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Implementing Logging

InvenioRDM enhances its logging capabilities with the invenio-logging module, improving the observability and reliability of your repository. This ensures a smoother operational experience for administrators and end-users alike.


invenio-logging is a key component of Invenio configures the Flask app logger, offering a standard Python logging interface for log creation and handler installation.

The following logger extensions exists:

InvenioLoggingConsole: Logs output to the console, useful for development environments.

InvenioLoggingFS: Logs are written to a file, ideal for production environments where log preservation is necessary.

InvenioLoggingSentry: Provides real-time error tracking and monitoring, allowing for immediate notification of issues.

Logging Levels

InvenioRDM supports different logging levels for controlling the verbosity of logs. The available levels, in increasing order of severity, are:

  • DEBUG: Detailed information for diagnosing issues.
  • INFO: Confirmation that things are working as expected.
  • WARNING: Indicating a potential issue that should be addressed.
  • ERROR: A fault that caused a feature to fail but didn't cause a complete failure.
  • CRITICAL: A serious error that caused a failure and should be investigated immediately.

You can set the desired logging level for your environment by configuring the LOGGING_CONSOLE_LEVEL and LOGGING_FS_LEVEL variables in your invenio.cfg file. For example, to set the console logging level to DEBUG, you can add the following line:


Setting the logging level to DEBUG can be particularly useful during development or when troubleshooting issues, as it provides more detailed information in the logs.

For more information on configuring logging levels and handlers, refer to the invenio-logging documentation.

File Logging

For file logging, you can configure the LOGGING_FS_LEVEL variable in a similar way:


This will set the file logging level to INFO, which is a good default for production environments.

The available logging levels are: CRITICAL, ERROR, WARNING, INFO, DEBUG, or NOTSET.

Python Warnings Logging

InvenioRDM can log Python warnings to the console and file logs. This behavior can be controlled using the LOGGING_CONSOLE_PYWARNINGS and LOGGING_FS_PYWARNINGS configuration variables.

File Logging Configuration

The maximum size of log files can be set using LOGGING_FS_MAXBYTES (default: 100MB). The number of rotated log files to keep can be set using LOGGING_FS_BACKUPCOUNT (default: 5).

Full configuration options can be found in


You can configure Celery, SQLAlchemy, and Redis to send logs to Sentry. This behavior can be controlled using the LOGGING_SENTRY_CELERY, LOGGING_SENTRY_SQLALCHEMY, and LOGGING_SENTRY_REDIS configuration variables, respectively.

Sentry Integration

Sentry is a popular error tracking and monitoring tool that provides real-time error tracking and monitoring capabilities. InvenioRDM integrates with Sentry through the invenio-logging module, allowing you to seamlessly incorporate Sentry into your logging setup.

Configuring Sentry

To enable Sentry integration, follow these steps:

  • Install the Sentry SDK: In your Pipefile, under the packages section, add the invenio-logging package with the sentry_sdk extra:
invenio-logging = {extras = ["sentry_sdk"], version = "~=2.0"}
  • Configure Logging Backends: In your invenio.cfg file, add the desired logging backends and configure the Sentry initialization options:
# Invenio-logging Sentry
# ----------------------
    # Add Sentry SDK configuration options here, e.g.:
    # Instruct Sentry to send user data attached to the event
    "send_default_pii": True,

You can find the full configuration options at Sentry configuration page

  • Set the Sentry DSN: In your .env file, override the SENTRY_DSN variable with your Sentry Data Source Name (DSN) to avoid CI errors:

Replace with your actual Sentry project ID.

Customizing the Sentry Extension

If needed, Additional options can be passed to the Sentry instance using the LOGGING_SENTRY_INIT_KWARGS configuration variable.

Best Practices

Environment-Specific Configuration Utilize different logging levels and backends for development, testing, and production environments to optimize performance and monitoring by switching the configurations above.

Sensitive Information Ensure that logging does not capture sensitive information, adhering to privacy and security best practices.