It is generally recommended to use static start dates to have more control over when the DAG is run, especially if you need to re-run jobs or backfill data. To solve this problem, you can either hard-code a static start date for the DAG or make sure that the dynamic start date is far enough in the past so that it is before the interval between executions. dag = DAG( 'run_job', default_args=default_args, catchup=False, ) This means that the first run of the DAG will be after the first interval rather than at the scheduled time. However, Airflow runs jobs at the end of an interval, not the beginning. In the provided code, the start date is set to the current date using the time module. One possible reason for this issue is the start date of the DAG. Airflow Runs Jobs at The End of An Interval Some common reason DAG Not Triggered at Scheduled Time are: 1. This article will provide examples of why DAGs may not be triggered, how to fix this issue, and introduce a tool called SQLake for simplifying data pipeline orchestration. It can be frustrating when the scheduler fails to trigger DAGs to run at the scheduled time, disrupting your workflows. Alternative Approach – Automated Orchestration.Airflow Webserver and Scheduler Misconfiguration Some common reason DAG Not Triggered at Scheduled Time are:.This will replace the default pod_template_file named in the airflow. You can also create custom pod_template_file on a per-task basis so that you can recycle the same base values between multiple tasks. apiVersion : v1 kind : Pod metadata : name : placeholder-name spec : containers : - env : - name : AIRFLOW_CORE_EXECUTOR value : LocalExecutor # Hard Coded Airflow Envs - name : AIRFLOW_CORE_FERNET_KEY valueFrom : secretKeyRef : name : RELEASE-NAME-fernet-key key : fernet-key - name : AIRFLOW_DATABASE_SQL_ALCHEMY_CONN valueFrom : secretKeyRef : name : RELEASE-NAME-airflow-metadata key : connection - name : AIRFLOW_CONN_AIRFLOW_DB valueFrom : secretKeyRef : name : RELEASE-NAME-airflow-metadata key : connection image : dummy_image imagePullPolicy : IfNotPresent name : base volumeMounts : - mountPath : "/opt/airflow/logs" name : airflow-logs - mountPath : /opt/airflow/airflow.cfg name : airflow-config readOnly : true subPath : airflow.cfg restartPolicy : Never securit圜ontext : runAsUser : 50000 fsGroup : 50000 serviceAccountName : "RELEASE-NAME-worker-serviceaccount" volumes : - emptyDir : " ) except ValueError as e : if i > 4 : raise e sidecar_task = test_sharedvolume_mount () Also, configuration information specific to the Kubernetes Executor, such as the worker namespace and image information, needs to be specified in the Airflow Configuration file.Īdditionally, the Kubernetes Executor enables specification of additional features on a per-task basis using the Executor config. One example of an Airflow deployment running on a distributed set of five nodes in a Kubernetes cluster is shown below.Ĭonsistent with the regular Airflow architecture, the Workers need access to the DAG files to execute the tasks within those DAGs and interact with the Metadata repository. ![]() The worker pod then runs the task, reports the result, and terminates. When a DAG submits a task, the KubernetesExecutor requests a worker pod from the Kubernetes API. KubernetesExecutor requires a non-sqlite database in the backend. Not necessarily need to be running on Kubernetes, but does need access to a Kubernetes cluster. KubernetesExecutor runs as a process in the Airflow Scheduler. The Kubernetes executor runs each task instance in its own pod on a Kubernetes cluster. ![]() Or by installing Airflow with the cncf.kubernetes extras: This can done by installing apache-airflow-providers-cncf-kubernetes>=7.4.0
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