scripts/kube/build/configmaps.yaml [175:331]:
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    dags_volume_subpath =
    logs_volume_claim = 
    logs_volume_subpath =
    dags_volume_host =
    logs_volume_host =
    in_cluster = True
    gcp_service_account_keys =

    # Example affinity and toleration definitions.
    affinity = {"nodeAffinity":{"requiredDuringSchedulingIgnoredDuringExecution":{"weight":1,"preference":[{"matchExpressions":[{"key":"lifecycle","operator":"In","value":"Ec2Spot"}]}],"nodeSelectorTerms":[{"matchExpressions":[{"key":"kubernetes.io/hostname","operator":"NotIn","values":["4e5e6a99-e28a-450b-bba9-e0124853de9b"]}]}]}}}
    tolerations = [{ "key": "spotInstance", "operator": "Equal", "value": "true", "effect": "PreferNoSchedule" },{ "key": "dedicated", "operator": "Equal", "value": "airflow", "effect": "NoSchedule" }, { "key": "prod", "operator": "Exists" }]
    # affinity = {"nodeAffinity":{"requiredDuringSchedulingIgnoredDuringExecution":{"nodeSelectorTerms":[{"matchExpressions":[{"key":"kubernetes.io/hostname","operator":"NotIn","values":["4e5e6a99-e28a-450b-bba9-e0124853de9b"]}]}]}}}
    # tolerations = [{ "key": "dedicated", "operator": "Equal", "value": "airflow", "effect": "NoSchedule" }, { "key": "prod", "operator": "Exists" }]

    # For cloning DAGs from git repositories into volumes: https://github.com/kubernetes/git-sync
    git_sync_container_repository = gcr.io/google-containers/git-sync-amd64
    git_sync_container_tag = v2.0.5
    git_sync_init_container_name = git-sync-clone

    [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_secrets]
    SQL_ALCHEMY_CONN = airflow-secrets=sql_alchemy_conn

    [hive]
    # Default mapreduce queue for HiveOperator tasks
    default_hive_mapred_queue =

    [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

    # 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 = sqla+mysql://airflow:airflow@localhost:3306/airflow

    # 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+mysql://airflow:airflow@localhost:3306/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

    [celery_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. Especially important in case of using Redis or SQS
    visibility_timeout = 21600

    # In case of using SSL
    ssl_active = False
    ssl_key =
    ssl_cert =
    ssl_cacert =

    [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 =

    [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

    [kerberos]
    ccache = /tmp/airflow_krb5_ccache
    # gets augmented with fqdn
    principal = airflow
    reinit_frequency = 3600
    kinit_path = kinit
    keytab = airflow.keytab

    [cli]
    api_client = airflow.api.client.json_client
    endpoint_url = http://0.0.0.0:8080

    [api]
    auth_backend = airflow.api.auth.backend.default

    [github_enterprise]
    api_rev = v3

    [admin]
    # UI to hide sensitive variable fields when set to True
    hide_sensitive_variable_fields = True

    [elasticsearch]
    elasticsearch_host =
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scripts/kube/templates/configmaps.template.yaml [175:331]:
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    dags_volume_subpath =
    logs_volume_claim = 
    logs_volume_subpath =
    dags_volume_host =
    logs_volume_host =
    in_cluster = True
    gcp_service_account_keys =

    # Example affinity and toleration definitions.
    affinity = {"nodeAffinity":{"requiredDuringSchedulingIgnoredDuringExecution":{"weight":1,"preference":[{"matchExpressions":[{"key":"lifecycle","operator":"In","value":"Ec2Spot"}]}],"nodeSelectorTerms":[{"matchExpressions":[{"key":"kubernetes.io/hostname","operator":"NotIn","values":["4e5e6a99-e28a-450b-bba9-e0124853de9b"]}]}]}}}
    tolerations = [{ "key": "spotInstance", "operator": "Equal", "value": "true", "effect": "PreferNoSchedule" },{ "key": "dedicated", "operator": "Equal", "value": "airflow", "effect": "NoSchedule" }, { "key": "prod", "operator": "Exists" }]
    # affinity = {"nodeAffinity":{"requiredDuringSchedulingIgnoredDuringExecution":{"nodeSelectorTerms":[{"matchExpressions":[{"key":"kubernetes.io/hostname","operator":"NotIn","values":["4e5e6a99-e28a-450b-bba9-e0124853de9b"]}]}]}}}
    # tolerations = [{ "key": "dedicated", "operator": "Equal", "value": "airflow", "effect": "NoSchedule" }, { "key": "prod", "operator": "Exists" }]

    # For cloning DAGs from git repositories into volumes: https://github.com/kubernetes/git-sync
    git_sync_container_repository = gcr.io/google-containers/git-sync-amd64
    git_sync_container_tag = v2.0.5
    git_sync_init_container_name = git-sync-clone

    [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_secrets]
    SQL_ALCHEMY_CONN = airflow-secrets=sql_alchemy_conn

    [hive]
    # Default mapreduce queue for HiveOperator tasks
    default_hive_mapred_queue =

    [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

    # 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 = sqla+mysql://airflow:airflow@localhost:3306/airflow

    # 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+mysql://airflow:airflow@localhost:3306/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

    [celery_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. Especially important in case of using Redis or SQS
    visibility_timeout = 21600

    # In case of using SSL
    ssl_active = False
    ssl_key =
    ssl_cert =
    ssl_cacert =

    [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 =

    [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

    [kerberos]
    ccache = /tmp/airflow_krb5_ccache
    # gets augmented with fqdn
    principal = airflow
    reinit_frequency = 3600
    kinit_path = kinit
    keytab = airflow.keytab

    [cli]
    api_client = airflow.api.client.json_client
    endpoint_url = http://0.0.0.0:8080

    [api]
    auth_backend = airflow.api.auth.backend.default

    [github_enterprise]
    api_rev = v3

    [admin]
    # UI to hide sensitive variable fields when set to True
    hide_sensitive_variable_fields = True

    [elasticsearch]
    elasticsearch_host =
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