.. _performance:

================================
Optimizing Kallithea performance
================================

When serving a large amount of big repositories, Kallithea can start performing
slower than expected. Because of the demanding nature of handling large amounts
of data from version control systems, here are some tips on how to get the best
performance.


Fast storage
------------

Kallithea is often I/O bound, and hence a fast disk (SSD/SAN) and plenty of RAM
is usually more important than a fast CPU.


Caching
-------

Tweak beaker cache settings in the ini file. The actual effect of that is
questionable.

.. note::

    Beaker has no upper bound on cache size and will never drop any caches. For
    memory cache, the only option is to regularly restart the worker process.
    For file cache, it must be cleaned manually, as described in the `Beaker
    documentation <https://beaker.readthedocs.io/en/latest/sessions.html#removing-expired-old-sessions>`_::

        find data/cache -type f -mtime +30 -print -exec rm {} \;


Database
--------

SQLite is a good option when having a small load on the system. But due to
locking issues with SQLite, it is not recommended to use it for larger
deployments.

Switching to MySQL or PostgreSQL will result in an immediate performance
increase. A tool like SQLAlchemyGrate_ can be used for migrating to another
database platform.


Horizontal scaling
------------------

Scaling horizontally means running several Kallithea instances and let them
share the load. That can give huge performance benefits when dealing with large
amounts of traffic (many users, CI servers, etc.). Kallithea can be scaled
horizontally on one (recommended) or multiple machines.

It is generally possible to run WSGI applications multithreaded, so that
several HTTP requests are served from the same Python process at once. That can
in principle give better utilization of internal caches and less process
overhead.

One danger of running multithreaded is that program execution becomes much more
complex; programs must be written to consider all combinations of events and
problems might depend on timing and be impossible to reproduce.

Kallithea can't promise to be thread-safe, just like the embedded Mercurial
backend doesn't make any strong promises when used as Kallithea uses it.
Instead, we recommend scaling by using multiple server processes.

Web servers with multiple worker processes (such as ``mod_wsgi`` with the
``WSGIDaemonProcess`` ``processes`` parameter) will work out of the box.

In order to scale horizontally on multiple machines, you need to do the
following:

    - Each instance's ``data`` storage needs to be configured to be stored on a
      shared disk storage, preferably together with repositories. This ``data``
      dir contains template caches, sessions, whoosh index and is used for
      task locking (so it is safe across multiple instances). Set the
      ``cache_dir``, ``index_dir``, ``beaker.cache.data_dir``, ``beaker.cache.lock_dir``
      variables in each .ini file to a shared location across Kallithea instances
    - If using several Celery instances,
      the message broker should be common to all of them (e.g.,  one
      shared RabbitMQ server)
    - Load balance using round robin or IP hash, recommended is writing LB rules
      that will separate regular user traffic from automated processes like CI
      servers or build bots.


Serve static files directly from the web server
-----------------------------------------------

With the default ``static_files`` ini setting, the Kallithea WSGI application
will take care of serving the static files from ``kallithea/public/`` at the
root of the application URL.

The actual serving of the static files is very fast and unlikely to be a
problem in a Kallithea setup - the responses generated by Kallithea from
database and repository content will take significantly more time and
resources.

To serve static files from the web server, use something like this Apache config
snippet::

        Alias /images/ /srv/kallithea/kallithea/kallithea/public/images/
        Alias /css/ /srv/kallithea/kallithea/kallithea/public/css/
        Alias /js/ /srv/kallithea/kallithea/kallithea/public/js/
        Alias /codemirror/ /srv/kallithea/kallithea/kallithea/public/codemirror/
        Alias /fontello/ /srv/kallithea/kallithea/kallithea/public/fontello/

Then disable serving of static files in the ``.ini`` ``app:main`` section::

        static_files = false

If using Kallithea installed as a package, you should be able to find the files
under ``site-packages/kallithea``, either in your Python installation or in your
virtualenv. When upgrading, make sure to update the web server configuration
too if necessary.

It might also be possible to improve performance by configuring the web server
to compress responses (served from static files or generated by Kallithea) when
serving them. That might also imply buffering of responses - that is more
likely to be a problem; large responses (clones or pulls) will have to be fully
processed and spooled to disk or memory before the client will see any
response. See the documentation for your web server.


.. _SQLAlchemyGrate: https://github.com/shazow/sqlalchemygrate