Airflow kubernetes github8/28/2023 # The base url of your website as airflow cannot guess what domain or # Default mapreduce queue for HiveOperator tasks # provided explicitly or passed via `default_args` # The default owner assigned to each new operator, unless # endpoint_url = # So api will look like: endpoint_url = Īuth_backend = .default # If you set web_server_url_prefix, do NOT forget to append it here, ex: # database directly, while the json_client will use the api running on theĪpi_client = _client # In what way should the cli access the API. # `airflow trigger_dag -c`, the key-value pairs will override the existing ones in params. If you pass some key-value pairs through `airflow backfill -c` or # Whether to override params with dag_run.conf. # it has to cleanup after it is sent a SIGTERM, before it is SIGKILLED # When a task is killed forcefully, this is the amount of time in seconds that This will be deprecated in Airflow 2.0 (be forced to False). # Whether to enable pickling for xcom (note that this is insecure and allows for # Name of handler to read task instance logs. # Turn unit test mode on (overwrites many configuration options with test # If set to False enables some unsecure features like Charts and Ad Hoc Queries. # What security module to use (for example kerberos): # Can be used to de-elevate a sudo user running Airflow when executing tasks # If set, tasks without a `run_as_user` argument will be run with this user # The class to use for running task instances in a subprocess # How long before timing out a python file import while filling the DagBag # Secret key to save connection passwords in the db Plugins_folder = /usr/local/airflow/plugins # get started, but you probably want to set this to False in a production # Whether to load the examples that ship with Airflow. # The maximum number of active DAG runs per DAG # whose size is guided by this config element # When not using pools, tasks are run in the "default pool", # The number of task instances allowed to run concurrently by the scheduler # the max number of task instances that should run simultaneously # The amount of parallelism as a setting to the executor. # How many seconds to retry re-establishing a DB connection after # a lower config value will allow the system to recover faster. If the number of DB connections is ever exceeded, # can be idle in the pool before it is invalidated. # The SqlAlchemy pool recycle is the number of seconds a connection # The SqlAlchemy pool size is the maximum number of database connections Sql_alchemy_conn = If SqlAlchemy should pool database connections. # SqlAlchemy supports many different database engine, more information # The SqlAlchemy connection string to the metadata database. # SequentialExecutor, LocalExecutor, CeleryExecutor, DaskExecutor # The executor class that airflow should use. # can be utc (default), system, or any IANA timezone string (e.g. # Default timezone in case supplied date times are naive # Hostname by providing a path to a callable, which will resolve the hostname It's also fun to see the jobs spin up with the watch command kubectl get pods -watch -n airflow View the logs for the individual pods to know when they're up ( kubectl logs -f ) Run the airflow jobĮnable the DAG by clicking the toggle control to the on stateĬlick the trigger dag icon to run the jobĭrill into the job and view the progress. Note: The various airflow containers will take a few minutes until their fully operable, even if the kubectl status is RUNNING. Helm install -namespace "airflow" -name "airflow" -f airflow.yaml ~/src/charts/incubator/airflow/ Git clone docker to execute within the minikube VMīuild the and tag the image within the minikube VMĭocker build -t airflow-docker-local:0.1.1-1 Install the helm airflow chart
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