I'm using Django with Python3 and Postgresql
I've read that Haystack uses Elastic Search (and I dislike Java),
but I see Xapian-Haystack doesn't work with Python3 (but I've heard about Xapian before and I think like it).
djorm-ext-pgfulltext is a database full text search module and I really don't know how is different from the previous option, in terms of efficiency.
(3. The option to build a simple search module would be the most inefficient, I believe.)
A list with the modules is displayed at:
https://www.djangopackages.com/grids/g/search/
I'm just wondering what is exactly the functionality that haystack provides and if I need it.
I mean the search and indexing is done by whoosh. As far as I can tell, haystack is just offering ready made views, and forms. If I want to write my own form and views do I still need haystack?
Am I missing something?
P.S. I don't plan to use any other search engine than whoosh so I also don't need haystacks's multiple search engine wrapping.
Besides views, forms and a search engine-agnostic layer, the other powerful thing about Haystack is its ability to map Django models to something the search index understands. Using Haystack, you can easily specify which fields in a model should be indexed and how (see the SearchIndex API - http://django-haystack.readthedocs.org/en/latest/searchindex_api.html).
Once you have done that, you can then leverage the built-in management commands to (re)index your data when required.
It also comes with some nice templatetags to help present search results, like highlighting the matching bits.
Is there a particular reason that you don't want to use Haystack? It is a pretty non-intrusive plugin that lets you use as much of it as you need, and makes it easy to use more advanced functionality when you need it later down the road. In one of the sites I built, I only used the SearchIndex and SearchQuerySet APIs; I built my own views and forms. Ultimately, if you end up writing your own indexing and searching code, views and forms, you have basically re-written a large part of Haystack, in which case, you may want to consider using something that is in use out there and reasonably well tested.
That said, I have rolled my own 'Haystack' like layer in another project, mainly because the data source didn't map to the Django ORM. In that case, I wrote my own indexing scripts, and used PySolr to interface with my Apache Solr instance.
Given that Whoosh is written in Python, I'd assume it has a decent Python interface, so it shouldn't be too hard to do. I would only do it if there's something special about your scenario though.
I'm build a Django app with Neo4j (along with Postgres), I found this Django integration called neo4django, I was wondering if it's possible to use neo4restclient only, like, what would be the disadvantages of not using Neo4django? Does using neo4-rest-client only, give me more flexibility?
When I was creating my models with Neo4Django, it seemed that there is no difference between modeling a graph db and relational db. Am I missing anything?
Thanks!
You can absolutely go ahead with neo4j-rest-client or py2neo, without using neo4django. In the same way, you can use any other database driver you'd like any time using Django, any REST client, etc.
What'll you lose? The model DSL, the built-in querying (eg, Person.objects.filter(name="Mohamed")), the built-in indexing, and the Lucene, Gremlin and Cypher behind that. Some things will be much easier- like setting an arbitrary property on a node- but you'll need to learn more about how Neo4j works.
You'll also lose some of the shortcuts Django provides that work with neo4django, like get_object_or_404() and some of the class-based views that work with querysets.
What'll you gain? Absolute power over the DB, and an easier time tweaking DB performance. Though neo4django isn't nearly as good a lib as some traditional ORMs in the Python sphere, the trade-off of power vs provided ease is similar.
That said, the two can work together- you can drop down from neo4django to the underlying REST client nodes and relationships anytime. Just use model_instance.node to get the underlying neo4j-rest-client node object from a model, and from neo4django.db import connection to get a wrapped neo4j-rest-client GraphDatabase.
On whether you're missing something: neo4django was written to re-use a powerful developer interface- the Django ORM- so it should feel similar to writing models for Postgres. I've written a bit about that odd feeling in the past. I think part of the problem might be that the lib doesn't highlight the graph terminology new graph-interested devs expect- like traversals and pattern matching- and instead dresses those techniques in Django query clothing.
I'd love your thoughts, or to know anything you'd like the library to do that it isn't doing :) Good luck!
I have recently added search capabilities to my django-powered site to allow employers to search for employees using keywords. When the user initially uploads their resume, I turn it into text, get rid of stop words, and then add the text to a TextField for that user. I used Django-Haystack with the Whoosh search back engine.
Three things-
1) Aside from extra features which I'll probably not use, is there any concrete advantage to switching to Solr or Xapian?
2) In turning the resume into text, I essentially index the pdf myself. I know both Xapian and Solr support .pdf indexing, however, from the looks of it Haystack does not. Any tips on how to get around this? Or should I keep indexing it myself? If so, should I be doing more than simply providing a text file of keywords?
3) Whoosh only return a result if the keyword matches itself exactly. If a user has 'mathematics' as his keyword, and I search 'math', I want that user to appear. I couldn't definitively tell whether Xapian or Solr support this. Thoughts?
Thanks for any suggestion. I'm going to continue digging into this myself for the time being.
Unfortunately I don't know enough to answer your other questions, however for point 3.) Whoosh actually does support this.
You would have to use the autocomplete function of SearchQuerySet.
Detailed here:
http://docs.haystacksearch.org/dev/autocomplete.html
I'm currently using Whoosh and matching on partial matches myself.
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.