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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 works fine and is about 90% complete.

HypeIndexer are not yet fully functional, this are about 50% complete.

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