#  Licensed to the Apache Software Foundation (ASF) under one   *
#  or more contributor license agreements.  See the NOTICE file *
#  distributed with this work for additional information        *
#  regarding copyright ownership.  The ASF licenses this file   *
#  to you under the Apache License, Version 2.0 (the            *
#  "License"); you may not use this file except in compliance   *
#  with the License.  You may obtain a copy of the License at   *
#                                                               *
#    http://www.apache.org/licenses/LICENSE-2.0                 *
#                                                               *
#  Unless required by applicable law or agreed to in writing,   *
#  software distributed under the License is distributed on an  *
#  "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY       *
#  KIND, either express or implied.  See the License for the    *
#  specific language governing permissions and limitations      *
#  under the License.                                           *
apiVersion: v1
kind: ConfigMap
metadata:
  name: airflow-configmap
  namespace: airflow
data:
  airflow.cfg: |
    [core]
    dags_folder = {{CONFIGMAP_DAGS_FOLDER}}
    base_log_folder = /root/airflow/logs
    logging_level = INFO
    executor = KubernetesExecutor
    parallelism = 32
    load_examples = False
    plugins_folder = /root/airflow/plugins
    sql_alchemy_conn = $SQL_ALCHEMY_CONN

    [scheduler]
    dag_dir_list_interval = 300
    child_process_log_directory = /root/airflow/logs/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
    max_threads = 2

    # 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 a new DAGs should be picked up from the filesystem
    min_file_process_interval = 0

    statsd_on = False
    statsd_host = localhost
    statsd_port = 8125
    statsd_prefix = airflow

    # How many seconds to wait between file-parsing loops to prevent the logs from being spammed.
    min_file_parsing_loop_time = 1

    print_stats_interval = 30
    scheduler_zombie_task_threshold = 300
    max_tis_per_query = 0
    authenticate = False

    # 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

    [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://0.0.0.0:8080
    rbac=True

    # 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
    expose_config = False

    # 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

    [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

    [kubernetes]
    airflow_configmap = airflow-configmap
    worker_container_repository = {{AIRFLOW_IMAGE}}
    worker_container_tag = {{AIRFLOW_TAG}}
    worker_container_image_pull_policy = Always
    worker_service_account_name = airflow
    namespace = airflow
    delete_worker_pods = True
    dags_in_image = False
    git_repo = https://github.com/{{CONFIGMAP_GIT_REPO}}.git
    git_branch = {{CONFIGMAP_BRANCH}}
    git_subpath = airflow/contrib/example_dags/
    git_user =
    git_password =
    git_sync_root = /git
    git_sync_path = repo
    git_dags_folder_mount_point = {{CONFIGMAP_GIT_DAGS_FOLDER_MOUNT_POINT}}
    dags_volume_claim = {{CONFIGMAP_DAGS_VOLUME_CLAIM}}
    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 =