Book Image

Building Django 2.0 Web Applications

By : Tom Aratyn
Book Image

Building Django 2.0 Web Applications

By: Tom Aratyn

Overview of this book

<p>This project-based guide will give you a sound understanding of Django 2.0 through three full-featured applications. It starts off by building a basic IMDB clone and adding users who can register, vote on their favorite movies, and upload associated pictures. You will learn how to use the votes that your users have cast to build a list of the top 10 movies. This book will also take you through deploying your app into a production environment using Docker containers hosted on the server in Amazon's Electric Computing Cloud (EC2). </p><p> </p><p>Next, you're going to build a Stack Overflow clone wherein registered users can ask and answer questions. You will learn how to enable a user asking a question to accept answers and mark them as useful. You will also learn how to add search functionality to help users find questions by using ElasticSearch. You'll discover ways to apply the principles of 12 factor apps while deploying Django on the most popular web server, Apache, with mod_wsgi. Lastly, you'll build a clone of MailChimp so users can send and create emails, and deploy it using AWS. </p><p> </p><p>Get set to take your basic Python skills to the next level with this comprehensive guide! </p><p></p>
Table of Contents (19 chapters)
Title Page
www.packtpub.com
Contributors
Preface
Index

Creating a search view


In this section, we'll create a view that will let users search our Questions and will display the matching results. To achieve this result, we will do the following things:

  • Add a search_for_question() function to our elasticsearch service
  • Make a search view
  • Make a template to display search results
  • Update the base template to have search available everywhere

Let's start by adding search to our elasticsearch service.

Creating a search function

The responsibility for querying our Elasticsearch server for a list of questions matching the user's query lies with our elasticsearch service.

Let's add a function that will send a search query and parse the results to django/qanda/service/elasticsearch.py:

def search_for_questions(query):
    client = get_client()
    result = client.search(index=settings.ES_INDEX, body={
      'query': {
          'match': {
              'text': query,
          },
      },
    })
    return (h['_source'] for h in result['hits']['hits'])

After we connect...