Default framework-side logging configuration

Kedro’s default logging configuration defines a handler called rich that uses the Rich logging handler to format messages. We also use the Rich traceback handler to render exceptions.

By default, Python only shows logging messages at level WARNING and above. Kedro’s logging configuration specifies that INFO level messages from Kedro should also be emitted. This makes it easier to track the progress of your pipeline when you perform a kedro run.

Project-side logging configuration

In addition to the rich handler defined in Kedro’s framework, the project-side conf/base/logging.yml defines two further logging handlers:

  • console: show logs on standard output (typically your terminal screen) without any rich formatting

  • info_file_handler: write logs of level INFO and above to info.log

The logging handlers that are actually used by default are rich and info_file_handler.

The project-side logging configuration also ensures that logs emitted from your project’s logger should be shown if they are INFO level or above (as opposed to the Python default of WARNING).

We now give some common examples of how you might like to change your project’s logging configuration.

Using KEDRO_LOGGING_CONFIG environment variable

KEDRO_LOGGING_CONFIG is an optional environment variable that you can use to specify the path of your logging configuration file, overriding the default path of conf/base/logging.yml.

To use this environment variable, set it to the path of your desired logging configuration file before running any Kedro commands. For example, if you have a logging configuration file located at /path/to/logging.yml, you can set KEDRO_LOGGING_CONFIG as follows:

export KEDRO_LOGGING_CONFIG=/path/to/logging.yml

After setting the environment variable, any subsequent Kedro commands will use the logging configuration file at the specified path.

Note

If the KEDRO_LOGGING_CONFIG environment variable is not set, Kedro will default to using the logging configuration file at the project’s default location of conf/base/logging.yml.

Disable file-based logging

You might sometimes need to disable file-based logging, e.g. if you are running Kedro on a read-only file system such as Databricks Repos. The simplest way to do this is to delete your conf/base/logging.yml file. With no project-side logging configuration specified, Kedro uses the default framework-side logging configuration, which does not include any file-based handlers.

Alternatively, if you would like to keep other configuration in conf/base/logging.yml and just disable file-based logging, then you can remove the file-based handlers from the root logger as follows:

 root:
-  handlers: [console, info_file_handler]
+  handlers: [console]

Use plain console logging

To use plain rather than rich logging, swap the rich handler for the console one as follows:

 root:
-  handlers: [rich, info_file_handler]
+  handlers: [console, info_file_handler]

Rich logging in a dumb terminal

Rich detects whether your terminal is capable of displaying richly formatted messages. If your terminal is “dumb” then formatting is automatically stripped out so that the logs are just plain text. This is likely to happen if you perform kedro run on CI (e.g. GitHub Actions or CircleCI).

If you find that the default wrapping of the log messages is too narrow but do not wish to switch to using the console logger on CI then the simplest way to control the log message wrapping is through altering the COLUMNS and LINES environment variables. For example:

export COLUMNS=120 LINES=25

Note

You must provide a value for both COLUMNS and LINES even if you only wish to change the width of the log message. Rich’s default values for these variables are COLUMNS=80 and LINE=25.

Rich logging in Jupyter

Rich also formats the logs in JupyterLab and Jupyter Notebook. The size of the output console does not adapt to your window but can be controlled through the JUPYTER_COLUMNS and JUPYTER_LINES environment variables. The default values (115 and 100 respectively) should be suitable for most users, but if you require a different output console size then you should alter the values of JUPYTER_COLUMNS and JUPYTER_LINES.

Perform logging in your project

To perform logging in your own code (e.g. in a node), you are advised to do as follows:

import logging

log = logging.getLogger(__name__)
log.warning("Issue warning")
log.info("Send information")

Note

The name of a logger corresponds to a key in the loggers section in logging.yml (e.g. kedro). See Python’s logging documentation for more information.

You can take advantage of rich’s console markup when enabled in your logging calls:

log.error("[bold red blink]Important error message![/]", extra={"markup": True})