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[core] |
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# The folder where your airflow pipelines live, most likely a |
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# subfolder in a code repository. This path must be absolute. |
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# dags_folder = /usr/local/airflow/dags |
|
|
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# Hostname by providing a path to a callable, which will resolve the hostname. |
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# The format is "package.function". |
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# |
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# For example, default value "socket.getfqdn" means that result from getfqdn() of "socket" |
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# package will be used as hostname. |
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# |
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# No argument should be required in the function specified. |
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# If using IP address as hostname is preferred, use value ``airflow.utils.net.get_host_ip_address`` |
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hostname_callable = socket.getfqdn |
|
|
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# Default timezone in case supplied date times are naive |
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# can be utc (default), system, or any IANA timezone string (e.g. Europe/Amsterdam) |
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default_timezone = utc |
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|
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# The executor class that airflow should use. Choices include |
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# ``SequentialExecutor``, ``LocalExecutor``, ``CeleryExecutor``, ``DaskExecutor``, |
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# ``KubernetesExecutor``, ``CeleryKubernetesExecutor`` or the |
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# full import path to the class when using a custom executor. |
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executor = SequentialExecutor |
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|
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# The SqlAlchemy connection string to the metadata database. |
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# SqlAlchemy supports many different database engine, more information |
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# their website |
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# sql_alchemy_conn = sqlite:////usr/local/airflow/airflow.db |
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|
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# The encoding for the databases |
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sql_engine_encoding = utf-8 |
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|
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# Collation for ``dag_id``, ``task_id``, ``key`` columns in case they have different encoding. |
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# This is particularly useful in case of mysql with utf8mb4 encoding because |
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# primary keys for XCom table has too big size and ``sql_engine_collation_for_ids`` should |
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# be set to ``utf8mb3_general_ci``. |
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# sql_engine_collation_for_ids = |
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|
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# If SqlAlchemy should pool database connections. |
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sql_alchemy_pool_enabled = True |
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|
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# The SqlAlchemy pool size is the maximum number of database connections |
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# in the pool. 0 indicates no limit. |
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sql_alchemy_pool_size = 5 |
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|
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# The maximum overflow size of the pool. |
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# When the number of checked-out connections reaches the size set in pool_size, |
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# additional connections will be returned up to this limit. |
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# When those additional connections are returned to the pool, they are disconnected and discarded. |
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# It follows then that the total number of simultaneous connections the pool will allow |
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# is pool_size + max_overflow, |
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# and the total number of "sleeping" connections the pool will allow is pool_size. |
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# max_overflow can be set to ``-1`` to indicate no overflow limit; |
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# no limit will be placed on the total number of concurrent connections. Defaults to ``10``. |
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sql_alchemy_max_overflow = 10 |
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|
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# The SqlAlchemy pool recycle is the number of seconds a connection |
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# can be idle in the pool before it is invalidated. This config does |
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# not apply to sqlite. If the number of DB connections is ever exceeded, |
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# a lower config value will allow the system to recover faster. |
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sql_alchemy_pool_recycle = 1800 |
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|
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# Check connection at the start of each connection pool checkout. |
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# Typically, this is a simple statement like "SELECT 1". |
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# More information here: |
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# https://docs.sqlalchemy.org/en/13/core/pooling.html#disconnect-handling-pessimistic |
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sql_alchemy_pool_pre_ping = True |
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|
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# The schema to use for the metadata database. |
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# SqlAlchemy supports databases with the concept of multiple schemas. |
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sql_alchemy_schema = |
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|
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# Import path for connect args in SqlAlchemy. Defaults to an empty dict. |
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# This is useful when you want to configure db engine args that SqlAlchemy won't parse |
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# in connection string. |
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# See https://docs.sqlalchemy.org/en/13/core/engines.html#sqlalchemy.create_engine.params.connect_args |
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# sql_alchemy_connect_args = |
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|
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# This defines the maximum number of task instances that can run concurrently in Airflow |
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# regardless of scheduler count and worker count. Generally, this value is reflective of |
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# the number of task instances with the running state in the metadata database. |
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parallelism = 200 |
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|
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# The maximum number of task instances allowed to run concurrently in each DAG. To calculate |
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# the number of tasks that is running concurrently for a DAG, add up the number of running |
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# tasks for all DAG runs of the DAG. This is configurable at the DAG level with ``concurrency``, |
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# which is defaulted as ``dag_concurrency``. |
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dag_concurrency = 16 |
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|
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# Are DAGs paused by default at creation |
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dags_are_paused_at_creation = True |
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|
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# The maximum number of active DAG runs per DAG. The scheduler will not create more DAG runs |
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# if it reaches the limit. This is configurable at the DAG level with ``max_active_runs``, |
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# which is defaulted as ``max_active_runs_per_dag``. |
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max_active_runs_per_dag = 16 |
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|
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# The maximum number of queued dagruns for a single DAG. The scheduler will not create more DAG runs |
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# if it reaches the limit. This is not configurable at the DAG level. |
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max_queued_runs_per_dag = 16 |
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|
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# Whether to load the DAG examples that ship with Airflow. It's good to |
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# get started, but you probably want to set this to ``False`` in a production |
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# environment |
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load_examples = True |
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|
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# Whether to load the default connections that ship with Airflow. It's good to |
|
# get started, but you probably want to set this to ``False`` in a production |
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# environment |
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load_default_connections = True |
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|
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# Path to the folder containing Airflow plugins |
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# plugins_folder = /usr/local/airflow/plugins |
|
|
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# Should tasks be executed via forking of the parent process ("False", |
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# the speedier option) or by spawning a new python process ("True" slow, |
|
# but means plugin changes picked up by tasks straight away) |
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execute_tasks_new_python_interpreter = False |
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|
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# Secret key to save connection passwords in the db |
|
fernet_key = |
|
|
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# Whether to disable pickling dags |
|
donot_pickle = True |
|
|
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# How long before timing out a python file import |
|
dagbag_import_timeout = 86400.0 |
|
|
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# Should a traceback be shown in the UI for dagbag import errors, |
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# instead of just the exception message |
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dagbag_import_error_tracebacks = True |
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|
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# If tracebacks are shown, how many entries from the traceback should be shown |
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dagbag_import_error_traceback_depth = 2 |
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|
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# How long before timing out a DagFileProcessor, which processes a dag file |
|
dag_file_processor_timeout = 50 |
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|
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# The class to use for running task instances in a subprocess. |
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# Choices include StandardTaskRunner, CgroupTaskRunner or the full import path to the class |
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# when using a custom task runner. |
|
task_runner = StandardTaskRunner |
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|
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# If set, tasks without a ``run_as_user`` argument will be run with this user |
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# Can be used to de-elevate a sudo user running Airflow when executing tasks |
|
default_impersonation = |
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|
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# What security module to use (for example kerberos) |
|
security = |
|
|
|
# Turn unit test mode on (overwrites many configuration options with test |
|
# values at runtime) |
|
unit_test_mode = False |
|
|
|
# Whether to enable pickling for xcom (note that this is insecure and allows for |
|
# RCE exploits). |
|
enable_xcom_pickling = False |
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|
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# 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 |
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killed_task_cleanup_time = 60 |
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|
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# Whether to override params with dag_run.conf. If you pass some key-value pairs |
|
# through ``airflow dags backfill -c`` or |
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# ``airflow dags trigger -c``, the key-value pairs will override the existing ones in params. |
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dag_run_conf_overrides_params = True |
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|
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# When discovering DAGs, ignore any files that don't contain the strings ``DAG`` and ``airflow``. |
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dag_discovery_safe_mode = True |
|
|
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# The number of retries each task is going to have by default. Can be overridden at dag or task level. |
|
default_task_retries = 0 |
|
|
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# Updating serialized DAG can not be faster than a minimum interval to reduce database write rate. |
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min_serialized_dag_update_interval = 30 |
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|
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# Fetching serialized DAG can not be faster than a minimum interval to reduce database |
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# read rate. This config controls when your DAGs are updated in the Webserver |
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min_serialized_dag_fetch_interval = 10 |
|
|
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# Whether to persist DAG files code in DB. |
|
# If set to True, Webserver reads file contents from DB instead of |
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# trying to access files in a DAG folder. |
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# (Default is ``True``) |
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# Example: store_dag_code = True |
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# store_dag_code = |
|
|
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# Maximum number of Rendered Task Instance Fields (Template Fields) per task to store |
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# 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. |
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max_num_rendered_ti_fields_per_task = 30 |
|
|
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# On each dagrun check against defined SLAs |
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check_slas = True |
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|
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# Path to custom XCom class that will be used to store and resolve operators results |
|
# Example: xcom_backend = path.to.CustomXCom |
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xcom_backend = airflow.models.xcom.BaseXCom |
|
|
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# By default Airflow plugins are lazily-loaded (only loaded when required). Set it to ``False``, |
|
# if you want to load plugins whenever 'airflow' is invoked via cli or loaded from module. |
|
lazy_load_plugins = True |
|
|
|
# By default Airflow providers are lazily-discovered (discovery and imports happen only when required). |
|
# Set it to False, if you want to discover providers whenever 'airflow' is invoked via cli or |
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# loaded from module. |
|
lazy_discover_providers = True |
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|
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# Number of times the code should be retried in case of DB Operational Errors. |
|
# Not all transactions will be retried as it can cause undesired state. |
|
# Currently it is only used in ``DagFileProcessor.process_file`` to retry ``dagbag.sync_to_db``. |
|
max_db_retries = 3 |
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|
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# Hide sensitive Variables or Connection extra json keys from UI and task logs when set to True |
|
# |
|
# (Connection passwords are always hidden in logs) |
|
hide_sensitive_var_conn_fields = True |
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|
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# A comma-separated list of extra sensitive keywords to look for in variables names or connection's |
|
# extra JSON. |
|
sensitive_var_conn_names = |
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|
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[logging] |
|
# The folder where airflow should store its log files |
|
# This path must be absolute |
|
# base_log_folder = /usr/local/airflow/logs |
|
|
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# Airflow can store logs remotely in AWS S3, Google Cloud Storage or Elastic Search. |
|
# Set this to True if you want to enable remote logging. |
|
remote_logging = False |
|
|
|
# Users must supply an Airflow connection id that provides access to the storage |
|
# location. |
|
remote_log_conn_id = |
|
|
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# 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. |
|
google_key_path = |
|
|
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# Storage bucket URL for remote logging |
|
# S3 buckets should start with "s3://" |
|
# Cloudwatch log groups should start with "cloudwatch://" |
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# 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://" |
|
remote_base_log_folder = |
|
|
|
# Use server-side encryption for logs stored in S3 |
|
encrypt_s3_logs = False |
|
|
|
# Logging level. |
|
# |
|
# Supported values: ``CRITICAL``, ``ERROR``, ``WARNING``, ``INFO``, ``DEBUG``. |
|
logging_level = INFO |
|
|
|
# Logging level for Flask-appbuilder UI. |
|
# |
|
# Supported values: ``CRITICAL``, ``ERROR``, ``WARNING``, ``INFO``, ``DEBUG``. |
|
fab_logging_level = WARN |
|
|
|
# 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 |
|
logging_config_class = |
|
|
|
# Flag to enable/disable Colored logs in Console |
|
# Colour the logs when the controlling terminal is a TTY. |
|
colored_console_log = True |
|
|
|
# Log format for when Colored logs is enabled |
|
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 |
|
colored_formatter_class = airflow.utils.log.colored_log.CustomTTYColoredFormatter |
|
|
|
# Format of Log line |
|
log_format = [%%(asctime)s] {%%(filename)s:%%(lineno)d} %%(levelname)s - %%(message)s |
|
simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s |
|
|
|
# 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} |
|
task_log_prefix_template = |
|
|
|
# Formatting for how airflow generates file names/paths for each task run. |
|
log_filename_template = {{ ti.dag_id }}/{{ ti.task_id }}/{{ ts }}/{{ try_number }}.log |
|
|
|
# Formatting for how airflow generates file names for log |
|
log_processor_filename_template = {{ filename }}.log |
|
|
|
# full path of dag_processor_manager logfile |
|
# dag_processor_manager_log_location = /usr/local/airflow/logs/dag_processor_manager/dag_processor_manager.log |
|
|
|
# Name of handler to read task instance logs. |
|
# Defaults to use ``task`` handler. |
|
task_log_reader = task |
|
|
|
# A comma\-separated list of third-party logger names that will be configured to print messages to |
|
# consoles\. |
|
# Example: extra_loggers = connexion,sqlalchemy |
|
extra_loggers = |
|
|
|
[metrics] |
|
|
|
# StatsD (https://github.com/etsy/statsd) integration settings. |
|
# Enables sending metrics to StatsD. |
|
statsd_on = False |
|
statsd_host = localhost |
|
statsd_port = 8125 |
|
statsd_prefix = airflow |
|
|
|
# If you want to avoid sending all the available metrics to StatsD, |
|
# 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") |
|
statsd_allow_list = |
|
|
|
# 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: |
|
stat_name_handler = |
|
|
|
# To enable datadog integration to send airflow metrics. |
|
statsd_datadog_enabled = False |
|
|
|
# List of datadog tags attached to all metrics(e.g: key1:value1,key2:value2) |
|
statsd_datadog_tags = |
|
|
|
# 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 |
|
# statsd_custom_client_path = |
|
|
|
[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 |
|
backend = airflow.secrets.local_filesystem.LocalFilesystemBackend |
|
|
|
# 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"}`` |
|
backend_kwargs = {"variables_file_path": "<airflow_home_path>/config/secrets.json", "connections_file_path": "<airflow_home_path>/config/connections.json"} |
|
|
|
[cli] |
|
# In what way should the cli access the API. The LocalClient will use the |
|
# database directly, while the json_client will use the api running on the |
|
# webserver |
|
api_client = airflow.api.client.local_client |
|
|
|
# If you set web_server_url_prefix, do NOT forget to append it here, ex: |
|
# ``endpoint_url = http://localhost:8080/myroot`` |
|
# So api will look like: ``http://localhost:8080/myroot/api/experimental/...`` |
|
endpoint_url = http://localhost:8080 |
|
|
|
[debug] |
|
# Used only with ``DebugExecutor``. If set to ``True`` DAG will fail with first |
|
# failed task. Helpful for debugging purposes. |
|
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 |
|
# `UPDATING.md <https://github.com/apache/airflow/blob/main/UPDATING.md>`_ |
|
enable_experimental_api = False |
|
|
|
# How to authenticate users of the API. See |
|
# https://airflow.apache.org/docs/apache-airflow/stable/security.html for possible values. |
|
# ("airflow.api.auth.backend.default" allows all requests for historic reasons) |
|
auth_backend = airflow.api.auth.backend.basic_auth |
|
|
|
# Used to set the maximum page limit for API requests |
|
maximum_page_limit = 100 |
|
|
|
# Used to set the default page limit when limit is zero. A default limit |
|
# of 100 is set on OpenApi spec. However, this particular default limit |
|
# only work when limit is set equal to zero(0) from API requests. |
|
# If no limit is supplied, the OpenApi spec default is used. |
|
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 |
|
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 |
|
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. |
|
access_control_allow_headers = |
|
|
|
# Specifies the method or methods allowed when accessing the resource. |
|
access_control_allow_methods = |
|
|
|
# Indicates whether the response can be shared with requesting code from the given origin. |
|
access_control_allow_origin = |
|
|
|
[lineage] |
|
# what lineage backend to use |
|
backend = |
|
|
|
[atlas] |
|
sasl_enabled = False |
|
host = |
|
port = 21000 |
|
username = |
|
password = |
|
|
|
[operators] |
|
# The default owner assigned to each new operator, unless |
|
# provided explicitly or passed via ``default_args`` |
|
default_owner = airflow |
|
default_cpus = 1 |
|
default_ram = 512 |
|
default_disk = 512 |
|
default_gpus = 0 |
|
|
|
# Default queue that tasks get assigned to and that worker listen on. |
|
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. |
|
allow_illegal_arguments = False |
|
|
|
[hive] |
|
# Default mapreduce queue for HiveOperator tasks |
|
default_hive_mapred_queue = |
|
|
|
# Template for mapred_job_name in HiveOperator, supports the following named parameters |
|
# hostname, dag_id, task_id, execution_date |
|
# mapred_job_name_template = |
|
|
|
[webserver] |
|
# The base url of your website as airflow cannot guess what domain or |
|
# cname you are using. This is used in automated emails that |
|
# airflow sends to point links to the right web server |
|
base_url = http://localhost:8080 |
|
|
|
# Default timezone to display all dates in the UI, can be UTC, system, or |
|
# any IANA timezone string (e.g. Europe/Amsterdam). If left empty the |
|
# default value of core/default_timezone will be used |
|
# Example: default_ui_timezone = America/New_York |
|
default_ui_timezone = UTC |
|
|
|
# The ip specified when starting the web server |
|
web_server_host = 0.0.0.0 |
|
|
|
# The port on which to run the web server |
|
web_server_port = 8080 |
|
|
|
# Paths to the SSL certificate and key for the web server. When both are |
|
# provided SSL will be enabled. This does not change the web server port. |
|
web_server_ssl_cert = |
|
|
|
# Paths to the SSL certificate and key for the web server. When both are |
|
# provided SSL will be enabled. This does not change the web server port. |
|
web_server_ssl_key = |
|
|
|
# Number of seconds the webserver waits before killing gunicorn master that doesn't respond |
|
web_server_master_timeout = 120 |
|
|
|
# Number of seconds the gunicorn webserver waits before timing out on a worker |
|
web_server_worker_timeout = 120 |
|
|
|
# Number of workers to refresh at a time. When set to 0, worker refresh is |
|
# disabled. When nonzero, airflow periodically refreshes webserver workers by |
|
# bringing up new ones and killing old ones. |
|
worker_refresh_batch_size = 1 |
|
|
|
# Number of seconds to wait before refreshing a batch of workers. |
|
worker_refresh_interval = 6000 |
|
|
|
# If set to True, Airflow will track files in plugins_folder directory. When it detects changes, |
|
# then reload the gunicorn. |
|
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". |
|
secret_key = /leJxGFPnISkvPzCCy1XSw== |
|
|
|
# Number of workers to run the Gunicorn web server |
|
workers = 4 |
|
|
|
# The worker class gunicorn should use. Choices include |
|
# sync (default), eventlet, gevent |
|
worker_class = sync |
|
|
|
# Log files for the gunicorn webserver. '-' means log to stderr. |
|
access_logfile = - |
|
|
|
# Log files for the gunicorn webserver. '-' means log to stderr. |
|
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 |
|
access_logformat = |
|
|
|
# Expose the configuration file in the web server |
|
expose_config = False |
|
|
|
# Expose hostname in the web server |
|
expose_hostname = True |
|
|
|
# Expose stacktrace in the web server |
|
expose_stacktrace = True |
|
|
|
# Default DAG view. Valid values are: ``tree``, ``graph``, ``duration``, ``gantt``, ``landing_times`` |
|
dag_default_view = tree |
|
|
|
# Default DAG orientation. Valid values are: |
|
# ``LR`` (Left->Right), ``TB`` (Top->Bottom), ``RL`` (Right->Left), ``BT`` (Bottom->Top) |
|
dag_orientation = LR |
|
|
|
# The amount of time (in secs) webserver will wait for initial handshake |
|
# while fetching logs from other worker machine |
|
log_fetch_timeout_sec = 5 |
|
|
|
# Time interval (in secs) to wait before next log fetching. |
|
log_fetch_delay_sec = 2 |
|
|
|
# Distance away from page bottom to enable auto tailing. |
|
log_auto_tailing_offset = 30 |
|
|
|
# Animation speed for auto tailing log display. |
|
log_animation_speed = 1000 |
|
|
|
# By default, the webserver shows paused DAGs. Flip this to hide paused |
|
# DAGs by default |
|
hide_paused_dags_by_default = False |
|
|
|
# Consistent page size across all listing views in the UI |
|
page_size = 100 |
|
|
|
# Define the color of navigation bar |
|
navbar_color = #fff |
|
|
|
# Default dagrun to show in UI |
|
default_dag_run_display_number = 25 |
|
|
|
# Enable werkzeug ``ProxyFix`` middleware for reverse proxy |
|
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/ |
|
proxy_fix_x_for = 1 |
|
|
|
# Number of values to trust for ``X-Forwarded-Proto`` |
|
proxy_fix_x_proto = 1 |
|
|
|
# Number of values to trust for ``X-Forwarded-Host`` |
|
proxy_fix_x_host = 1 |
|
|
|
# Number of values to trust for ``X-Forwarded-Port`` |
|
proxy_fix_x_port = 1 |
|
|
|
# Number of values to trust for ``X-Forwarded-Prefix`` |
|
proxy_fix_x_prefix = 1 |
|
|
|
# Set secure flag on session cookie |
|
cookie_secure = False |
|
|
|
# Set samesite policy on session cookie |
|
cookie_samesite = Lax |
|
|
|
# Default setting for wrap toggle on DAG code and TI log views. |
|
default_wrap = False |
|
|
|
# Allow the UI to be rendered in a frame |
|
x_frame_enabled = True |
|
|
|
# Send anonymous user activity to your analytics tool |
|
# choose from google_analytics, segment, or metarouter |
|
# analytics_tool = |
|
|
|
# Unique ID of your account in the analytics tool |
|
# analytics_id = |
|
|
|
# 'Recent Tasks' stats will show for old DagRuns if set |
|
show_recent_stats_for_completed_runs = True |
|
|
|
# Update FAB permissions and sync security manager roles |
|
# on webserver startup |
|
update_fab_perms = True |
|
|
|
# The UI cookie lifetime in minutes. User will be logged out from UI after |
|
# ``session_lifetime_minutes`` of non-activity |
|
session_lifetime_minutes = 43200 |
|
|
|
# Sets a custom page title for the DAGs overview page and site title for all pages |
|
# instance_name = |
|
|
|
[email] |
|
|
|
# Configuration email backend and whether to |
|
# send email alerts on retry or failure |
|
# Email backend to use |
|
email_backend = airflow.utils.email.send_email_smtp |
|
|
|
# Email connection to use |
|
email_conn_id = smtp_default |
|
|
|
# Whether email alerts should be sent when a task is retried |
|
default_email_on_retry = True |
|
|
|
# Whether email alerts should be sent when a task failed |
|
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 |
|
# 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 |
|
# html_content_template = |
|
|
|
[smtp] |
|
|
|
# If you want airflow to send emails on retries, failure, and you want to use |
|
# the airflow.utils.email.send_email_smtp function, you have to configure an |
|
# smtp server here |
|
smtp_host = localhost |
|
smtp_starttls = True |
|
smtp_ssl = False |
|
# Example: smtp_user = airflow |
|
# smtp_user = |
|
# Example: smtp_password = airflow |
|
# smtp_password = |
|
smtp_port = 25 |
|
smtp_mail_from = [email protected] |
|
smtp_timeout = 30 |
|
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``, ``before_send``, ``transport``. |
|
# Enable error reporting to Sentry |
|
sentry_on = false |
|
sentry_dsn = |
|
|
|
[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`` |
|
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 |
|
celery_app_name = airflow.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 |
|
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. |
|
# http://docs.celeryproject.org/en/latest/reference/celery.bin.worker.html#cmdoption-celery-worker-autoscale |
|
# Example: worker_autoscale = 16,12 |
|
# 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.celeryproject.org/en/stable/userguide/optimizing.html#prefetch-limits |
|
# Example: worker_prefetch_multiplier = 1 |
|
# worker_prefetch_multiplier = |
|
|
|
# When you start an airflow worker, airflow starts a tiny web server |
|
# subprocess to serve the workers local log files to the airflow main |
|
# web server, who then builds pages and sends them to users. This defines |
|
# the port on which the logs are served. It needs to be unused, and open |
|
# visible from the main web server to connect into the workers. |
|
worker_log_server_port = 8793 |
|
|
|
# Umask that will be used when starting workers with the ``airflow celery worker`` |
|
# in daemon mode. This control the file-creation mode mask which determines the initial |
|
# value of file permission bits for newly created files. |
|
worker_umask = 0o077 |
|
|
|
# The Celery broker URL. Celery supports RabbitMQ, Redis and experimentally |
|
# a sqlalchemy database. Refer to the Celery documentation for more information. |
|
broker_url = redis://redis:6379/0 |
|
|
|
# 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 |
|
# http://docs.celeryproject.org/en/latest/userguide/configuration.html#task-result-backend-settings |
|
result_backend = db+postgresql://postgres:airflow@postgres/airflow |
|
|
|
# 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 |
|
flower_host = 0.0.0.0 |
|
|
|
# The root URL for Flower |
|
# Example: flower_url_prefix = /flower |
|
flower_url_prefix = |
|
|
|
# This defines the port that Celery Flower runs on |
|
flower_port = 5555 |
|
|
|
# Securing Flower with Basic Authentication |
|
# Accepts user:password pairs separated by a comma |
|
# Example: flower_basic_auth = user1:password1,user2:password2 |
|
flower_basic_auth = |
|
|
|
# Default queue that tasks get assigned to and that worker listen on. |
|
default_queue = default |
|
|
|
# How many processes CeleryExecutor uses to sync task state. |
|
# 0 means to use max(1, number of cores - 1) processes. |
|
sync_parallelism = 0 |
|
|
|
# Import path for celery configuration options |
|
celery_config_options = airflow.config_templates.default_celery.DEFAULT_CELERY_CONFIG |
|
ssl_active = False |
|
ssl_key = |
|
ssl_cert = |
|
ssl_cacert = |
|
|
|
# Celery Pool implementation. |
|
# Choices include: ``prefork`` (default), ``eventlet``, ``gevent`` or ``solo``. |
|
# See: |
|
# https://docs.celeryproject.org/en/latest/userguide/workers.html#concurrency |
|
# https://docs.celeryproject.org/en/latest/userguide/concurrency/eventlet.html |
|
pool = prefork |
|
|
|
# The number of seconds to wait before timing out ``send_task_to_executor`` or |
|
# ``fetch_celery_task_state`` operations. |
|
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. |
|
task_track_started = True |
|
|
|
# Time in seconds after which Adopted tasks are cleared by CeleryExecutor. This is helpful to clear |
|
# stalled tasks. |
|
task_adoption_timeout = 600 |
|
|
|
# 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. |
|
task_publish_max_retries = 3 |
|
|
|
# Worker initialisation check to validate Metadata Database connection |
|
worker_precheck = False |
|
|
|
[celery_broker_transport_options] |
|
|
|
# This section is for specifying options which can be passed to the |
|
# underlying celery broker transport. See: |
|
# http://docs.celeryproject.org/en/latest/userguide/configuration.html#std:setting-broker_transport_options |
|
# The visibility timeout defines the number of seconds to wait for the worker |
|
# to acknowledge the task before the message is redelivered to another worker. |
|
# Make sure to increase the visibility timeout to match the time of the longest |
|
# ETA you're planning to use. |
|
# visibility_timeout is only supported for Redis and SQS celery brokers. |
|
# See: |
|
# http://docs.celeryproject.org/en/master/userguide/configuration.html#std:setting-broker_transport_options |
|
# Example: visibility_timeout = 21600 |
|
# visibility_timeout = |
|
|
|
[dask] |
|
|
|
# This section only applies if you are using the DaskExecutor in |
|
# [core] section above |
|
# The IP address and port of the Dask cluster's scheduler. |
|
cluster_address = 127.0.0.1:8786 |
|
|
|
# TLS/ SSL settings to access a secured Dask scheduler. |
|
tls_ca = |
|
tls_cert = |
|
tls_key = |
|
|
|
[scheduler] |
|
# Task instances listen for external kill signal (when you clear tasks |
|
# from the CLI or the UI), this defines the frequency at which they should |
|
# listen (in seconds). |
|
job_heartbeat_sec = 5 |
|
|
|
# How often (in seconds) to check and tidy up 'running' TaskInstancess |
|
# that no longer have a matching DagRun |
|
clean_tis_without_dagrun_interval = 15.0 |
|
|
|
# The scheduler constantly tries to trigger new tasks (look at the |
|
# scheduler section in the docs for more information). This defines |
|
# how often the scheduler should run (in seconds). |
|
scheduler_heartbeat_sec = 5 |
|
|
|
# The number of times to try to schedule each DAG file |
|
# -1 indicates unlimited number |
|
num_runs = -1 |
|
|
|
# The number of seconds to wait between consecutive DAG file processing |
|
processor_poll_interval = 1 |
|
|
|
# Number of seconds after which a DAG file is parsed. The DAG file is parsed every |
|
# ``min_file_process_interval`` number of seconds. Updates to DAGs are reflected after |
|
# this interval. Keeping this number low will increase CPU usage. |
|
min_file_process_interval = 30 |
|
|
|
# How often (in seconds) to scan the DAGs directory for new files. Default to 5 minutes. |
|
dag_dir_list_interval = 300 |
|
|
|
# How often should stats be printed to the logs. Setting to 0 will disable printing stats |
|
print_stats_interval = 30 |
|
|
|
# How often (in seconds) should pool usage stats be sent to statsd (if statsd_on is enabled) |
|
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 |
|
scheduler_health_check_threshold = 30 |
|
|
|
# How often (in seconds) should the scheduler check for orphaned tasks and SchedulerJobs |
|
orphaned_tasks_check_interval = 300.0 |
|
# child_process_log_directory = /usr/local/airflow/logs/scheduler |
|
|
|
# Local task jobs periodically heartbeat to the DB. If the job has |
|
# not heartbeat in this many seconds, the scheduler will mark the |
|
# associated task instance as failed and will re-schedule the task. |
|
scheduler_zombie_task_threshold = 300 |
|
|
|
# Turn off scheduler catchup by setting this to ``False``. |
|
# Default behavior is unchanged and |
|
# Command Line Backfills still work, but the scheduler |
|
# will not do scheduler catchup if this is ``False``, |
|
# however it can be set on a per DAG basis in the |
|
# DAG definition (catchup) |
|
catchup_by_default = True |
|
|
|
# This changes the batch size of queries in the scheduling main loop. |
|
# If this is too high, SQL query performance may be impacted by one |
|
# or more of the following: |
|
# - reversion to full table scan |
|
# - complexity of query predicate |
|
# - excessive locking |
|
# Additionally, you may hit the maximum allowable query length for your db. |
|
# Set this to 0 for no limit (not advised) |
|
max_tis_per_query = 512 |
|
|
|
# Should the scheduler issue ``SELECT ... FOR UPDATE`` in relevant queries. |
|
# If this is set to False then you should not run more than a single |
|
# scheduler at once |
|
use_row_level_locking = True |
|
|
|
# Max number of DAGs to create DagRuns for per scheduler loop. |
|
max_dagruns_to_create_per_loop = 10 |
|
|
|
# How many DagRuns should a scheduler examine (and lock) when scheduling |
|
# and queuing tasks. |
|
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 |
|
schedule_after_task_execution = True |
|
|
|
# The scheduler can run multiple processes in parallel to parse dags. |
|
# This defines how many processes will run. |
|
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 |
|
file_parsing_sort_mode = modified_time |
|
|
|
# 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. |
|
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 |
|
allow_trigger_in_future = False |
|
|
|
# DAG dependency detector class to use |
|
dependency_detector = airflow.serialization.serialized_objects.DependencyDetector |
|
|
|
[kerberos] |
|
ccache = /tmp/airflow_krb5_ccache |
|
|
|
# gets augmented with fqdn |
|
principal = airflow |
|
reinit_frequency = 3600 |
|
kinit_path = kinit |
|
keytab = airflow.keytab |
|
|
|
[github_enterprise] |
|
api_rev = v3 |
|
|
|
[elasticsearch] |
|
# Elasticsearch host |
|
host = |
|
|
|
# Format of the log_id, which is used to query for a given tasks logs |
|
log_id_template = {dag_id}-{task_id}-{execution_date}-{try_number} |
|
|
|
# Used to mark the end of a log stream for a task |
|
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: The code will prefix the https:// automatically, don't include that here. |
|
frontend = |
|
|
|
# Write the task logs to the stdout of the worker, rather than the default files |
|
write_stdout = False |
|
|
|
# Instead of the default log formatter, write the log lines as JSON |
|
json_format = False |
|
|
|
# Log fields to also attach to the json output, if enabled |
|
json_fields = asctime, filename, lineno, levelname, message |
|
|
|
# The field where host name is stored (normally either `host` or `host.name`) |
|
host_field = host |
|
|
|
# The field where offset is stored (normally either `offset` or `log.offset`) |
|
offset_field = offset |
|
|
|
[elasticsearch_configs] |
|
use_ssl = False |
|
verify_certs = True |
|
|
|
[kubernetes] |
|
# Path to the YAML pod file. If set, all other kubernetes-related fields are ignored. |
|
pod_template_file = |
|
|
|
# The repository of the Kubernetes Image for the Worker to Run |
|
worker_container_repository = |
|
|
|
# The tag of the Kubernetes Image for the Worker to Run |
|
worker_container_tag = |
|
|
|
# The Kubernetes namespace where airflow workers should be created. Defaults to ``default`` |
|
namespace = default |
|
|
|
# If True, all worker pods will be deleted upon termination |
|
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. |
|
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. |
|
worker_pods_creation_batch_size = 1 |
|
|
|
# Allows users to launch pods in multiple namespaces. |
|
# Will require creating a cluster-role for the scheduler |
|
multi_namespace_mode = False |
|
|
|
# Use the service account kubernetes gives to pods to connect to kubernetes cluster. |
|
# It's intended for clients that expect to be running inside a pod running on kubernetes. |
|
# It will raise an exception if called from a process not running in a kubernetes environment. |
|
in_cluster = True |
|
|
|
# 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. |
|
# cluster_context = |
|
|
|
# Path to the kubernetes configfile to be used when ``in_cluster`` is set to False |
|
# 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 |
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kube_client_request_args = |
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# Optional keyword arguments to pass to the ``delete_namespaced_pod`` kubernetes client |
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# ``core_v1_api`` method when using the Kubernetes Executor. |
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# This should be an object and can contain any of the options listed in the ``v1DeleteOptions`` |
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# class defined here: |
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# https://github.com/kubernetes-client/python/blob/41f11a09995efcd0142e25946adc7591431bfb2f/kubernetes/client/models/v1_delete_options.py#L19 |
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# Example: delete_option_kwargs = {"grace_period_seconds": 10} |
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delete_option_kwargs = |
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# Enables TCP keepalive mechanism. This prevents Kubernetes API requests to hang indefinitely |
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# when idle connection is time-outed on services like cloud load balancers or firewalls. |
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enable_tcp_keepalive = True |
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# When the `enable_tcp_keepalive` option is enabled, TCP probes a connection that has |
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# been idle for `tcp_keep_idle` seconds. |
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tcp_keep_idle = 120 |
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# When the `enable_tcp_keepalive` option is enabled, if Kubernetes API does not respond |
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# to a keepalive probe, TCP retransmits the probe after `tcp_keep_intvl` seconds. |
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tcp_keep_intvl = 30 |
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# When the `enable_tcp_keepalive` option is enabled, if Kubernetes API does not respond |
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# to a keepalive probe, TCP retransmits the probe `tcp_keep_cnt number` of times before |
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# a connection is considered to be broken. |
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tcp_keep_cnt = 6 |
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# Set this to false to skip verifying SSL certificate of Kubernetes python client. |
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verify_ssl = True |
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# How long in seconds a worker can be in Pending before it is considered a failure |
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worker_pods_pending_timeout = 300 |
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# How often in seconds to check if Pending workers have exceeded their timeouts |
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worker_pods_pending_timeout_check_interval = 120 |
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# How many pending pods to check for timeout violations in each check interval. |
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# You may want this higher if you have a very large cluster and/or use ``multi_namespace_mode``. |
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worker_pods_pending_timeout_batch_size = 100 |
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[smart_sensor] |
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# When `use_smart_sensor` is True, Airflow redirects multiple qualified sensor tasks to |
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# smart sensor task. |
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use_smart_sensor = False |
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# `shard_code_upper_limit` is the upper limit of `shard_code` value. The `shard_code` is generated |
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# by `hashcode % shard_code_upper_limit`. |
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shard_code_upper_limit = 10000 |
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# The number of running smart sensor processes for each service. |
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shards = 5 |
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# comma separated sensor classes support in smart_sensor. |
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sensors_enabled = NamedHivePartitionSensor |
