Mailing List Stats (mlstats) is a tool to parse and analyze mail boxes. It is useful to retrieve and analyze mailing list archives. The parsed mail boxes are stored in a database.

mlstats is able to retrieve mailing lists from the web, and store the data of every email in a database, from where to obtain different kind of reports.

Licensed under GNU General Public License (GPL), version 2 or later.

Releases:

• https://github.com/MetricsGrimoire/MailingListStats/releases

Latest version:

$ git clone git://github.com/MetricsGrimoire/MailingListStats.git

mlstats needs the following dependencies (either MySQL or PostgreSQL):

• Python package "SQLAlchemy"
• MySQL:
  • Python package "MySQL-python"
  • MySQL-Server
• PostgreSQL:
  • Python package "psycopg2"
  • PostgreSQL-Server (tested with 8.4 and 9.1)

You can install MLStats running setup.py script:

$ python setup.py install

If you don't have root privileges, use the --prefix option to indicate the directory where mlstats will be installed. For more details, take a look at the help of the installer:

$ python setup.py install --help

You are ready to use mlstats.

• max_allowed_packet: Raise your max_allowed_packet-setting of your database. 1 or 16 MB might be to low (depends on your mailinglist). In the most cases 50 MB is a good value.

More options, and a more detailed info about the options, can be learnt by running mlstats --help

The backend postgres requires the database already exists. The creation of tables must be done manually. There is a SQL script with the schema in db/data_model_pg.sql that can be used for this purpose.

[To be written]

Source code, wiki and submission of bug reports are accessible in GitHub.

If you want to receive updates about new versions, and keep in touch with the development team, consider subscribing to the MetricsGrimoire mailing list. It is a very low traffic list, usually with less than one message per day.

When making contributions, please follow the PEP8 specification. To check your code follows the specification, use a tool like pep8 or flake8.

When adding new features, add tests for the new feature or fix, and check that the existing tests pass. Tests live in pymlstats/tests and you can run them with:

$ python -m unittest discover

Please, also consider to add tests for the current features available.

mlstats has been originally developed by the GSyC/LibreSoft group at the Universidad Rey Juan Carlos in Mostoles, near Madrid (Spain). It is part of a wider research on libre software engineering, aimed to gain knowledge on how libre software is developed and maintained.

• Israel Herraiz
• Jorge Gascon Perez

• Dave Neary
• Germán Poo-Caamaño
• Luis Cañas