kppullin · September 12, 2022 19:47 · trejas · Sep 4, 2019 · Dhopal · Oct 15, 2019
diff --git a/airflow-k8s-executor-minikube-helm.md b/airflow-k8s-executor-minikube-helm.md
diff --git a/configmap-airflow-worker.yaml b/configmap-airflow-worker.yaml
 apiVersion: v1
 kind: ConfigMap
 metadata:
  name: "{{ template "airflow.fullname" . }}-worker"
 data:
  airflow.cfg: |
    [core]
    # The home folder for airflow, default is ~/airflow
    airflow_home = /usr/local/airflow

    # The folder where your airflow pipelines live, most likely a
    # subfolder in a code repository
    # This path must be absolute
    dags_folder = /usr/local/airflow/dags

    # The folder where airflow should store its log files
    # This path must be absolute
    base_log_folder = /usr/local/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

    # 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 = utc

    # The executor class that airflow should use. Choices include
    # SequentialExecutor, LocalExecutor, CeleryExecutor, DaskExecutor
    executor = SequentialExecutor

    # The SqlAlchemy connection string to the metadata database.
    # SqlAlchemy supports many different database engine, more information
    # their website
    sql_alchemy_conn = postgresql+psycopg2://postgres:airflow@airflow-postgresql:5432/airflow

    # If SqlAlchemy should pool database connections.
    sql_alchemy_pool_enabled = True

    # 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 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 = True

    # 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 = /usr/local/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 = 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

    [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 =

    [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
    expose_config = False

    # Set to true to turn on authentication:
    # https://airflow.incubator.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

    [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 = [email protected]

    [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.
    broker_url = redis://redis:6379/1

    # Another key Celery setting
    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

    # Default queue that tasks get assigned to and that worker listen on.
    default_queue = default

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

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

    dag_dir_list_interval = 300

    # How often should stats be printed to the logs
    print_stats_interval = 30

    child_process_log_directory = /usr/local/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.
    # This depends on query length limits and how long you are willing to hold locks.
    # 0 for no limit
    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

    [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

    [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 and tag of the Kubernetes Image for the Worker to Run
    worker_container_repository =
    worker_container_tag =

    # If True (default), worker pods will be deleted upon termination
    delete_worker_pods = True

    # 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 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 volume claim)
    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 =

    # Git credentials and repository for DAGs mounted via Git (mutually exclusive with volume claim)
    git_repo =
    git_branch =
    git_user =
    git_password =
    git_subpath =

    # 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

    # 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

    [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.
	apiVersion: v1
	kind: ConfigMap
	metadata:
	name: "{{ template "airflow.fullname" . }}-worker"
	data:
	airflow.cfg: \|
	[core]
	# The home folder for airflow, default is ~/airflow
	airflow_home = /usr/local/airflow

	# The folder where your airflow pipelines live, most likely a
	# subfolder in a code repository
	# This path must be absolute
	dags_folder = /usr/local/airflow/dags

	# The folder where airflow should store its log files
	# This path must be absolute
	base_log_folder = /usr/local/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

	# 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 = utc

	# The executor class that airflow should use. Choices include
	# SequentialExecutor, LocalExecutor, CeleryExecutor, DaskExecutor
	executor = SequentialExecutor

	# The SqlAlchemy connection string to the metadata database.
	# SqlAlchemy supports many different database engine, more information
	# their website
	sql_alchemy_conn = postgresql+psycopg2://postgres:airflow@airflow-postgresql:5432/airflow

	# If SqlAlchemy should pool database connections.
	sql_alchemy_pool_enabled = True

	# 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 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 = True

	# 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 = /usr/local/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 = 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

	[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 =

	[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
	expose_config = False

	# Set to true to turn on authentication:
	# https://airflow.incubator.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

	[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 = [email protected]

	[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.
	broker_url = redis://redis:6379/1

	# Another key Celery setting
	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

	# Default queue that tasks get assigned to and that worker listen on.
	default_queue = default

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

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

	dag_dir_list_interval = 300

	# How often should stats be printed to the logs
	print_stats_interval = 30

	child_process_log_directory = /usr/local/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.
	# This depends on query length limits and how long you are willing to hold locks.
	# 0 for no limit
	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

	[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

	[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 and tag of the Kubernetes Image for the Worker to Run
	worker_container_repository =
	worker_container_tag =

	# If True (default), worker pods will be deleted upon termination
	delete_worker_pods = True

	# 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 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 volume claim)
	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 =

	# Git credentials and repository for DAGs mounted via Git (mutually exclusive with volume claim)
	git_repo =
	git_branch =
	git_user =
	git_password =
	git_subpath =

	# 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

	# 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

	[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.