Lakehouse/config/airflow/airflow.cfg

[core]
# The folder where your airflow pipelines live, most likely a
# subfolder in a code repository. This path must be absolute.
#
# Variable: AIRFLOW__CORE__DAGS_FOLDER
#
dags_folder = /root/airflow/dags

# Hostname by providing a path to a callable, which will resolve the hostname.
# The format is "package.function".
# 
# For example, default value ``airflow.utils.net.getfqdn`` means that result from patched
# version of `socket.getfqdn() <https://docs.python.org/3/library/socket.html#socket.getfqdn>`__,
# see related `CPython Issue <https://github.com/python/cpython/issues/49254>`__.
# 
# 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``
#
# Variable: AIRFLOW__CORE__HOSTNAME_CALLABLE
#
hostname_callable = airflow.utils.net.getfqdn

# A callable to check if a python file has airflow dags defined or not and should
# return ``True`` if it has dags otherwise ``False``.
# If this is not provided, Airflow uses its own heuristic rules.
# 
# The function should have the following signature
# 
# .. code-block:: python
# 
#     def func_name(file_path: str, zip_file: zipfile.ZipFile | None = None) -> bool: ...
#
# Variable: AIRFLOW__CORE__MIGHT_CONTAIN_DAG_CALLABLE
#
might_contain_dag_callable = airflow.utils.file.might_contain_dag_via_default_heuristic

# Default timezone in case supplied date times are naive
# can be `UTC` (default), `system`, or any `IANA <https://www.iana.org/time-zones>`
# timezone string (e.g. Europe/Amsterdam)
#
# Variable: AIRFLOW__CORE__DEFAULT_TIMEZONE
#
default_timezone = utc

# The executor class that airflow should use. Choices include
# ``LocalExecutor``, ``CeleryExecutor``,
# ``KubernetesExecutor`` or the full import path to the class when using a custom executor.
#
# Variable: AIRFLOW__CORE__EXECUTOR
#
executor = LocalExecutor

# The auth manager class that airflow should use. Full import path to the auth manager class.
#
# Variable: AIRFLOW__CORE__AUTH_MANAGER
#
auth_manager = airflow.api_fastapi.auth.managers.simple.simple_auth_manager.SimpleAuthManager

# The list of users and their associated role in simple auth manager. If the simple auth manager is
# used in your environment, this list controls who can access the environment.
# 
# List of user-role delimited with a comma. Each user-role is a colon delimited couple of username and
# role. Roles are predefined in simple auth managers: viewer, user, op, admin.
#
# Example: simple_auth_manager_users = bob:admin,peter:viewer
#
# Variable: AIRFLOW__CORE__SIMPLE_AUTH_MANAGER_USERS
#
simple_auth_manager_users = admin:admin,mo:admin,Bart:admin

# Whether to disable authentication and allow everyone as admin in the environment.
#
# Variable: AIRFLOW__CORE__SIMPLE_AUTH_MANAGER_ALL_ADMINS
#
simple_auth_manager_all_admins = False

# The json file where the simple auth manager stores passwords for the configured users.
# By default this is ``AIRFLOW_HOME/simple_auth_manager_passwords.json.generated``.
#
# Example: simple_auth_manager_passwords_file = /path/to/passwords.json
#
# Variable: AIRFLOW__CORE__SIMPLE_AUTH_MANAGER_PASSWORDS_FILE
#
# simple_auth_manager_passwords_file = 

# This defines the maximum number of task instances that can run concurrently per scheduler in
# Airflow, regardless of the worker count. Generally this value, multiplied by the number of
# schedulers in your cluster, is the maximum number of task instances with the running
# state in the metadata database. The value must be larger or equal 1.
#
# Variable: AIRFLOW__CORE__PARALLELISM
#
parallelism = 32

# The maximum number of task instances allowed to run concurrently in each DAG. To calculate
# the number of tasks that is running concurrently for a DAG, add up the number of running
# tasks for all DAG runs of the DAG. This is configurable at the DAG level with ``max_active_tasks``,
# which is defaulted as ``[core] max_active_tasks_per_dag``.
# 
# An example scenario when this would be useful is when you want to stop a new dag with an early
# start date from stealing all the executor slots in a cluster.
#
# Variable: AIRFLOW__CORE__MAX_ACTIVE_TASKS_PER_DAG
#
max_active_tasks_per_dag = 16

# Are DAGs paused by default at creation
#
# Variable: AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION
#
dags_are_paused_at_creation = True

# The maximum number of active DAG runs per DAG. The scheduler will not create more DAG runs
# if it reaches the limit. This is configurable at the DAG level with ``max_active_runs``,
# which is defaulted as ``[core] max_active_runs_per_dag``.
#
# Variable: AIRFLOW__CORE__MAX_ACTIVE_RUNS_PER_DAG
#
max_active_runs_per_dag = 16

# (experimental) The maximum number of consecutive DAG failures before DAG is automatically paused.
# This is also configurable per DAG level with ``max_consecutive_failed_dag_runs``,
# which is defaulted as ``[core] max_consecutive_failed_dag_runs_per_dag``.
# If not specified, then the value is considered as 0,
# meaning that the dags are never paused out by default.
#
# Variable: AIRFLOW__CORE__MAX_CONSECUTIVE_FAILED_DAG_RUNS_PER_DAG
#
max_consecutive_failed_dag_runs_per_dag = 0

# The name of the method used in order to start Python processes via the multiprocessing module.
# This corresponds directly with the options available in the Python docs:
# `multiprocessing.set_start_method
# <https://docs.python.org/3/library/multiprocessing.html#multiprocessing.set_start_method>`__
# must be one of the values returned by `multiprocessing.get_all_start_methods()
# <https://docs.python.org/3/library/multiprocessing.html#multiprocessing.get_all_start_methods>`__.
#
# Example: mp_start_method = fork
#
# Variable: AIRFLOW__CORE__MP_START_METHOD
#
# mp_start_method = 

# Whether to load the DAG examples that ship with Airflow. It's good to
# get started, but you probably want to set this to ``False`` in a production
# environment
#
# Variable: AIRFLOW__CORE__LOAD_EXAMPLES
#
load_examples = False

# Path to the folder containing Airflow plugins
#
# Variable: AIRFLOW__CORE__PLUGINS_FOLDER
#
plugins_folder = /root/airflow/plugins

# Should tasks be executed via forking of the parent process
# 
# * ``False``: Execute via forking of the parent process
# * ``True``: Spawning a new python process, slower than fork, but means plugin changes picked
#   up by tasks straight away
#
# Variable: AIRFLOW__CORE__EXECUTE_TASKS_NEW_PYTHON_INTERPRETER
#
execute_tasks_new_python_interpreter = False

# Secret key to save connection passwords in the db
#
# Variable: AIRFLOW__CORE__FERNET_KEY
#
fernet_key = f4n7uT66HPrl8yxqKHVtme_qINN9YlZ1xUQcZy761MY=

# Whether to disable pickling dags
#
# Variable: AIRFLOW__CORE__DONOT_PICKLE
#
donot_pickle = True

# How long before timing out a python file import
#
# Variable: AIRFLOW__CORE__DAGBAG_IMPORT_TIMEOUT
#
dagbag_import_timeout = 30.0

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

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

# 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
#
# Variable: AIRFLOW__CORE__DEFAULT_IMPERSONATION
#
default_impersonation = 

# What security module to use (for example kerberos)
#
# Variable: AIRFLOW__CORE__SECURITY
#
security = 

# Turn unit test mode on (overwrites many configuration options with test
# values at runtime)
#
# Variable: AIRFLOW__CORE__UNIT_TEST_MODE
#
unit_test_mode = False

# Space-separated list of classes that may be imported during deserialization. Items can be glob
# expressions. Python built-in classes (like dict) are always allowed.
#
# Example: allowed_deserialization_classes = airflow.* my_mod.my_other_mod.TheseClasses*
#
# Variable: AIRFLOW__CORE__ALLOWED_DESERIALIZATION_CLASSES
#
allowed_deserialization_classes = airflow.*

# Space-separated list of classes that may be imported during deserialization. Items are processed
# as regex expressions. Python built-in classes (like dict) are always allowed.
# This is a secondary option to ``[core] allowed_deserialization_classes``.
#
# Variable: AIRFLOW__CORE__ALLOWED_DESERIALIZATION_CLASSES_REGEXP
#
allowed_deserialization_classes_regexp = 

# 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
#
# Variable: AIRFLOW__CORE__KILLED_TASK_CLEANUP_TIME
#
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.
#
# Variable: AIRFLOW__CORE__DAG_RUN_CONF_OVERRIDES_PARAMS
#
dag_run_conf_overrides_params = True

# If enabled, Airflow will only scan files containing both ``DAG`` and ``airflow`` (case-insensitive).
#
# Variable: AIRFLOW__CORE__DAG_DISCOVERY_SAFE_MODE
#
dag_discovery_safe_mode = True

# The pattern syntax used in the
# `.airflowignore
# <https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dags.html#airflowignore>`__
# files in the DAG directories. Valid values are ``regexp`` or ``glob``.
#
# Variable: AIRFLOW__CORE__DAG_IGNORE_FILE_SYNTAX
#
dag_ignore_file_syntax = glob

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

# The number of seconds each task is going to wait by default between retries. Can be overridden at
# dag or task level.
#
# Variable: AIRFLOW__CORE__DEFAULT_TASK_RETRY_DELAY
#
default_task_retry_delay = 300

# The maximum delay (in seconds) each task is going to wait by default between retries.
# This is a global setting and cannot be overridden at task or DAG level.
#
# Variable: AIRFLOW__CORE__MAX_TASK_RETRY_DELAY
#
max_task_retry_delay = 86400

# The weighting method used for the effective total priority weight of the task
#
# Variable: AIRFLOW__CORE__DEFAULT_TASK_WEIGHT_RULE
#
default_task_weight_rule = downstream

# Maximum possible time (in seconds) that task will have for execution of auxiliary processes
# (like listeners, mini scheduler...) after task is marked as success..
#
# Variable: AIRFLOW__CORE__TASK_SUCCESS_OVERTIME
#
task_success_overtime = 20

# The default task execution_timeout value for the operators. Expected an integer value to
# be passed into timedelta as seconds. If not specified, then the value is considered as None,
# meaning that the operators are never timed out by default.
#
# Variable: AIRFLOW__CORE__DEFAULT_TASK_EXECUTION_TIMEOUT
#
default_task_execution_timeout = 

# Updating serialized DAG can not be faster than a minimum interval to reduce database write rate.
#
# Variable: AIRFLOW__CORE__MIN_SERIALIZED_DAG_UPDATE_INTERVAL
#
min_serialized_dag_update_interval = 30

# If ``True``, serialized DAGs are compressed before writing to DB.
# 
# .. note::
# 
#     This will disable the DAG dependencies view
#
# Variable: AIRFLOW__CORE__COMPRESS_SERIALIZED_DAGS
#
compress_serialized_dags = False

# 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
#
# Variable: AIRFLOW__CORE__MIN_SERIALIZED_DAG_FETCH_INTERVAL
#
min_serialized_dag_fetch_interval = 10

# 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.
#
# Variable: AIRFLOW__CORE__MAX_NUM_RENDERED_TI_FIELDS_PER_TASK
#
max_num_rendered_ti_fields_per_task = 30

# Path to custom XCom class that will be used to store and resolve operators results
#
# Example: xcom_backend = path.to.CustomXCom
#
# Variable: AIRFLOW__CORE__XCOM_BACKEND
#
xcom_backend = airflow.sdk.execution_time.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.
#
# Variable: AIRFLOW__CORE__LAZY_LOAD_PLUGINS
#
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.
#
# Variable: AIRFLOW__CORE__LAZY_DISCOVER_PROVIDERS
#
lazy_discover_providers = True

# Hide sensitive **Variables** or **Connection extra json keys** from UI
# and task logs when set to ``True``
# 
# .. note::
# 
#     Connection passwords are always hidden in logs
#
# Variable: AIRFLOW__CORE__HIDE_SENSITIVE_VAR_CONN_FIELDS
#
hide_sensitive_var_conn_fields = True

# A comma-separated list of extra sensitive keywords to look for in variables names or connection's
# extra JSON.
#
# Variable: AIRFLOW__CORE__SENSITIVE_VAR_CONN_NAMES
#
sensitive_var_conn_names = 

# Task Slot counts for ``default_pool``. This setting would not have any effect in an existing
# deployment where the ``default_pool`` is already created. For existing deployments, users can
# change the number of slots using Webserver, API or the CLI
#
# Variable: AIRFLOW__CORE__DEFAULT_POOL_TASK_SLOT_COUNT
#
default_pool_task_slot_count = 128

# The maximum list/dict length an XCom can push to trigger task mapping. If the pushed list/dict has a
# length exceeding this value, the task pushing the XCom will be failed automatically to prevent the
# mapped tasks from clogging the scheduler.
#
# Variable: AIRFLOW__CORE__MAX_MAP_LENGTH
#
max_map_length = 1024

# The default umask to use for process when run in daemon mode (scheduler, worker,  etc.)
# 
# This controls the file-creation mode mask which determines the initial value of file permission bits
# for newly created files.
# 
# This value is treated as an octal-integer.
#
# Variable: AIRFLOW__CORE__DAEMON_UMASK
#
daemon_umask = 0o077

# Class to use as asset manager.
#
# Example: asset_manager_class = airflow.assets.manager.AssetManager
#
# Variable: AIRFLOW__CORE__ASSET_MANAGER_CLASS
#
# asset_manager_class = 

# Kwargs to supply to asset manager.
#
# Example: asset_manager_kwargs = {"some_param": "some_value"}
#
# Variable: AIRFLOW__CORE__ASSET_MANAGER_KWARGS
#
# asset_manager_kwargs = 

# (experimental) Whether components should use Airflow Internal API for DB connectivity.
#
# Variable: AIRFLOW__CORE__DATABASE_ACCESS_ISOLATION
#
database_access_isolation = False

# (experimental) Airflow Internal API url.
# Only used if ``[core] database_access_isolation`` is ``True``.
#
# Example: internal_api_url = http://localhost:8080
#
# Variable: AIRFLOW__CORE__INTERNAL_API_URL
#
# internal_api_url = 

# Secret key used to authenticate internal API clients to core. It should be as random as possible.
# However, when running more than 1 instances of webserver / internal API services, make sure all
# of them use the same ``secret_key`` otherwise calls will fail on authentication.
# The authentication token generated using the secret key has a short expiry time though - make
# sure that time on ALL the machines that you run airflow components on is synchronized
# (for example using ntpd) otherwise you might get "forbidden" errors when the logs are accessed.
#
# Variable: AIRFLOW__CORE__INTERNAL_API_SECRET_KEY
#
internal_api_secret_key = 010RL08807/JBjH4cWzNaw==

# The ability to allow testing connections across Airflow UI, API and CLI.
# Supported options: ``Disabled``, ``Enabled``, ``Hidden``. Default: Disabled
# Disabled - Disables the test connection functionality and disables the Test Connection button in UI.
# Enabled - Enables the test connection functionality and shows the Test Connection button in UI.
# Hidden - Disables the test connection functionality and hides the Test Connection button in UI.
# Before setting this to Enabled, make sure that you review the users who are able to add/edit
# connections and ensure they are trusted. Connection testing can be done maliciously leading to
# undesired and insecure outcomes.
# See `Airflow Security Model: Capabilities of authenticated UI users
# <https://airflow.apache.org/docs/apache-airflow/stable/security/security_model.html#capabilities-of-authenticated-ui-users>`__
# for more details.
#
# Variable: AIRFLOW__CORE__TEST_CONNECTION
#
test_connection = Disabled

# The maximum length of the rendered template field. If the value to be stored in the
# rendered template field exceeds this size, it's redacted.
#
# Variable: AIRFLOW__CORE__MAX_TEMPLATED_FIELD_LENGTH
#
max_templated_field_length = 4096

# The url of the execution api server. Default is ``{BASE_URL}/execution/``
# where ``{BASE_URL}`` is the base url of the API Server. If ``{BASE_URL}`` is not set,
# it will use ``http://localhost:8080`` as the default base url.
#
# Variable: AIRFLOW__CORE__EXECUTION_API_SERVER_URL
#
# execution_api_server_url = 

[database]
# Path to the ``alembic.ini`` file. You can either provide the file path relative
# to the Airflow home directory or the absolute path if it is located elsewhere.
#
# Variable: AIRFLOW__DATABASE__ALEMBIC_INI_FILE_PATH
#
alembic_ini_file_path = alembic.ini

# The SQLAlchemy connection string to the metadata database.
# SQLAlchemy supports many different database engines.
# See: `Set up a Database Backend: Database URI
# <https://airflow.apache.org/docs/apache-airflow/stable/howto/set-up-database.html#database-uri>`__
# for more details.
#
# Variable: AIRFLOW__DATABASE__SQL_ALCHEMY_CONN
#
sql_alchemy_conn = sqlite:////root/airflow/airflow.db

# Extra engine specific keyword args passed to SQLAlchemy's create_engine, as a JSON-encoded value
#
# Example: sql_alchemy_engine_args = {"arg1": true}
#
# Variable: AIRFLOW__DATABASE__SQL_ALCHEMY_ENGINE_ARGS
#
# sql_alchemy_engine_args = 

# The encoding for the databases
#
# Variable: AIRFLOW__DATABASE__SQL_ENGINE_ENCODING
#
sql_engine_encoding = utf-8

# Collation for ``dag_id``, ``task_id``, ``key``, ``external_executor_id`` columns
# in case they have different encoding.
# By default this collation is the same as the database collation, however for ``mysql`` and ``mariadb``
# the default is ``utf8mb3_bin`` so that the index sizes of our index keys will not exceed
# the maximum size of allowed index when collation is set to ``utf8mb4`` variant, see
# `GitHub Issue Comment <https://github.com/apache/airflow/pull/17603#issuecomment-901121618>`__
# for more details.
#
# Variable: AIRFLOW__DATABASE__SQL_ENGINE_COLLATION_FOR_IDS
#
# sql_engine_collation_for_ids = 

# If SQLAlchemy should pool database connections.
#
# Variable: AIRFLOW__DATABASE__SQL_ALCHEMY_POOL_ENABLED
#
sql_alchemy_pool_enabled = True

# The SQLAlchemy pool size is the maximum number of database connections
# in the pool. 0 indicates no limit.
#
# Variable: AIRFLOW__DATABASE__SQL_ALCHEMY_POOL_SIZE
#
sql_alchemy_pool_size = 5

# The maximum overflow size of the pool.
# When the number of checked-out connections reaches the size set in pool_size,
# additional connections will be returned up to this limit.
# When those additional connections are returned to the pool, they are disconnected and discarded.
# It follows then that the total number of simultaneous connections the pool will allow
# is **pool_size** + **max_overflow**,
# and the total number of "sleeping" connections the pool will allow is pool_size.
# max_overflow can be set to ``-1`` to indicate no overflow limit;
# no limit will be placed on the total number of concurrent connections. Defaults to ``10``.
#
# Variable: AIRFLOW__DATABASE__SQL_ALCHEMY_MAX_OVERFLOW
#
sql_alchemy_max_overflow = 10

# 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.
#
# Variable: AIRFLOW__DATABASE__SQL_ALCHEMY_POOL_RECYCLE
#
sql_alchemy_pool_recycle = 1800

# Check connection at the start of each connection pool checkout.
# Typically, this is a simple statement like "SELECT 1".
# See `SQLAlchemy Pooling: Disconnect Handling - Pessimistic
# <https://docs.sqlalchemy.org/en/14/core/pooling.html#disconnect-handling-pessimistic>`__
# for more details.
#
# Variable: AIRFLOW__DATABASE__SQL_ALCHEMY_POOL_PRE_PING
#
sql_alchemy_pool_pre_ping = True

# The schema to use for the metadata database.
# SQLAlchemy supports databases with the concept of multiple schemas.
#
# Variable: AIRFLOW__DATABASE__SQL_ALCHEMY_SCHEMA
#
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. This can be set by passing a dictionary containing the create engine parameters.
# For more details about passing create engine parameters (keepalives variables, timeout etc)
# in Postgres DB Backend see `Setting up a PostgreSQL Database
# <https://airflow.apache.org/docs/apache-airflow/stable/howto/set-up-database.html#setting-up-a-postgresql-database>`__
# e.g ``connect_args={"timeout":30}`` can be defined in ``airflow_local_settings.py`` and
# can be imported as shown below
#
# Example: sql_alchemy_connect_args = airflow_local_settings.connect_args
#
# Variable: AIRFLOW__DATABASE__SQL_ALCHEMY_CONNECT_ARGS
#
# sql_alchemy_connect_args = 

# Important Warning: Use of sql_alchemy_session_maker Highly Discouraged
# Import path for function which returns 'sqlalchemy.orm.sessionmaker'.
# Improper configuration of sql_alchemy_session_maker can lead to serious issues,
# including data corruption, unrecoverable application crashes. Please review the SQLAlchemy
# documentation for detailed guidance on proper configuration and best practices.
#
# Example: sql_alchemy_session_maker = airflow_local_settings._sessionmaker
#
# Variable: AIRFLOW__DATABASE__SQL_ALCHEMY_SESSION_MAKER
#
# sql_alchemy_session_maker = 

# 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``.
#
# Variable: AIRFLOW__DATABASE__MAX_DB_RETRIES
#
max_db_retries = 3

# Whether to run alembic migrations during Airflow start up. Sometimes this operation can be expensive,
# and the users can assert the correct version through other means (e.g. through a Helm chart).
# Accepts ``True`` or ``False``.
#
# Variable: AIRFLOW__DATABASE__CHECK_MIGRATIONS
#
check_migrations = True

# List of DB managers to use to migrate external tables in airflow database. The managers must inherit
# from BaseDBManager. If ``FabAuthManager`` is configured in the environment,
# ``airflow.providers.fab.auth_manager.models.db.FABDBManager`` is automatically added.
#
# Example: external_db_managers = airflow.providers.fab.auth_manager.models.db.FABDBManager
#
# Variable: AIRFLOW__DATABASE__EXTERNAL_DB_MANAGERS
#
# external_db_managers = 

# The number of rows to process in each batch when performing a migration.
# This is useful for large tables to avoid locking and failure due to query timeouts.
#
# Variable: AIRFLOW__DATABASE__MIGRATION_BATCH_SIZE
#
migration_batch_size = 10000

[logging]
# The folder where airflow should store its log files.
# This path must be absolute.
# There are a few existing configurations that assume this is set to the default.
# If you choose to override this you may need to update the
# ``[logging] dag_processor_manager_log_location`` and
# ``[logging] dag_processor_child_process_log_directory settings`` as well.
#
# Variable: AIRFLOW__LOGGING__BASE_LOG_FOLDER
#
base_log_folder = /root/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.
#
# Variable: AIRFLOW__LOGGING__REMOTE_LOGGING
#
remote_logging = False

# Users must supply an Airflow connection id that provides access to the storage
# location. Depending on your remote logging service, this may only be used for
# reading logs, not writing them.
#
# Variable: AIRFLOW__LOGGING__REMOTE_LOG_CONN_ID
#
remote_log_conn_id = 

# Whether the local log files for GCS, S3, WASB, HDFS and OSS remote logging should be deleted after
# they are uploaded to the remote location.
#
# Variable: AIRFLOW__LOGGING__DELETE_LOCAL_LOGS
#
delete_local_logs = False

# Path to Google Credential JSON file. If omitted, authorization based on `the Application Default
# Credentials
# <https://cloud.google.com/docs/authentication/application-default-credentials>`__ will
# be used.
#
# Variable: AIRFLOW__LOGGING__GOOGLE_KEY_PATH
#
google_key_path = 

# Storage bucket URL for remote logging
# S3 buckets should start with **s3://**
# Cloudwatch log groups should start with **cloudwatch://**
# GCS buckets should start with **gs://**
# WASB buckets should start with **wasb** just to help Airflow select correct handler
# Stackdriver logs should start with **stackdriver://**
#
# Variable: AIRFLOW__LOGGING__REMOTE_BASE_LOG_FOLDER
#
remote_base_log_folder = 

# The remote_task_handler_kwargs param is loaded into a dictionary and passed to the ``__init__``
# of remote task handler and it overrides the values provided by Airflow config. For example if you set
# ``delete_local_logs=False`` and you provide ``{"delete_local_copy": true}``, then the local
# log files will be deleted after they are uploaded to remote location.
#
# Example: remote_task_handler_kwargs = {"delete_local_copy": true}
#
# Variable: AIRFLOW__LOGGING__REMOTE_TASK_HANDLER_KWARGS
#
remote_task_handler_kwargs = 

# Use server-side encryption for logs stored in S3
#
# Variable: AIRFLOW__LOGGING__ENCRYPT_S3_LOGS
#
encrypt_s3_logs = False

# Logging level.
# 
# Supported values: ``CRITICAL``, ``ERROR``, ``WARNING``, ``INFO``, ``DEBUG``.
#
# Variable: AIRFLOW__LOGGING__LOGGING_LEVEL
#
logging_level = INFO

# Logging level for celery. If not set, it uses the value of logging_level
# 
# Supported values: ``CRITICAL``, ``ERROR``, ``WARNING``, ``INFO``, ``DEBUG``.
#
# Variable: AIRFLOW__LOGGING__CELERY_LOGGING_LEVEL
#
celery_logging_level = 

# Logging level for Flask-appbuilder UI.
# 
# Supported values: ``CRITICAL``, ``ERROR``, ``WARNING``, ``INFO``, ``DEBUG``.
#
# Variable: AIRFLOW__LOGGING__FAB_LOGGING_LEVEL
#
fab_logging_level = WARNING

# Logging class
# Specify the class that will specify the logging configuration
# This class has to be on the python classpath
#
# Example: logging_config_class = my.path.default_local_settings.LOGGING_CONFIG
#
# Variable: AIRFLOW__LOGGING__LOGGING_CONFIG_CLASS
#
logging_config_class = 

# Flag to enable/disable Colored logs in Console
# Colour the logs when the controlling terminal is a TTY.
#
# Variable: AIRFLOW__LOGGING__COLORED_CONSOLE_LOG
#
colored_console_log = True

# Log format for when Colored logs is enabled
#
# Variable: AIRFLOW__LOGGING__COLORED_LOG_FORMAT
#
colored_log_format = [%%(blue)s%%(asctime)s%%(reset)s] {%%(blue)s%%(filename)s:%%(reset)s%%(lineno)d} %%(log_color)s%%(levelname)s%%(reset)s - %%(log_color)s%%(message)s%%(reset)s

# Specifies the class utilized by Airflow to implement colored logging
#
# Variable: AIRFLOW__LOGGING__COLORED_FORMATTER_CLASS
#
colored_formatter_class = airflow.utils.log.colored_log.CustomTTYColoredFormatter

# Format of Log line
#
# Variable: AIRFLOW__LOGGING__LOG_FORMAT
#
log_format = [%%(asctime)s] {%%(filename)s:%%(lineno)d} %%(levelname)s - %%(message)s

# Defines the format of log messages for simple logging configuration
#
# Variable: AIRFLOW__LOGGING__SIMPLE_LOG_FORMAT
#
simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s

# Where to send dag parser logs. If "file", logs are sent to log files defined by child_process_log_directory.
#
# Variable: AIRFLOW__LOGGING__DAG_PROCESSOR_LOG_TARGET
#
dag_processor_log_target = file

# Format of Dag Processor Log line
#
# Variable: AIRFLOW__LOGGING__DAG_PROCESSOR_LOG_FORMAT
#
dag_processor_log_format = [%%(asctime)s] [SOURCE:DAG_PROCESSOR] {%%(filename)s:%%(lineno)d} %%(levelname)s - %%(message)s

# Determines the directory where logs for the child processes of the dag processor will be stored
#
# Variable: AIRFLOW__LOGGING__DAG_PROCESSOR_CHILD_PROCESS_LOG_DIRECTORY
#
dag_processor_child_process_log_directory = /root/airflow/logs/dag_processor

# Determines the formatter class used by Airflow for structuring its log messages
# The default formatter class is timezone-aware, which means that timestamps attached to log entries
# will be adjusted to reflect the local timezone of the Airflow instance
#
# Variable: AIRFLOW__LOGGING__LOG_FORMATTER_CLASS
#
log_formatter_class = airflow.utils.log.timezone_aware.TimezoneAware

# An import path to a function to add adaptations of each secret added with
# ``airflow.sdk.execution_time.secrets_masker.mask_secret`` to be masked in log messages.
# The given function is expected to require a single parameter: the secret to be adapted.
# It may return a single adaptation of the secret or an iterable of adaptations to each be
# masked as secrets. The original secret will be masked as well as any adaptations returned.
#
# Example: secret_mask_adapter = urllib.parse.quote
#
# Variable: AIRFLOW__LOGGING__SECRET_MASK_ADAPTER
#
secret_mask_adapter = 

# The minimum length of a secret to be masked in log messages.
# Secrets shorter than this length will not be masked.
#
# Variable: AIRFLOW__LOGGING__MIN_LENGTH_MASKED_SECRET
#
min_length_masked_secret = 5

# Specify prefix pattern like mentioned below with stream handler ``TaskHandlerWithCustomFormatter``
#
# Example: task_log_prefix_template = {{ti.dag_id}}-{{ti.task_id}}-{{logical_date}}-{{ti.try_number}}
#
# Variable: AIRFLOW__LOGGING__TASK_LOG_PREFIX_TEMPLATE
#
task_log_prefix_template = 

# Formatting for how airflow generates file names/paths for each task run.
#
# Variable: AIRFLOW__LOGGING__LOG_FILENAME_TEMPLATE
#
log_filename_template = dag_id={{ ti.dag_id }}/run_id={{ ti.run_id }}/task_id={{ ti.task_id }}/{%% if ti.map_index >= 0 %%}map_index={{ ti.map_index }}/{%% endif %%}attempt={{ try_number|default(ti.try_number) }}.log

# Name of handler to read task instance logs.
# Defaults to use ``task`` handler.
#
# Variable: AIRFLOW__LOGGING__TASK_LOG_READER
#
task_log_reader = task

# A comma\-separated list of third-party logger names that will be configured to print messages to
# consoles\.
#
# Example: extra_logger_names = fastapi,sqlalchemy
#
# Variable: AIRFLOW__LOGGING__EXTRA_LOGGER_NAMES
#
extra_logger_names = 

# 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.
#
# Variable: AIRFLOW__LOGGING__WORKER_LOG_SERVER_PORT
#
worker_log_server_port = 8793

# Port to serve logs from for triggerer.
# See ``[logging] worker_log_server_port`` description for more info.
#
# Variable: AIRFLOW__LOGGING__TRIGGER_LOG_SERVER_PORT
#
trigger_log_server_port = 8794

# We must parse timestamps to interleave logs between trigger and task.  To do so,
# we need to parse timestamps in log files. In case your log format is non-standard,
# you may provide import path to callable which takes a string log line and returns
# the timestamp (datetime.datetime compatible).
#
# Example: interleave_timestamp_parser = path.to.my_func
#
# Variable: AIRFLOW__LOGGING__INTERLEAVE_TIMESTAMP_PARSER
#
# interleave_timestamp_parser = 

# Permissions in the form or of octal string as understood by chmod. The permissions are important
# when you use impersonation, when logs are written by a different user than airflow. The most secure
# way of configuring it in this case is to add both users to the same group and make it the default
# group of both users. Group-writeable logs are default in airflow, but you might decide that you are
# OK with having the logs other-writeable, in which case you should set it to ``0o777``. You might
# decide to add more security if you do not use impersonation and change it to ``0o755`` to make it
# only owner-writeable. You can also make it just readable only for owner by changing it to ``0o700``
# if all the access (read/write) for your logs happens from the same user.
#
# Example: file_task_handler_new_folder_permissions = 0o775
#
# Variable: AIRFLOW__LOGGING__FILE_TASK_HANDLER_NEW_FOLDER_PERMISSIONS
#
file_task_handler_new_folder_permissions = 0o775

# Permissions in the form or of octal string as understood by chmod. The permissions are important
# when you use impersonation, when logs are written by a different user than airflow. The most secure
# way of configuring it in this case is to add both users to the same group and make it the default
# group of both users. Group-writeable logs are default in airflow, but you might decide that you are
# OK with having the logs other-writeable, in which case you should set it to ``0o666``. You might
# decide to add more security if you do not use impersonation and change it to ``0o644`` to make it
# only owner-writeable. You can also make it just readable only for owner by changing it to ``0o600``
# if all the access (read/write) for your logs happens from the same user.
#
# Example: file_task_handler_new_file_permissions = 0o664
#
# Variable: AIRFLOW__LOGGING__FILE_TASK_HANDLER_NEW_FILE_PERMISSIONS
#
file_task_handler_new_file_permissions = 0o664

# By default Celery sends all logs into stderr.
# If enabled any previous logging handlers will get *removed*.
# With this option AirFlow will create new handlers
# and send low level logs like INFO and WARNING to stdout,
# while sending higher severity logs to stderr.
#
# Variable: AIRFLOW__LOGGING__CELERY_STDOUT_STDERR_SEPARATION
#
celery_stdout_stderr_separation = False

# A comma separated list of keywords related to errors whose presence should display the line in red
# color in UI
#
# Variable: AIRFLOW__LOGGING__COLOR_LOG_ERROR_KEYWORDS
#
color_log_error_keywords = error,exception

# A comma separated list of keywords related to warning whose presence should display the line in yellow
# color in UI
#
# Variable: AIRFLOW__LOGGING__COLOR_LOG_WARNING_KEYWORDS
#
color_log_warning_keywords = warn

[metrics]
# `StatsD <https://github.com/statsd/statsd>`__ integration settings.

# Configure an allow list (comma separated regex patterns to match) to send only certain metrics.
#
# Example: metrics_allow_list = "scheduler,executor,dagrun,pool,triggerer,celery" or "^scheduler,^executor,heartbeat|timeout"
#
# Variable: AIRFLOW__METRICS__METRICS_ALLOW_LIST
#
metrics_allow_list = 

# Configure a block list (comma separated regex patterns to match) to block certain metrics
# from being emitted.
# If ``[metrics] metrics_allow_list`` and ``[metrics] metrics_block_list`` are both configured,
# ``[metrics] metrics_block_list`` is ignored.
#
# Example: metrics_block_list = "scheduler,executor,dagrun,pool,triggerer,celery" or "^scheduler,^executor,heartbeat|timeout"
#
# Variable: AIRFLOW__METRICS__METRICS_BLOCK_LIST
#
metrics_block_list = 

# Enables sending metrics to StatsD.
#
# Variable: AIRFLOW__METRICS__STATSD_ON
#
statsd_on = False

# Specifies the host address where the StatsD daemon (or server) is running
#
# Variable: AIRFLOW__METRICS__STATSD_HOST
#
statsd_host = localhost

# Enables the statsd host to be resolved into IPv6 address
#
# Variable: AIRFLOW__METRICS__STATSD_IPV6
#
statsd_ipv6 = False

# Specifies the port on which the StatsD daemon (or server) is listening to
#
# Variable: AIRFLOW__METRICS__STATSD_PORT
#
statsd_port = 8125

# Defines the namespace for all metrics sent from Airflow to StatsD
#
# Variable: AIRFLOW__METRICS__STATSD_PREFIX
#
statsd_prefix = airflow

# A function that validate the StatsD stat name, apply changes to the stat name if necessary and return
# the transformed stat name.
# 
# The function should have the following signature
# 
# .. code-block:: python
# 
#     def func_name(stat_name: str) -> str: ...
#
# Variable: AIRFLOW__METRICS__STAT_NAME_HANDLER
#
stat_name_handler = 

# To enable datadog integration to send airflow metrics.
#
# Variable: AIRFLOW__METRICS__STATSD_DATADOG_ENABLED
#
statsd_datadog_enabled = False

# List of datadog tags attached to all metrics(e.g: ``key1:value1,key2:value2``)
#
# Variable: AIRFLOW__METRICS__STATSD_DATADOG_TAGS
#
statsd_datadog_tags = 

# Set to ``False`` to disable metadata tags for some of the emitted metrics
#
# Variable: AIRFLOW__METRICS__STATSD_DATADOG_METRICS_TAGS
#
statsd_datadog_metrics_tags = True

# If you want to utilise your own custom StatsD client set the relevant
# module path below.
# Note: The module path must exist on your
# `PYTHONPATH <https://docs.python.org/3/using/cmdline.html#envvar-PYTHONPATH>`
# for Airflow to pick it up
#
# Variable: AIRFLOW__METRICS__STATSD_CUSTOM_CLIENT_PATH
#
# statsd_custom_client_path = 

# If you want to avoid sending all the available metrics tags to StatsD,
# you can configure a block list of prefixes (comma separated) to filter out metric tags
# that start with the elements of the list (e.g: ``job_id,run_id``)
#
# Example: statsd_disabled_tags = job_id,run_id,dag_id,task_id
#
# Variable: AIRFLOW__METRICS__STATSD_DISABLED_TAGS
#
statsd_disabled_tags = job_id,run_id

# To enable sending Airflow metrics with StatsD-Influxdb tagging convention.
#
# Variable: AIRFLOW__METRICS__STATSD_INFLUXDB_ENABLED
#
statsd_influxdb_enabled = False

# Enables sending metrics to OpenTelemetry.
#
# Variable: AIRFLOW__METRICS__OTEL_ON
#
otel_on = False

# Specifies the hostname or IP address of the OpenTelemetry Collector to which Airflow sends
# metrics and traces.
#
# Variable: AIRFLOW__METRICS__OTEL_HOST
#
otel_host = localhost

# Specifies the port of the OpenTelemetry Collector that is listening to.
#
# Variable: AIRFLOW__METRICS__OTEL_PORT
#
otel_port = 8889

# The prefix for the Airflow metrics.
#
# Variable: AIRFLOW__METRICS__OTEL_PREFIX
#
otel_prefix = airflow

# Defines the interval, in milliseconds, at which Airflow sends batches of metrics and traces
# to the configured OpenTelemetry Collector.
#
# Variable: AIRFLOW__METRICS__OTEL_INTERVAL_MILLISECONDS
#
otel_interval_milliseconds = 60000

# If ``True``, all metrics are also emitted to the console. Defaults to ``False``.
#
# Variable: AIRFLOW__METRICS__OTEL_DEBUGGING_ON
#
otel_debugging_on = False

# The default service name of traces.
#
# Variable: AIRFLOW__METRICS__OTEL_SERVICE
#
otel_service = Airflow

# If ``True``, SSL will be enabled. Defaults to ``False``.
# To establish an HTTPS connection to the OpenTelemetry collector,
# you need to configure the SSL certificate and key within the OpenTelemetry collector's
# ``config.yml`` file.
#
# Variable: AIRFLOW__METRICS__OTEL_SSL_ACTIVE
#
otel_ssl_active = False

[traces]
# Distributed traces integration settings.

# Enables sending traces to OpenTelemetry.
#
# Variable: AIRFLOW__TRACES__OTEL_ON
#
otel_on = False

# Specifies the hostname or IP address of the OpenTelemetry Collector to which Airflow sends
# traces.
#
# Variable: AIRFLOW__TRACES__OTEL_HOST
#
otel_host = localhost

# Specifies the port of the OpenTelemetry Collector that is listening to.
#
# Variable: AIRFLOW__TRACES__OTEL_PORT
#
otel_port = 8889

# The default service name of traces.
#
# Variable: AIRFLOW__TRACES__OTEL_SERVICE
#
otel_service = Airflow

# If True, all traces are also emitted to the console. Defaults to False.
#
# Variable: AIRFLOW__TRACES__OTEL_DEBUGGING_ON
#
otel_debugging_on = False

# If True, SSL will be enabled.  Defaults to False.
# To establish an HTTPS connection to the OpenTelemetry collector,
# you need to configure the SSL certificate and key within the OpenTelemetry collector's
# config.yml file.
#
# Variable: AIRFLOW__TRACES__OTEL_SSL_ACTIVE
#
otel_ssl_active = False

[secrets]
# Full class name of secrets backend to enable (will precede env vars and metastore in search path)
#
# Example: backend = airflow.providers.amazon.aws.secrets.systems_manager.SystemsManagerParameterStoreBackend
#
# Variable: AIRFLOW__SECRETS__BACKEND
#
backend = 

# The backend_kwargs param is loaded into a dictionary and passed to ``__init__``
# of secrets backend class. See documentation for the secrets backend you are using.
# JSON is expected.
# 
# Example for AWS Systems Manager ParameterStore:
# ``{"connections_prefix": "/airflow/connections", "profile_name": "default"}``
#
# Variable: AIRFLOW__SECRETS__BACKEND_KWARGS
#
backend_kwargs = 

# .. note:: |experimental|
# 
# Enables local caching of Variables, when parsing DAGs only.
# Using this option can make dag parsing faster if Variables are used in top level code, at the expense
# of longer propagation time for changes.
# Please note that this cache concerns only the DAG parsing step. There is no caching in place when DAG
# tasks are run.
#
# Variable: AIRFLOW__SECRETS__USE_CACHE
#
use_cache = False

# .. note:: |experimental|
# 
# When the cache is enabled, this is the duration for which we consider an entry in the cache to be
# valid. Entries are refreshed if they are older than this many seconds.
# It means that when the cache is enabled, this is the maximum amount of time you need to wait to see a
# Variable change take effect.
#
# Variable: AIRFLOW__SECRETS__CACHE_TTL_SECONDS
#
cache_ttl_seconds = 900

[api]
# Boolean for running SwaggerUI in the webserver.
#
# Variable: AIRFLOW__API__ENABLE_SWAGGER_UI
#
enable_swagger_ui = True

# Secret key used to run your api server. It should be as random as possible. However, when running
# more than 1 instances of the api, make sure all of them use the same ``secret_key`` otherwise
# one of them will error with "CSRF session token is missing".
# The api key is also used to authorize requests to Celery workers when logs are retrieved.
# The token generated using the secret key has a short expiry time though - make sure that time on
# ALL the machines that you run airflow components on is synchronized (for example using ntpd)
# otherwise you might get "forbidden" errors when the logs are accessed.
#
# Variable: AIRFLOW__API__SECRET_KEY
#
secret_key = 010RL08807/JBjH4cWzNaw==

# Expose the configuration file in the web server. Set to ``non-sensitive-only`` to show all values
# except those that have security implications. ``True`` shows all values. ``False`` hides the
# configuration completely.
#
# Variable: AIRFLOW__API__EXPOSE_CONFIG
#
expose_config = True

# Expose stacktrace in the web server
#
# Variable: AIRFLOW__API__EXPOSE_STACKTRACE
#
expose_stacktrace = False

# The base url of the API server. Airflow cannot guess what domain or CNAME you are using.
# If the Airflow console (the front-end) and the API server are on a different domain, this config
# should contain the API server endpoint.
#
# Example: base_url = https://my-airflow.company.com
#
# Variable: AIRFLOW__API__BASE_URL
#
# base_url = 

# The ip specified when starting the api server
#
# Variable: AIRFLOW__API__HOST
#
host = 0.0.0.0

# The port on which to run the api server
#
# Variable: AIRFLOW__API__PORT
#
port = 8080

# Number of workers to run on the API server
#
# Variable: AIRFLOW__API__WORKERS
#
workers = 4

# Number of seconds the API server waits before timing out on a worker
#
# Variable: AIRFLOW__API__WORKER_TIMEOUT
#
worker_timeout = 120

# Log files for the api server. '-' means log to stderr.
#
# Variable: AIRFLOW__API__ACCESS_LOGFILE
#
access_logfile = -

# Paths to the SSL certificate and key for the api server. When both are
# provided SSL will be enabled. This does not change the api server port.
# The same SSL certificate will also be loaded into the worker to enable
# it to be trusted when a self-signed certificate is used.
#
# Variable: AIRFLOW__API__SSL_CERT
#
ssl_cert = 

# Paths to the SSL certificate and key for the api server. When both are
# provided SSL will be enabled. This does not change the api server port.
#
# Variable: AIRFLOW__API__SSL_KEY
#
ssl_key = 

# Used to set the maximum page limit for API requests. If limit passed as param
# is greater than maximum page limit, it will be ignored and maximum page limit value
# will be set as the limit
#
# Variable: AIRFLOW__API__MAXIMUM_PAGE_LIMIT
#
maximum_page_limit = 100

# Used to set the default page limit when limit param is zero or not provided in API
# requests. Otherwise if positive integer is passed in the API requests as limit, the
# smallest number of user given limit or maximum page limit is taken as limit.
#
# Variable: AIRFLOW__API__FALLBACK_PAGE_LIMIT
#
fallback_page_limit = 50

# Used in response to a preflight request to indicate which HTTP
# headers can be used when making the actual request. This header is
# the server side response to the browser's
# Access-Control-Request-Headers header.
#
# Variable: AIRFLOW__API__ACCESS_CONTROL_ALLOW_HEADERS
#
access_control_allow_headers = 

# Specifies the method or methods allowed when accessing the resource.
#
# Variable: AIRFLOW__API__ACCESS_CONTROL_ALLOW_METHODS
#
access_control_allow_methods = 

# Indicates whether the response can be shared with requesting code from the given origins.
# Separate URLs with space.
#
# Variable: AIRFLOW__API__ACCESS_CONTROL_ALLOW_ORIGINS
#
access_control_allow_origins = 

# Indicates whether the **xcomEntries** endpoint supports the **deserialize**
# flag. If set to ``False``, setting this flag in a request would result in a
# 400 Bad Request error.
#
# Variable: AIRFLOW__API__ENABLE_XCOM_DESERIALIZE_SUPPORT
#
enable_xcom_deserialize_support = False

[workers]
# Configuration related to workers that run Airflow tasks.

# Full class name of secrets backend to enable for workers (will precede env vars backend)
#
# Example: secrets_backend = airflow.providers.amazon.aws.secrets.systems_manager.SystemsManagerParameterStoreBackend
#
# Variable: AIRFLOW__WORKERS__SECRETS_BACKEND
#
secrets_backend = 

# The secrets_backend_kwargs param is loaded into a dictionary and passed to ``__init__``
# of secrets backend class. See documentation for the secrets backend you are using.
# JSON is expected.
# 
# Example for AWS Systems Manager ParameterStore:
# ``{"connections_prefix": "/airflow/connections", "profile_name": "default"}``
#
# Variable: AIRFLOW__WORKERS__SECRETS_BACKEND_KWARGS
#
secrets_backend_kwargs = 

# The minimum interval (in seconds) at which the worker checks the task instance's
# heartbeat status with the API server to confirm it is still alive.
#
# Variable: AIRFLOW__WORKERS__MIN_HEARTBEAT_INTERVAL
#
min_heartbeat_interval = 5

# The maximum number of consecutive failed heartbeats before terminating the task instance process.
#
# Variable: AIRFLOW__WORKERS__MAX_FAILED_HEARTBEATS
#
max_failed_heartbeats = 3

# The maximum number of retry attempts to the execution API server.
#
# Variable: AIRFLOW__WORKERS__EXECUTION_API_RETRIES
#
execution_api_retries = 5

# The minimum amount of time (in seconds) to wait before retrying a failed API request.
#
# Variable: AIRFLOW__WORKERS__EXECUTION_API_RETRY_WAIT_MIN
#
execution_api_retry_wait_min = 1.0

# The maximum amount of time (in seconds) to wait before retrying a failed API request.
#
# Variable: AIRFLOW__WORKERS__EXECUTION_API_RETRY_WAIT_MAX
#
execution_api_retry_wait_max = 90.0

# Number of seconds to wait after a task process exits before forcibly closing any
# remaining communication sockets. This helps prevent the task supervisor from hanging
# indefinitely due to missed EOF signals.
#
# Variable: AIRFLOW__WORKERS__SOCKET_CLEANUP_TIMEOUT
#
socket_cleanup_timeout = 60.0

[api_auth]
# Settings relating to authentication on the Airflow APIs

# The audience claim to use when generating and validating JWTs for the API.
# 
# This variable can be a single value, or a comma-separated string, in which case the first value is the
# one that will be used when generating, and the others are accepted at validation time.
# 
# Not required, but strongly encouraged.
# 
# See also :ref:`config:execution_api__jwt_audience`
#
# Example: jwt_audience = my-unique-airflow-id
#
# Variable: AIRFLOW__API_AUTH__JWT_AUDIENCE
#
# jwt_audience = 

# Issuer claim to use when generating and validating JWTs for the API.
#
# Variable: AIRFLOW__API_AUTH__JWT_ISSUER
#
jwt_issuer = airflow

# Number in seconds until the JWTs used for authentication expires. When the token expires,
# all API calls using this token will fail on authentication.
# 
# Make sure that time on ALL the machines that you run airflow components on is synchronized
# (for example using ntpd) otherwise you might get "forbidden" errors.
# 
# See also :ref:`config:execution_api__jwt_expiration_time`
#
# Variable: AIRFLOW__API_AUTH__JWT_EXPIRATION_TIME
#
jwt_expiration_time = 86400

# Number in seconds until the JWTs used for authentication expires for CLI commands.
# When the token expires, all CLI calls using this token will fail on authentication.
# 
# Make sure that time on ALL the machines that you run airflow components on is synchronized
# (for example using ntpd) otherwise you might get "forbidden" errors.
#
# Variable: AIRFLOW__API_AUTH__JWT_CLI_EXPIRATION_TIME
#
jwt_cli_expiration_time = 3600

# Secret key used to encode and decode JWTs to authenticate to public and private APIs.
# 
# It should be as random as possible. However, when running more than 1 instances of API services,
# make sure all of them use the same ``jwt_secret`` otherwise calls will fail on authentication.
# 
# Mutually exclusive with ``jwt_private_key_path``.
#
# Variable: AIRFLOW__API_AUTH__JWT_SECRET
#
jwt_secret = LUPyvA9pvI084a6b0PKUwA==

# The path to a file containing a PEM-encoded private key use when generating Task Identity tokens in
# the executor.
# 
# Mutually exclusive with ``jwt_secret``.
#
# Example: jwt_private_key_path = /path/to/private_key.pem
#
# Variable: AIRFLOW__API_AUTH__JWT_PRIVATE_KEY_PATH
#
# jwt_private_key_path = 

# The algorithm name use when generating and validating JWT Task Identities.
# 
# This value must be appropriate for the given private key type.
# 
# If this is not specified Airflow makes some guesses as what algorithm is best based on the key type.
# 
# ("HS512" if ``jwt_secret`` is set, otherwise a key-type specific guess)
#
# Example: jwt_algorithm = "EdDSA" or "HS512"
#
# Variable: AIRFLOW__API_AUTH__JWT_ALGORITHM
#
# jwt_algorithm = 

# The Key ID to place in header when generating JWTs. Not used in the validation path.
# 
# If this is not specified the RFC7638 thumbprint of the private key will be used.
# 
# Ignored when ``jwt_secret`` is used.
#
# Example: jwt_kid = my-key-id
#
# Variable: AIRFLOW__API_AUTH__JWT_KID
#
# jwt_kid = 

# The public signing keys of Task Execution token issuers to trust. It must contain the public key
# related to ``jwt_private_key_path`` else tasks will be unlikely to execute successfully.
# 
# Can be a local file path (without the ``file://`` prefix) or an http or https URL.
# 
# If a remote URL is given it will be polled periodically for changes.
# 
# Mutually exclusive with ``jwt_secret``.
# 
# If a ``jwt_private_key_path`` is given but this settings is not set then the private key will be
# trusted. If this is provided it is your responsibility to ensure that the private key used for
# generation is in this list.
#
# Example: trusted_jwks_url = "/path/to/public-jwks.json" or "https://my-issuer/.well-known/jwks.json"
#
# Variable: AIRFLOW__API_AUTH__TRUSTED_JWKS_URL
#
# trusted_jwks_url = 

# Issuer of the JWT. This becomes the ``iss`` claim of generated tokens, and is validated on incoming
# requests.
# 
# Ideally this should be unique per individual airflow deployment
# 
# Not required, but strongly recommended to be set.
# 
# See also :ref:`config:api_auth__jwt_audience`
#
# Example: jwt_issuer = http://my-airflow.mycompany.com
#
# Variable: AIRFLOW__API_AUTH__JWT_ISSUER
#
# jwt_issuer = 

# Number of seconds leeway in validating expiry time of JWTs to account for clock skew between
# client and server
#
# Variable: AIRFLOW__API_AUTH__JWT_LEEWAY
#
jwt_leeway = 10

[execution_api]
# Settings related to the Execution API server.
# 
# The ExecutionAPI also uses a lot of settings from the :ref:`config:api_auth` section.

# Number in seconds until the JWT used for authentication expires. When the token expires,
# all API calls using this token will fail on authentication.
# 
# Make sure that time on ALL the machines that you run airflow components on is synchronized
# (for example using ntpd) otherwise you might get "forbidden" errors.
#
# Variable: AIRFLOW__EXECUTION_API__JWT_EXPIRATION_TIME
#
jwt_expiration_time = 600

# The audience claim to use when generating and validating JWTs for the Execution API.
# 
# This variable can be a single value, or a comma-separated string, in which case the first value is the
# one that will be used when generating, and the others are accepted at validation time.
# 
# Not required, but strongly encouraged
# 
# See also :ref:`config:api_auth__jwt_audience`
#
# Variable: AIRFLOW__EXECUTION_API__JWT_AUDIENCE
#
jwt_audience = urn:airflow.apache.org:task

[lineage]
# what lineage backend to use
#
# Variable: AIRFLOW__LINEAGE__BACKEND
#
backend = 

[operators]
# The default owner assigned to each new operator, unless
# provided explicitly or passed via ``default_args``
#
# Variable: AIRFLOW__OPERATORS__DEFAULT_OWNER
#
default_owner = airflow

# The default value of attribute "deferrable" in operators and sensors.
#
# Variable: AIRFLOW__OPERATORS__DEFAULT_DEFERRABLE
#
default_deferrable = false

# Indicates the default number of CPU units allocated to each operator when no specific CPU request
# is specified in the operator's configuration
#
# Variable: AIRFLOW__OPERATORS__DEFAULT_CPUS
#
default_cpus = 1

# Indicates the default number of RAM allocated to each operator when no specific RAM request
# is specified in the operator's configuration
#
# Variable: AIRFLOW__OPERATORS__DEFAULT_RAM
#
default_ram = 512

# Indicates the default number of disk storage allocated to each operator when no specific disk request
# is specified in the operator's configuration
#
# Variable: AIRFLOW__OPERATORS__DEFAULT_DISK
#
default_disk = 512

# Indicates the default number of GPUs allocated to each operator when no specific GPUs request
# is specified in the operator's configuration
#
# Variable: AIRFLOW__OPERATORS__DEFAULT_GPUS
#
default_gpus = 0

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

[webserver]
# Sorting order in grid view. Valid values are: ``topological``, ``hierarchical_alphabetical``
#
# Variable: AIRFLOW__WEBSERVER__GRID_VIEW_SORTING_ORDER
#
grid_view_sorting_order = topological

# The amount of time (in secs) webserver will wait for initial handshake
# while fetching logs from other worker machine
#
# Variable: AIRFLOW__WEBSERVER__LOG_FETCH_TIMEOUT_SEC
#
log_fetch_timeout_sec = 5

# By default, the webserver shows paused DAGs. Flip this to hide paused
# DAGs by default
#
# Variable: AIRFLOW__WEBSERVER__HIDE_PAUSED_DAGS_BY_DEFAULT
#
hide_paused_dags_by_default = False

# Consistent page size across all listing views in the UI
#
# Variable: AIRFLOW__WEBSERVER__PAGE_SIZE
#
page_size = 50

# Default setting for wrap toggle on DAG code and TI log views.
#
# Variable: AIRFLOW__WEBSERVER__DEFAULT_WRAP
#
default_wrap = False

# Sets a custom page title for the DAGs overview page and site title for all pages
#
# Variable: AIRFLOW__WEBSERVER__INSTANCE_NAME
#
# instance_name = 

# Whether the custom page title for the DAGs overview page contains any Markup language
#
# Variable: AIRFLOW__WEBSERVER__INSTANCE_NAME_HAS_MARKUP
#
instance_name_has_markup = False

# How frequently, in seconds, the DAG data will auto-refresh in graph or grid view
# when auto-refresh is turned on
#
# Variable: AIRFLOW__WEBSERVER__AUTO_REFRESH_INTERVAL
#
auto_refresh_interval = 3

# Boolean for displaying warning for publicly viewable deployment
#
# Variable: AIRFLOW__WEBSERVER__WARN_DEPLOYMENT_EXPOSURE
#
warn_deployment_exposure = True

# Comma separated string of view events to exclude from dag audit view.
# All other events will be added minus the ones passed here.
# The audit logs in the db will not be affected by this parameter.
#
# Example: audit_view_excluded_events = cli_task_run,running,success
#
# Variable: AIRFLOW__WEBSERVER__AUDIT_VIEW_EXCLUDED_EVENTS
#
# audit_view_excluded_events = 

# Comma separated string of view events to include in dag audit view.
# If passed, only these events will populate the dag audit view.
# The audit logs in the db will not be affected by this parameter.
#
# Example: audit_view_included_events = dagrun_cleared,failed
#
# Variable: AIRFLOW__WEBSERVER__AUDIT_VIEW_INCLUDED_EVENTS
#
# audit_view_included_events = 

# Require confirmation when changing a DAG in the web UI. This is to prevent accidental changes
# to a DAG that may be running on sensitive environments like production.
# When set to ``True``, confirmation dialog will be shown when a user tries to Pause/Unpause,
# Trigger a DAG
#
# Variable: AIRFLOW__WEBSERVER__REQUIRE_CONFIRMATION_DAG_CHANGE
#
require_confirmation_dag_change = False

[email]
# Configuration email backend and whether to
# send email alerts on retry or failure

# Email backend to use
#
# Variable: AIRFLOW__EMAIL__EMAIL_BACKEND
#
email_backend = airflow.utils.email.send_email_smtp

# Email connection to use
#
# Variable: AIRFLOW__EMAIL__EMAIL_CONN_ID
#
email_conn_id = smtp_default

# Whether email alerts should be sent when a task is retried
#
# Variable: AIRFLOW__EMAIL__DEFAULT_EMAIL_ON_RETRY
#
default_email_on_retry = True

# Whether email alerts should be sent when a task failed
#
# Variable: AIRFLOW__EMAIL__DEFAULT_EMAIL_ON_FAILURE
#
default_email_on_failure = True

# File that will be used as the template for Email subject (which will be rendered using Jinja2).
# If not set, Airflow uses a base template.
#
# Example: subject_template = /path/to/my_subject_template_file
#
# Variable: AIRFLOW__EMAIL__SUBJECT_TEMPLATE
#
# subject_template = 

# File that will be used as the template for Email content (which will be rendered using Jinja2).
# If not set, Airflow uses a base template.
#
# Example: html_content_template = /path/to/my_html_content_template_file
#
# Variable: AIRFLOW__EMAIL__HTML_CONTENT_TEMPLATE
#
# html_content_template = 

# Email address that will be used as sender address.
# It can either be raw email or the complete address in a format ``Sender Name <sender@email.com>``
#
# Example: from_email = Airflow <airflow@example.com>
#
# Variable: AIRFLOW__EMAIL__FROM_EMAIL
#
# from_email = 

# ssl context to use when using SMTP and IMAP SSL connections. By default, the context is "default"
# which sets it to ``ssl.create_default_context()`` which provides the right balance between
# compatibility and security, it however requires that certificates in your operating system are
# updated and that SMTP/IMAP servers of yours have valid certificates that have corresponding public
# keys installed on your machines. You can switch it to "none" if you want to disable checking
# of the certificates, but it is not recommended as it allows MITM (man-in-the-middle) attacks
# if your infrastructure is not sufficiently secured. It should only be set temporarily while you
# are fixing your certificate configuration. This can be typically done by upgrading to newer
# version of the operating system you run Airflow components on,by upgrading/refreshing proper
# certificates in the OS or by updating certificates for your mail servers.
#
# Example: ssl_context = default
#
# Variable: AIRFLOW__EMAIL__SSL_CONTEXT
#
ssl_context = default

[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

# Specifies the host server address used by Airflow when sending out email notifications via SMTP.
#
# Variable: AIRFLOW__SMTP__SMTP_HOST
#
smtp_host = localhost

# Determines whether to use the STARTTLS command when connecting to the SMTP server.
#
# Variable: AIRFLOW__SMTP__SMTP_STARTTLS
#
smtp_starttls = True

# Determines whether to use an SSL connection when talking to the SMTP server.
#
# Variable: AIRFLOW__SMTP__SMTP_SSL
#
smtp_ssl = False

# Defines the port number on which Airflow connects to the SMTP server to send email notifications.
#
# Variable: AIRFLOW__SMTP__SMTP_PORT
#
smtp_port = 25

# Specifies the default **from** email address used when Airflow sends email notifications.
#
# Variable: AIRFLOW__SMTP__SMTP_MAIL_FROM
#
smtp_mail_from = airflow@example.com

# Determines the maximum time (in seconds) the Apache Airflow system will wait for a
# connection to the SMTP server to be established.
#
# Variable: AIRFLOW__SMTP__SMTP_TIMEOUT
#
smtp_timeout = 30

# Defines the maximum number of times Airflow will attempt to connect to the SMTP server.
#
# Variable: AIRFLOW__SMTP__SMTP_RETRY_LIMIT
#
smtp_retry_limit = 5

[sentry]
# `Sentry <https://docs.sentry.io>`__ integration. Here you can supply
# additional configuration options based on the Python platform.
# See `Python / Configuration / Basic Options
# <https://docs.sentry.io/platforms/python/configuration/options/>`__ for more details.
# Unsupported options: ``integrations``, ``in_app_include``, ``in_app_exclude``,
# ``ignore_errors``, ``before_breadcrumb``, ``transport``.

# Enable error reporting to Sentry
#
# Variable: AIRFLOW__SENTRY__SENTRY_ON
#
sentry_on = false

#
# Variable: AIRFLOW__SENTRY__SENTRY_DSN
#
sentry_dsn = 

# Dotted path to a before_send function that the sentry SDK should be configured to use.
#
# Variable: AIRFLOW__SENTRY__BEFORE_SEND
#
# before_send = 

[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).
#
# Variable: AIRFLOW__SCHEDULER__JOB_HEARTBEAT_SEC
#
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).
#
# Variable: AIRFLOW__SCHEDULER__SCHEDULER_HEARTBEAT_SEC
#
scheduler_heartbeat_sec = 5

# The frequency (in seconds) at which the LocalTaskJob should send heartbeat signals to the
# scheduler to notify it's still alive. If this value is set to 0, the heartbeat interval will default
# to the value of ``[scheduler] task_instance_heartbeat_timeout``.
#
# Variable: AIRFLOW__SCHEDULER__TASK_INSTANCE_HEARTBEAT_SEC
#
task_instance_heartbeat_sec = 0

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

# Controls how long the scheduler will sleep between loops, but if there was nothing to do
# in the loop. i.e. if it scheduled something then it will start the next loop
# iteration straight away.
#
# Variable: AIRFLOW__SCHEDULER__SCHEDULER_IDLE_SLEEP_TIME
#
scheduler_idle_sleep_time = 1

# 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 assets that are no longer
# referenced and should be marked as orphaned.
#
# Variable: AIRFLOW__SCHEDULER__PARSING_CLEANUP_INTERVAL
#
parsing_cleanup_interval = 60

# How often (in seconds) should pool usage stats be sent to StatsD (if statsd_on is enabled)
#
# Variable: AIRFLOW__SCHEDULER__POOL_METRICS_INTERVAL
#
pool_metrics_interval = 5.0

# How often (in seconds) should running task instance stats be sent to StatsD (if statsd_on is enabled)
#
# Variable: AIRFLOW__SCHEDULER__RUNNING_METRICS_INTERVAL
#
running_metrics_interval = 30.0

# If the last scheduler heartbeat happened more than ``[scheduler] 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.
#
# Variable: AIRFLOW__SCHEDULER__SCHEDULER_HEALTH_CHECK_THRESHOLD
#
scheduler_health_check_threshold = 30

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

# When you start a scheduler, airflow starts a tiny web server
# subprocess to serve a health check on this host
#
# Variable: AIRFLOW__SCHEDULER__SCHEDULER_HEALTH_CHECK_SERVER_HOST
#
scheduler_health_check_server_host = 0.0.0.0

# When you start a scheduler, airflow starts a tiny web server
# subprocess to serve a health check on this port
#
# Variable: AIRFLOW__SCHEDULER__SCHEDULER_HEALTH_CHECK_SERVER_PORT
#
scheduler_health_check_server_port = 8974

# How often (in seconds) should the scheduler check for orphaned tasks and SchedulerJobs
#
# Variable: AIRFLOW__SCHEDULER__ORPHANED_TASKS_CHECK_INTERVAL
#
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.
#
# Variable: AIRFLOW__SCHEDULER__TASK_INSTANCE_HEARTBEAT_TIMEOUT
#
task_instance_heartbeat_timeout = 300

# How often (in seconds) should the scheduler check for task instances whose heartbeats have timed out.
#
# Variable: AIRFLOW__SCHEDULER__TASK_INSTANCE_HEARTBEAT_TIMEOUT_DETECTION_INTERVAL
#
task_instance_heartbeat_timeout_detection_interval = 10.0

# Turn on scheduler catchup by setting this to ``True``.
# 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)
#
# Variable: AIRFLOW__SCHEDULER__CATCHUP_BY_DEFAULT
#
catchup_by_default = False

# Setting this to ``True`` will make first task instance of a task
# ignore depends_on_past setting. A task instance will be considered
# as the first task instance of a task when there is no task instance
# in the DB with a logical_date earlier than it., i.e. no manual marking
# success will be needed for a newly added task to be scheduled.
#
# Variable: AIRFLOW__SCHEDULER__IGNORE_FIRST_DEPENDS_ON_PAST_BY_DEFAULT
#
ignore_first_depends_on_past_by_default = True

# This determines the number of task instances to be evaluated for scheduling
# during each scheduler loop.
# Set this to 0 to use the value of ``[core] parallelism``
#
# Variable: AIRFLOW__SCHEDULER__MAX_TIS_PER_QUERY
#
max_tis_per_query = 16

# 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
#
# Variable: AIRFLOW__SCHEDULER__USE_ROW_LEVEL_LOCKING
#
use_row_level_locking = True

# Max number of DAGs to create DagRuns for per scheduler loop.
#
# Variable: AIRFLOW__SCHEDULER__MAX_DAGRUNS_TO_CREATE_PER_LOOP
#
max_dagruns_to_create_per_loop = 10

# How many DagRuns should a scheduler examine (and lock) when scheduling
# and queuing tasks.
#
# Variable: AIRFLOW__SCHEDULER__MAX_DAGRUNS_PER_LOOP_TO_SCHEDULE
#
max_dagruns_per_loop_to_schedule = 20

# Time in seconds after which dags, which were not updated by Dag Processor are deactivated.
#
# Variable: AIRFLOW__SCHEDULER__DAG_STALE_NOT_SEEN_DURATION
#
dag_stale_not_seen_duration = 600

# 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.
#
# Variable: AIRFLOW__SCHEDULER__USE_JOB_SCHEDULE
#
use_job_schedule = True

# How often to check for expired trigger requests that have not run yet.
#
# Variable: AIRFLOW__SCHEDULER__TRIGGER_TIMEOUT_CHECK_INTERVAL
#
trigger_timeout_check_interval = 15

# Amount of time a task can be in the queued state before being retried or set to failed.
#
# Variable: AIRFLOW__SCHEDULER__TASK_QUEUED_TIMEOUT
#
task_queued_timeout = 600.0

# How often to check for tasks that have been in the queued state for
# longer than ``[scheduler] task_queued_timeout``.
#
# Variable: AIRFLOW__SCHEDULER__TASK_QUEUED_TIMEOUT_CHECK_INTERVAL
#
task_queued_timeout_check_interval = 120.0

# The run_id pattern used to verify the validity of user input to the run_id parameter when
# triggering a DAG. This pattern cannot change the pattern used by scheduler to generate run_id
# for scheduled DAG runs or DAG runs triggered without changing the run_id parameter.
#
# Variable: AIRFLOW__SCHEDULER__ALLOWED_RUN_ID_PATTERN
#
allowed_run_id_pattern = ^[A-Za-z0-9_.~:+-]+$

# Whether to create DAG runs that span an interval or one single point in time for cron schedules, when
# a cron string is provided to ``schedule`` argument of a DAG.
# 
# * ``True``: **CronDataIntervalTimetable** is used, which is suitable
#   for DAGs with well-defined data interval. You get contiguous intervals from the end of the previous
#   interval up to the scheduled datetime.
# * ``False``: **CronTriggerTimetable** is used, which is closer to the behavior of cron itself.
# 
# Notably, for **CronTriggerTimetable**, the logical date is the same as the time the DAG Run will
# try to schedule, while for **CronDataIntervalTimetable**, the logical date is the beginning of
# the data interval, but the DAG Run will try to schedule at the end of the data interval.
#
# Variable: AIRFLOW__SCHEDULER__CREATE_CRON_DATA_INTERVALS
#
create_cron_data_intervals = False

# Whether to create DAG runs that span an interval or one single point in time when a timedelta or
# relativedelta is provided to ``schedule`` argument of a DAG.
# 
# * ``True``: **DeltaDataIntervalTimetable** is used, which is suitable for DAGs with well-defined data
#   interval. You get contiguous intervals from the end of the previous interval up to the scheduled
#   datetime.
# * ``False``: **DeltaTriggerTimetable** is used, which is suitable for DAGs that simply want to say
#   e.g. "run this every day" and do not care about the data interval.
# 
# Notably, for **DeltaTriggerTimetable**, the logical date is the same as the time the DAG Run will
# try to schedule, while for **DeltaDataIntervalTimetable**, the logical date is the beginning of
# the data interval, but the DAG Run will try to schedule at the end of the data interval.
#
# Variable: AIRFLOW__SCHEDULER__CREATE_DELTA_DATA_INTERVALS
#
create_delta_data_intervals = False

# Whether to enable memory allocation tracing in the scheduler. If enabled, Airflow will start
# tracing memory allocation and log the top 10 memory usages at the error level upon receiving the
# signal SIGUSR1.
# This is an expensive operation and generally should not be used except for debugging purposes.
#
# Variable: AIRFLOW__SCHEDULER__ENABLE_TRACEMALLOC
#
enable_tracemalloc = False

[triggerer]
# How many triggers a single Triggerer will run at once, by default.
#
# Variable: AIRFLOW__TRIGGERER__CAPACITY
#
capacity = 1000

# How often to heartbeat the Triggerer job to ensure it hasn't been killed.
#
# Variable: AIRFLOW__TRIGGERER__JOB_HEARTBEAT_SEC
#
job_heartbeat_sec = 5

# If the last triggerer heartbeat happened more than ``[triggerer] triggerer_health_check_threshold``
# ago (in seconds), triggerer is considered unhealthy.
# This is used by the health check in the **/health** endpoint and in ``airflow jobs check`` CLI
# for TriggererJob.
#
# Variable: AIRFLOW__TRIGGERER__TRIGGERER_HEALTH_CHECK_THRESHOLD
#
triggerer_health_check_threshold = 30

[kerberos]
# Location of your ccache file once kinit has been performed.
#
# Variable: AIRFLOW__KERBEROS__CCACHE
#
ccache = /tmp/airflow_krb5_ccache

# gets augmented with fqdn
#
# Variable: AIRFLOW__KERBEROS__PRINCIPAL
#
principal = airflow

# Determines the frequency at which initialization or re-initialization processes occur.
#
# Variable: AIRFLOW__KERBEROS__REINIT_FREQUENCY
#
reinit_frequency = 3600

# Path to the kinit executable
#
# Variable: AIRFLOW__KERBEROS__KINIT_PATH
#
kinit_path = kinit

# Designates the path to the Kerberos keytab file for the Airflow user
#
# Variable: AIRFLOW__KERBEROS__KEYTAB
#
keytab = airflow.keytab

# Allow to disable ticket forwardability.
#
# Variable: AIRFLOW__KERBEROS__FORWARDABLE
#
forwardable = True

# Allow to remove source IP from token, useful when using token behind NATted Docker host.
#
# Variable: AIRFLOW__KERBEROS__INCLUDE_IP
#
include_ip = True

[sensors]
# Sensor default timeout, 7 days by default (7 * 24 * 60 * 60).
#
# Variable: AIRFLOW__SENSORS__DEFAULT_TIMEOUT
#
default_timeout = 604800

[dag_processor]
# Configuration for the Airflow DAG processor. This includes, for example:
#   - DAG bundles, which allows Airflow to load DAGs from different sources
#   - Parsing configuration, like:
#      - how often to refresh DAGs from those sources
#      - how many files to parse concurrently

# String path to folder where Airflow bundles can store files locally. Not templated.
# If no path is provided, Airflow will use ``Path(tempfile.gettempdir()) / "airflow"``.
# This path must be absolute.
#
# Example: dag_bundle_storage_path = /tmp/some-place
#
# Variable: AIRFLOW__DAG_PROCESSOR__DAG_BUNDLE_STORAGE_PATH
#
# dag_bundle_storage_path = 

# List of backend configs.  Must supply name, classpath, and kwargs for each backend.
# 
# By default, ``refresh_interval`` is set to ``[dag_processor] refresh_interval``, but that can
# also be overridden in kwargs if desired.
# 
# The default is the dags folder dag bundle.
# 
# Note: As shown below, you can split your json config over multiple lines by indenting.
# See configparser documentation for an example:
# https://docs.python.org/3/library/configparser.html#supported-ini-file-structure.
#
# Example: dag_bundle_config_list = [
#       {
#         "name": "my-git-repo",
#         "classpath": "airflow.providers.git.bundles.git.GitDagBundle",
#         "kwargs": {
#           "subdir": "dags",
#           "tracking_ref": "main",
#           "refresh_interval": 0
#         }
#       }
#     ]
#
# Variable: AIRFLOW__DAG_PROCESSOR__DAG_BUNDLE_CONFIG_LIST
#
dag_bundle_config_list = [
      {
        "name": "dags-folder",
        "classpath": "airflow.dag_processing.bundles.local.LocalDagBundle",
        "kwargs": {}
      }
    ]


# How often (in seconds) to refresh, or look for new files, in a DAG bundle.
#
# Variable: AIRFLOW__DAG_PROCESSOR__REFRESH_INTERVAL
#
refresh_interval = 300

# The DAG processor can run multiple processes in parallel to parse dags.
# This defines how many processes will run.
#
# Variable: AIRFLOW__DAG_PROCESSOR__PARSING_PROCESSES
#
parsing_processes = 2

# One of ``modified_time``, ``random_seeded_by_host`` and ``alphabetical``.
# The DAG processor will list and sort the dag files to decide the parsing order.
# 
# * ``modified_time``: Sort by modified time of the files. This is useful on large scale to parse the
#   recently modified DAGs first.
# * ``random_seeded_by_host``: Sort randomly across multiple DAG processors but with same order on the
#   same host, allowing each processor to parse the files in a different order.
# * ``alphabetical``: Sort by filename
#
# Variable: AIRFLOW__DAG_PROCESSOR__FILE_PARSING_SORT_MODE
#
file_parsing_sort_mode = modified_time

# The maximum number of callbacks that are fetched during a single loop.
#
# Variable: AIRFLOW__DAG_PROCESSOR__MAX_CALLBACKS_PER_LOOP
#
max_callbacks_per_loop = 20

# Number of seconds after which a DAG file is parsed. The DAG file is parsed every
# ``[dag_processor] min_file_process_interval`` number of seconds. Updates to DAGs are reflected after
# this interval. Keeping this number low will increase CPU usage.
#
# Variable: AIRFLOW__DAG_PROCESSOR__MIN_FILE_PROCESS_INTERVAL
#
min_file_process_interval = 30

# 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_processor] dag_file_processor_timeout``, but when you have a long timeout configured,
# it results in a significant delay in the deactivation of stale dags.
#
# Variable: AIRFLOW__DAG_PROCESSOR__STALE_DAG_THRESHOLD
#
stale_dag_threshold = 50

# How long before timing out a DagFileProcessor, which processes a dag file
#
# Variable: AIRFLOW__DAG_PROCESSOR__DAG_FILE_PROCESSOR_TIMEOUT
#
dag_file_processor_timeout = 50

# How often should DAG processor stats be printed to the logs. Setting to 0 will disable printing stats
#
# Variable: AIRFLOW__DAG_PROCESSOR__PRINT_STATS_INTERVAL
#
print_stats_interval = 30

# Always run tasks with the latest code.  If set to True, the bundle version will not
# be stored on the dag run and therefore, the latest code will always be used.
#
# Variable: AIRFLOW__DAG_PROCESSOR__DISABLE_BUNDLE_VERSIONING
#
disable_bundle_versioning = False

# How often the DAG processor should check if any DAG bundles are ready for a refresh, either by hitting
# the bundles refresh_interval or because another DAG processor has seen a newer version of the bundle.
# A low value means we check more frequently, and have a smaller window of time where DAG processors are
# out of sync with each other, parsing different versions of the same bundle.
#
# Variable: AIRFLOW__DAG_PROCESSOR__BUNDLE_REFRESH_CHECK_INTERVAL
#
bundle_refresh_check_interval = 5

# On shared workers, bundle copies accumulate in local storage as tasks run
# and version of the bundle changes.
# This setting represents the delta in seconds between checks for these stale bundles.
# Bundles which are older than `stale_bundle_cleanup_age_threshold` may be removed. But
# we always keep `stale_bundle_cleanup_min_versions` versions locally.
# Set to 0 or negative to disable.
#
# Variable: AIRFLOW__DAG_PROCESSOR__STALE_BUNDLE_CLEANUP_INTERVAL
#
stale_bundle_cleanup_interval = 1800

# Bundle versions used more recently than this threshold will not be removed.
# Recency of use is determined by when the task began running on the worker,
# that age is compared with this setting, given as time delta in seconds.
#
# Variable: AIRFLOW__DAG_PROCESSOR__STALE_BUNDLE_CLEANUP_AGE_THRESHOLD
#
stale_bundle_cleanup_age_threshold = 21600

# Minimum number of local bundle versions to retain on disk.
# Local bundle versions older than `stale_bundle_cleanup_age_threshold` will
# only be deleted we have more than `stale_bundle_cleanup_min_versions` versions
# accumulated on the worker.
#
# Variable: AIRFLOW__DAG_PROCESSOR__STALE_BUNDLE_CLEANUP_MIN_VERSIONS
#
stale_bundle_cleanup_min_versions = 10

# The dag_processor reads dag files to extract the airflow modules that are going to be used,
# and imports them ahead of time to avoid having to re-do it for each parsing process.
# This flag can be set to ``False`` to disable this behavior in case an airflow module needs
# to be freshly imported each time (at the cost of increased DAG parsing time).
#
# Variable: AIRFLOW__DAG_PROCESSOR__PARSING_PRE_IMPORT_MODULES
#
parsing_pre_import_modules = True

[common.io]
# Common IO configuration section

# Path to a location on object storage where XComs can be stored in url format.
#
# Example: xcom_objectstorage_path = s3://conn_id@bucket/path
#
# Variable: AIRFLOW__COMMON.IO__XCOM_OBJECTSTORAGE_PATH
#
xcom_objectstorage_path = 

# Threshold in bytes for storing XComs in object storage. -1 means always store in the
# database. 0 means always store in object storage. Any positive number means
# it will be stored in object storage if the size of the value is greater than the threshold.
#
# Example: xcom_objectstorage_threshold = 1000000
#
# Variable: AIRFLOW__COMMON.IO__XCOM_OBJECTSTORAGE_THRESHOLD
#
xcom_objectstorage_threshold = -1

# Compression algorithm to use when storing XComs in object storage. Supported algorithms
# are a.o.: snappy, zip, gzip, bz2, and lzma. If not specified, no compression will be used.
# Note that the compression algorithm must be available in the Python installation (e.g.
# python-snappy for snappy). Zip, gz, bz2 are available by default.
#
# Example: xcom_objectstorage_compression = gz
#
# Variable: AIRFLOW__COMMON.IO__XCOM_OBJECTSTORAGE_COMPRESSION
#
xcom_objectstorage_compression = 

[imap]
# Options for IMAP provider.

# ssl_context = 

[standard]
# Options for the standard provider operators.

# Which python tooling should be used to install the virtual environment.
# 
# The following options are available:
# - ``auto``: Automatically select, use ``uv`` if available, otherwise use ``pip``.
# - ``pip``: Use pip to install the virtual environment.
# - ``uv``: Use uv to install the virtual environment. Must be available in environment PATH.
#
# Example: venv_install_method = uv
#
# Variable: AIRFLOW__STANDARD__VENV_INSTALL_METHOD
#
venv_install_method = auto