Django2.0手册:Search

Standard textual queries¶

Text-based fields have a selection of simple 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 (icontains), but this is only marginally better.

A database’s more advanced comparison functions¶

If you’re using PostgreSQL, Django provides a selection of database
specific tools 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.

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 unaccented comparison:

>>> 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 trigram_similar comparison, which compares
sequences of letters.

例如:

>>> 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 which 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¶

Simple database operations are too simple an 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.

>>> Entry.objects.filter(body_text__search='cheese')
[<Entry: Cheese on Toast recipes>, <Entry: Pizza recipes>]

>>> 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>,
]