Deploy a Django Application on AWS Elastic Beanstalk and scale it with Memcache

Want to deploy a Django application on AWS Elastic Beanstalk that is ready to scale? We’ll explore how to set up your Elastic Beanstalk environment, hook it up to a database, deploy your application, and finally how to use Memcache to speed it up.

We’ll walk you through creating the application from start to finish, but you can view the finished product source code here.

Memcache is a technology that improves the performance and scalability of web apps and mobile app backends. You should consider using Memcache when your pages are loading too slowly or your app is having scalability issues. Even for small sites, Memcache can make page loads snappy and help future-proof your app.


Before you complete the steps in this guide, make sure you have all of the following:

  • Familiarity with Python (and ideally Django)
  • An AWS account. If you haven’t used AWS before, you can set up an account here.
  • The AWS CLI installed and configured on your computer.
  • Python, git, and the EB CLI installed on your computer.

Required Versions:

  • Python 3.6
  • Pip 18.0

Since Elastic Beanstalk has specific requirements, if you’re running a different version of Python on your machine, consider using a tool like pyenv, virtualenv, or python’s own venv.

Create a Django application for Elastic Beanstalk

The following commands will create an isolated Python environment and bootstrap an empty Django app:

Visiting http://localhost:8000 will show a “hello, world” landing page.

Create an Elastic Beanstalk app

Associate your Django project with a new Elastic Beanstalk app with the following steps:

  1. Use pip freeze to write your dependencies to a file named requirements.txt.

    This file is required in order for Elastic Beanstalk to know what to install during deployment.

  2. Create an .ebextensions folder and add a django.config file:

    Set the WSGIPath in .ebextensions/django.config so Elastic Beanstalk can start your application:

  3. Initialize a Git repository and commit the skeleton. Start by adding a .gitignore file to make sure you don’t commit files you don’t want to. Paste the following into it:


    Now commit all files to the Git repository:

  4. Create an Elastic Beanstalk repo:

    This will set up a new application called django-memcache. Feel free to use a different region. Then we’ll create an environment to run our application in:

    Notice that we’re adding a MySQL database to our EB environment. You’ll be prompted for a username and password for the database. You can set them to whatever you like.

    Be careful when choosing your password. AWS does not handle symbols very well (! $ @ etc.), and can cause some unexpected behavior. Stick to letters and numbers, and make sure it’s at least eight characters long.

    This will create a AWS Relational Database Service (RDS) instance that is associated with this application. When you terminate this application, the database instance will be destroyed as well. If you need a RDS instance that is independent of your Elastic Beanstalk application, create one via the AWS RDS interface.

    This configuration process will take about five minutes. Go refill your coffee, stretch your legs, and come back later.

Configure Django for Elastic Beanstalk

You will need to make two changes to the vanilla Django skeleton for it to work on Elastic Beanstalk.

  1. Allow any app domain name

    You need to allow Django to run on any host. Do this by setting ALLOWED_HOSTS = ['*'] in django_tasklist/

    Note, once you have a domain name for your page you should use that instead of a wildcard.

  2. Set up the MySQL database

    Django comes with SQLite configured by default. This will not work out of the box on Elastic Beanstalk. Since our EB environment already has a MySQL database initialized, we’ll configure this database in Django.

    To use our database in Django, we need to install the mysqlclient:

    Finally, configure the database in django_tasklist/ (replace current SQLite configuration):

Save the changes to git:

Deploy the Django app on Elastic Beanstalk

Deploying the Django application on EB is easily done by running the deploy command:

You can now open the application and see if it’s working:

You should now see the same landing page with the little rocket as when you ran the Django app locally.

If you get a 500 error when you open the application, check the logs. They’re located in the EB console in the side menu labeled Logs.

Add task list functionality

The Django application we are building is a task list. In addition to displaying the list, it will have actions to add new tasks and to remove them. To accomplish this, we need to:

  1. Create a task list app
  2. Create a Task model
  3. Create the route, view, and controller logic

Create a task list app

Django has the concept of apps and we need to create one in order to add any functionality. We will create a mc_tasklist app:

Add mc_tasklist to the list of installed apps in django_tasklist/

Create the Task model

To create and store tasks, we need to do two things:

  1. Create a simple Task model in mc_tasklist/

  2. Use makemigrations and migrate to create a migration for the mc_tasklist app as well as create the mc_tasklist_task table, along with all other default Django tables:

    To run the migrations when deploying on Elastic Beanstalk create .ebextensions/task_list.config with the following content:

        command: " migrate"
        leader_only: true
        DJANGO_SETTINGS_MODULE: django_tasklist.settings

Create the task list application

The actual application consists of a view that is displayed in the front end and a controller that implements the functionality in the back end. You also need to tell Django which controller corresponds to which URL.

  1. Setup the routes for add, remove, and index methods in django_tasklist/

  2. Add corresponding view controllers in mc_tasklist/

  3. Create a template with display code in mc_tasklist/templates/index.html:

    <!DOCTYPE html>
      <meta charset="utf-8">
      <title>MemCachier Django tutorial</title>
      <!-- Fonts -->
      <link href=""
            rel='stylesheet' type='text/css' />
      <!-- Bootstrap CSS -->
      <link href=""
            rel="stylesheet" />
      <div class="container">
        <!-- New Task Card -->
        <div class="card">
          <div class="card-body">
            <h5 class="card-title">New Task</h5>
            <form action="add" method="POST">
              {% csrf_token %}
              <div class="form-group">
                <input type="text" class="form-control" placeholder="Task Name"
                       name="name" required>
              <button type="submit" class="btn btn-default">
                <i class="fa fa-plus"></i> Add Task
        <!-- Current Tasks -->
        {% if tasks %}
        <div class="card">
          <div class="card-body">
            <h5 class="card-title">Current Tasks</h5>
            <table class="table table-striped">
              {% for task in tasks %}
                <!-- Task Name -->
                <td class="table-text">{{ }}</td>
                <!-- Delete Button -->
                  <form action="remove" method="POST">
                    {% csrf_token %}
                    <input type="hidden" name="id" value="{{ }}">
                    <button type="submit" class="btn btn-danger">
                      <i class="fa fa-trash"></i> Delete
              {% endfor %}
        {% endif %}
      <!-- Bootstrap related JavaScript -->
      <script src=""></script>
      <script src=""></script>
      <script src=""></script>

    The view consists of two cards: one that contains a form to create new tasks, and another that contains a table with existing tasks and a delete button associated with each task.

    Note that Django will automatically check each apps templates folder for templates.

Our task list is now functional. Save the changes so far with:

Deploy and view the task list on Elastic Beanstalk:

Test the app by adding a few tasks. We now have a functioning task. With this complete, we can learn how to improve its performance with Memcache.

Add caching to Django

Memcache is an in-memory, distributed cache. Its primary API consists of two operations: SET(key, value) and GET(key). Memcache is like a hashmap (or dictionary) that is spread across multiple servers, where operations are still performed in constant time.

The most common use for Memcache is to cache the results of expensive database queries and HTML renders so that these expensive operations don’t need to happen over and over again.

Set up Memcache

To use Memcache in Django, you first need to provision an actual Memcached cache. You can easily get one for free from MemCachier. MemCachier provides easy to use, performant caches that are compatible with the popular memcached protocol. It allows you to just use a cache without having to setup and maintain actual Memcached servers yourself.

There are three config variables you’ll need for your application to be able to connect to your cache: MEMCACHIER_SERVERS, MEMCACHIER_USERNAME, and MEMCACHIER_PASSWORD. You’ll need to add these variables to EB.

We can confirm that they’ve been set by running:

Then we need to configure the appropriate dependencies.

Since EB does not play nicely with pylibmc, we’ll also need to upgrade pip and install libmemcached using ebextensions/config files.

Inside .ebextensions/upgrade_pip.config, include:

We’ll also need to add the following to .ebextensions/task_list.config:

Configure Django with MemCachier

Configure your cache by adding the following to the end of django_tasklist/

This configures the cache for both development and production. If the MEMCACHIER_* environment variables exist, the cache will be setup with pylibmc, connecting to MemCachier. Whereas, if the MEMCACHIER_* environment variables don’t exist – hence development mode – Django’s simple in-memory cache is used instead.

Cache expensive database queries

Memcache is often used to cache expensive database queries. This simple example doesn’t include any expensive queries, but for the sake of learning, let’s assume that getting all tasks from the database is an expensive operation.

The task list database query code in mc_tasklist/ can be modified to check the cache first like so:

The above code first checks the cache to see if the tasks.all key exists in the cache. If it does not, a database query is executed and the cache is updated. Subsequent pageloads will not need to perform the database query. The time.sleep(2) only exists to simulate a slow query.

Re-deploy the app to Elastic Beanstalk with

and test the new functionality. To see what’s going on in your cache, open the MemCachier dashboard for your cache.

The first time you loaded your task list, you should have gotten an increase for the get miss and set commands. Every subsequent reload of the task list should increase get hits (refresh the stats in the dashboard).

Our cache is working, but there is still a major problem. Add a new task and see what happens. No new task appears on the current tasks list! The new task was created in the database, but the app is serving the stale task list from the cache.

Clear stale data

There are many techniques for dealing with an out-of-date cache.

  1. Expiration: The easiest way to make sure the cache does not get stale is by setting an expiration time. The cache.set method can take an optional third argument, which is the time in seconds that the cache key should stay in the cache. If this option is not specified, the default TIMEOUT value in will be used instead.

    You could modify the cache.set method to look like this:

    But this functionality only works when it is known for how long the cached value is valid. In our case however, the cache gets stale upon user interaction (add, remove a task).

  2. Delete cached value: A straight forward strategy is to invalidate the tasks.all key when you know the cache is out of date – namely, to modify the add and remove views to delete the tasks.all key:

  3. Key based expiration: Another technique to invalidate stale data is to change the key:

    The upside of key based expiration is that you do not have to interact with the cache to expire the value. The LRU eviction of Memcache will clean out the old keys eventually.

  4. Update cache: Instead of invalidating the key, the value can also be updated to reflect the new task list:

    Updating the value instead of deleting it will allow the first pageload to avoid having to go to the database

You can use option 2, 3, or 4 to make sure the cache will not ever be out-of-date. As usual, redeploy the app afterwards with eb deploy.

Now when you add a new task, all the tasks you’ve added since implementing caching will appear.

Use Django’s integrated caching

Django also has a few built in ways to use your Memcache to improve performance. These mainly target the rendering of HTML which is an expensive operation that is taxing for the CPU.

Caching and CSRF

You cannot cache any views or fragments that contain forms with CSRF tokens because the token changes with each request. For the sake of learning how to use Django’s integrated caching we will disable Django’s CSRF middleware. Since this task list is public, this is not a big deal but do not do this in any serious production application.

Comment CsrfViewMiddleware in django_tasklist/

Cache template fragments

Django allows you to cache rendered template fragments. This is similar to snippet caching in Flask, or caching rendered partials in Laravel. To enable fragment caching add {% load cache %} to the top of your template.

Do not cache fragments that include forms with CSRF tokens.

To cache a rendered set of task entries, we use a {% cache timeout key %} statement in mc_tasklist/templates/index.html:

Here the timeout is None and the key is a list of strings that will be concatenated. As long as task IDs are never reused, this is all there is to caching rendered snippets. The MySQL database we use on Elastic Beanstalk does not reuse IDs, so we’re all set.

If you use a database that does reuse IDs, you need to delete the fragment when its respective task is deleted. You can do this by adding the following code to the task deletion logic:

Let’s see the effect of caching the fragments in our application. You should now observe an additional get hit for each task in your list whenever you reload the page (except the first reload).

Cache entire views

We can go one step further and cache entire views instead of fragments. This should be done with care, because it can result in unintended side effects if a view frequently changes or contains forms for user input. In our task list example, both of these conditions are true because the task list changes each time a task is added or deleted, and the view contains forms to add and delete a task.

Do not cache views that include forms with CSRF tokens.

You can cache the task list view with the @cache_page(timeout) decorator in mc_tasklist/

Because the view changes whenever we add or remove a task, we need to delete the cached view whenever this happens. This is not straight forward. We need to learn the key when the view is cached in order to be then able to delete it:

To see the effect of view caching, reload your application. On the first refresh, you should see the get hit counter increase according to the number of tasks you have, as well as an additional get miss and set, which correspond to the view that is now cached. Any subsequent reload will increase the get hit counter by just two, because the entire view is retrieved with two get commands.

Note that view caching does not obsolete the caching of expensive operations or template fragments. It is good practice to cache smaller operations within cached larger operations, or smaller fragments within larger fragments. This technique (called Russian doll caching) helps with performance if a larger operation, fragment, or view is removed from the cache, because the building blocks do not have to be recreated from scratch.

Using Memcache for session storage

Memcache works well for storing information for short-lived sessions that time out. However, because Memcache is a cache and therefore not persistent, long-lived sessions are better suited to permanent storage options, such as your database.

For short-lived sessions configure SESSION_ENGINE to use the cache backend in django_tasklist/

For long-lived sessions, Django allows you to use a write-through cache, backed by a database. This is the best option for performance while guaranteeing persistence. To use the write-through cache, configure the SESSION_ENGINE in django_tasklist/ like so:

For more information on how to use sessions in Django, please see the Django Session Documentation

Clean up

Once you’re done with this tutorial and don’t want to use it anymore, you can clean up your EB instance by using:

This will clean up all of the AWS resources.

Further reading and resources