| [core] |
| # The folder where your airflow pipelines live, most likely a |
| # subfolder in a code repository |
| # This path must be absolute |
| dags_folder = /home/airflow/airflow/dags |
| |
| # The folder where airflow should store its log files |
| # This path must be absolute |
| base_log_folder = /home/airflow/airflow/logs |
| |
| # Airflow can store logs remotely in AWS S3, Google Cloud Storage or Elastic Search. |
| # Users must supply an Airflow connection id that provides access to the storage |
| # location. If remote_logging is set to true, see UPDATING.md for additional |
| # configuration requirements. |
| remote_logging = False |
| remote_log_conn_id = |
| remote_base_log_folder = |
| encrypt_s3_logs = False |
| |
| # Logging level |
| logging_level = INFO |
| fab_logging_level = WARN |
| |
| # Logging class |
| # Specify the class that will specify the logging configuration |
| # This class has to be on the python classpath |
| # logging_config_class = my.path.default_local_settings.LOGGING_CONFIG |
| logging_config_class = |
| |
| # Log format |
| # we need to escape the curly braces by adding an additional curly brace |
| log_format = [%%(asctime)s] {{%%(filename)s:%%(lineno)d}} %%(levelname)s - %%(message)s |
| simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s |
| |
| # Log filename format |
| # we need to escape the curly braces by adding an additional curly brace |
| log_filename_template = {{ ti.dag_id }}/{{ ti.task_id }}/{{ ts }}/{{ try_number }}.log |
| log_processor_filename_template = {{ filename }}.log |
| dag_processor_manager_log_location = /home/airflow/airflow/logs/dag_processor_manager/dag_processor_manager.log |
| |
| # Hostname by providing a path to a callable, which will resolve the hostname |
| hostname_callable = socket:getfqdn |
| |
| # Default timezone in case supplied date times are naive |
| # can be utc (default), system, or any IANA timezone string (e.g. Europe/Amsterdam) |
| default_timezone = system |
| |
| # The executor class that airflow should use. Choices include |
| # SequentialExecutor, LocalExecutor, CeleryExecutor, DaskExecutor |
| executor = LocalExecutor |
| |
| # The SqlAlchemy connection string to the metadata database. |
| # SqlAlchemy supports many different database engine, more information |
| # their website |
| #sql_alchemy_conn = sqlite:////tmp/airflow.db |
| |
| |
| # If SqlAlchemy should pool database connections. |
| sql_alchemy_pool_enabled = True |
| |
| # The encoding for the databases |
| sql_engine_encoding = utf-8 |
| |
| # The SqlAlchemy pool size is the maximum number of database connections |
| # in the pool. 0 indicates no limit. |
| sql_alchemy_pool_size = 5 |
| |
| # The SqlAlchemy pool recycle is the number of seconds a connection |
| # can be idle in the pool before it is invalidated. This config does |
| # not apply to sqlite. If the number of DB connections is ever exceeded, |
| # a lower config value will allow the system to recover faster. |
| sql_alchemy_pool_recycle = 1800 |
| |
| # How many seconds to retry re-establishing a DB connection after |
| # disconnects. Setting this to 0 disables retries. |
| sql_alchemy_reconnect_timeout = 300 |
| |
| # The schema to use for the metadata database |
| # SqlAlchemy supports databases with the concept of multiple schemas. |
| sql_alchemy_schema = |
| |
| # The amount of parallelism as a setting to the executor. This defines |
| # the max number of task instances that should run simultaneously |
| # on this airflow installation |
| parallelism = 32 |
| |
| # The number of task instances allowed to run concurrently by the scheduler |
| dag_concurrency = 16 |
| |
| # Are DAGs paused by default at creation |
| dags_are_paused_at_creation = False |
| |
| # When not using pools, tasks are run in the "default pool", |
| # whose size is guided by this config element |
| non_pooled_task_slot_count = 128 |
| |
| # The maximum number of active DAG runs per DAG |
| max_active_runs_per_dag = 16 |
| |
| # Whether to load the examples that ship with Airflow. It's good to |
| # get started, but you probably want to set this to False in a production |
| # environment |
| load_examples = True |
| |
| # Where your Airflow plugins are stored |
| plugins_folder = /home/airflow/airflow/plugins |
| |
| # Secret key to save connection passwords in the db |
| fernet_key = $FERNET_KEY |
| |
| # Whether to disable pickling dags |
| donot_pickle = False |
| |
| # How long before timing out a python file import while filling the DagBag |
| dagbag_import_timeout = 30 |
| |
| # The class to use for running task instances in a subprocess |
| #task_runner = StandardTaskRunner |
| # use BashTaskRunner for 1.10.2 |
| task_runner = BashTaskRunner |
| |
| # If set, tasks without a `run_as_user` argument will be run with this user |
| # Can be used to de-elevate a sudo user running Airflow when executing tasks |
| default_impersonation = |
| |
| # What security module to use (for example kerberos): |
| security = |
| |
| # If set to False enables some unsecure features like Charts and Ad Hoc Queries. |
| # In 2.0 will default to True. |
| secure_mode = False |
| |
| # Turn unit test mode on (overwrites many configuration options with test |
| # values at runtime) |
| unit_test_mode = False |
| |
| # Name of handler to read task instance logs. |
| # Default to use task handler. |
| task_log_reader = task |
| |
| # Whether to enable pickling for xcom (note that this is insecure and allows for |
| # RCE exploits). This will be deprecated in Airflow 2.0 (be forced to False). |
| enable_xcom_pickling = True |
| |
| # When a task is killed forcefully, this is the amount of time in seconds that |
| # it has to cleanup after it is sent a SIGTERM, before it is SIGKILLED |
| killed_task_cleanup_time = 60 |
| |
| # Whether to override params with dag_run.conf. If you pass some key-value pairs through `airflow backfill -c` or |
| # `airflow trigger_dag -c`, the key-value pairs will override the existing ones in params. |
| dag_run_conf_overrides_params = False |
| |
| # Worker initialisation check to validate Metadata Database connection |
| worker_precheck = False |
| |
| # When discovering DAGs, ignore any files that don't contain the strings `DAG` and `airflow`. |
| dag_discovery_safe_mode = True |
| |
| [cli] |
| # In what way should the cli access the API. The LocalClient will use the |
| # database directly, while the json_client will use the api running on the |
| # webserver |
| api_client = airflow.api.client.local_client |
| |
| # If you set web_server_url_prefix, do NOT forget to append it here, ex: |
| # endpoint_url = http://localhost:8080/myroot |
| # So api will look like: http://localhost:8080/myroot/api/experimental/... |
| endpoint_url = http://localhost:8080 |
| |
| [api] |
| # How to authenticate users of the API |
| auth_backend = airflow.api.auth.backend.default |
| |
| [lineage] |
| # what lineage backend to use |
| backend = |
| |
| [atlas] |
| sasl_enabled = False |
| host = |
| port = 21000 |
| username = |
| password = |
| |
| [operators] |
| # The default owner assigned to each new operator, unless |
| # provided explicitly or passed via `default_args` |
| default_owner = Airflow |
| default_cpus = 1 |
| default_ram = 512 |
| default_disk = 512 |
| default_gpus = 0 |
| |
| [hive] |
| # Default mapreduce queue for HiveOperator tasks |
| default_hive_mapred_queue = |
| # Template for mapred_job_name in HiveOperator, supports the following named parameters: |
| # hostname, dag_id, task_id, execution_date |
| mapred_job_name_template = Airflow HiveOperator task for {hostname}.{dag_id}.{task_id}.{execution_date} |
| |
| [webserver] |
| # The base url of your website as airflow cannot guess what domain or |
| # cname you are using. This is used in automated emails that |
| # airflow sends to point links to the right web server |
| base_url = http://localhost:8080 |
| |
| # The ip specified when starting the web server |
| web_server_host = 0.0.0.0 |
| |
| # The port on which to run the web server |
| web_server_port = 8080 |
| |
| # Paths to the SSL certificate and key for the web server. When both are |
| # provided SSL will be enabled. This does not change the web server port. |
| web_server_ssl_cert = |
| web_server_ssl_key = |
| |
| # Number of seconds the webserver waits before killing gunicorn master that doesn't respond |
| web_server_master_timeout = 120 |
| |
| # Number of seconds the gunicorn webserver waits before timing out on a worker |
| web_server_worker_timeout = 120 |
| |
| # Number of workers to refresh at a time. When set to 0, worker refresh is |
| # disabled. When nonzero, airflow periodically refreshes webserver workers by |
| # bringing up new ones and killing old ones. |
| worker_refresh_batch_size = 1 |
| |
| # Number of seconds to wait before refreshing a batch of workers. |
| worker_refresh_interval = 30 |
| |
| # Secret key used to run your flask app |
| secret_key = temporary_key |
| |
| # Number of workers to run the Gunicorn web server |
| workers = 4 |
| |
| # The worker class gunicorn should use. Choices include |
| # sync (default), eventlet, gevent |
| worker_class = sync |
| |
| # Log files for the gunicorn webserver. '-' means log to stderr. |
| access_logfile = - |
| error_logfile = - |
| |
| # Expose the configuration file in the web server |
| # This is only applicable for the flask-admin based web UI (non FAB-based). |
| # In the FAB-based web UI with RBAC feature, |
| # access to configuration is controlled by role permissions. |
| expose_config = True |
| |
| # Set to true to turn on authentication: |
| # https://airflow.apache.org/security.html#web-authentication |
| authenticate = False |
| |
| # Filter the list of dags by owner name (requires authentication to be enabled) |
| filter_by_owner = False |
| |
| # Filtering mode. Choices include user (default) and ldapgroup. |
| # Ldap group filtering requires using the ldap backend |
| # |
| # Note that the ldap server needs the "memberOf" overlay to be set up |
| # in order to user the ldapgroup mode. |
| owner_mode = user |
| |
| # Default DAG view. Valid values are: |
| # tree, graph, duration, gantt, landing_times |
| dag_default_view = tree |
| |
| # Default DAG orientation. Valid values are: |
| # LR (Left->Right), TB (Top->Bottom), RL (Right->Left), BT (Bottom->Top) |
| dag_orientation = LR |
| |
| # Puts the webserver in demonstration mode; blurs the names of Operators for |
| # privacy. |
| demo_mode = False |
| |
| # The amount of time (in secs) webserver will wait for initial handshake |
| # while fetching logs from other worker machine |
| log_fetch_timeout_sec = 5 |
| |
| # By default, the webserver shows paused DAGs. Flip this to hide paused |
| # DAGs by default |
| hide_paused_dags_by_default = False |
| |
| # Consistent page size across all listing views in the UI |
| page_size = 100 |
| |
| # Use FAB-based webserver with RBAC feature |
| rbac = False |
| |
| # Define the color of navigation bar |
| navbar_color = #007A87 |
| |
| # Default dagrun to show in UI |
| default_dag_run_display_number = 25 |
| |
| # Enable werkzeug `ProxyFix` middleware |
| enable_proxy_fix = False |
| |
| # Set secure flag on session cookie |
| cookie_secure = False |
| |
| # Set samesite policy on session cookie |
| cookie_samesite = |
| |
| [email] |
| email_backend = airflow.utils.email.send_email_smtp |
| |
| [smtp] |
| # If you want airflow to send emails on retries, failure, and you want to use |
| # the airflow.utils.email.send_email_smtp function, you have to configure an |
| # smtp server here |
| smtp_host = localhost |
| smtp_starttls = True |
| smtp_ssl = False |
| # Uncomment and set the user/pass settings if you want to use SMTP AUTH |
| # smtp_user = airflow |
| # smtp_password = airflow |
| smtp_port = 25 |
| smtp_mail_from = airflow@example.com |
| |
| [celery] |
| # This section only applies if you are using the CeleryExecutor in |
| # [core] section above |
| |
| # The app name that will be used by celery |
| celery_app_name = airflow.executors.celery_executor |
| |
| # The concurrency that will be used when starting workers with the |
| # "airflow worker" command. This defines the number of task instances that |
| # a worker will take, so size up your workers based on the resources on |
| # your worker box and the nature of your tasks |
| worker_concurrency = 16 |
| |
| # The maximum and minimum concurrency that will be used when starting workers with the |
| # "airflow worker" command (always keep minimum processes, but grow to maximum if necessary). |
| # Note the value should be "max_concurrency,min_concurrency" |
| # Pick these numbers based on resources on worker box and the nature of the task. |
| # If autoscale option is available, worker_concurrency will be ignored. |
| # http://docs.celeryproject.org/en/latest/reference/celery.bin.worker.html#cmdoption-celery-worker-autoscale |
| # worker_autoscale = 16,12 |
| |
| # When you start an airflow worker, airflow starts a tiny web server |
| # subprocess to serve the workers local log files to the airflow main |
| # web server, who then builds pages and sends them to users. This defines |
| # the port on which the logs are served. It needs to be unused, and open |
| # visible from the main web server to connect into the workers. |
| worker_log_server_port = 8793 |
| |
| # The Celery broker URL. Celery supports RabbitMQ, Redis and experimentally |
| # a sqlalchemy database. Refer to the Celery documentation for more |
| # information. |
| # http://docs.celeryproject.org/en/latest/userguide/configuration.html#broker-settings |
| broker_url = redis://redis:6379/1 |
| |
| # The Celery result_backend. When a job finishes, it needs to update the |
| # metadata of the job. Therefore it will post a message on a message bus, |
| # or insert it into a database (depending of the backend) |
| # This status is used by the scheduler to update the state of the task |
| # The use of a database is highly recommended |
| # http://docs.celeryproject.org/en/latest/userguide/configuration.html#task-result-backend-settings |
| result_backend = db+postgresql://airflow:airflow@postgres/airflow |
| |
| # Celery Flower is a sweet UI for Celery. Airflow has a shortcut to start |
| # it `airflow flower`. This defines the IP that Celery Flower runs on |
| flower_host = 0.0.0.0 |
| |
| # The root URL for Flower |
| # Ex: flower_url_prefix = /flower |
| flower_url_prefix = |
| |
| # This defines the port that Celery Flower runs on |
| flower_port = 5555 |
| |
| # Securing Flower with Basic Authentication |
| # Accepts user:password pairs separated by a comma |
| # Example: flower_basic_auth = user1:password1,user2:password2 |
| flower_basic_auth = |
| |
| # Default queue that tasks get assigned to and that worker listen on. |
| default_queue = default |
| |
| # How many processes CeleryExecutor uses to sync task state. |
| # 0 means to use max(1, number of cores - 1) processes. |
| sync_parallelism = 0 |
| |
| # Import path for celery configuration options |
| celery_config_options = airflow.config_templates.default_celery.DEFAULT_CELERY_CONFIG |
| |
| # In case of using SSL |
| ssl_active = False |
| ssl_key = |
| ssl_cert = |
| ssl_cacert = |
| |
| [celery_broker_transport_options] |
| # This section is for specifying options which can be passed to the |
| # underlying celery broker transport. See: |
| # http://docs.celeryproject.org/en/latest/userguide/configuration.html#std:setting-broker_transport_options |
| |
| # The visibility timeout defines the number of seconds to wait for the worker |
| # to acknowledge the task before the message is redelivered to another worker. |
| # Make sure to increase the visibility timeout to match the time of the longest |
| # ETA you're planning to use. |
| # |
| # visibility_timeout is only supported for Redis and SQS celery brokers. |
| # See: |
| # http://docs.celeryproject.org/en/master/userguide/configuration.html#std:setting-broker_transport_options |
| # |
| #visibility_timeout = 21600 |
| |
| [dask] |
| # This section only applies if you are using the DaskExecutor in |
| # [core] section above |
| |
| # The IP address and port of the Dask cluster's scheduler. |
| cluster_address = 127.0.0.1:8786 |
| # TLS/ SSL settings to access a secured Dask scheduler. |
| tls_ca = |
| tls_cert = |
| tls_key = |
| |
| [scheduler] |
| # Task instances listen for external kill signal (when you clear tasks |
| # from the CLI or the UI), this defines the frequency at which they should |
| # listen (in seconds). |
| job_heartbeat_sec = 5 |
| |
| # The scheduler constantly tries to trigger new tasks (look at the |
| # scheduler section in the docs for more information). This defines |
| # how often the scheduler should run (in seconds). |
| scheduler_heartbeat_sec = 5 |
| |
| # after how much time should the scheduler terminate in seconds |
| # -1 indicates to run continuously (see also num_runs) |
| run_duration = -1 |
| |
| # after how much time (seconds) a new DAGs should be picked up from the filesystem |
| min_file_process_interval = 0 |
| |
| # How often (in seconds) to scan the DAGs directory for new files. Default to 5 minutes. |
| dag_dir_list_interval = 300 |
| |
| # How often should stats be printed to the logs |
| print_stats_interval = 30 |
| |
| # If the last scheduler heartbeat happened more than scheduler_health_check_threshold ago (in seconds), |
| # scheduler is considered unhealthy. |
| # This is used by the health check in the "/health" endpoint |
| # This is used by the health check in the "/health" endpoint |
| scheduler_health_check_threshold = 30 |
| |
| child_process_log_directory = /home/airflow/airflow/logs/scheduler |
| |
| # Local task jobs periodically heartbeat to the DB. If the job has |
| # not heartbeat in this many seconds, the scheduler will mark the |
| # associated task instance as failed and will re-schedule the task. |
| scheduler_zombie_task_threshold = 300 |
| |
| # Turn off scheduler catchup by setting this to False. |
| # Default behavior is unchanged and |
| # Command Line Backfills still work, but the scheduler |
| # will not do scheduler catchup if this is False, |
| # however it can be set on a per DAG basis in the |
| # DAG definition (catchup) |
| catchup_by_default = True |
| |
| # This changes the batch size of queries in the scheduling main loop. |
| # If this is too high, SQL query performance may be impacted by one |
| # or more of the following: |
| # - reversion to full table scan |
| # - complexity of query predicate |
| # - excessive locking |
| # |
| # Additionally, you may hit the maximum allowable query length for your db. |
| # |
| # Set this to 0 for no limit (not advised) |
| max_tis_per_query = 512 |
| |
| # Statsd (https://github.com/etsy/statsd) integration settings |
| statsd_on = False |
| statsd_host = localhost |
| statsd_port = 8125 |
| statsd_prefix = airflow |
| |
| # The scheduler can run multiple threads in parallel to schedule dags. |
| # This defines how many threads will run. |
| max_threads = 2 |
| |
| authenticate = False |
| |
| # Turn off scheduler use of cron intervals by setting this to False. |
| # DAGs submitted manually in the web UI or with trigger_dag will still run. |
| use_job_schedule = True |
| |
| [ldap] |
| # set this to ldaps://<your.ldap.server>:<port> |
| uri = |
| user_filter = objectClass=* |
| user_name_attr = uid |
| group_member_attr = memberOf |
| superuser_filter = |
| data_profiler_filter = |
| bind_user = cn=Manager,dc=example,dc=com |
| bind_password = insecure |
| basedn = dc=example,dc=com |
| cacert = /etc/ca/ldap_ca.crt |
| search_scope = LEVEL |
| |
| # This setting allows the use of LDAP servers that either return a |
| # broken schema, or do not return a schema. |
| ignore_malformed_schema = False |
| |
| [mesos] |
| # Mesos master address which MesosExecutor will connect to. |
| master = localhost:5050 |
| |
| # The framework name which Airflow scheduler will register itself as on mesos |
| framework_name = Airflow |
| |
| # Number of cpu cores required for running one task instance using |
| # 'airflow run <dag_id> <task_id> <execution_date> --local -p <pickle_id>' |
| # command on a mesos slave |
| task_cpu = 1 |
| |
| # Memory in MB required for running one task instance using |
| # 'airflow run <dag_id> <task_id> <execution_date> --local -p <pickle_id>' |
| # command on a mesos slave |
| task_memory = 256 |
| |
| # Enable framework checkpointing for mesos |
| # See http://mesos.apache.org/documentation/latest/slave-recovery/ |
| checkpoint = False |
| |
| # Failover timeout in milliseconds. |
| # When checkpointing is enabled and this option is set, Mesos waits |
| # until the configured timeout for |
| # the MesosExecutor framework to re-register after a failover. Mesos |
| # shuts down running tasks if the |
| # MesosExecutor framework fails to re-register within this timeframe. |
| # failover_timeout = 604800 |
| |
| # Enable framework authentication for mesos |
| # See http://mesos.apache.org/documentation/latest/configuration/ |
| authenticate = False |
| |
| # Mesos credentials, if authentication is enabled |
| # default_principal = admin |
| # default_secret = admin |
| |
| # Optional Docker Image to run on slave before running the command |
| # This image should be accessible from mesos slave i.e mesos slave |
| # should be able to pull this docker image before executing the command. |
| # docker_image_slave = puckel/docker-airflow |
| |
| [kerberos] |
| ccache = /tmp/airflow_krb5_ccache |
| # gets augmented with fqdn |
| principal = airflow |
| reinit_frequency = 3600 |
| kinit_path = kinit |
| keytab = airflow.keytab |
| |
| [github_enterprise] |
| api_rev = v3 |
| |
| [admin] |
| # UI to hide sensitive variable fields when set to True |
| hide_sensitive_variable_fields = True |
| |
| [elasticsearch] |
| elasticsearch_host = |
| # we need to escape the curly braces by adding an additional curly brace |
| elasticsearch_log_id_template = {dag_id}-{task_id}-{execution_date}-{try_number} |
| elasticsearch_end_of_log_mark = end_of_log |
| |
| [kubernetes] |
| # The repository, tag and imagePullPolicy of the Kubernetes Image for the Worker to Run |
| worker_container_repository = |
| worker_container_tag = |
| worker_container_image_pull_policy = IfNotPresent |
| |
| # If True (default), worker pods will be deleted upon termination |
| delete_worker_pods = True |
| |
| # Number of Kubernetes Worker Pod creation calls per scheduler loop |
| worker_pods_creation_batch_size = 1 |
| |
| # The Kubernetes namespace where airflow workers should be created. Defaults to `default` |
| namespace = default |
| |
| # The name of the Kubernetes ConfigMap Containing the Airflow Configuration (this file) |
| airflow_configmap = |
| |
| # For docker image already contains DAGs, this is set to `True`, and the worker will search for dags in dags_folder, |
| # otherwise use git sync or dags volume claim to mount DAGs |
| dags_in_image = False |
| |
| # For either git sync or volume mounted DAGs, the worker will look in this subpath for DAGs |
| dags_volume_subpath = |
| |
| # For DAGs mounted via a volume claim (mutually exclusive with git-sync and host path) |
| dags_volume_claim = |
| |
| # For volume mounted logs, the worker will look in this subpath for logs |
| logs_volume_subpath = |
| |
| # A shared volume claim for the logs |
| logs_volume_claim = |
| |
| |
| # For DAGs mounted via a hostPath volume (mutually exclusive with volume claim and git-sync) |
| # Useful in local environment, discouraged in production |
| dags_volume_host = |
| |
| # A hostPath volume for the logs |
| # Useful in local environment, discouraged in production |
| logs_volume_host = |
| |
| # A list of configMapsRefs to envFrom. If more than one configMap is |
| # specified, provide a comma separated list: configmap_a,configmap_b |
| env_from_configmap_ref = |
| |
| # A list of secretRefs to envFrom. If more than one secret is |
| # specified, provide a comma separated list: secret_a,secret_b |
| env_from_secret_ref = |
| |
| # Git credentials and repository for DAGs mounted via Git (mutually exclusive with volume claim) |
| git_repo = |
| git_branch = |
| git_subpath = |
| # Use git_user and git_password for user authentication or git_ssh_key_secret_name and git_ssh_key_secret_key |
| # for SSH authentication |
| git_user = |
| git_password = |
| git_sync_root = /git |
| git_sync_dest = repo |
| # Mount point of the volume if git-sync is being used. |
| # i.e. /root/airflow/dags |
| git_dags_folder_mount_point = |
| |
| # To get Git-sync SSH authentication set up follow this format |
| # |
| # airflow-secrets.yaml: |
| # --- |
| # apiVersion: v1 |
| # kind: Secret |
| # metadata: |
| # name: airflow-secrets |
| # data: |
| # # key needs to be gitSshKey |
| # gitSshKey: <base64_encoded_data> |
| # --- |
| # airflow-configmap.yaml: |
| # apiVersion: v1 |
| # kind: ConfigMap |
| # metadata: |
| # name: airflow-configmap |
| # data: |
| # known_hosts: | |
| # github.com ssh-rsa <...> |
| # airflow.cfg: | |
| # ... |
| # |
| # git_ssh_key_secret_name = airflow-secrets |
| # git_ssh_known_hosts_configmap_name = airflow-configmap |
| git_ssh_key_secret_name = |
| git_ssh_known_hosts_configmap_name = |
| |
| # For cloning DAGs from git repositories into volumes: https://github.com/kubernetes/git-sync |
| git_sync_container_repository = k8s.gcr.io/git-sync |
| git_sync_container_tag = v3.1.1 |
| git_sync_init_container_name = git-sync-clone |
| |
| # The name of the Kubernetes service account to be associated with airflow workers, if any. |
| # Service accounts are required for workers that require access to secrets or cluster resources. |
| # See the Kubernetes RBAC documentation for more: |
| # https://kubernetes.io/docs/admin/authorization/rbac/ |
| worker_service_account_name = |
| |
| # Any image pull secrets to be given to worker pods, If more than one secret is |
| # required, provide a comma separated list: secret_a,secret_b |
| image_pull_secrets = |
| |
| # GCP Service Account Keys to be provided to tasks run on Kubernetes Executors |
| # Should be supplied in the format: key-name-1:key-path-1,key-name-2:key-path-2 |
| gcp_service_account_keys = |
| |
| # Use the service account kubernetes gives to pods to connect to kubernetes cluster. |
| # It's intended for clients that expect to be running inside a pod running on kubernetes. |
| # It will raise an exception if called from a process not running in a kubernetes environment. |
| in_cluster = True |
| |
| # When running with in_cluster=False change the default cluster_context or config_file |
| # options to Kubernetes client. Leave blank these to use default behaviour like `kubectl` has. |
| # cluster_context = |
| # config_file = |
| |
| |
| # Affinity configuration as a single line formatted JSON object. |
| # See the affinity model for top-level key names (e.g. `nodeAffinity`, etc.): |
| # https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.12/#affinity-v1-core |
| affinity = |
| |
| # A list of toleration objects as a single line formatted JSON array |
| # See: |
| # https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.12/#toleration-v1-core |
| tolerations = |
| |
| # Worker pods security context options |
| # See: |
| # https://kubernetes.io/docs/tasks/configure-pod-container/security-context/ |
| |
| # Specifies the uid to run the first process of the worker pods containers as |
| run_as_user = |
| |
| # Specifies a gid to associate with all containers in the worker pods |
| # if using a git_ssh_key_secret_name use an fs_group |
| # that allows for the key to be read, e.g. 65533 |
| fs_group = |
| |
| [kubernetes_node_selectors] |
| # The Key-value pairs to be given to worker pods. |
| # The worker pods will be scheduled to the nodes of the specified key-value pairs. |
| # Should be supplied in the format: key = value |
| |
| [kubernetes_annotations] |
| # The Key-value annotations pairs to be given to worker pods. |
| # Should be supplied in the format: key = value |
| |
| [kubernetes_environment_variables] |
| # The scheduler sets the following environment variables into your workers. You may define as |
| # many environment variables as needed and the kubernetes launcher will set them in the launched workers. |
| # Environment variables in this section are defined as follows |
| # <environment_variable_key> = <environment_variable_value> |
| # |
| # For example if you wanted to set an environment variable with value `prod` and key |
| # `ENVIRONMENT` you would follow the following format: |
| # ENVIRONMENT = prod |
| # |
| # Additionally you may override worker airflow settings with the AIRFLOW__<SECTION>__<KEY> |
| # formatting as supported by airflow normally. |
| |
| [kubernetes_secrets] |
| # The scheduler mounts the following secrets into your workers as they are launched by the |
| # scheduler. You may define as many secrets as needed and the kubernetes launcher will parse the |
| # defined secrets and mount them as secret environment variables in the launched workers. |
| # Secrets in this section are defined as follows |
| # <environment_variable_mount> = <kubernetes_secret_object>=<kubernetes_secret_key> |
| # |
| # For example if you wanted to mount a kubernetes secret key named `postgres_password` from the |
| # kubernetes secret object `airflow-secret` as the environment variable `POSTGRES_PASSWORD` into |
| # your workers you would follow the following format: |
| # POSTGRES_PASSWORD = airflow-secret=postgres_credentials |
| # |
| # Additionally you may override worker airflow settings with the AIRFLOW__<SECTION>__<KEY> |
| # formatting as supported by airflow normally. |