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== Introduction ==

=== Why do we need an abstraction layer for full-text indexing? ===

Developers often must implement search methods for their database, but unfortunately so far it has been easier to roll your own simple search than to deploy an existing project such as Lucene. Merquery provides an abstraction layer for popular text indexing engines and makes it easy for anyone to attach them to their database.

== Using Merquery ==

You can enable Lucene indexing of a database table like so (assuming the library is in contrib.search):

{{{
#!python
from contrib.search.backends import LuceneIndexer

class Person(models.Model):
    first_name = models.CharField(maxlength=30)
    last_name = models.CharField(maxlength=30)
    biography = models.TextField()

indexer = LuceneIndexer('/tmp/person-index', Person,
                        {'first': 'Person.first_name',
                         'last': 'Person.last_name'},
                        text_fields=['Person.biography']
                       )
indexer.update()
indexer.search("brian -last:beck", order_by='last_name')
}}}

== Current Status ==

Merquery is in a working state but needs some polish.

XapianIndexer and LuceneIndexer currently work fine and are about 90% complete. XapianIndexer also has a fork called [http://code.google.com/p/djapian/ Djapian]

HypeIndexer is not yet fully functional; this is about 50% complete.

Note: You should see the ticket #2707 if you want use XapianIndexer.

More info from the author in [http://blog.case.edu/bmb12/2006/08/merquery_summer_of_code_results this blog post].