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[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 = /opt/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() - see 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 | |
# with argument as: `(file_path: str, zip_file: zipfile.ZipFile | None = None)` | |
# return True if it has dags otherwise False | |
# If this is not provided, Airflow uses its own heuristic rules. | |
# | |
# 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 timezone string (e.g. Europe/Amsterdam) | |
# | |
# Variable: AIRFLOW__CORE__DEFAULT_TIMEZONE | |
# | |
default_timezone = utc | |
# The executor class that airflow should use. Choices include | |
# ``SequentialExecutor``, ``LocalExecutor``, ``CeleryExecutor``, | |
# ``KubernetesExecutor``, ``CeleryKubernetesExecutor``, ``LocalKubernetesExecutor`` or the | |
# full import path to the class when using a custom executor. | |
# | |
# Variable: AIRFLOW__CORE__EXECUTOR | |
# | |
executor = SequentialExecutor | |
# The auth manager class that airflow should use. Full import path to the auth manager class. | |
# | |
# Variable: AIRFLOW__CORE__AUTH_MANAGER | |
# | |
auth_manager = airflow.auth.managers.fab.fab_auth_manager.FabAuthManager | |
# 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. | |
# | |
# 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 ``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 ``max_active_runs_per_dag``. | |
# | |
# Variable: AIRFLOW__CORE__MAX_ACTIVE_RUNS_PER_DAG | |
# | |
max_active_runs_per_dag = 16 | |
# 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: | |
# https://docs.python.org/3/library/multiprocessing.html#multiprocessing.set_start_method. | |
# Must be one of the values returned by: | |
# 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 = True | |
# Path to the folder containing Airflow plugins | |
# | |
# Variable: AIRFLOW__CORE__PLUGINS_FOLDER | |
# | |
plugins_folder = /opt/airflow/plugins | |
# Should tasks be executed via forking of the parent process ("False", | |
# the speedier option) or by spawning a new python process ("True" slow, | |
# 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 = | |
# 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 | |
# How long before timing out a DagFileProcessor, which processes a dag file | |
# | |
# Variable: AIRFLOW__CORE__DAG_FILE_PROCESSOR_TIMEOUT | |
# | |
dag_file_processor_timeout = 50 | |
# The class to use for running task instances in a subprocess. | |
# Choices include StandardTaskRunner, CgroupTaskRunner or the full import path to the class | |
# when using a custom task runner. | |
# | |
# Variable: AIRFLOW__CORE__TASK_RUNNER | |
# | |
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 | |
# | |
# 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 | |
# Whether to enable pickling for xcom (note that this is insecure and allows for | |
# RCE exploits). | |
# | |
# Variable: AIRFLOW__CORE__ENABLE_XCOM_PICKLING | |
# | |
enable_xcom_pickling = False | |
# What classes can be imported during deserialization. This is a multi line value. | |
# The individual items will be parsed as regexp. Python built-in classes (like dict) | |
# are always allowed. Bare "." will be replaced so you can set airflow.* . | |
# | |
# Variable: AIRFLOW__CORE__ALLOWED_DESERIALIZATION_CLASSES | |
# | |
allowed_deserialization_classes = airflow\..* | |
# 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" files in the DAG directories. Valid values are | |
# ``regexp`` or ``glob``. | |
# | |
# Variable: AIRFLOW__CORE__DAG_IGNORE_FILE_SYNTAX | |
# | |
dag_ignore_file_syntax = regexp | |
# 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 | |
# 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 | |
# On each dagrun check against defined SLAs | |
# | |
# Variable: AIRFLOW__CORE__CHECK_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 | |
# | |
# Variable: AIRFLOW__CORE__XCOM_BACKEND | |
# | |
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. | |
# | |
# 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 | |
# | |
# (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 dataset manager. | |
# | |
# Example: dataset_manager_class = airflow.datasets.manager.DatasetManager | |
# | |
# Variable: AIRFLOW__CORE__DATASET_MANAGER_CLASS | |
# | |
# dataset_manager_class = | |
# Kwargs to supply to dataset manager. | |
# | |
# Example: dataset_manager_kwargs = {"some_param": "some_value"} | |
# | |
# Variable: AIRFLOW__CORE__DATASET_MANAGER_KWARGS | |
# | |
# dataset_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 = | |
# 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. For more information on capabilities of users, see the documentation: | |
# https://airflow.apache.org/docs/apache-airflow/stable/security/security_model.html#capabilities-of-authenticated-ui-users | |
# | |
# Variable: AIRFLOW__CORE__TEST_CONNECTION | |
# | |
test_connection = Disabled | |
[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. | |
# More information here: | |
# http://airflow.apache.org/docs/apache-airflow/stable/howto/set-up-database.html#database-uri | |
# | |
# Variable: AIRFLOW__DATABASE__SQL_ALCHEMY_CONN | |
# | |
sql_alchemy_conn = sqlite:////opt/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 https://github.com/apache/airflow/pull/17603#issuecomment-901121618). | |
# | |
# 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". | |
# More information here: | |
# https://docs.sqlalchemy.org/en/14/core/pooling.html#disconnect-handling-pessimistic | |
# | |
# 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. | |
# See https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine.params.connect_args | |
# | |
# Example: sql_alchemy_connect_args = {"timeout": 30} | |
# | |
# Variable: AIRFLOW__DATABASE__SQL_ALCHEMY_CONNECT_ARGS | |
# | |
# sql_alchemy_connect_args = | |
# Whether to load the default connections that ship with Airflow when ``airflow db init`` is called. | |
# It's good to get started, but you probably want to set this to ``False`` in a production environment. | |
# | |
# Variable: AIRFLOW__DATABASE__LOAD_DEFAULT_CONNECTIONS | |
# | |
load_default_connections = True | |
# 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 | |
[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 dag_processor_manager_log_location and | |
# child_process_log_directory settings as well. | |
# | |
# Variable: AIRFLOW__LOGGING__BASE_LOG_FOLDER | |
# | |
base_log_folder = /opt/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 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/production#finding_credentials_automatically>`__ 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 __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 = INFO | |
# Logging level for Flask-appbuilder UI. | |
# | |
# Supported values: ``CRITICAL``, ``ERROR``, ``WARNING``, ``INFO``, ``DEBUG``. | |
# | |
# Variable: AIRFLOW__LOGGING__FAB_LOGGING_LEVEL | |
# | |
fab_logging_level = INFO | |
# 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 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.utils.log.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 = | |
# Specify prefix pattern like mentioned below with stream handler TaskHandlerWithCustomFormatter | |
# | |
# Example: task_log_prefix_template = {ti.dag_id}-{ti.task_id}-{execution_date}-{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 }}.log | |
# Formatting for how airflow generates file names for log | |
# | |
# Variable: AIRFLOW__LOGGING__LOG_PROCESSOR_FILENAME_TEMPLATE | |
# | |
log_processor_filename_template = {{ filename }}.log | |
# Full path of dag_processor_manager logfile. | |
# | |
# Variable: AIRFLOW__LOGGING__DAG_PROCESSOR_MANAGER_LOG_LOCATION | |
# | |
dag_processor_manager_log_location = /opt/airflow/logs/dag_processor_manager/dag_processor_manager.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 = connexion,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 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 | |
# If enabled, Airflow may ship messages to task logs from outside the task run context, e.g. from | |
# the scheduler, executor, or callback execution context. This can help in circumstances such as | |
# when there's something blocking the execution of the task and ordinarily there may be no task | |
# logs at all. | |
# This is set to True by default. If you encounter issues with this feature | |
# (e.g. scheduler performance issues) it can be disabled. | |
# | |
# Variable: AIRFLOW__LOGGING__ENABLE_TASK_CONTEXT_LOGGER | |
# | |
enable_task_context_logger = True | |
[metrics] | |
# StatsD (https://github.com/etsy/statsd) integration settings. | |
# If you want to avoid emitting all the available metrics, you can configure an | |
# allow list of prefixes (comma separated) to send only the metrics that start | |
# with the elements of the list (e.g: "scheduler,executor,dagrun") | |
# | |
# Variable: AIRFLOW__METRICS__METRICS_ALLOW_LIST | |
# | |
metrics_allow_list = | |
# If you want to avoid emitting all the available metrics, you can configure a | |
# block list of prefixes (comma separated) to filter out metrics that start with | |
# the elements of the list (e.g: "scheduler,executor,dagrun"). | |
# If metrics_allow_list and metrics_block_list are both configured, metrics_block_list is ignored. | |
# | |
# 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 | |
# 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: | |
# 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 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 | |
# | |
# Variable: AIRFLOW__METRICS__OTEL_HOST | |
# | |
otel_host = localhost | |
# Specifies the hostname or IP address of the OpenTelemetry Collector to which Airflow sends | |
# metrics and traces | |
# | |
# Variable: AIRFLOW__METRICS__OTEL_PORT | |
# | |
otel_port = 8889 | |
# Specifies the port of the OpenTelemetry Collector that is listening to | |
# | |
# 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 | |
# 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 | |
[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 | |
[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 | |
# | |
# Variable: AIRFLOW__CLI__API_CLIENT | |
# | |
api_client = airflow.api.client.local_client | |
# If you set web_server_url_prefix, do NOT forget to append it here, ex: | |
# ``endpoint_url = http://localhost:8080/myroot`` | |
# So api will look like: ``http://localhost:8080/myroot/api/experimental/...`` | |
# | |
# Variable: AIRFLOW__CLI__ENDPOINT_URL | |
# | |
endpoint_url = http://localhost:8080 | |
[debug] | |
# Used only with ``DebugExecutor``. If set to ``True`` DAG will fail with first | |
# failed task. Helpful for debugging purposes. | |
# | |
# Variable: AIRFLOW__DEBUG__FAIL_FAST | |
# | |
fail_fast = False | |
[api] | |
# Enables the deprecated experimental API. Please note that these APIs do not have access control. | |
# The authenticated user has full access. | |
# | |
# .. warning:: | |
# | |
# This `Experimental REST API <https://airflow.readthedocs.io/en/latest/rest-api-ref.html>`__ is | |
# deprecated since version 2.0. Please consider using | |
# `the Stable REST API <https://airflow.readthedocs.io/en/latest/stable-rest-api-ref.html>`__. | |
# For more information on migration, see | |
# `RELEASE_NOTES.rst <https://github.com/apache/airflow/blob/main/RELEASE_NOTES.rst>`_ | |
# | |
# Variable: AIRFLOW__API__ENABLE_EXPERIMENTAL_API | |
# | |
enable_experimental_api = False | |
# Comma separated list of auth backends to authenticate users of the API. See | |
# https://airflow.apache.org/docs/apache-airflow/stable/security/api.html for possible values. | |
# ("airflow.api.auth.backend.default" allows all requests for historic reasons) | |
# | |
# Variable: AIRFLOW__API__AUTH_BACKENDS | |
# | |
auth_backends = airflow.api.auth.backend.session | |
# 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 = 100 | |
# The intended audience for JWT token credentials used for authorization. This value must match on the client and server sides. If empty, audience will not be tested. | |
# | |
# Example: google_oauth2_audience = project-id-random-value.apps.googleusercontent.com | |
# | |
# Variable: AIRFLOW__API__GOOGLE_OAUTH2_AUDIENCE | |
# | |
google_oauth2_audience = | |
# Path to Google Cloud Service Account key file (JSON). If omitted, authorization based on | |
# `the Application Default Credentials | |
# <https://cloud.google.com/docs/authentication/production#finding_credentials_automatically>`__ will | |
# be used. | |
# | |
# Example: google_key_path = /files/service-account-json | |
# | |
# Variable: AIRFLOW__API__GOOGLE_KEY_PATH | |
# | |
google_key_path = | |
# 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 | |
[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 | |
# Is allowed to pass additional/unused arguments (args, kwargs) to the BaseOperator operator. | |
# If set to False, an exception will be thrown, otherwise only the console message will be displayed. | |
# | |
# Variable: AIRFLOW__OPERATORS__ALLOW_ILLEGAL_ARGUMENTS | |
# | |
allow_illegal_arguments = False | |
[webserver] | |
# The message displayed when a user attempts to execute actions beyond their authorised privileges. | |
# | |
# Variable: AIRFLOW__WEBSERVER__ACCESS_DENIED_MESSAGE | |
# | |
access_denied_message = Access is Denied | |
# Path of webserver config file used for configuring the webserver parameters | |
# | |
# Variable: AIRFLOW__WEBSERVER__CONFIG_FILE | |
# | |
config_file = /opt/airflow/webserver_config.py | |
# 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 | |
# | |
# Variable: AIRFLOW__WEBSERVER__BASE_URL | |
# | |
base_url = http://localhost:8080 | |
# 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 | |
# | |
# Variable: AIRFLOW__WEBSERVER__DEFAULT_UI_TIMEZONE | |
# | |
default_ui_timezone = UTC | |
# The ip specified when starting the web server | |
# | |
# Variable: AIRFLOW__WEBSERVER__WEB_SERVER_HOST | |
# | |
web_server_host = 0.0.0.0 | |
# The port on which to run the web server | |
# | |
# Variable: AIRFLOW__WEBSERVER__WEB_SERVER_PORT | |
# | |
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. | |
# | |
# Variable: AIRFLOW__WEBSERVER__WEB_SERVER_SSL_CERT | |
# | |
web_server_ssl_cert = | |
# 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. | |
# | |
# Variable: AIRFLOW__WEBSERVER__WEB_SERVER_SSL_KEY | |
# | |
web_server_ssl_key = | |
# The type of backend used to store web session data, can be `database` or `securecookie`. For the | |
# `database` backend, sessions are store in the database (in `session` table) and they can be | |
# managed there (for example when you reset password of the user, all sessions for that user are | |
# deleted). For the `securecookie` backend, sessions are stored in encrypted cookies on the client | |
# side. The `securecookie` mechanism is 'lighter' than database backend, but sessions are not deleted | |
# when you reset password of the user, which means that other than waiting for expiry time, the only | |
# way to invalidate all sessions for a user is to change secret_key and restart webserver (which | |
# also invalidates and logs out all other user's sessions). | |
# | |
# When you are using `database` backend, make sure to keep your database session table small | |
# by periodically running `airflow db clean --table session` command, especially if you have | |
# automated API calls that will create a new session for each call rather than reuse the sessions | |
# stored in browser cookies. | |
# | |
# Example: session_backend = securecookie | |
# | |
# Variable: AIRFLOW__WEBSERVER__SESSION_BACKEND | |
# | |
session_backend = database | |
# Number of seconds the webserver waits before killing gunicorn master that doesn't respond | |
# | |
# Variable: AIRFLOW__WEBSERVER__WEB_SERVER_MASTER_TIMEOUT | |
# | |
web_server_master_timeout = 120 | |
# Number of seconds the gunicorn webserver waits before timing out on a worker | |
# | |
# Variable: AIRFLOW__WEBSERVER__WEB_SERVER_WORKER_TIMEOUT | |
# | |
web_server_worker_timeout = 120 | |
# Number of workers to refresh at a time. When set to 0, worker refresh is | |
# disabled. When nonzero, airflow periodically refreshes webserver workers by | |
# bringing up new ones and killing old ones. | |
# | |
# Variable: AIRFLOW__WEBSERVER__WORKER_REFRESH_BATCH_SIZE | |
# | |
worker_refresh_batch_size = 1 | |
# Number of seconds to wait before refreshing a batch of workers. | |
# | |
# Variable: AIRFLOW__WEBSERVER__WORKER_REFRESH_INTERVAL | |
# | |
worker_refresh_interval = 6000 | |
# If set to True, Airflow will track files in plugins_folder directory. When it detects changes, | |
# then reload the gunicorn. If set to True, gunicorn starts without preloading, which is slower, uses | |
# more memory, and may cause race conditions. Avoid setting this to True in production. | |
# | |
# Variable: AIRFLOW__WEBSERVER__RELOAD_ON_PLUGIN_CHANGE | |
# | |
reload_on_plugin_change = False | |
# Secret key used to run your flask app. It should be as random as possible. However, when running | |
# more than 1 instances of webserver, make sure all of them use the same ``secret_key`` otherwise | |
# one of them will error with "CSRF session token is missing". | |
# The webserver 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__WEBSERVER__SECRET_KEY | |
# | |
secret_key = RkOr8insrj7nygm7oe1brg== | |
# Number of workers to run the Gunicorn web server | |
# | |
# Variable: AIRFLOW__WEBSERVER__WORKERS | |
# | |
workers = 4 | |
# The worker class gunicorn should use. Choices include | |
# sync (default), eventlet, gevent. Note when using gevent you might also want to set the | |
# "_AIRFLOW_PATCH_GEVENT" environment variable to "1" to make sure gevent patching is done as | |
# early as possible. | |
# | |
# Variable: AIRFLOW__WEBSERVER__WORKER_CLASS | |
# | |
worker_class = sync | |
# Log files for the gunicorn webserver. '-' means log to stderr. | |
# | |
# Variable: AIRFLOW__WEBSERVER__ACCESS_LOGFILE | |
# | |
access_logfile = - | |
# Log files for the gunicorn webserver. '-' means log to stderr. | |
# | |
# Variable: AIRFLOW__WEBSERVER__ERROR_LOGFILE | |
# | |
error_logfile = - | |
# Access log format for gunicorn webserver. | |
# default format is %%(h)s %%(l)s %%(u)s %%(t)s "%%(r)s" %%(s)s %%(b)s "%%(f)s" "%%(a)s" | |
# documentation - https://docs.gunicorn.org/en/stable/settings.html#access-log-format | |
# | |
# Variable: AIRFLOW__WEBSERVER__ACCESS_LOGFORMAT | |
# | |
access_logformat = | |
# 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__WEBSERVER__EXPOSE_CONFIG | |
# | |
expose_config = False | |
# Expose hostname in the web server | |
# | |
# Variable: AIRFLOW__WEBSERVER__EXPOSE_HOSTNAME | |
# | |
expose_hostname = False | |
# Expose stacktrace in the web server | |
# | |
# Variable: AIRFLOW__WEBSERVER__EXPOSE_STACKTRACE | |
# | |
expose_stacktrace = False | |
# Default DAG view. Valid values are: ``grid``, ``graph``, ``duration``, ``gantt``, ``landing_times`` | |
# | |
# Variable: AIRFLOW__WEBSERVER__DAG_DEFAULT_VIEW | |
# | |
dag_default_view = grid | |
# Default DAG orientation. Valid values are: | |
# ``LR`` (Left->Right), ``TB`` (Top->Bottom), ``RL`` (Right->Left), ``BT`` (Bottom->Top) | |
# | |
# Variable: AIRFLOW__WEBSERVER__DAG_ORIENTATION | |
# | |
dag_orientation = LR | |
# 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 | |
# Time interval (in secs) to wait before next log fetching. | |
# | |
# Variable: AIRFLOW__WEBSERVER__LOG_FETCH_DELAY_SEC | |
# | |
log_fetch_delay_sec = 2 | |
# Distance away from page bottom to enable auto tailing. | |
# | |
# Variable: AIRFLOW__WEBSERVER__LOG_AUTO_TAILING_OFFSET | |
# | |
log_auto_tailing_offset = 30 | |
# Animation speed for auto tailing log display. | |
# | |
# Variable: AIRFLOW__WEBSERVER__LOG_ANIMATION_SPEED | |
# | |
log_animation_speed = 1000 | |
# 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 = 100 | |
# Define the color of navigation bar | |
# | |
# Variable: AIRFLOW__WEBSERVER__NAVBAR_COLOR | |
# | |
navbar_color = #fff | |
# Define the color of text in the navigation bar | |
# | |
# Variable: AIRFLOW__WEBSERVER__NAVBAR_TEXT_COLOR | |
# | |
navbar_text_color = #51504f | |
# Default dagrun to show in UI | |
# | |
# Variable: AIRFLOW__WEBSERVER__DEFAULT_DAG_RUN_DISPLAY_NUMBER | |
# | |
default_dag_run_display_number = 25 | |
# Enable werkzeug ``ProxyFix`` middleware for reverse proxy | |
# | |
# Variable: AIRFLOW__WEBSERVER__ENABLE_PROXY_FIX | |
# | |
enable_proxy_fix = False | |
# Number of values to trust for ``X-Forwarded-For``. | |
# More info: https://werkzeug.palletsprojects.com/en/0.16.x/middleware/proxy_fix/ | |
# | |
# Variable: AIRFLOW__WEBSERVER__PROXY_FIX_X_FOR | |
# | |
proxy_fix_x_for = 1 | |
# Number of values to trust for ``X-Forwarded-Proto`` | |
# | |
# Variable: AIRFLOW__WEBSERVER__PROXY_FIX_X_PROTO | |
# | |
proxy_fix_x_proto = 1 | |
# Number of values to trust for ``X-Forwarded-Host`` | |
# | |
# Variable: AIRFLOW__WEBSERVER__PROXY_FIX_X_HOST | |
# | |
proxy_fix_x_host = 1 | |
# Number of values to trust for ``X-Forwarded-Port`` | |
# | |
# Variable: AIRFLOW__WEBSERVER__PROXY_FIX_X_PORT | |
# | |
proxy_fix_x_port = 1 | |
# Number of values to trust for ``X-Forwarded-Prefix`` | |
# | |
# Variable: AIRFLOW__WEBSERVER__PROXY_FIX_X_PREFIX | |
# | |
proxy_fix_x_prefix = 1 | |
# Set secure flag on session cookie | |
# | |
# Variable: AIRFLOW__WEBSERVER__COOKIE_SECURE | |
# | |
cookie_secure = False | |
# Set samesite policy on session cookie | |
# | |
# Variable: AIRFLOW__WEBSERVER__COOKIE_SAMESITE | |
# | |
cookie_samesite = Lax | |
# Default setting for wrap toggle on DAG code and TI log views. | |
# | |
# Variable: AIRFLOW__WEBSERVER__DEFAULT_WRAP | |
# | |
default_wrap = False | |
# Allow the UI to be rendered in a frame | |
# | |
# Variable: AIRFLOW__WEBSERVER__X_FRAME_ENABLED | |
# | |
x_frame_enabled = True | |
# Send anonymous user activity to your analytics tool | |
# choose from google_analytics, segment, or metarouter | |
# | |
# Variable: AIRFLOW__WEBSERVER__ANALYTICS_TOOL | |
# | |
# analytics_tool = | |
# Unique ID of your account in the analytics tool | |
# | |
# Variable: AIRFLOW__WEBSERVER__ANALYTICS_ID | |
# | |
# analytics_id = | |
# 'Recent Tasks' stats will show for old DagRuns if set | |
# | |
# Variable: AIRFLOW__WEBSERVER__SHOW_RECENT_STATS_FOR_COMPLETED_RUNS | |
# | |
show_recent_stats_for_completed_runs = True | |
# Update FAB permissions and sync security manager roles | |
# on webserver startup | |
# | |
# Variable: AIRFLOW__WEBSERVER__UPDATE_FAB_PERMS | |
# | |
update_fab_perms = True | |
# The UI cookie lifetime in minutes. User will be logged out from UI after | |
# ``session_lifetime_minutes`` of non-activity | |
# | |
# Variable: AIRFLOW__WEBSERVER__SESSION_LIFETIME_MINUTES | |
# | |
session_lifetime_minutes = 43200 | |
# 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. | |
# | |
# Variable: AIRFLOW__WEBSERVER__AUDIT_VIEW_EXCLUDED_EVENTS | |
# | |
audit_view_excluded_events = gantt,landing_times,tries,duration,calendar,graph,grid,tree,tree_data | |
# 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 = | |
# Boolean for running SwaggerUI in the webserver. | |
# | |
# Variable: AIRFLOW__WEBSERVER__ENABLE_SWAGGER_UI | |
# | |
enable_swagger_ui = True | |
# Boolean for running Internal API in the webserver. | |
# | |
# Variable: AIRFLOW__WEBSERVER__RUN_INTERNAL_API | |
# | |
run_internal_api = False | |
# Boolean for enabling rate limiting on authentication endpoints. | |
# | |
# Variable: AIRFLOW__WEBSERVER__AUTH_RATE_LIMITED | |
# | |
auth_rate_limited = True | |
# Rate limit for authentication endpoints. | |
# | |
# Variable: AIRFLOW__WEBSERVER__AUTH_RATE_LIMIT | |
# | |
auth_rate_limit = 5 per 40 second | |
# The caching algorithm used by the webserver. Must be a valid hashlib function name. | |
# | |
# Example: caching_hash_method = sha256 | |
# | |
# Variable: AIRFLOW__WEBSERVER__CACHING_HASH_METHOD | |
# | |
caching_hash_method = md5 | |
# Behavior of the trigger DAG run button for DAGs without params. False to skip and trigger | |
# without displaying a form to add a dag_run.conf, True to always display the form. | |
# The form is displayed always if parameters are defined. | |
# | |
# Variable: AIRFLOW__WEBSERVER__SHOW_TRIGGER_FORM_IF_NO_PARAMS | |
# | |
show_trigger_form_if_no_params = False | |
# A DAG author is able to provide any raw HTML into ``doc_md`` or params description in | |
# ``description_md`` for text formatting. This is including potentially unsafe javascript. | |
# Displaying the DAG or trigger form in web UI provides the DAG author the potential to | |
# inject malicious code into clients browsers. To ensure the web UI is safe by default, | |
# raw HTML is disabled by default. If you trust your DAG authors, you can enable HTML | |
# support in markdown by setting this option to True. | |
# | |
# This parameter also enables the deprecated fields ``description_html`` and | |
# ``custom_html_form`` in DAG params until the feature is removed in a future version. | |
# | |
# Example: allow_raw_html_descriptions = False | |
# | |
# Variable: AIRFLOW__WEBSERVER__ALLOW_RAW_HTML_DESCRIPTIONS | |
# | |
allow_raw_html_descriptions = 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 | |
# Username to authenticate when connecting to smtp server. | |
# | |
# Example: smtp_user = airflow | |
# | |
# Variable: AIRFLOW__SMTP__SMTP_USER | |
# | |
# smtp_user = | |
# Password to authenticate when connecting to smtp server. | |
# | |
# Example: smtp_password = airflow | |
# | |
# Variable: AIRFLOW__SMTP__SMTP_PASSWORD | |
# | |
# smtp_password = | |
# 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: | |
# https://docs.sentry.io/error-reporting/configuration/?platform=python. | |
# 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_zombie_task_threshold. | |
# | |
# Variable: AIRFLOW__SCHEDULER__LOCAL_TASK_JOB_HEARTBEAT_SEC | |
# | |
local_task_job_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 | |
# 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. | |
# | |
# Variable: AIRFLOW__SCHEDULER__MIN_FILE_PROCESS_INTERVAL | |
# | |
min_file_process_interval = 30 | |
# 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. | |
# | |
# Variable: AIRFLOW__SCHEDULER__PARSING_CLEANUP_INTERVAL | |
# | |
parsing_cleanup_interval = 60 | |
# 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. | |
# | |
# Variable: AIRFLOW__SCHEDULER__STALE_DAG_THRESHOLD | |
# | |
stale_dag_threshold = 50 | |
# How often (in seconds) to scan the DAGs directory for new files. Default to 5 minutes. | |
# | |
# Variable: AIRFLOW__SCHEDULER__DAG_DIR_LIST_INTERVAL | |
# | |
dag_dir_list_interval = 300 | |
# How often should stats be printed to the logs. Setting to 0 will disable printing stats | |
# | |
# Variable: AIRFLOW__SCHEDULER__PRINT_STATS_INTERVAL | |
# | |
print_stats_interval = 30 | |
# 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 | |
# 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. | |
# | |
# 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 | |
# Determines the directory where logs for the child processes of the scheduler will be stored | |
# | |
# Variable: AIRFLOW__SCHEDULER__CHILD_PROCESS_LOG_DIRECTORY | |
# | |
child_process_log_directory = /opt/airflow/logs/scheduler | |
# Local task jobs periodically heartbeat to the DB. If the job has | |
# not heartbeat in this many seconds, the scheduler will mark the | |
# associated task instance as failed and will re-schedule the task. | |
# | |
# Variable: AIRFLOW__SCHEDULER__SCHEDULER_ZOMBIE_TASK_THRESHOLD | |
# | |
scheduler_zombie_task_threshold = 300 | |
# How often (in seconds) should the scheduler check for zombie tasks. | |
# | |
# Variable: AIRFLOW__SCHEDULER__ZOMBIE_DETECTION_INTERVAL | |
# | |
zombie_detection_interval = 10.0 | |
# 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) | |
# | |
# Variable: AIRFLOW__SCHEDULER__CATCHUP_BY_DEFAULT | |
# | |
catchup_by_default = True | |
# 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 an execution_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 changes the batch size of queries in the scheduling main loop. | |
# This should not be greater than ``core.parallelism``. | |
# If this is too high, SQL query performance may be impacted by | |
# complexity of query predicate, and/or excessive locking. | |
# Additionally, you may hit the maximum allowable query length for your db. | |
# 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 | |
# Should the Task supervisor process perform a "mini scheduler" to attempt to schedule more tasks of the | |
# same DAG. Leaving this on will mean tasks in the same DAG execute quicker, but might starve out other | |
# dags in some circumstances | |
# | |
# Variable: AIRFLOW__SCHEDULER__SCHEDULE_AFTER_TASK_EXECUTION | |
# | |
schedule_after_task_execution = True | |
# The scheduler 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__SCHEDULER__PARSING_PRE_IMPORT_MODULES | |
# | |
parsing_pre_import_modules = True | |
# The scheduler can run multiple processes in parallel to parse dags. | |
# This defines how many processes will run. | |
# | |
# Variable: AIRFLOW__SCHEDULER__PARSING_PROCESSES | |
# | |
parsing_processes = 2 | |
# One of ``modified_time``, ``random_seeded_by_host`` and ``alphabetical``. | |
# The scheduler 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 Schedulers but with same order on the | |
# same host. This is useful when running with Scheduler in HA mode where each scheduler can | |
# parse different DAG files. | |
# * ``alphabetical``: Sort by filename | |
# | |
# Variable: AIRFLOW__SCHEDULER__FILE_PARSING_SORT_MODE | |
# | |
file_parsing_sort_mode = modified_time | |
# Whether the dag processor is running as a standalone process or it is a subprocess of a scheduler | |
# job. | |
# | |
# Variable: AIRFLOW__SCHEDULER__STANDALONE_DAG_PROCESSOR | |
# | |
standalone_dag_processor = False | |
# Only applicable if `[scheduler]standalone_dag_processor` is true and callbacks are stored | |
# in database. Contains maximum number of callbacks that are fetched during a single loop. | |
# | |
# Variable: AIRFLOW__SCHEDULER__MAX_CALLBACKS_PER_LOOP | |
# | |
max_callbacks_per_loop = 20 | |
# Only applicable if `[scheduler]standalone_dag_processor` is true. | |
# 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 | |
# Allow externally triggered DagRuns for Execution Dates in the future | |
# Only has effect if schedule_interval is set to None in DAG | |
# | |
# Variable: AIRFLOW__SCHEDULER__ALLOW_TRIGGER_IN_FUTURE | |
# | |
allow_trigger_in_future = False | |
# 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_.~:+-]+$ | |
[triggerer] | |
# How many triggers a single Triggerer will run at once, by default. | |
# | |
# Variable: AIRFLOW__TRIGGERER__DEFAULT_CAPACITY | |
# | |
default_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_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 | |
[aws] | |
# This section contains settings for Amazon Web Services (AWS) integration. | |
# session_factory = | |
cloudwatch_task_handler_json_serializer = airflow.providers.amazon.aws.log.cloudwatch_task_handler.json_serialize_legacy | |
[aws_ecs_executor] | |
# This section only applies if you are using the AwsEcsExecutor in | |
# Airflow's ``[core]`` configuration. | |
# For more information on any of these execution parameters, see the link below: | |
# https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/ecs/client/run_task.html | |
# For boto3 credential management, see | |
# https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html | |
conn_id = aws_default | |
# region_name = | |
assign_public_ip = False | |
# cluster = | |
# container_name = | |
launch_type = FARGATE | |
platform_version = LATEST | |
# security_groups = | |
# subnets = | |
# task_definition = | |
max_run_task_attempts = 3 | |
# run_task_kwargs = | |
check_health_on_startup = True | |
[aws_auth_manager] | |
# This section only applies if you are using the AwsAuthManager. In other words, if you set | |
# ``[core] auth_manager = airflow.providers.amazon.aws.auth_manager.aws_auth_manager.AwsAuthManager`` in | |
# Airflow's configuration. | |
enable = False | |
conn_id = aws_default | |
# saml_metadata_url = | |
# avp_policy_store_id = | |
[celery_kubernetes_executor] | |
# This section only applies if you are using the ``CeleryKubernetesExecutor`` in | |
# ``[core]`` section above | |
# Define when to send a task to ``KubernetesExecutor`` when using ``CeleryKubernetesExecutor``. | |
# When the queue of a task is the value of ``kubernetes_queue`` (default ``kubernetes``), | |
# the task is executed via ``KubernetesExecutor``, | |
# otherwise via ``CeleryExecutor`` | |
# | |
# Variable: AIRFLOW__CELERY_KUBERNETES_EXECUTOR__KUBERNETES_QUEUE | |
# | |
kubernetes_queue = kubernetes | |
[celery] | |
# This section only applies if you are using the CeleryExecutor in | |
# ``[core]`` section above | |
# The app name that will be used by celery | |
# | |
# Variable: AIRFLOW__CELERY__CELERY_APP_NAME | |
# | |
celery_app_name = airflow.providers.celery.executors.celery_executor | |
# The concurrency that will be used when starting workers with the | |
# ``airflow celery 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 | |
# | |
# Variable: AIRFLOW__CELERY__WORKER_CONCURRENCY | |
# | |
worker_concurrency = 16 | |
# The maximum and minimum concurrency that will be used when starting workers with the | |
# ``airflow celery worker`` command (always keep minimum processes, but grow | |
# to maximum if necessary). Note the value should be max_concurrency,min_concurrency | |
# Pick these numbers based on resources on worker box and the nature of the task. | |
# If autoscale option is available, worker_concurrency will be ignored. | |
# https://docs.celeryq.dev/en/latest/reference/celery.bin.worker.html#cmdoption-celery-worker-autoscale | |
# | |
# Example: worker_autoscale = 16,12 | |
# | |
# Variable: AIRFLOW__CELERY__WORKER_AUTOSCALE | |
# | |
# worker_autoscale = | |
# Used to increase the number of tasks that a worker prefetches which can improve performance. | |
# The number of processes multiplied by worker_prefetch_multiplier is the number of tasks | |
# that are prefetched by a worker. A value greater than 1 can result in tasks being unnecessarily | |
# blocked if there are multiple workers and one worker prefetches tasks that sit behind long | |
# running tasks while another worker has unutilized processes that are unable to process the already | |
# claimed blocked tasks. | |
# https://docs.celeryq.dev/en/stable/userguide/optimizing.html#prefetch-limits | |
# | |
# Variable: AIRFLOW__CELERY__WORKER_PREFETCH_MULTIPLIER | |
# | |
worker_prefetch_multiplier = 1 | |
# Specify if remote control of the workers is enabled. | |
# In some cases when the broker does not support remote control, Celery creates lots of | |
# ``.*reply-celery-pidbox`` queues. You can prevent this by setting this to false. | |
# However, with this disabled Flower won't work. | |
# https://docs.celeryq.dev/en/stable/getting-started/backends-and-brokers/index.html#broker-overview | |
# | |
# Variable: AIRFLOW__CELERY__WORKER_ENABLE_REMOTE_CONTROL | |
# | |
worker_enable_remote_control = true | |
# The Celery broker URL. Celery supports RabbitMQ, Redis and experimentally | |
# a sqlalchemy database. Refer to the Celery documentation for more information. | |
# | |
# Variable: AIRFLOW__CELERY__BROKER_URL | |
# | |
broker_url = redis://:@redis.service.consul:6379/0 | |
# The Celery result_backend. When a job finishes, it needs to update the | |
# metadata of the job. Therefore it will post a message on a message bus, | |
# or insert it into a database (depending of the backend) | |
# This status is used by the scheduler to update the state of the task | |
# The use of a database is highly recommended | |
# When not specified, sql_alchemy_conn with a db+ scheme prefix will be used | |
# https://docs.celeryq.dev/en/latest/userguide/configuration.html#task-result-backend-settings | |
# | |
# Example: result_backend = db+postgresql://postgres:airflow@postgres/airflow | |
# | |
# Variable: AIRFLOW__CELERY__RESULT_BACKEND | |
# | |
result_backend = db+postgresql://airflow:airflow@postgres.service.consul/airflow | |
# Optional configuration dictionary to pass to the Celery result backend SQLAlchemy engine. | |
# | |
# Example: result_backend_sqlalchemy_engine_options = {"pool_recycle": 1800} | |
# | |
# Variable: AIRFLOW__CELERY__RESULT_BACKEND_SQLALCHEMY_ENGINE_OPTIONS | |
# | |
result_backend_sqlalchemy_engine_options = | |
# Celery Flower is a sweet UI for Celery. Airflow has a shortcut to start | |
# it ``airflow celery flower``. This defines the IP that Celery Flower runs on | |
# | |
# Variable: AIRFLOW__CELERY__FLOWER_HOST | |
# | |
flower_host = 0.0.0.0 | |
# The root URL for Flower | |
# | |
# Example: flower_url_prefix = /flower | |
# | |
# Variable: AIRFLOW__CELERY__FLOWER_URL_PREFIX | |
# | |
flower_url_prefix = | |
# This defines the port that Celery Flower runs on | |
# | |
# Variable: AIRFLOW__CELERY__FLOWER_PORT | |
# | |
flower_port = 5555 | |
# Securing Flower with Basic Authentication | |
# Accepts user:password pairs separated by a comma | |
# | |
# Example: flower_basic_auth = user1:password1,user2:password2 | |
# | |
# Variable: AIRFLOW__CELERY__FLOWER_BASIC_AUTH | |
# | |
flower_basic_auth = | |
# How many processes CeleryExecutor uses to sync task state. | |
# 0 means to use max(1, number of cores - 1) processes. | |
# | |
# Variable: AIRFLOW__CELERY__SYNC_PARALLELISM | |
# | |
sync_parallelism = 0 | |
# Import path for celery configuration options | |
# | |
# Variable: AIRFLOW__CELERY__CELERY_CONFIG_OPTIONS | |
# | |
celery_config_options = airflow.providers.celery.executors.default_celery.DEFAULT_CELERY_CONFIG | |
# | |
# Variable: AIRFLOW__CELERY__SSL_ACTIVE | |
# | |
ssl_active = False | |
# Path to the client key. | |
# | |
# Variable: AIRFLOW__CELERY__SSL_KEY | |
# | |
ssl_key = | |
# Path to the client certificate. | |
# | |
# Variable: AIRFLOW__CELERY__SSL_CERT | |
# | |
ssl_cert = | |
# Path to the CA certificate. | |
# | |
# Variable: AIRFLOW__CELERY__SSL_CACERT | |
# | |
ssl_cacert = | |
# Celery Pool implementation. | |
# Choices include: ``prefork`` (default), ``eventlet``, ``gevent`` or ``solo``. | |
# See: | |
# https://docs.celeryq.dev/en/latest/userguide/workers.html#concurrency | |
# https://docs.celeryq.dev/en/latest/userguide/concurrency/eventlet.html | |
# | |
# Variable: AIRFLOW__CELERY__POOL | |
# | |
pool = prefork | |
# The number of seconds to wait before timing out ``send_task_to_executor`` or | |
# ``fetch_celery_task_state`` operations. | |
# | |
# Variable: AIRFLOW__CELERY__OPERATION_TIMEOUT | |
# | |
operation_timeout = 1.0 | |
# Celery task will report its status as 'started' when the task is executed by a worker. | |
# This is used in Airflow to keep track of the running tasks and if a Scheduler is restarted | |
# or run in HA mode, it can adopt the orphan tasks launched by previous SchedulerJob. | |
# | |
# Variable: AIRFLOW__CELERY__TASK_TRACK_STARTED | |
# | |
task_track_started = True | |
# The Maximum number of retries for publishing task messages to the broker when failing | |
# due to ``AirflowTaskTimeout`` error before giving up and marking Task as failed. | |
# | |
# Variable: AIRFLOW__CELERY__TASK_PUBLISH_MAX_RETRIES | |
# | |
task_publish_max_retries = 3 | |
# Worker initialisation check to validate Metadata Database connection | |
# | |
# Variable: AIRFLOW__CELERY__WORKER_PRECHECK | |
# | |
worker_precheck = False | |
[celery_broker_transport_options] | |
# This section is for specifying options which can be passed to the | |
# underlying celery broker transport. See: | |
# https://docs.celeryq.dev/en/latest/userguide/configuration.html#std:setting-broker_transport_options | |
# The visibility timeout defines the number of seconds to wait for the worker | |
# to acknowledge the task before the message is redelivered to another worker. | |
# Make sure to increase the visibility timeout to match the time of the longest | |
# ETA you're planning to use. | |
# visibility_timeout is only supported for Redis and SQS celery brokers. | |
# See: | |
# https://docs.celeryq.dev/en/stable/getting-started/backends-and-brokers/redis.html#visibility-timeout | |
# | |
# Example: visibility_timeout = 21600 | |
# | |
# Variable: AIRFLOW__CELERY_BROKER_TRANSPORT_OPTIONS__VISIBILITY_TIMEOUT | |
# | |
# visibility_timeout = | |
# The sentinel_kwargs parameter allows passing additional options to the Sentinel client. | |
# In a typical scenario where Redis Sentinel is used as the broker and Redis servers are | |
# password-protected, the password needs to be passed through this parameter. Although its | |
# type is string, it is required to pass a string that conforms to the dictionary format. | |
# See: | |
# https://docs.celeryq.dev/en/stable/getting-started/backends-and-brokers/redis.html#configuration | |
# | |
# Example: sentinel_kwargs = {"password": "password_for_redis_server"} | |
# | |
# Variable: AIRFLOW__CELERY_BROKER_TRANSPORT_OPTIONS__SENTINEL_KWARGS | |
# | |
# sentinel_kwargs = | |
[local_kubernetes_executor] | |
# This section only applies if you are using the ``LocalKubernetesExecutor`` in | |
# ``[core]`` section above | |
# Define when to send a task to ``KubernetesExecutor`` when using ``LocalKubernetesExecutor``. | |
# When the queue of a task is the value of ``kubernetes_queue`` (default ``kubernetes``), | |
# the task is executed via ``KubernetesExecutor``, | |
# otherwise via ``LocalExecutor`` | |
# | |
# Variable: AIRFLOW__LOCAL_KUBERNETES_EXECUTOR__KUBERNETES_QUEUE | |
# | |
kubernetes_queue = kubernetes | |
[kubernetes_executor] | |
# Kwargs to override the default urllib3 Retry used in the kubernetes API client | |
# | |
# Example: api_client_retry_configuration = { "total": 3, "backoff_factor": 0.5 } | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__API_CLIENT_RETRY_CONFIGURATION | |
# | |
api_client_retry_configuration = | |
# Flag to control the information added to kubernetes executor logs for better traceability | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__LOGS_TASK_METADATA | |
# | |
logs_task_metadata = False | |
# Path to the YAML pod file that forms the basis for KubernetesExecutor workers. | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__POD_TEMPLATE_FILE | |
# | |
pod_template_file = | |
# The repository of the Kubernetes Image for the Worker to Run | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__WORKER_CONTAINER_REPOSITORY | |
# | |
worker_container_repository = | |
# The tag of the Kubernetes Image for the Worker to Run | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__WORKER_CONTAINER_TAG | |
# | |
worker_container_tag = | |
# The Kubernetes namespace where airflow workers should be created. Defaults to ``default`` | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__NAMESPACE | |
# | |
namespace = default | |
# If True, all worker pods will be deleted upon termination | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__DELETE_WORKER_PODS | |
# | |
delete_worker_pods = True | |
# If False (and delete_worker_pods is True), | |
# failed worker pods will not be deleted so users can investigate them. | |
# This only prevents removal of worker pods where the worker itself failed, | |
# not when the task it ran failed. | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__DELETE_WORKER_PODS_ON_FAILURE | |
# | |
delete_worker_pods_on_failure = False | |
# Number of Kubernetes Worker Pod creation calls per scheduler loop. | |
# Note that the current default of "1" will only launch a single pod | |
# per-heartbeat. It is HIGHLY recommended that users increase this | |
# number to match the tolerance of their kubernetes cluster for | |
# better performance. | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__WORKER_PODS_CREATION_BATCH_SIZE | |
# | |
worker_pods_creation_batch_size = 1 | |
# Allows users to launch pods in multiple namespaces. | |
# Will require creating a cluster-role for the scheduler, | |
# or use multi_namespace_mode_namespace_list configuration. | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__MULTI_NAMESPACE_MODE | |
# | |
multi_namespace_mode = False | |
# If multi_namespace_mode is True while scheduler does not have a cluster-role, | |
# give the list of namespaces where the scheduler will schedule jobs | |
# Scheduler needs to have the necessary permissions in these namespaces. | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__MULTI_NAMESPACE_MODE_NAMESPACE_LIST | |
# | |
multi_namespace_mode_namespace_list = | |
# Use the service account kubernetes gives to pods to connect to kubernetes cluster. | |
# It's intended for clients that expect to be running inside a pod running on kubernetes. | |
# It will raise an exception if called from a process not running in a kubernetes environment. | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__IN_CLUSTER | |
# | |
in_cluster = True | |
# When running with in_cluster=False change the default cluster_context or config_file | |
# options to Kubernetes client. Leave blank these to use default behaviour like ``kubectl`` has. | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__CLUSTER_CONTEXT | |
# | |
# cluster_context = | |
# Path to the kubernetes configfile to be used when ``in_cluster`` is set to False | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__CONFIG_FILE | |
# | |
# config_file = | |
# Keyword parameters to pass while calling a kubernetes client core_v1_api methods | |
# from Kubernetes Executor provided as a single line formatted JSON dictionary string. | |
# List of supported params are similar for all core_v1_apis, hence a single config | |
# variable for all apis. See: | |
# https://raw.githubusercontent.com/kubernetes-client/python/41f11a09995efcd0142e25946adc7591431bfb2f/kubernetes/client/api/core_v1_api.py | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__KUBE_CLIENT_REQUEST_ARGS | |
# | |
kube_client_request_args = | |
# Optional keyword arguments to pass to the ``delete_namespaced_pod`` kubernetes client | |
# ``core_v1_api`` method when using the Kubernetes Executor. | |
# This should be an object and can contain any of the options listed in the ``v1DeleteOptions`` | |
# class defined here: | |
# https://github.com/kubernetes-client/python/blob/41f11a09995efcd0142e25946adc7591431bfb2f/kubernetes/client/models/v1_delete_options.py#L19 | |
# | |
# Example: delete_option_kwargs = {"grace_period_seconds": 10} | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__DELETE_OPTION_KWARGS | |
# | |
delete_option_kwargs = | |
# Enables TCP keepalive mechanism. This prevents Kubernetes API requests to hang indefinitely | |
# when idle connection is time-outed on services like cloud load balancers or firewalls. | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__ENABLE_TCP_KEEPALIVE | |
# | |
enable_tcp_keepalive = True | |
# When the `enable_tcp_keepalive` option is enabled, TCP probes a connection that has | |
# been idle for `tcp_keep_idle` seconds. | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__TCP_KEEP_IDLE | |
# | |
tcp_keep_idle = 120 | |
# When the `enable_tcp_keepalive` option is enabled, if Kubernetes API does not respond | |
# to a keepalive probe, TCP retransmits the probe after `tcp_keep_intvl` seconds. | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__TCP_KEEP_INTVL | |
# | |
tcp_keep_intvl = 30 | |
# When the `enable_tcp_keepalive` option is enabled, if Kubernetes API does not respond | |
# to a keepalive probe, TCP retransmits the probe `tcp_keep_cnt number` of times before | |
# a connection is considered to be broken. | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__TCP_KEEP_CNT | |
# | |
tcp_keep_cnt = 6 | |
# Set this to false to skip verifying SSL certificate of Kubernetes python client. | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__VERIFY_SSL | |
# | |
verify_ssl = True | |
# How often in seconds to check for task instances stuck in "queued" status without a pod | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__WORKER_PODS_QUEUED_CHECK_INTERVAL | |
# | |
worker_pods_queued_check_interval = 60 | |
# Path to a CA certificate to be used by the Kubernetes client to verify the server's SSL certificate. | |
# | |
# Variable: AIRFLOW__KUBERNETES_EXECUTOR__SSL_CA_CERT | |
# | |
ssl_ca_cert = | |
[elasticsearch] | |
# Elasticsearch host | |
# | |
# Variable: AIRFLOW__ELASTICSEARCH__HOST | |
# | |
host = | |
# Format of the log_id, which is used to query for a given tasks logs | |
# | |
# Variable: AIRFLOW__ELASTICSEARCH__LOG_ID_TEMPLATE | |
# | |
log_id_template = {dag_id}-{task_id}-{run_id}-{map_index}-{try_number} | |
# Used to mark the end of a log stream for a task | |
# | |
# Variable: AIRFLOW__ELASTICSEARCH__END_OF_LOG_MARK | |
# | |
end_of_log_mark = end_of_log | |
# Qualified URL for an elasticsearch frontend (like Kibana) with a template argument for log_id | |
# Code will construct log_id using the log_id template from the argument above. | |
# NOTE: scheme will default to https if one is not provided | |
# | |
# Example: frontend = http://localhost:5601/app/kibana#/discover?_a=(columns:!(message),query:(language:kuery,query:'log_id: "{log_id}"'),sort:!(log.offset,asc)) | |
# | |
# Variable: AIRFLOW__ELASTICSEARCH__FRONTEND | |
# | |
frontend = | |
# Write the task logs to the stdout of the worker, rather than the default files | |
# | |
# Variable: AIRFLOW__ELASTICSEARCH__WRITE_STDOUT | |
# | |
write_stdout = False | |
# Instead of the default log formatter, write the log lines as JSON | |
# | |
# Variable: AIRFLOW__ELASTICSEARCH__JSON_FORMAT | |
# | |
json_format = False | |
# Log fields to also attach to the json output, if enabled | |
# | |
# Variable: AIRFLOW__ELASTICSEARCH__JSON_FIELDS | |
# | |
json_fields = asctime, filename, lineno, levelname, message | |
# The field where host name is stored (normally either `host` or `host.name`) | |
# | |
# Variable: AIRFLOW__ELASTICSEARCH__HOST_FIELD | |
# | |
host_field = host | |
# The field where offset is stored (normally either `offset` or `log.offset`) | |
# | |
# Variable: AIRFLOW__ELASTICSEARCH__OFFSET_FIELD | |
# | |
offset_field = offset | |
# Comma separated list of index patterns to use when searching for logs (default: `_all`). | |
# | |
# Example: index_patterns = something-* | |
# | |
# Variable: AIRFLOW__ELASTICSEARCH__INDEX_PATTERNS | |
# | |
index_patterns = _all | |
[elasticsearch_configs] | |
# | |
# Variable: AIRFLOW__ELASTICSEARCH_CONFIGS__HTTP_COMPRESS | |
# | |
http_compress = False | |
# | |
# Variable: AIRFLOW__ELASTICSEARCH_CONFIGS__VERIFY_CERTS | |
# | |
verify_certs = True | |
[imap] | |
# Options for IMAP provider. | |
# ssl_context = | |
[azure_remote_logging] | |
# Configuration that needs to be set for enable remote logging in Azure Blob Storage | |
remote_wasb_log_container = airflow-logs | |
[openlineage] | |
# This section applies settings for OpenLineage integration. | |
# For backwards compatibility with `openlineage-python` one can still use | |
# `openlineage.yml` file or `OPENLINEAGE_` environment variables. However, below | |
# configuration takes precedence over those. | |
# More in documentation - https://openlineage.io/docs/client/python#configuration. | |
# Set this to true if you don't want OpenLineage to emit events. | |
# | |
# Variable: AIRFLOW__OPENLINEAGE__DISABLED | |
# | |
disabled = False | |
# Semicolon separated string of Airflow Operator names to disable | |
# | |
# Example: disabled_for_operators = airflow.operators.bash.BashOperator;airflow.operators.python.PythonOperator | |
# | |
# Variable: AIRFLOW__OPENLINEAGE__DISABLED_FOR_OPERATORS | |
# | |
disabled_for_operators = | |
# OpenLineage namespace | |
# | |
# Example: namespace = food_delivery | |
# | |
# Variable: AIRFLOW__OPENLINEAGE__NAMESPACE | |
# | |
# namespace = | |
# Semicolon separated paths to custom OpenLineage extractors. | |
# | |
# Example: extractors = full.path.to.ExtractorClass;full.path.to.AnotherExtractorClass | |
# | |
# Variable: AIRFLOW__OPENLINEAGE__EXTRACTORS | |
# | |
extractors = | |
# Path to YAML config. This provides backwards compatibility to pass config as | |
# `openlineage.yml` file. | |
# | |
# Variable: AIRFLOW__OPENLINEAGE__CONFIG_PATH | |
# | |
config_path = | |
# OpenLineage Client transport configuration. It should contain type | |
# and additional options per each type. | |
# | |
# Currently supported types are: | |
# | |
# * HTTP | |
# * Kafka | |
# * Console | |
# | |
# Example: transport = {"type": "http", "url": "http://localhost:5000"} | |
# | |
# Variable: AIRFLOW__OPENLINEAGE__TRANSPORT | |
# | |
transport = | |
# If disabled, OpenLineage events do not contain source code of particular | |
# operators, like PythonOperator. | |
# | |
# Variable: AIRFLOW__OPENLINEAGE__DISABLE_SOURCE_CODE | |
# | |
# disable_source_code = | |
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