This is the tenth article in the series in which I document my experience writing web applications in Python using the Flask microframework.

The goal of the tutorial series is to develop a decently featured microblogging application that demonstrating total lack of originality I have decided to call microblog.

Here is an index of all the articles in the series that have been published to date:

• Part I: Hello, World!
• Part II: Templates
• Part III: Web Forms
• Part IV: Database
• Part V: User Logins
• Part VI: Profile Page And Avatars
• Part VII: Unit Testing
• Part VIII: Followers, Contacts And Friends
• Part IX: Pagination
• Part X: Full Text Search (this article)
• Part XI: Email Support
• Part XII: Facelift
• Part XIII: Dates and Times
• Part XIV: I18n and L10n
• Part XV: Ajax
• Part XVI: Debugging, Testing and Profiling

Table of Contents:

[TOC]

Recap

In the previous article in the series we've enhanced our database queries so that we can get results on pages.

Today, we are going to continue working on our database, but in a different area. All applications that store content must provide a search capability.

For many types of web sites it is possible to just let Google, Bing, etc. index all the content and provide the search results. This works well for sites that have mostly static pages, like a forum. In our little microblog application the basic unit of content is just a short user post, not a whole page. The type of search results that we want are dynamic. For example, if we search for the word "dog" we want to see blog posts from any users that include that word. It is obvious that until someone searches for that word there is no page that the big search engines could have indexed with these results, so clearly we have no choice other than rolling our own search.

Introduction to full text search engines

Unfortunately support for full text search in relational databases is not well standardized. Each database implements full text search in its own way, and SQLAlchemy at this time does not have a full text search abstration.

We are currently using SQLite for our database, so we could just create a full text index using the facilities provided by SQLite, bypassing SQLAlchemy. But that isn't a good idea, because if one day we decide to switch to another database we would need to rewrite our full text search capability for another database.

So instead, we are going to let our database deal with the regular data, and we are going to create a specialized database that will be dedicated to text searches.

There are a few open source full text search engines. The only one that to my knowledge has a Flask extension is Whoosh, an engine also written in Python. The advantage of using a pure Python engine is that it will install and run anywhere a Python interpreter is available. The disadvantage is that search performance will not be up to par with other engines that are written in C or C++. In my opinion the ideal solution would be to have a Flask extension that can connect to several engines and abstract us from dealing with a particular one in the same way Flask-SQLAlchemy gives us the freedom to use several database engines, but nothing of that kind seems to be available for full text searching at this time. Django developers do have a very nice extension that supports several full text search engines called django-haystack. Maybe one day someone will create a similar extension for Flask.

But for now, we'll implement our text searching with Whoosh. The extension that we are going to use is Flask-WhooshAlchemy, which integrates a Whoosh database with Flask-SQLAlchemy models.

If you don't have Flask-WhooshAlchemy installed in your virtual environment yet go ahead and install it now. Windows users should run this:

flask\Scripts\pip install Flask-WhooshAlchemy

All others must run this:

flask/bin/pip install Flask-WhooshAlchemy

Configuration

Configuration for Flask-WhooshAlchemy is pretty simple. We just need to tell the extension what is the name of the full text search database (file config.py):

WHOOSH_BASE = os.path.join(basedir, 'search.db')

Model changes

Since Flask-WhooshAlchemy integrates with Flask-SQLAlchemy, we indicate what data is to be indexed for searching in the proper model class (file app/models.py):

from app import app
import flask.ext.whooshalchemy as whooshalchemy

class Post(db.Model):
    __searchable__ = ['body']

    id = db.Column(db.Integer, primary_key = True)
    body = db.Column(db.String(140))
    timestamp = db.Column(db.DateTime)
    user_id = db.Column(db.Integer, db.ForeignKey('user.id'))

    def __repr__(self):
        return '<Post %r>' % (self.body)

whooshalchemy.whoosh_index(app, Post)

The model has a new __searchable__ field, which is an array with all the database fields that will be in the searchable index. In our case we only want to index the body field of our posts.

We also have to initialize the full text index for this model by calling the whoosh_index function.

Since this isn't a change that affects the format of our relational database we do not need to record a new migration.

Unfortunately any posts that were in the database before the full text engine was added will not be indexed. To make sure the database and the full text engine are synchronized we are going to delete all posts from the database and start over. First we start the Python interpreter. For Windows users:

flask\Scripts\python

And for everyone else:

flask/bin/python

Then in the Python prompt we delete all the posts:

>>> from app.models import Post
>>> from app import db
>>> for post in Post.query.all():
...    db.session.delete(post)
>>> db.session.commit()

Searching

And now we are ready to start searching. First let's add a few new posts to the database. We have two options to do this. We can just start the application and enter posts via the web browser, as regular users would do, or we can also do it in the Python prompt.

From the Python prompt we can do it as follows:

>>> from app.models import User, Post
>>> from app import db
>>> import datetime
>>> u = User.query.get(1)
>>> p = Post(body='my first post', timestamp=datetime.datetime.utcnow(), author=u)
>>> db.session.add(p)
>>> p = Post(body='my second post', timestamp=datetime.datetime.utcnow(), author=u)
>>> db.session.add(p)
>>> p = Post(body='my third and last post', timestamp=datetime.datetime.utcnow(), author=u)
>>> db.session.add(p)
>>> db.session.commit()

The Flask-WhooshAlchemy extension is nice, because it hooks up into Flask-SQLAlchemy commits automatically. We do not need to maintain the full text index, it is all done for us transparently.

Now that we have a few posts in our full text index we can issue searches:

>>> Post.query.whoosh_search('post').all()
[<Post u'my second post'>, <Post u'my first post'>, <Post u'my third and last post'>]
>>> Post.query.whoosh_search('second').all()
[<Post u'my second post'>]
>>> Post.query.whoosh_search('second OR last').all()
[<Post u'my second post'>, <Post u'my third and last post'>]

As you can see in the examples above, the queries do not need to be limited to single words. In fact, Whoosh supports a pretty powerful search query language.

Integrating full text searches into the application

To make the searching capability available to our application's users we have to add just a few small changes.

Configuration

As far as configuration, we'll just indicate how many search results should be returned as a maximum (file config.py):

MAX_SEARCH_RESULTS = 50

Search form

We are going to add a search form to the navigation bar at the top of the page. Putting the search box at the top is nice, because then the search will be accessible from all pages.

First we add a search form class (file app/forms.py):

class SearchForm(Form):
    search = TextField('search', validators = [Required()])

Then we need to create a search form object and make it available to all templates, since we will be putting the search form in the navigation bar that is common to all pages. The easiest way to achieve this is to create the form in the before_request handler, and then stick it in Flask's global g (file app/views.py):

from forms import SearchForm

@app.before_request
def before_request():
    g.user = current_user
    if g.user.is_authenticated():
        g.user.last_seen = datetime.utcnow()
        db.session.add(g.user)
        db.session.commit()
        g.search_form = SearchForm()

Then we add the form to our template (file app/templates/base.html):

<div>Microblog:
    <a href="{{ url_for('index') }}">Home</a>
    {% if g.user.is_authenticated() %}
    | <a href="{{ url_for('user', nickname = g.user.nickname) }}">Your Profile</a>
    | <form style="display: inline;" action="{{url_for('search')}}" method="post" name="search">{{g.search_form.hidden_tag()}}{{g.search_form.search(size=20)}}<input type="submit" value="Search"></form>
    | <a href="{{ url_for('logout') }}">Logout</a>
    {% endif %}
</div>

Note that we only display the form when we have a logged in user. Likewise, the before_request handler will only create a form when a user is logged in, since our application does not show any content to guests that are not authenticated.

Search view function

The action field of our form was set above to send all search requests the the search view function. This is where we will be issuing our full text queries (file app/views.py):

@app.route('/search', methods = ['POST'])
@login_required
def search():
    if not g.search_form.validate_on_submit():
        return redirect(url_for('index'))
    return redirect(url_for('search_results', query = g.search_form.search.data))

This function doesn't really do much, it just collects the search query from the form and then redirects to another page passing this query as an argument. The reason the search work isn't done directly here is that if a user then hits the refresh button the browser will put up a warning indicating that form data will be resubmitted. This is avoided when the response to a POST request is a redirect, because after the redirect the browser's refresh button will reload the redirected page.

Search results page

Once a query string has been received the form POST handler sends it via page redirection to the search_results handler (file app/views.py):

from config import MAX_SEARCH_RESULTS

@app.route('/search_results/<query>')
@login_required
def search_results(query):
    results = Post.query.whoosh_search(query, MAX_SEARCH_RESULTS).all()
    return render_template('search_results.html',
        query = query,
        results = results)

The search results view function sends the query into Whoosh, passing a maximum number of search results, since we don't want to be presenting a potentially large number of hits, we are happy showing just the first fifty.

The final piece is the search results template (file app/templates/search_results.html):

<!-- extend base layout -->
{% extends "base.html" %}

{% block content %}
<h1>Search results for "{{query}}":</h1>
{% for post in results %}
    {% include 'post.html' %}
{% endfor %}
{% endblock %}

And here, once again, we can reuse our post.html sub-template, so we don't need to worry about rendering avatars or other formatting elements, since all of that is done in a generic way in the sub-template.

Final words

We now have completed yet another important, though often overlooked piece that any decent web application must have.

The source code for the updated microblog application is available below:

Download microblog-0.10.zip.

As always, the above download does not include a database or a flask virtual environment. See previous articles in the series to learn how to create these.

I hope you enjoyed this tutorial. If you have any questions feel free to write in the comments below. Thank you for reading, and I will be seeing you again in the next installment!

Miguel

Origin: http://blog.miguelgrinberg.com/post/the-flask-mega-tutorial-part-x-full-text-search