The point of this is to keep notes/urls pointing to particular parts of the documentation that people want to refer to in the future. For example, when something is a complex feature that requires a little bit of review most of the times you work with it.
Let's take an example. I search for Django STATICFILES_DIR:
https://www.google.com/search?q=django+STATICFILES_DIRS
Pretty quickly I get exactly what I want:
https://docs.djangoproject.com/en/4.1/ref/contrib/staticfiles/
which has a STATICFILES_DIR configuration entry.
But, notice from the url, this is for Django 4.1. And it says so on the page too.
But maybe there are a current version? Let's look.
There isn't.
Contrast with Python, which points to a very generic, 3 version. Not to Python 3.10 or 3.11.
https://docs.python.org/3/tutorial/datastructures.html#dictionaries
Or postgres (looking for create table):
https://www.postgresql.org/docs/14/sql-createtable.html
OK, yes, I have a version 14, but...
I can click on that current and that will NOT pin me to a particular version.
https://www.postgresql.org/docs/current/sql-createtable.html
In the case of Django, Python and Postgresql, I am pretty confident a generic, version-less documentation page will serve my purposes just fine 90% of the time - those are pretty stable APIs by now.
Often searching gets you to ancient postgresql versions like 9.2, but you can always find a current link.
Am I looking in the wrong places for a permanent link for Django docs?
Yes, there is dev link on the Django versions, but that's living a bit dangerously, I assume people are potentially working updating the docs on the current version on that URL. Or should I use that after all?
Going to use the dev tag for now. Django is stable enough that I expect minimal problems there and I can always go back to my version of interest from there. But at least I don't have bookmarks pointing to a bunch of different historical versions.
There are two main schools of thought for doing A/B (Split) Testing:
Javascript-based solutions such as Optimizely, Google Analytics Content Experiments.
Server-side solutions such as Django-AB, Splango, and django-lean. (Also, writing your own.)
My understanding is that Javascript-based solutions are spectacular for "which color button converts better," but not so great for switching out entire page layouts, and completely unworkable for trying out large functional changes such as the sequence of pages in a funnel.
That leads me towards a server-side solution. I'm not crazy about coding my own, and will do so only if there is no other option. I'm trying to add value by improving the core functionality of my site, not by creating a better split-testing framework.
The Django apps I've found for split testing are various mixtures of unmaintained, undocumented, documented incorrectly, and incompatible with Django 1.5. This surprises me, because the Django and Python communities seem to have a strong focus on good documentation. I'm also very surprised that none of the testing frameworks I've tried has been compatible with Django 1.5 -- is testing not as core a part of the philosophy in the Django/Python world as it is in Rails?
Here's what I've found:
Splango https://github.com/shimon/Splango -- Not compatible with Django 1.5 (although most compatibility bugs I found were trivial to fix). Largely un-touched since October 2010, except for a fix August 2012 which claims to make sure templates get included in the install. Since templates don't get included in the install when Splango is installed via PyPI, either the fix didn't work or didn't get submitted to PyPI. Documentation is largely accurate, but doesn't completely cover how to set up tests and get reports. It tells you how to configure the template to gather the data, but there appears to be additional steps required in the admin interface which are completely undocumented, and I'm not sure I've done them properly.
Django-lean. Original at https://bitbucket.org/akoha/django-lean has not been updated since July 2010. There is an apparently "blessed" fork at https://github.com/anandhenry2002/django-lean which has not been changed since May 2012, when it was copied over from the original. The original's documentation is incorrect in ways that make following the examples impossible. (Though you can probably muddle your way through, as I did.) The new version's documentation has formatting problems that make it difficult to read on github. (This appears to be because it's the unchanged documentation from the old project, and BitBucket syntax doesn't work on Github.) The django-lean Google Group has not had a message since July 2012.
django-mini-lean https://github.com/DanAncona/django-mini-lean -- Updated as recently as February 2013, but undocumented.
Leaner - https://bitbucket.org/brianjinwright/leaner -- Last updated July 2012, and no docs.
Django-AB -- Last updated May 2009. Is not a package, and can't be installed via PIP or PyPI. After placing the checkout in my django app folder (and renaming the folder to ab) and following the installation instructions, I get an error loading the template loader that I have not tracked down further.
So far Splango appears to be the winner, as I've actually been able to get it more-or-less working (by manually installing the templates, and then editing them to fix Django 1.5 incompatibilities).
Can anyone point me to anything I've missed?
You have missed this app : https://github.com/mixcloud/django-experiments + https://github.com/disqus/gargoyle/
And then there's waffle: http://waffle.readthedocs.org/
It's simple, updated, maintained, but not very feature rich, it doesn't have any analytics/reporting stuff integrated. But then again, google analytics or mixpanel type of service is better for this.
I first looked at Django-AB and that is almost what I wanted, but I couldn't get it to work either. After looking at django-experiments and deciding I didn't want to mess around with redis yet, I decided to roll my own. I've tried to package it up nicely and make it easy to use for the beginner. It's super basic.
https://github.com/crobertsbmw/RobertsAB
You can swap out entirely different page layouts with Google Analytics Experiments (their default experiment setup will redirect users to a different URL for each variation you have), although in general its much easier to interpret why something is more successful if you test smaller things against each other.
You are right that testing different funnels and user flows against each other using Google Analytics would require a lot of manual setup; although theoretically you could do it by swapping out different links and tracking your users with UTM campaigns.
For smaller A/B tests within the same page, I ended up using Google Analytics Experiments and writing a custom Django CMS plugin for adding a few variant options to a template, which queries the Google Analytics API and displays the correct variant using Javascript.
I'm currently using 1.4.3. 1.5 will make my front-end development much easier because of the new verbatim template tag.
The release candidate was posted a few weeks ago: https://www.djangoproject.com/weblog/2013/jan/04/15-rc-1/
Is it advisable to upgrade? Where can I find a build status or see outstanding bugs or anything else that's holding up the final release?
This is mostly a judgment call based on your project requirements and your schedule. Technically, I consider 1.5 to be pretty good and the improvements, especially to user model extension, to be invaluable. But it's still under development and bugs are still being found.
If you are developing an app that you don't plan to launch within a month, I would definitely upgrade. I like the new 1.5 features and it will probably be officially released fairly soon.
If you are developing an app that you need to launch sooner than that in a high-stress environment, I would pass on upgrading until after 1.5 is stable.
If you are developing an app that isn't going to see heavy use very soon, then you could go either way, but I would lean towards upgrading.
I have installed FaLang component to use multi-language support everything is working fine except the alias structure. When we are switching the language the URL not get changes as per the added alias conversion added into admin section. I tried almost all activities enabling both plugins but still the problem is as it is.
I believe Shaz is correct, there is no solution within FaLang at this time.
I asked the developer about this in Feb 2012 and he mentioned he was still working on it, but currently, with FaLang v 1.1.7 it still doesn't happen.
Initially I was really excited when FaLang appeared, but now I'm not so sure. I work on a lot of multilingual sites, and I don't want to get get stranded with multilingual Joomla 2.5 sites as has happened to me with many Joomla 1.5 sites I developed with JoomFish.
I know many well respected developers like FaLang, but FWIW I now favour the native language capabability built into Joomla 2.5. It's not perfect, but for now they have my confidence that it will improve.
Unfortunately this doesn't solve your problem, but if you need an introduction to how it works, here is one good link:
[http://bit.ly/LpPoV2][1]
and another one
http://www.youtube.com/watch?v=gnhVVtyejPg
Initially I found the process a bit cumbersome, but once I got used to it, it's works quite well ... and yes, the aliases work as you would expect.
Good luck!
The story so far:
Decided to go with Xapian as search backend because it has all search-engine features I was looking for, knows about Unicode, stemming, has few dependencies and requires no bloated app-server installation on top of it.
Tried Django and Haystack (plus xapian-haystack, the backend glue code to tie Haystack to Xapian) because it was advertised on quite some blogs as "working". Did not work. Neither django-haystack nor the xapian-haystack project provide a version combination that actually works together. MASTER from both projects yields an error from Xapian, so it's not stable at all. Haystack 1.0.1 and xapian-haystack 1.0.x/1.1.0 are not API-compatible. Plus, in a minimally working installation of Haystack 1.0.1 and xapian-haystack MASTER, any complex query yields zero results due to errors in either django-haystack or xapian-haystack (I double-verified this), maybe because the unit-tests actually test very simple cases, and no edge-cases at all.
Tried Djapian. The source-code is riddled with spelling errors (mind you, in variable names, not comments), documentation is also riddled with ambiguities and outdated information that will never lead to a working installation. Not surprisingly, users rarely ask for features but how to get it working in the first place.
Next on the plate: exploring Solr (installing a Java environment plus Tomcat gives me headaches, the machine is RAM- and CPU-constrained), or Lucene (slightly less headaches, but still).
Before I proceed spending more time with a solution that might or might not work as advertised, I'd like to know: Did anyone ever get an actual, real-world search solution working in Django? I'm serious. I find it really frustrating reading about "large problems mostly solved", and then realizing that you will never get a working installation from the source-code because, actually, all bloggers dealing with those "mostly solved problems" never went past basic installation and copy-pasting the official tutorials.
So here are the requirements:
must be able to search for 10-100 terms in one query
must handle + (term must be present) and - (term must not be present), AND/OR
must handle arbitrary grouping (i.e. parentheses around AND/OR)
must allow for Django-ORM filtering before or after fulltext-search (i.e. pre-/post-processing of results with the full set of filters that Django knows about)
alternatively, there must be a facility to bulk-fetch the result set and transform it into a QuerySet
should be light on the machine, so preferably no humongous JVM and Java-based app-server installation
Is there anything out there that does this? I'm not interested in anecdotal evidence, or references to some blog posts that claim it should be working. I'd like to hear from someone who actually has a fully-functional setup working in the real world, under real conditions, with real queries.
EDIT:
Let me repeat again that I'm not so much interested in anecdotal evidence that someone, somewhere has a somewhat running installation working with unspecified properties. I already went there, I read all the blog posts, mailing lists, I contacted the authors, but when it came to actual implementation of real-world scenarios, nothing ever worked as advertised.
Also, and a user below brought that point up as well, considering the TCO of any project, I'm definitely not interested in hearing that someone, somewhere was able to pull it off once a vendor parachuted in an unknown number of specialists to monkey-patch the whole installation with specific domain-knowledge that's documented nowhere.
So, please, if you claim you have a working installation that actually satisfies minimum requirements for a full-fledged search (see requirements above), please provide the following so that we can all benefit from a search solution for Django that actually solves the problem:
exact Linux distribution, release version,
exact release version of Haystack (or equivalent) and release version of search backend,
exact release version of the search engine
publicly (!) available documentation how to set up all components exactly in the way that your installation was set up such that the minimal requirements above are met.
Thank you.
I have developed some Django applications with xapian support too. The biggest of them has a xapian database with an index of 8G storing 2.4M documents (including forum posts, wiki entries, planet entries and blog entries) - still growing.
Overall I am quite happy with xapian. It performs extremely well and is easy to use. The only thing I don't like is that xapian won't work with mod_wsgi (except of the global mode) because of a deadlock. So you are forced to use fastcgi (or connect to xapian-tcpsrv or write your own service).
I recommend you, to use the xapian-bindings directly. Xapian nowadays offers quite a lot of useful helpers (TermGenerator, QueryParser etc), which makes both the indexing and the querying simple. In fact, there is nothing I can imaging which would justify an additional library. In my opinion they are all more complicated and don't allow you to index efficiently.
The only thing you need, is some understanding of the way how xapian is working. (What are terms? What are values? What is stemming and where should I use it? and so on). You can find all those topics on the xapian website, and as soon as you understand those concepts, dealing with xapian will become easy.
Also, the xapian API is extremly stable. I've started using it a long time before the 1.0 release and never had any problems with API changes or version conflicts. The only thing which has changed is that all those helpers (query parser, tokenizer, etc.) I have once written for my Django project are now useless, because similar classes have made their way into the xapian core.
So, to summarize, just give the direct usage of xapian-bindings a try.
I can vouch for Django-Haystack with the Xapian backend (In the interest of full disclosure, I am the author of the xapian-haystack backend) in a real life, production environment. We currently use Haystack/Xapian on several sites, the largest of which has more than 20,000 registered users and a Xapian database with 20,000+ documents containing more than 143,000 unique terms for a total size of ~141mb.
As for not being able to get any combination of Haystack and the Xapian backend running, I'll admit that I was not as diligent as I should have been with my tagging and so there is some confusion with the versions. You should, however, be able to use the current master of both codebases without any issue. If this is not case, I'd be more than happy to assist with problems. You'll need to be a little bit more specific about the issue though. Simply saying "it did not work" is not enough information.
Daniel and I both do our best to respond to any issues opened on Github within a timely manner. Also, we're both usually available on the #haystack IRC channel during the day and the django-haystack Google Group.
Versions used:
Haystack 1.0BETA with Xapian-Haystack 1.1.0BETA
Haystack 1.0.1FINAL with Xapian-Haystack 1.1.3BETA
Most of the sites we've deployed with Haystack have been running Ubuntu 8.04 LTS with Xapian 1.0.5
Short answer: No.
We bailed and went with a Google Custom Search. Although the site has over 10,000 possible page views, we keep the sitemap feed down to the main 4,000 pages or so and it costs $250/year, which is about 2 hours of my time. The customer is happy and he feels comfortable with the results.
I'd love to see someone come up with a good FOSS solution, but in a commercial situation the TCO has got to make economic sense.
The details you requested.
exact Linux distribution, release version - Ubuntu 9.04 & 9.10
exact release version of Haystack (or equivalent) - Haystack 1.0 as well as master
release version of search backend - The Solr & Whoosh backends included with Haystack
exact release version of the search engine - Solr 1.3, Solr 1.4 & Whoosh 0.3.15
publicly (!) available documentation how to set up all components exactly in the way that your installation was set up such that the minimal requirements above are met.
http://docs.haystacksearch.org/dev/installing_search_engines.html#solr (or #whoosh)
Beyond this, it's the standard configuration bits from the tutorial, plus any additional overrides from (which I can't link to, thanks Stack Overflow) as needed.
As the maintainer of Haystack, I'm actively running all of the above previous setups. The smallest Haystack installation (Haystack 1.0 + Whoosh) is ~600 documents. A slightly larger one (Haystack master + Solr 1.4) is ~4000 documents. The largest deployment I'm aware of (Haystack master + Solr 1.4) is ~3 million documents.
I generally try to avoid Stack Overflow, so don't be surprised if you see nothing further from me. The mailing list is the best place for support, but given your responses thus far, I'm sure you'd rather just trash me here.
I (and my colleagues) have successfully used Haystack to achieve a fairly good search functionality.
It is easy to start with haystack and whoosh backend; and change to the Apache-Solr backend when performance of whoosh is not acceptable.
We really got to get around to write a detailed post about it with links to the projects where it works.
For now I can suggest you to have a look at this search: http://www.webdevjobshq.com/search/?q=rails implemented using Haystack with Apache-Solr backend. Or this: http://www.govbuddy.com/search/?q=Roy
Have you considered Sphinx? What are you using as you data store? It has a MySQL engine that works terrific. I think it meet most of your requirements except I'm not exactly certain how nicely it can be tied into Django-ORM.
I'm heavily considering using Sphinx in one of my own Django Apps to improve performance on an auto-suggest field that does a prefix and infix search on a corpus of 3.5 million records. But I haven't got around to implementing it yet, so I can't speak to Django+Sphinx integration. My only Sphinx experience is with the MySQL Engine and directly querying MySQL.
I use Djapian. It was quite simple to install and works great. There is an actual tutorial that covers basic use-cases and shows entire integration process.
Yes, it has some ambiguities but issue tracker is open and authors rapidly fixes bugs and add features.