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branches: fix performance of branch selectors with many branches - only show the first 200 results
The way we use select2, it will cause browser performance problems when a
select list contains thousands of entries. The primary bottleneck is the DOM
creation, secondarily for the query to filter through the entries and decide
what to show. We thus primarily have to limit how many entries we put in the
drop-down, secondarily limit the iteration over data.
One tricky case is where the user specifies a short but full branch name (like
'trunk') but many other branches contains the same string (not necessarily at
the beginning, like 'for-trunk-next-week') which come before the perfect match
in the branch list. It is thus not a solution to just stop searching when a
fixed amount of matches have been found.
Instead, we limit the amount of ordinary query matches, but always show all
prefix matches. We thus always have to iterate through all entries, but we
start using the (presumably) cheaper prefix search when the limit has been
reached.
There is no filtering initially when there is no query term, so that case has
to be handled specially.
Upstream select2 is now at 4.x. Upgrading is not trivial, and getting this
fixed properly upstream is not a short term solution. Instead, we customize our
copy. The benefit from this patch is bigger than the overhead of "maintaining"
it locally.
The way we use select2, it will cause browser performance problems when a
select list contains thousands of entries. The primary bottleneck is the DOM
creation, secondarily for the query to filter through the entries and decide
what to show. We thus primarily have to limit how many entries we put in the
drop-down, secondarily limit the iteration over data.
One tricky case is where the user specifies a short but full branch name (like
'trunk') but many other branches contains the same string (not necessarily at
the beginning, like 'for-trunk-next-week') which come before the perfect match
in the branch list. It is thus not a solution to just stop searching when a
fixed amount of matches have been found.
Instead, we limit the amount of ordinary query matches, but always show all
prefix matches. We thus always have to iterate through all entries, but we
start using the (presumably) cheaper prefix search when the limit has been
reached.
There is no filtering initially when there is no query term, so that case has
to be handled specially.
Upstream select2 is now at 4.x. Upgrading is not trivial, and getting this
fixed properly upstream is not a short term solution. Instead, we customize our
copy. The benefit from this patch is bigger than the overhead of "maintaining"
it locally.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 | .. _overview:
=====================
Installation overview
=====================
Some overview and some details that can help understanding the options when
installing Kallithea.
Python environment
------------------
**Kallithea** is written entirely in Python_ and requires Python version
2.6 or higher. Python 3.x is currently not supported.
Given a Python installation, there are different ways of providing the
environment for running Python applications. Each of them pretty much
corresponds to a ``site-packages`` directory somewhere where packages can be
installed.
Kallithea itself can be run from source or be installed, but even when running
from source, there are some dependencies that must be installed in the Python
environment used for running Kallithea.
- Packages *could* be installed in Python's ``site-packages`` directory ... but
that would require running pip_ as root and it would be hard to uninstall or
upgrade and is probably not a good idea unless using a package manager.
- Packages could also be installed in ``~/.local`` ... but that is probably
only a good idea if using a dedicated user per application or instance.
- Finally, it can be installed in a virtualenv_. That is a very lightweight
"container" where each Kallithea instance can get its own dedicated and
self-contained virtual environment.
We recommend using virtualenv for installing Kallithea.
Installation methods
--------------------
Kallithea must be installed on a server. Kallithea is installed in a Python
environment so it can use packages that are installed there and make itself
available for other packages.
Two different cases will pretty much cover the options for how it can be
installed.
- The Kallithea source repository can be cloned and used -- it is kept stable and
can be used in production. The Kallithea maintainers use the development
branch in production. The advantage of installation from source and regularly
updating it is that you take advantage of the most recent improvements. Using
it directly from a DVCS also means that it is easy to track local customizations.
Running ``pip install -e .`` in the source will use pip to install the
necessary dependencies in the Python environment and create a
``.../site-packages/Kallithea.egg-link`` file there that points at the Kallithea
source.
- Kallithea can also be installed from ready-made packages using a package manager.
The official released versions are available on PyPI_ and can be downloaded and
installed with all dependencies using ``pip install kallithea``.
With this method, Kallithea is installed in the Python environment as any
other package, usually as a ``.../site-packages/Kallithea-X-py2.7.egg/``
directory with Python files and everything else that is needed.
(``pip install kallithea`` from a source tree will do pretty much the same
but build the Kallithea package itself locally instead of downloading it.)
Web server
----------
Kallithea is (primarily) a WSGI_ application that must be run from a web
server that serves WSGI applications over HTTP.
Kallithea itself is not serving HTTP (or HTTPS); that is the web server's
responsibility. Kallithea does however need to know its own user facing URL
(protocol, address, port and path) for each HTTP request. Kallithea will
usually use its own HTML/cookie based authentication but can also be configured
to use web server authentication.
There are several web server options:
- Kallithea uses the Paste_ tool as command line interface. Paste provides
``paster serve`` as a convenient way to launch a Python WSGI / web server
from the command line. That is perfect for development and evaluation.
Actual use in production might have different requirements and need extra
work to make it manageable as a scalable system service.
Paste comes with its own built-in web server but Kallithea defaults to use
Waitress_. Gunicorn_ is also an option. These web servers have different
limited feature sets.
The web server used by ``paster`` is configured in the ``.ini`` file passed
to it. The entry point for the WSGI application is configured
in ``setup.py`` as ``kallithea.config.middleware:make_app``.
- `Apache httpd`_ can serve WSGI applications directly using mod_wsgi_ and a
simple Python file with the necessary configuration. This is a good option if
Apache is an option.
- uWSGI_ is also a full web server with built-in WSGI module.
- IIS_ can also server WSGI applications directly using isapi-wsgi_.
- A `reverse HTTP proxy <https://en.wikipedia.org/wiki/Reverse_proxy>`_
can be put in front of another web server which has WSGI support.
Such a layered setup can be complex but might in some cases be the right
option, for example to standardize on one internet-facing web server, to add
encryption or special authentication or for other security reasons, to
provide caching of static files, or to provide load balancing or fail-over.
Nginx_, Varnish_ and HAProxy_ are often used for this purpose, often in front
of a ``paster`` server that somehow is wrapped as a service.
The best option depends on what you are familiar with and the requirements for
performance and stability. Also, keep in mind that Kallithea mainly is serving
dynamically generated pages from a relatively slow Python process. Kallithea is
also often used inside organizations with a limited amount of users and thus no
continuous hammering from the internet.
.. _Python: http://www.python.org/
.. _Gunicorn: http://gunicorn.org/
.. _Waitress: http://waitress.readthedocs.org/en/latest/
.. _virtualenv: http://pypi.python.org/pypi/virtualenv
.. _Paste: http://pythonpaste.org/
.. _PyPI: https://pypi.python.org/pypi
.. _Apache httpd: http://httpd.apache.org/
.. _mod_wsgi: https://code.google.com/p/modwsgi/
.. _isapi-wsgi: https://github.com/hexdump42/isapi-wsgi
.. _uWSGI: https://uwsgi-docs.readthedocs.org/en/latest/
.. _nginx: http://nginx.org/en/
.. _iis: http://en.wikipedia.org/wiki/Internet_Information_Services
.. _pip: http://en.wikipedia.org/wiki/Pip_%28package_manager%29
.. _WSGI: http://en.wikipedia.org/wiki/Web_Server_Gateway_Interface
.. _pylons: http://www.pylonsproject.org/
.. _HAProxy: http://www.haproxy.org/
.. _Varnish: https://www.varnish-cache.org/
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