airflow.cfg

[core]
# 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
# Hostname by providing a path to a callable, which will resolve the hostname.
# The format is "package.function".
#
# For example, default value "socket.getfqdn" means that result from getfqdn() of "socket"
# package will be used as hostname.
#
# No argument should be required in the function specified.
# If using IP address as hostname is preferred, use value ``airflow.utils.net.get_host_ip_address``
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
executor = CeleryExecutor

# 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 = 10000

# The number of task instances allowed to run concurrently by the scheduler
max_active_tasks_per_dag=10000

# 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
default_pool_task_slot_count = 10000

# The maximum number of active DAG runs per DAG
max_active_runs_per_dag = 1000

# 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 = False

# 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 = True

# How long before timing out a python file import
dagbag_import_timeout = 600.0

# Should a traceback be shown in the UI for dagbag import errors,
# instead of just the exception message
dagbag_import_error_tracebacks = True

# If tracebacks are shown, how many entries from the traceback should be shown
dagbag_import_error_traceback_depth = 2

# How long before timing out a DagFileProcessor, which processes a dag file
dag_file_processor_timeout = 100

# The class to use for running task instances in a subprocess
task_runner = StandardTaskRunner

# 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 =

# 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 file task handler.

# 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 = False

# 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 dags backfill -c`` or
# ``airflow dags trigger -c``, the key-value pairs will override the existing ones in params.
dag_run_conf_overrides_params = True

# When discovering DAGs, ignore any files that don't contain the strings ``DAG`` and ``airflow``.
dag_discovery_safe_mode = True

# The number of retries each task is going to have by default. Can be overridden at dag or task level.
default_task_retries = 0

# Updating serialized DAG can not be faster than a minimum interval to reduce database write rate.
min_serialized_dag_update_interval = 5

# Fetching serialized DAG can not be faster than a minimum interval to reduce database
# read rate. This config controls when your DAGs are updated in the Webserver
min_serialized_dag_fetch_interval = 5

# Whether to persist DAG files code in DB.
# If set to True, Webserver reads file contents from DB instead of
# trying to access files in a DAG folder.
# Example: store_dag_code = False
# store_dag_code =

# Maximum number of Rendered Task Instance Fields (Template Fields) per task to store
# in the Database.
# All the template_fields for each of Task Instance are stored in the Database.
# Keeping this number small may cause an error when you try to view ``Rendered`` tab in
# TaskInstance view for older tasks.
max_num_rendered_ti_fields_per_task = 30

# On each dagrun check against defined SLAs
check_slas = True

# Path to custom XCom class that will be used to store and resolve operators results
# Example: xcom_backend = path.to.CustomXCom
xcom_backend = airflow.models.xcom.BaseXCom

# By default Airflow plugins are lazily-loaded (only loaded when required). Set it to ``False``,
# if you want to load plugins whenever 'airflow' is invoked via cli or loaded from module.
lazy_load_plugins = True

# By default Airflow providers are lazily-discovered (discovery and imports happen only when required).
# Set it to False, if you want to discover providers whenever 'airflow' is invoked via cli or
# loaded from module.
lazy_discover_providers = True

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

[database]
# The SqlAlchemy connection string to the metadata database.
# SqlAlchemy supports many different database engine, more information
# their website
#sql_alchemy_conn = mysql+mysqldb://airflow:airflow@172.31.31.249:3306/airflow
#sql_alchemy_conn = postgresql+psycopg2://airflow:airflow@172.31.31.249/airflow
# The encoding for the databases
sql_engine_encoding = utf-8


# The SqlAlchemy pool size is the maximum number of database connections
# in the pool.
sql_alchemy_pool_size = 300

# 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.
sql_alchemy_pool_recycle = 3600

# Check connection at the start of each connection pool checkout.
# Typically, this is a simple statement like "SELECT 1".
# More information here:
# https://docs.sqlalchemy.org/en/13/core/pooling.html#disconnect-handling-pessimistic
sql_alchemy_pool_pre_ping = True

# The schema to use for the metadata database.
# SqlAlchemy supports databases with the concept of multiple schemas.
sql_alchemy_schema =

# Import path for connect args in SqlAlchemy. Defaults to an empty dict.
# This is useful when you want to configure db engine args that SqlAlchemy won't parse
# in connection string.
# See https://docs.sqlalchemy.org/en/13/core/engines.html#sqlalchemy.create_engine.params.connect_args
# sql_alchemy_connect_args =
# How many seconds to retry re-establishing a DB connection after
# disconnects. Setting this to 0 disables retries.
sql_alchemy_reconnect_timeout = 300

# Number of times the code should be retried in case of DB Operational Errors.
# Not all transactions will be retried as it can cause undesired state.
# Currently it is only used in ``DagFileProcessor.process_file`` to retry ``dagbag.sync_to_db``.
max_db_retries = 10


[logging]

# 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.
# Set this to True if you want to enable remote logging.
#remote_logging = True

# Airflow can store logs remotely in AWS S3 or Google Cloud Storage. Users
# must supply an Airflow connection id that provides access to the storage
# location.

#remote_log_conn_id = GCSConn
#remote_base_log_folder = $REMOTE_LOG_FOLDER
encrypt_s3_logs = False

# Logging level
logging_level = INFO

# 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

# Log format
log_format = [%%(asctime)s] {{%%(filename)s:%%(lineno)d}} %%(levelname)s - %%(message)s
simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s

# 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


[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
endpoint_url = http://localhost:8080

[api]
# How to authenticate users of the API
auth_backends = airflow.api.auth.backend.basic_auth,airflow.api.auth.backend.session

[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

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


[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://proxy/airflow

# Default timezone to display all dates in the UI, can be UTC, system, or
# any IANA timezone string (e.g. Europe/Amsterdam). If left empty the
# default value of core/default_timezone will be used
# Example: default_ui_timezone = America/New_York
default_ui_timezone = America/New_York

# 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 gunicorn webserver waits before timing out on a worker
web_server_worker_timeout = 600

# 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 = 600

# Secret key used to run your flask app
# secret_key = $SECRET_KEY

# Number of workers to run the Gunicorn web server
workers = 1

# 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:
# http://pythonhosted.org/airflow/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 = graph

# 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

# 'Recent Tasks' stats will show for old DagRuns if set
show_recent_stats_for_completed_runs = True

enable_proxy_fix = True

# Consistent page size across all listing views in the UI
page_size = 100

[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@airflow.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.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
celeryd_concurrency = 1

# 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
#broker_url = amqp://172.31.31.249:5672
task_acks_late = True
# Another key Celery setting
#celery_result_backend = db+mysql://airflow:airflow@172.31.31.249:3306/airflow
#celery_result_backend = db+postgresql://airflow:airflow@172.31.31.249/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

# This defines the port that Celery Flower runs on
flower_port = 5555

# Import path for celery configuration options
#celery_config_options = celeryconfig.CELERY_CONFIG
#celery_config_options = airflow.config_templates.default_celery.DEFAULT_CELERY_CONFIG
ssl_active = False

[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

[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 = 60

# How often (in seconds) to check and tidy up 'running' TaskInstancess
# that no longer have a matching DagRun
clean_tis_without_dagrun_interval = 15.0

# 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 = 1

# 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 and in `airflow jobs check` CLI
# for SchedulerJob.
scheduler_health_check_threshold = 300

# When you start a scheduler, airflow starts a tiny web server
# subprocess to serve a health check if this is set to True
enable_health_check = True

# The number of times to try to schedule each DAG file
# -1 indicates unlimited number
num_runs = -1

# The number of seconds to wait between consecutive DAG file processing
scheduler_idle_sleep_time = 1


# Number of seconds after which a DAG file is parsed. The DAG file is parsed every
# ``min_file_process_interval`` number of seconds. Updates to DAGs are reflected after
# this interval. Keeping this number low will increase CPU usage.
min_file_process_interval = 10

# How often (in seconds) to check for stale DAGs (DAGs which are no longer present in
# the expected files) which should be deactivated, as well as datasets that are no longer
# referenced and should be marked as orphaned.
parsing_cleanup_interval = 120

# How long (in seconds) to wait after we have re-parsed a DAG file before deactivating stale
# DAGs (DAGs which are no longer present in the expected files). The reason why we need
# this threshold is to account for the time between when the file is parsed and when the
# DAG is loaded. The absolute maximum that this could take is `dag_file_processor_timeout`,
# but when you have a long timeout configured, it results in a significant delay in the
# deactivation of stale dags.
stale_dag_threshold = 100

# 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 = 300

child_process_log_directory = /usr/local/airflow/logs/scheduler

# How often (in seconds) should the scheduler check for orphaned tasks and SchedulerJobs
orphaned_tasks_check_interval = 300.0

# 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 = 120

zombie_detection_interval = 60.0

# 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 = False
# 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

# Should the scheduler issue ``SELECT ... FOR UPDATE`` in relevant queries.
# If this is set to False then you should not run more than a single
# scheduler at once
use_row_level_locking = True

# The scheduler can run multiple threads in parallel to schedule dags.
# This defines how many threads will run. However airflow will never
# use more threads than the amount of cpu cores available.
parsing_processes=4

authenticate = False

# Time limit in seconds for inserting tasks instances into the DB.
# Verify integrity is called to ensure that all tasks in the dag are saved in the db.
# If set, this limits the amount of time this function is allowed to insert in seconds
# Because verify integrity is run on a loop by the scheduler the function becomes a
# "best effort" operation  -1 for no limit
verify_integrity_insert_time_limit = 60

# Amount of time a task can be in the queued state before being retried or set to failed.
task_queued_timeout = 36000.0

# How often to check for tasks that have been in the queued state for
# longer than `[scheduler] task_queued_timeout`.
task_queued_timeout_check_interval = 3600.0

[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


[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]