======

Search

======

A common task for web applications is to search some data in the database with

user input. In a simple case, this could be filtering a list of objects by a

category. A more complex use case might require searching with weighting,

categorization, highlighting, multiple languages, and so on. This document

explains some of the possible use cases and the tools you can use.

We'll refer to the same models used in :doc:`/topics/db/queries`.

Use Cases

=========

Standard textual queries

------------------------

Text-based fields have a selection of matching operations. For example, you may

wish to allow lookup up an author like so::

    >>> Author.objects.filter(name__contains='Terry')

    [<Author: Terry Gilliam>, <Author: Terry Jones>]

This is a very fragile solution as it requires the user to know an exact

substring of the author's name. A better approach could be a case-insensitive

match (:lookup:`icontains`), but this is only marginally better.

A database's more advanced comparison functions

-----------------------------------------------

If you're using PostgreSQL, Django provides :doc:`a selection of database

specific tools </ref/contrib/postgres/search>` to allow you to leverage more

complex querying options. Other databases have different selections of tools,

possibly via plugins or user-defined functions. Django doesn't include any

support for them at this time. We'll use some examples from PostgreSQL to

demonstrate the kind of functionality databases may have.

.. admonition:: Searching in other databases

    All of the searching tools provided by :mod:`django.contrib.postgres` are

    constructed entirely on public APIs such as :doc:`custom lookups

    </ref/models/lookups>` and :doc:`database functions

    </ref/models/database-functions>`. Depending on your database, you should

    be able to construct queries to allow similar APIs. If there are specific

    things which cannot be achieved this way, please open a ticket.

In the above example, we determined that a case insensitive lookup would be

more useful. When dealing with non-English names, a further improvement is to

use :lookup:`unaccented comparison <unaccent>`::

    >>> Author.objects.filter(name__unaccent__icontains='Helen')

    [<Author: Helen Mirren>, <Author: Helena Bonham Carter>, <Author: Hélène Joy>]

This shows another issue, where we are matching against a different spelling of

the name. In this case we have an asymmetry though - a search for ``Helen``

will pick up ``Helena`` or ``Hélène``, but not the reverse. Another option

would be to use a :lookup:`trigram_similar` comparison, which compares

sequences of letters.

For example::

    >>> Author.objects.filter(name__unaccent__lower__trigram_similar='Hélène')

    [<Author: Helen Mirren>, <Author: Hélène Joy>]

Now we have a different problem - the longer name of "Helena Bonham Carter"

doesn't show up as it is much longer. Trigram searches consider all

combinations of three letters, and compares how many appear in both search and

source strings. For the longer name, there are more combinations that don't

appear in the source string, so it is no longer considered a close match.

The correct choice of comparison functions here depends on your particular data

set, for example the language(s) used and the type of text being searched. All

of the examples we've seen are on short strings where the user is likely to

enter something close (by varying definitions) to the source data.

Document-based search

---------------------

Standard database operations stop being a useful approach when you start

considering large blocks of text. Whereas the examples above can be thought of

as operations on a string of characters, full text search looks at the actual

words. Depending on the system used, it's likely to use some of the following

ideas:

- Ignoring "stop words" such as "a", "the", "and".

- Stemming words, so that "pony" and "ponies" are considered similar.

- Weighting words based on different criteria such as how frequently they

  appear in the text, or the importance of the fields, such as the title or

  keywords, that they appear in.

There are many alternatives for using searching software, some of the most

prominent are Elastic_ and Solr_. These are full document-based search

solutions. To use them with data from Django models, you'll need a layer which

translates your data into a textual document, including back-references to the

database ids. When a search using the engine returns a certain document, you

can then look it up in the database. There are a variety of third-party

libraries which are designed to help with this process.

.. _Elastic: https://www.elastic.co/

.. _Solr: https://solr.apache.org/

PostgreSQL support

~~~~~~~~~~~~~~~~~~

PostgreSQL has its own full text search implementation built-in. While not as

powerful as some other search engines, it has the advantage of being inside

your database and so can easily be combined with other relational queries such

as categorization.

The :mod:`django.contrib.postgres` module provides some helpers to make these

queries. For example, a query might select all the blog entries which mention

"cheese"::

    >>> Entry.objects.filter(body_text__search='cheese')

    [<Entry: Cheese on Toast recipes>, <Entry: Pizza recipes>]

You can also filter on a combination of fields and on related models::

    >>> Entry.objects.annotate(

    ...     search=SearchVector('blog__tagline', 'body_text'),

    ... ).filter(search='cheese')

    [

        <Entry: Cheese on Toast recipes>,

        <Entry: Pizza Recipes>,

        <Entry: Dairy farming in Argentina>,

    ]

See the ``contrib.postgres`` :doc:`/ref/contrib/postgres/search` document for

complete details.