docs: clarify section on horizontal scaling - multi threading is not supported at all

docs/usage/performance.rst

@@ -1,127 +1,125 b''
 .. _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 PostgreSQL or MariaDB/MySQL 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.
+Scaling horizontally means running several Kallithea instances (also known as
+worker processes) and let them share the load. That is essential to serve other
+users while processing a long-running request from a user. Usually, the
+bottleneck on a Kallithea server is not CPU but I/O speed - especially network
+speed. It is thus a good idea to run multiple worker processes on one server.
 
-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.
+.. note::
 
-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 and the embedded Mercurial backend are not thread-safe. Each
+    worker process must thus be single-threaded.
 
-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 can usually launch multiple worker processes - for example ``mod_wsgi`` with the
+``WSGIDaemonProcess`` ``processes`` parameter or ``uWSGI`` or ``gunicorn`` with
+their ``workers`` setting.
 
-Web servers with multiple worker processes (such as ``mod_wsgi`` with the
-``WSGIDaemonProcess`` ``processes`` parameter) will work out of the box.
-
+Kallithea can also be scaled horizontally across multiple machines.
 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
+.. _mod_wsgi: https://modwsgi.readthedocs.io/
+.. _uWSGI: https://uwsgi-docs.readthedocs.io/
+.. _gunicorn: http://pypi.python.org/pypi/gunicorn