airflow-core/docs/administration-and-deployment/logging-monitoring/advanced-logging-configuration.rst
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.. _write-logs-advanced:
Not all configuration options are available from the airflow.cfg file. The config file describes
how to configure logging for tasks, because the logs generated by tasks are not only logged in separate
files by default but has to be also accessible via the webserver.
By default standard Airflow component logs are written to the $AIRFLOW_HOME/logs directory, but you
can also customize it and configure it as you want by overriding Python logger configuration that can
be configured by providing custom logging configuration object. You can also create and use logging configuration
for specific operators and tasks.
Some configuration options require that the logging config class be overwritten. You can do it by copying the default configuration of Airflow and modifying it to suit your needs.
The default configuration can be seen in the
airflow_local_settings.py template <https://github.com/apache/airflow/blob/|airflow-version|/airflow-core/src/airflow/config_templates/airflow_local_settings.py>_
and you can see the loggers and handlers used there.
See :ref:Configuring local settings <set-config:configuring-local-settings> for details on how to
configure local settings.
Except the custom loggers and handlers configurable there via the airflow.cfg, the logging methods in Airflow follow the usual Python logging convention,
that Python objects log to loggers that follow naming convention of <package>.<module_name>.
You can read more about standard python logging classes (Loggers, Handlers, Formatters) in the
Python logging documentation <https://docs.python.org/library/logging.html>_.
Configuring your logging classes can be done via the logging_config_class option in airflow.cfg file.
This configuration should specify the import path to a configuration compatible with
:func:logging.config.dictConfig. If your file is a standard import location, then you should set a
:envvar:PYTHONPATH environment variable.
Follow the steps below to enable custom logging config class:
#. Start by setting environment variable to known directory e.g. ~/airflow/
.. code-block:: bash
export PYTHONPATH=~/airflow/
#. Create a directory to store the config file e.g. ~/airflow/config
#. Create file called ~/airflow/config/log_config.py with following the contents:
.. code-block:: python
from copy import deepcopy
from airflow.config_templates.airflow_local_settings import DEFAULT_LOGGING_CONFIG
LOGGING_CONFIG = deepcopy(DEFAULT_LOGGING_CONFIG)
#. At the end of the file, add code to modify the default dictionary configuration.
#. Update $AIRFLOW_HOME/airflow.cfg to contain:
.. code-block:: ini
[logging]
logging_config_class = log_config.LOGGING_CONFIG
You can also use the logging_config_class together with remote logging if you plan to just extend/update
the configuration with remote logging enabled. Then the deep-copied dictionary will contain the remote logging
configuration generated for you and your modification will apply after remote logging configuration has
been added:
.. code-block:: ini
[logging]
remote_logging = True
logging_config_class = log_config.LOGGING_CONFIG
#. Restart the application.
See :doc:../modules_management for details on how Python and Airflow manage modules.
.. note::
You can override the way both standard logs of the components and "task" logs are handled.
You can create custom logging handlers and apply them to specific Operators, Hooks and tasks. By default, the Operators
and Hooks loggers are child of the airflow.task logger: They follow respectively the naming convention
airflow.task.operators.<package>.<module_name> and airflow.task.hooks.<package>.<module_name>. After
:doc:creating a custom logging class </administration-and-deployment/logging-monitoring/advanced-logging-configuration>,
you can assign specific loggers to them.
Example of custom logging for the SQLExecuteQueryOperator and the HttpHook:
.. code-block:: python
from copy import deepcopy
from pydantic.utils import deep_update
from airflow.config_templates.airflow_local_settings import DEFAULT_LOGGING_CONFIG
LOGGING_CONFIG = deep_update(
deepcopy(DEFAULT_LOGGING_CONFIG),
{
"loggers": {
"airflow.task.operators.airflow.providers.common.sql.operators.sql.SQLExecuteQueryOperator": {
"handlers": ["task"],
"level": "DEBUG",
"propagate": True,
},
"airflow.task.hooks.airflow.providers.http.hooks.http.HttpHook": {
"handlers": ["task"],
"level": "WARNING",
"propagate": False,
},
}
},
)
You can also set a custom name to a Dag's task with the logger_name attribute. This can be useful if multiple tasks
are using the same Operator, but you want to disable logging for some of them.
Example of custom logger name:
.. code-block:: python
# In your Dag file
SQLExecuteQueryOperator(..., logger_name="sql.big_query")
# In your custom `log_config.py`
LOGGING_CONFIG = deep_update(
deepcopy(DEFAULT_LOGGING_CONFIG),
{
"loggers": {
"airflow.task.operators.sql.big_query": {
"handlers": ["task"],
"level": "WARNING",
"propagate": True,
},
}
},
)
If you want to limit the log size of the tasks, you can add the handlers.task.max_bytes parameter.
Example of limiting the size of tasks:
.. code-block:: python
from copy import deepcopy
from pydantic.utils import deep_update
from airflow.config_templates.airflow_local_settings import DEFAULT_LOGGING_CONFIG
LOGGING_CONFIG = deep_update(
deepcopy(DEFAULT_LOGGING_CONFIG),
{
"handlers": {
"task": {"max_bytes": 104857600, "backup_count": 1} # 100MB and keep 1 history rotate log.
}
},
)