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https://github.com/andrew-chen-wang/cookiecutter-django-ec2-github
Deploying a Django project to AWS using GitHub Actions and CodeDeploy Blue/Green Deployment + EC2 (Works w/ Flask/FastAPI/Rails)
https://github.com/andrew-chen-wang/cookiecutter-django-ec2-github
aws blue-green blue-green-deployment django ec2 fastapi flask github-actions ruby-on-rails
Last synced: about 2 months ago
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Deploying a Django project to AWS using GitHub Actions and CodeDeploy Blue/Green Deployment + EC2 (Works w/ Flask/FastAPI/Rails)
- Host: GitHub
- URL: https://github.com/andrew-chen-wang/cookiecutter-django-ec2-github
- Owner: Andrew-Chen-Wang
- License: apache-2.0
- Created: 2021-07-31T05:51:27.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-03-07T02:59:20.000Z (almost 2 years ago)
- Last Synced: 2024-10-12T07:12:36.824Z (2 months ago)
- Topics: aws, blue-green, blue-green-deployment, django, ec2, fastapi, flask, github-actions, ruby-on-rails
- Language: Python
- Homepage:
- Size: 249 KB
- Stars: 48
- Watchers: 3
- Forks: 11
- Open Issues: 5
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
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README
GitHub Actions EC2 Blue/Green Deployment
========================================
================ =====================================================================
Created by `Andrew-Chen-Wang`_
Created on 4 August 2021 (for posterity since AWS changes stuff now and then)
Finished Writing 8 August 2021 (I slacked off for a day :P)
================ =====================================================================
Cookiecutter Django with GitHub Actions CD (on push to main/master and manual deployment
using a button) to EC2 using CodeDeploy Blue/Green deployment method for zero downtime.
This tutorial covers a lot from the ground up, including: setting up our web server for
automatic deployment on push to main/master, maintaining good security practices from
short explanations, baby steps on which values you should input, setting up a buffering
reverse proxy called NGINX (sync framework only, which will be explained later), setting
up a database using AWS RDS, setting up something I coined single-execution
per-deployment scripts, and a neat FAQ from 0-years of experience in DevOps explaining
how to run Celery alongside your app in either the same server or different servers.
.. image:: https://img.shields.io/badge/built%20with-Cookiecutter%20Django-ff69b4.svg?logo=cookiecutter
:target: https://github.com/pydanny/cookiecutter-django/
:alt: Built with Cookiecutter Django
.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
:target: https://github.com/ambv/black
:alt: Black code style
:License: Apache Software License 2.0
Introduction
------------
We'll be deploying a Django application from GitHub Actions on main branch push or
manual button push using the Blue/Green deployment method (no downtime) using
CodeDeploy, EC2 (not ECS; visit my other `tutorial for ECS`_), Parameter Store
(for environment variables), ALB/ELB (AWS load balancers that'll manager your SSL and
for easier configuration IP address configuration for A records) and AWS RDS PostgreSQL.
Blue/Green deployment guarantees no downtime with a catch; you'll need to code with
no-downtime in mind meaning your new database schema/migration or new views/endpoints
shouldn't crash servers that haven't received the new code yet. Visit
`GitLab's database migration docs`_ for a nice guide (GitLab uses Ruby on Rails, but
the concepts are the same for the Ruby + Python ecosystem).
.. _tutorial for ECS: https://github.com/Andrew-Chen-Wang/cookiecutter-django-ecs-github
.. _GitLab's database migration docs: https://docs.gitlab.com/ee/development/migration_style_guide.html#avoiding-downtime
This uses a Django application for deployment; if you're using some other framework
like Ruby on Rails or Flask, I will mention where you can change the code to adjust
to your own. I will be using t2/t3.micro over the course of this tutorial. To properly
estimate your billing, visit `https://calculator.aws/ `_
Why did I write this tutorial? Like the ECS tutorial, I was just afraid I forgot all
the concepts. Plus, it's really useful when you have a new project and need to speedrun
getting the application up. The ECS version takes at most 30 minutes (proficiently 15).
This one should take about 20-40 minutes, but first-time readers will take 1-2 hours.
To me, this is a preferable method over my `ECS (containerized/Docker) tutorial`_ which
forces you to save your Docker images somewhere, costing you some extra bucks. Besides
that, both methods are pretty neat.
.. _ECS (containerized/Docker) tutorial: https://github.com/Andrew-Chen-Wang/cookiecutter-django-ecs-github
I prefer to use AWS via the console for two reasons:
1. I am too lazy to learn how to use AWS Cloudformation or Terraform, and I don't
believe in copying code that I can't eventually understand (i.e. I don't think I'll
ever be learning the aforementioned code-based services, ansible, etc.)
2. AWS updates their dashboard often, so going to their dashboard often is a good way
of keeping up with updates. Also knowing your options and the descriptions provided
is enough for me to be a keeper for the AWS Console.
3. Going through the console offers an opportunity to learn how the entire AWS
infrastructure works and is nice in comparison to code-based deployment which can
screw everything up all at once.
What we'll be setting up
^^^^^^^^^^^^^^^^^^^^^^^^
I'll assume you have a production ready setup (cookiecutter-django provides
production.py which will configure your application properties properly) on GitHub.
I'll also assume you have some kind of domain to use and registered in AWS Route 53
with a Hosted Zone (where you'll be charged 50 cents per month just to keep DNS records.
I know... and I don't know why...).
Explanation (you can skip this and head to the `Deployment <#Deployment>`_ section):
We'll be adding a buffering reverse proxy called Nginx. You can think of it as another
load balancer; if you've ever used Sentry and not have a buffer proxy (not the same as
a WSGI/ASGI server like gunicorn/uwsgi), you'll know that a bunch of bots keep
attacking an IP address of one of your servers instead of through your ALB/ELB (i.e.
external load balancer). That's where your server may crash if you didn't have a
certificate configured with it.
We'll also be adding an HTTP server that is production suitable. The default local
server your web framework provides does not give many options that a dedicated server
can give such as threading, different methods of distributing HTTP requests (i.e. worker
types like Uvicorn workers + Gunicorn for ASGI/PubSub type work), how much resources to
the server should be allowed to consume like cores and memory, etc.. You may be
wondering why your framework doesn't offer these things.
Your web framework is dedicated to being a web framework, not a server. Through the
open source philosophy, we've packaged our things into small-ish modules that have
their own dedicated work. Hence, you'll see Django, Flask, and Rails application say
to use Gunicorn/Unicorn all the time regardless of the web framework in use.
Deployment
----------
Pre-requisites:
* You are logged in to AWS Console
* Have a domain, preferably in Route 53. If you just registered a domain from a
different registrar like Google Domains, you need to wait 60 days before being able
to transfer as required by ICANN. It's manageable not being on AWS Route 53, but will
take more steps that I'm not going to bother explaining. (i.e. I made that stupid
mistake of using Google Domains knowing I'd be running on AWS lmao). I'll explain
what you can try to do in those sections.
GitHub Integration
^^^^^^^^^^^^^^^^^^
1. Head to the AWS Console
2. Search for CodeDeploy. Go to the Applications tab.
3. Create an application. The name can be anything like your project name. Compute
platform should be "EC2/On-premises"
4. You may end up in Applications/application tab. If so, go back to the Applications
tab to see all your Applications listed.
5. Copy your Application name. Head to your repository on GitHub. Go to settings. In
the Secrets tab, add a repository secret. The name should be
``AWS_CODEDEPLOY_APPLICATION_NAME``, and the value should be your CodeDeploy
application name.
6. Go back to AWS CodeDeploy dashboard. Select your application and press "Deploy
application." Don't worry! This will not create a deployment! Just press it and
relax :)
7. You should now be in the Deployment settings. Find Revision type and select the one
about GitHub (e.g. "My application is stored in GitHub").
8. In the search box that appears, type in your GitHub username / organization name
just to quickly check if you've made a connection before. If nothing appears, then
press Connect to GitHub and grant OAuth access to the necessary organizations. If
you stop using this method, you can head to your personal / organization settings and
revoke access (`if you need to revoke, follow this`_).
9. After confirming the request for authorization, you'll be back in the menu. Press
Cancel. Yes, cancel the deployment and discard (it's a button in the confirmation
modal).
.. _if you need to revoke, follow this: https://docs.aws.amazon.com/codedeploy/latest/userguide/integrations-partners-github.html#behaviors-authentication
Setting up credentials
^^^^^^^^^^^^^^^^^^^^^^
AWS uses roles and IAM users. Users can run certain programs like the AWS CLI and roles
are attached to users and can also be used for selecting how AWS interprets
authorization level for specific functionalities like CodeDeploy which AWS manages --
thus not requiring an IAM user -- but for security sake you can attach a role saying the
manager only has certain permissions.
1. In the AWS Console, search for IAM.
2. Create a User Group. The name can just be your "project-name-Deployment".
3. Scroll to the permissions section and filter by "CodeDeploy" in the search field.
Make sure to press enter. Check mark the role called ``AWSCodeDeployDeployerAccess``.
If it's not there, view [1]_ at the
`additional notes section at the bottom <#additional-notes>`_.
4. Select Users or find a button saying Create User (DO NOT create a User Group)
5. Give it a username like "project-name-CodeDeploy" and give it Programmatic Access.
Press next.
6. In the Permission tab, select your user group in the table below. Press next until
you create the user (you can add tags if you want). Before closing, copy the access
key ID and the secret access key. Store it somewhere temporarily (like in a txt
file). If you end up forgetting which type of key is one of them, access key IDs
typically are shorter and usually start with AKI.
7. Go to your GitHub repository. Go to the Settings. Go to the Secrets tab. Add a
repository secret with name ``AWS_ACCESS_KEY_ID``, and the value is your access key
id. Create another secret called ``AWS_SECRET_ACCESS_KEY``, and the value is your
secret access key. Finally, create another secret called ``AWS_CODEDEPLOY_REGION``,
and write your CodeDeploy/your account's region. You can find it at the top right.
If it says ``global``, go to CodeDeploy again and check again. Note: you will never
see these values again at both GitHub and AWS.
8. Back in the IAM dashboard, go to the Roles tab. Then press Create role.
9. There should be a bunch of services. Since we're using bare EC2 instances, find the
service CodeDeploy and select it. At the bottom, select "CodeDeploy" (DO NOT select
ECS or lambda). Then keep going until you need to name your role. I would call it
"project-CodeDeploy". Then press Create role.
10. After creating the role, we need to add an inline policy. For simplicity sake,
go to the role's detail. Create an inline policy. Add the following:
.. code-block:: json
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"iam:PassRole",
"ec2:CreateTags",
"ec2:RunInstances"
],
"Resource": "*"
}
]
}
11. We need to create another role that'll be used in our EC2 instance itself.
Select EC2. In the policy section, if you're using django-storages (e.g. if you're
using cookiecutter-django), then search up S3 and attach S3 full access role.
If you don't have that, just press next and now no policy/permission has been given
to your EC2 instance. Call it "project-ec2". Later on, you can change the policies
on this role, so don't worry!
12. After creating this role, in the next few steps, we'll be needing access to
Parameter Store, the place where we store our servers' environment variables. To do
this, go to the role and create an inline policy. Use JSON and replace the Resource
value with your account ID, change the region (e.g. us-east-2) and change "PROJECT"
to your path prefix. Your path prefix might be your project's name but all
uppercase. This will be better known once we get to the step about Parameter Store.
The role can be called "project-read-parameters":
.. code-block:: json
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": "ssm:GetParametersByPath",
"Resource": "arn:aws:ssm:REGION:ACCOUNT_ID:parameter/PROJECT/*"
}
]
}
To specify which region this role is allowed to access/manage CodeDeploy, follow this
guide: https://docs.aws.amazon.com/codedeploy/latest/userguide/getting-started-create-service-role.html#getting-started-create-service-role-console
Setting up a VPC
^^^^^^^^^^^^^^^^
If you have different companies, I suggest you make different AWS accounts. You'll still
want to follow the following steps. A VPC here will help us split our code
bases/websites under a single AWS account. In other words, every server-based website
you host should probably have its own VPC. More details about why this is can be found
in the next section about security groups.
1. From the search bar, head to VPC and select "Your VPCs".
2. Name tag can be "project-vpc-01". IPv4 CIDR block should be ``10.0.0.0/16`` (I'm
using the VPC wizard to check my values). Create the VPC.
3. Head to Subnets and press "Create subnet." Select the VPC you just made. I'm going
to create 3 subnets since I'm using us-east-2. (if you're in us-east-1, you can
create 6, one for each availability zone). For each subnet, name it
``us-east-2-subnet-a`` where us-east-2 is my region and "a" represents the
availability zone. The IPv4 CIDR block should be using the ``10.0.0.0/24`` block.
For the second one, use "-b" and a different block: ``10.0.1.0/24``. Continue this
pattern until there are no more subnets.
You can read about creating subnets here: https://docs.aws.amazon.com/vpc/latest/userguide/working-with-vpcs.html#AddaSubnet
You can read more about the CIDR blocks here: https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html#vpc-sizing-ipv4
4. Once you've created your subnets, click one subnet, press Actions, press View
details, press Modify auto-assign IP settings, and enable auto-assign IPv4 addresses.
Do this for each subnet. Go to the Internet Gateway tab and press Create
internet gateway. The name can just be your project's name. Press Create.
5. There should be a banner at the top saying "Attach to a VPC". If not, go to your
internet gateway. On the top right, there should be a button saying "Action." Press
that then press "Attach to VPC" and attach it to your custom VPC.
6. Go to the Route Tables tab and create one. The name
can be "project-route-table-01". Select your custom VPC then press create.
7. You should be in the details for the route table; if so, scroll down to see Routes
and press "Edit Routes". If not, select your route table and edit its routes.
8. Press "Add route" with destination ``0.0.0.0/0`` and the target is Internet Gateway.
You might see a popup for your internet gateway; if so, select it and press Save
Changes.
* If you didn't see your Internet Gateway, cancel this edit. Head to the Internet
Gateway tab. Get the Internet Gateway's ID (not the name). It should look like
igw-etc. Then repeat this step and this time manually fill out the target. If
there's an error, that means either your internet gateway or your route table
is not associated with the right VPC.
9. Head to the Subnet association tab and let's edit that. Check all your subnets
and press "Save associations".
Setting up ACM for HTTPS certificates and EC2 Target Groups
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The following assumes your domain is in Route 53. This is the first time you will
start getting charged. Every month, Route 53 Hosted Zones will start charging 50
cents and your Load Balancer will start charging quite a bit. Maybe $12 per month?
1. Search up ACM, a free service that gets you SSL certificates and renews them like
Let's Encrypt. Press Get started under "Provision certificates." If you're on a
dashboard-like screen, just press "Request a certificate."
2. Press "Request a public certificate." Add your domain name and its "www." form as
well (i.e. write: www.example.com). For the second one press, "Add another name to
this certificate." Press next; select DNS validation. BUT WAIT! Don't click next
too fast. When we get to Step 5/Validation, you'll have to add CNAME records.
3. Select each domain by hitting the arrow button and add those CNAME records to your
DNS. If you're using Route 53, just press the button saying to add it. If you're
using Route 53 but the button's not there, make sure you have a hosted zone for your
domain. A hosted zone in Route 53 allows you to customize the DNS records for your
domain. Finally, press continue. (When you're back on the ACM dashboard You don't
need for the validation to happen; it'll take time. Just go to the next step)
4. Search up EC2 and head to the Target Groups tab. Then press Create a target group.
5. Target type is Instances since we're only working with EC2 instances. The name can
be "project-01". This time, that "01" is important. The protocol should be HTTP
and port 80 since we let our ALB/ELB/Load Balancer handle HTTPS. Select your VPC and
use protocol version HTTP1 (again since we let ALB handle HTTPS). The default health
check of HTTP and path being "/" is good enough. Press next and then press "Create
target group"
6. Go to Route 53 and enter the hosted zone for your domain. Create a new A record.
Mark it as an alias. Select the option for load balancer. Select the region your
load balancer is in, then select your load balancer.
Setting up Security Groups
^^^^^^^^^^^^^^^^^^^^^^^^^^
This is really important. Like so important that when I wrote my ECS tutorial it took
me a full week to figure out why I couldn't connect to my server and why my server
couldn't connect to my AWS RDS database and Redis ElastiCache.
In the next paragraph, I'll explain security groups, and you should DEFINITELY READ IT.
This is super crucial to understand when you're trying to fix bugs and maintain security.
What are security groups? They define how we communicate within AWS infrastructure.
If you've ever worked on servers before, you may know about blocking all ports except
for some specific ones. The exact same thing is happening here with the great addition
of specifying groups of servers that can access an instance/server. So my database can
open port 5432 with a security group attached saying "Only EC2 instances from my custom
VPC that have a security group called XYZ can call to my port here." Note the added
vocab VPC. That's right, each security group can only be used within a single VPC --
which also means all instances you ever create for a single website will be limited to
communication to other instances in the same VPCs.
This not only prevents confusion between different projects, but also makes sure that
unauthorized access from, potentially, a hacked server from a different project cannot
access your servers/databases in other VPCs.
Additionally, we can attach security groups to a single EC2 instance. In this tutorial,
out of many groups we create, we'll be creating a security group but not actually use
it. It'll be for SSHing into an EC2 instance in case you need to view some debugging
information or run ``python manage.py shell`` to access production data.
1. Search up EC2 if you're not already there. Head to the Load Balancer tab from the
side bar. Press "Security Groups".
2. We need to expose our future Load Balancer (explained next) to the public. Create
a security group with name "WebAccess" and a description saying "It's for ALB".
Select your custom VPC. The inbound traffic should consist of 4 rules:
* Type HTTP, with custom source type on source 0.0.0.0/0
* Type HTTP, with custom source type on source ::/0
* Type HTTPS, with custom source type on source 0.0.0.0/0
* Type HTTPS, with custom source type on source ::/0
The outbound rules should be left on default, but just in case:
* Type All traffic, destination type Custom at Source 0.0.0.0/0
3. Create another security group. This is for your actual EC2 instances. Name it
"project-reverse-proxy". Select your custom VPC. The outbound traffic is the same as
the first one (the default I believe). The inbound traffic should be 1 rule only:
* Type All Traffic, with custom source type, and find your first security group
in the Source (it should say the name of the security group you just created).
* Later on, if you ever need to access the terminal/shell inside any of your instances,
head to Security Groups again, select and edit this group's inbound rules, and
attach the SSH protocol type with CIDR 0.0.0.0/0. Then, go to an EC2 instance,
select connect, and you should now be able to connect via SSH.
4. This step may be optional depending on your use-case. For many Django applications
we typically use the database; this step covers both the database and cache. Just
note that AWS RDS and ElastiCache is expensive -- $50/month for RDS + $18 for cache.
You can also host your own database, and, when you grow large, you can perform an
extraction of all your data and import it into AWS RDS.
Create another security group. Name it "project-database" (if you also want to set
this up for caching, you'll need to create another security group). Select your
custom VPC. The outbound traffic is the same as the first one (the default I
believe). The inbound traffic should be 1 rule only, regardless if you're hosting
your database on EC2 or RDS or ElastiCache:
* Type PostgreSQL or Redis or whatever database service(s) you're using, with custom
source type, and find your EC2 security group in the Source (it should say the name
of the security group you just created in Step 3).
Setting up ACM/ELB (Application/Elastic Load Balancer)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
What is a load balancer? It's kinda what it sounds like; it distributes HTTP requests
across all your target groups and within each group is a server/instance. There are
different styles like Round Robbin, but the ALB is special in that it has an in-house
solution of detecting crashed servers (with health checks).
Note: your Load Balancer will start charging quite a bit. Maybe $12 per month?
1. Search up EC2 if you're not already there. Head to the Load Balancer tab from the
side bar. Press "Create Load Balancer"
2. Select "Application Load Balancer." Give it a name like "project-alb-01"
with scheme internet-facing and IP address type ipv4. In listeners, add a listener
for HTTP with port 80 and HTTPS with port 443. Make sure both are there. Under
Availability Zones, select your custom VPC and checkmark all your custom subnets.
Continue. In the next section, choose a certificate from ACM and your certificate
name should match your domain from the ACM section.
3. In Configure Security Groups, select the one that had the description about ALB.
4. Configure Routing: select the target group you first made. Finally, hit next and
create your load balancer.
5. Make sure you're still in the load balancer tab. Select your load balancer,
head to the listeners tab, find the row with port 80 listener, and press View/edit
rules (you'll need to scroll right).
6. Press the edit button (it can also look like a pencil icon). Delete the current
action. Press Add action. Choose Redirect To. Type in port 443. Make sure you still
have original host, path, and query and status code 301. Then press the checkmark
button. On the top, press the Update button.
The point of this redirect is to avoid having to go through the actual EC2 instance's
reverse proxy and simply let AWS ALB handle this.
Setting up EC2 Auto Scaling Group
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Autoscaling helps us automatically create or remove servers based on server load. Server
load is based on resource metrics (this is correlated with number of requests). This
helps makes sure requests don't take too long to get a response for due to servers
not having enough resources to effectively run all their requests and generate an HTTP
response.
1. Search up EC2 and head to the Launch Templates tab (from the side bar). Press Create
Launch Template.
2. The template name can be like "project-MainLaunchTemplate". The description can be
"Initial production webserver for PROJECT." There may be an option saying
"Auto Scaling guidance". If you want, you can checkmark it; I did :)
3. Select an AMI; I HIGHLY recommend you choose an Ubuntu LTS (the latest LTS)
4. Your instance type is up to you. For this tutorial, I'm using ``t2.micro`` which has
1 GiB Memory and 1 core. I highly recommend something a little better (obviously you
can create a new Launch Template. Don't worry. Everything can be changed!). For Key
Pair, give a decent key pair name like "project-ec2-keypair". Choose ``.pem`` if
you're on a Mac/Linux/WSL and ``.ppk`` if you're on Windows. The default volume of
8 GiB is enough. In Advanced details, for Purchasing option,
DO NOT checkmark Request Spot Instances
since it's configurable in your autoscaling group. If you're curious what pricing
spot instances are, head to EC2/Spot Requests and find Pricing History at the top.
Select the IAM instance profile we made in `Setting up Credentials`_ step 10. Find
the User Data section and copy and paste this code (it's from `here `_).
Make sure you change the region. Finally, Press "Create launch template":
.. code-block:: shell
#!/bin/bash
sudo apt update
sudo apt -y upgrade
sudo apt install -y ruby-full wget
cd /home/ubuntu/
wget https://aws-codedeploy-us-east-2.s3.us-east-2.amazonaws.com/latest/install
chmod +x ./install
sudo ./install auto > /tmp/logfile
sudo service codedeploy-agent start
5. Search up EC2 and head to the Auto Scaling Groups tab (from the side bar menu at the
very bottom). Press Create Auto Scaling Group.
6. The name can be something like "project-EC2AutoScalingGroup". Select the Launch
Template you just made. Use the "Latest" version and just press Next.
7. Instance purchase options should have an option saying "Combine purchase options and
instance types" so that you can use both On-Demand and Spot instances. For us, we're
going to have 0 On-Demand instances. "Spot allocation strategy per Availability Zone"
should use lowest price with a pool of 2. `Click here to learn more about the pool`_.
Give 100% to our Spot instances. Enable Capacity rebalance so that we have true
Blue/Green (if you used On Demand only, then this wouldn't be necessary. Because
we're using Spot instances, an instance could be taken down and another could never
come back up if we don't bid for another instance. In that case, this option lets us
wait the deployment out until we can get a new instance.).
8. Keep the default instance types. Choose your custom VPC and select all subnets.
Click next. Choose existing load balancer. Select Choose from your load balancer
target group. Make sure to select your target group. Click next. Our group size
should always be 1. Don't enable Scaling policies. You can set it up later. Don't
enable scale-in protection. It basically means that, based on metrics/current
resource consumption, if it drops lower, then don't bring down instances. I think
having dynamic scale-in, scale-out policy is better to conserve costs though.
Finally, create your Autoscaling Group.
9. Go to your GitHub secrets and add a new secret called ``AWS_AUTOSCALING_GROUP_NAME``
with the value being the name you gave the autoscaling group
"project-EC2AutoScalingGroup"
.. _Click here to learn more about the pool: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-fleet-configuration-strategies.html#plan-ec2-fleet
Creating a CodeDeploy Deployment Group
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
1. Search up CodeDeploy and head to your application. Create a deployment group.
2. Enter a deployment group name and set that to something like
"project-DeploymentGroup-01". Use the service role we created in
`Setting up Credentials`_, step 9. It should look like: "project-CodeDeploy". The
deployment type should be Blue/Green. The environment configuration should be copying
Autoscaling group. Choose your Autoscaling group. Re-route traffic immediately.
Terminate the original instance in 10 minutes.
3. In this tutorial, we can just select the Deployment Configuration for half. However,
in the future, you should create a custom percentage based configuration with
minimum healthy instances being 80% (so 20% at a time. This can be lowered to 10%).
4. Choose your load balancer and the custom target group. In advanced configuration,
head to Rollbacks and uncheck "Disable rollbacks." Enable "Roll back when a
deployment fails." Then, press Create deployment group.
5. Head to GitHub Secrets and add a new key ``AWS_CODEDEPLOY_DEPLOYMENT_GROUP`` with
the value being the name you set up for the deployment group, it was something like
"project-DeploymentGroup-01"
Setting up the Database (AWS RDS)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
If you don't need a database then you can skip this portion. I will not be covering a
cache setup, but the concepts here remain the same: VPC, security group attachment,
and checking your costs.
This is the most expensive piece: the database. I'm going to use the cheapest option,
a db.t3.micro instance, costing my $13 per month. Check your metrics everyday to see if you
need to upgrade your instance, and, yes, it's configurable/updatable. Don't worry.
1. Search up RDS. Go to the Databases tab and press Create database.
2. Choose Standard create. I'm selecting PostgreSQL to match with my security group and
needs. I always prefer the latest database version since database services are very
good with backwards compatibility. Use the Production template. Call the database
instance "project-database-01" (in case you need to set up read replicas later on).
Use PostgreSQL v12 so that we can use the AWS free tier.
3. You should create a random username and password. I highly recommend you run this
script to do so (there is also an option to let AWS generate a password. If you
select that option instead, then, after you create the database, the password will
appear in a banner or modal). This is Python. You can copy and paste this into
IPython or use `Programiz.com's`_ online Python "compiler":
.. _Programiz.com's: https://www.programiz.com/python-programming/online-compiler/
.. code-block:: python
import string, random
def generate_random_string(
length, using_digits=False, using_ascii_letters=False, using_punctuation=False
):
"""
Example:
opting out for 50 symbol-long, [a-z][A-Z][0-9] string
would yield log_2((26+26+50)^50) ~= 334 bit strength.
"""
symbols = []
if using_digits:
symbols += string.digits
if using_ascii_letters:
symbols += string.ascii_letters
if using_punctuation:
all_punctuation = set(string.punctuation)
# These symbols can cause issues in environment variables
unsuitable = {"'", '"', "\\", "$"}
suitable = all_punctuation.difference(unsuitable)
symbols += "".join(suitable)
return "".join([random.choice(symbols) for _ in range(length)])
print("username", generate_random_string(32, using_ascii_letters=True))
print(
"password",
generate_random_string(64, using_digits=True, using_ascii_letters=True)
)
4. My db instance class was a Burstable db.t3.micro for $13, excluding
monthly storage costs (which is cheap if you constantly increment your storage by
monitoring storage consumption). If you need help with calculating prices, visit
`calculator.aws `_. For my Storage type, I chose General
Purpose (SSD) with an allocated storage of 20 GiB, the minimum, costing me $4.60.
Enable storage autoscaling. To prevent spam attacks from ruining my bill, I've made
the maximum storage threshold be 100 GiB. I chose not to use a Multi-AZ deployment
approach, and instead if my database goes down then it goes down... The Multi-AZ
approach creates a backup database in a different region in case master goes down.
5. Choose your custom VPC. Let AWS create a new DB Subnet Group. No public access.
Then choose your security group for the database. Enable Password authentication
only.
6. Open the Additional configuration dropdown. You need to name your database. You can
run the above Python script again like so:
``print(generate_random_string(62, using_digits=True, using_ascii_letters=True))``
7. Enable automated backups. The backup window should be at a specific time when the
least number of users are predicted to be online. Make sure to enable auto minor
version upgrade. Also be sure to select a maintenance window (something AWS would
randomly perform otherwise). Leave the rest of the defaults alone and press Create.
8. It'll take a couple minutes to create the database, so keep going. When you need to
construct your ``DATABASE_URL`` for Parameter Store, your URL will look like this:
``postgresql://username:password@endpoint:port/database_name``
Make sure you did not delete those generated values. We still need to store everything
inside Parameter Store, our environment variable / secrets manager.
For restoring your backups, visit the docs: https://docs.aws.amazon.com/aws-backup/latest/devguide/restoring-rds.html
Setting up our CI/CD using GitHub Actions
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TL;DR copy and paste the `CI File`_ at `.github/workflows/ci.yml`_ if you're using
Django. If you're using a different framework, look in the file and change the portions
I tell you in the comments inside the huge dashed line sections to change.
Next, we need to set up the actual deployment pipeline. In our small scale, we're going
to push to our production servers on push to main/master branch. For some larger
organizations, you can instead create a deployment via GitHub releases. Either work.
The current CI/CD configuration is tailored to Django applications and is in
`.github/workflows/ci.yml`_. Copy and paste it in your
repository and in the exact same path (as required by GitHub actions). There are
comments in the file surrounded by huge dashed lines that'll direct you to which
portions to change. For the most part, the AWS portion should remain untouched.
.. _CI File: ./.github/workflows/ci.yml
.. _.github/workflows/ci.yml: ./.github/workflows/ci.yml
The way the workflow works is that every time you push a new commit, we run the CI. If
the tests pass (specifically pytest for testing our application and our linter using
pre-commit which I recommend for everyone regardless of the lang you're using), then we
immediately run our deployment workflow. This workflow file also runs for pull requests,
but deployments are not run.
Note that you can also use GitLab with their CI. Just know that the steps for GitHub
integrations won't be necessary; you'll instead need to choose S3 and your CI file will
need to specify an S3 bucket. That S3 bucket then needs to store your project files.
Adding our environment variables for our servers
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
We now need to add our environment variables for use on our servers. You can view the
parameter store values I used here for a default cookiecutter-django project: [3]_.
1. Search for Systems Manager. Go to the Parameter Store tab.
2. Double check that you are in the correct region.
3. For each environment variable you need, press Create parameter. I **highly highly**
recommend that all your parameter names have a prefix like ``/PROJECT/SECRET`` where
"SECRET" is the actual name you want the environment variable to be. This allows for
easier identification of parameters between projects hosted on the same AWS account.
Use Standard tier and text data type and paste your environment variables.
4. That prefix is extremely important in all your secret values. In `start_server`_,
change that prefix value to yours. In my Python application, I'm using
``os.environ["DATABASE_URL"]`` but my parameter store key is called
``/PREFIX/DATABASE_URL``. This is because I grabbed all the parameters via the path
prefix (i.e. the ``/PROJECT/``) and stored it as a JSON in the path
``/home/ubuntu/.env.json``. Also change the region from us-east-2 to wherever your
parameter values are located (yes, they are split by region).
For cookiecutter-django or django-environ users, I've created a class ``Env`` which
takes that JSON file and inserts the key/values into ``os.environ`` (you can view
the class in `config/settings/base.py`_.
5. In `the django-storages section in config/settings/production.py `_,
I made some changes with django-storages.
Because our EC2 instances already have its AWS credentials and region set up, I
chose to omit the AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_S3_REGION_NAME,
hence not needing it in Parameter Store. (I wrote an if condition saying if
``if "AWS_ACCESS_KEY_ID" not in os.environ``, then I'd require the parameter store
values. The region should match with your S3 bucket region. If not, then you'll
still want to manually set it with a Parameter Store value.
6. In Parameter Store, make sure you don't enable ``DJANGO_SECURE_SSL_REDIRECT`` or the
actual Django setting ``SECURE_SSL_REDIRECT``. Otherwise, you'll be in a 301 loop
of death. Either turn it off with a hard coded ``False`` or disabe it.
7. If you recall from `Setting up Credentials`_, we made a path prefix there for our
resource. The point of having it there was so that we restrict our EC2 instance from
seeing other environment variables not designated for this project to see. Double
check your path prefix is the same.
.. _config/settings/base.py: ./config/settings/base.py
.. _config/settings/production.py: ./config/settings/production.py
If you'd like, in your `start_server`_ file, you can specify a region where you created
your parameters by appending ``--region us-east-2``.
Setting up Scripts to setup our Servers
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In our Launch Template step, we wanted Ubuntu servers. Here, we're going to install our
(i.e. my) preferred Python version, installing our necessary packages, and finally
running the server.
1. Copy `appspec.yml <./appspec.yml>`_. There are comments inside explaining what's
going on. In a nutshell, this is how CodeDeploy knows 1. where to store our project
files on the server and 2. where and what scripts to run using specific hooks which
have a unique lifecycle (e.g. ApplicationStop will trigger ApplicationStart but skips
AfterInstall).
2. Copy the `scripts <./scripts>`_ directory. In `install_dependencies`_, you can see me
updating and upgrading Ubuntu packages, setting up my preferred Python version,
collecting staticfiles, and installing my Python packages. Adjust to your needs.
`start_server`_ is preparing the actual start command for my server (for Django,
it's gunicorn). If you choose to delete the ``stop_server`` script, then make sure
to update ``appspec.yml``. Additionally, you'll need to change the region in the
CodeDeploy installation at the top.
3. In `install_dependencies`_, change the domain in the NGINX configuration iff you are
using sync. If you have websockets, long polling, or are using an async framework,
there are additional configurations you can add to your NGINX configuration if you
want to continue using it (or you can simply delete the NGINX configuration and set
the gunicorn/web server port from port 5000 to 80 since NGINX used to listen on port
80 for our web server (as the reverse proxy)).
4. In `start_server`_, we're grabbing all our environment variables from Parameter
Store and injecting it into our local environment. Adjust to your needs.
You may also notice us installing NGINX in the `install_dependencies`_ script. NGINX is
our buffering reverse proxy. Please read the comments in the script file. Note: it is
only necessary if you're using a sync server (i.e. if you're using Ruby and you're not
doing long polling, websockets, or comet, or if you're on Django or Flask and you're
using gunicorn and not uvicorn i.e. not using ASGI or any async def/coroutine stuff).
.. _install_dependencies: ./scripts/install_dependencies
.. _start_server: ./scripts/start_server
Finalizing Single-Execution Per-Deployment Script
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
We're going to take advantage of GitHub Actions one more time. For many frameworks, we
have a form of migrations like Django migrations, Alembic, etc. for a new database
schema. But we don't want all our instances running it. In that case, Django and Flask
provide a great utility for calling manage.py commands without the CLI. For Django,
it's ``call_command`` and Flask should use Alembic itself. I can't disclose how I did it
for my previous employer for Flask, but the solution is actually quite simple to create.
1. In this repo, visit `my_awesome_project/users/views.py`_ and take a look at the
``migrate`` function. In Django and Flask terminology, this is a "view" or an
endpoint. In this view, we check the headers for an Authorization token. The settings
for this authorization token is not set, but, so long as you read my tutorial for the
environment variable section, it shouldn't be a problem to add to Parameter Store.
Once authorized, we run the migrate command and send a JSON response specifying the
latest migration name/revision ID on our production database. I've also set up
another view for ``collectstatic`` which generates our static files and transfers it
to S3.
2. Additionally, visit `my_awesome_project/users/management/commands/deployment.py`_.
There, you see me running an entire deployment script. It migrates our database
on to the CI database (this is a Django only thing). Then, we can grab the latest
migration/revision ID. It loops (with 1 second delay) until we receive the right
data from our endpoint that matches the migration we need.
3. Go back to our GitHub action workflow file and adjust the last run step to your need,
including what secrets value to use and the Django command to run.
Note: the ``collectstatic`` command, in my opinion, should actually be run in the GitHub
action itself for greater security in case the server gets hacked. This makes better
separation of roles. However, for the simplicity of this lengthy tutorial, I've kept it
here. (When I mean run in the GitHub action, I mean set the ``DJANGO_SETTINGS_MODULE``
environment variable to our production settings and run collectstatic; in that case,
though, you'd need to constantly update the workflow file with new environment
variables which people will definitely forget).
.. _my_awesome_project/users/views.py: ./my_awesome_project/users/views.py
.. _my_awesome_project/users/management/commands/deployment.py: ./my_awesome_project/users/management/commands/deployment.py
Let's Run It!
^^^^^^^^^^^^^
Congrats on getting everything prepared! All that's left to do is to deploy our
application.
You have two options:
1. Go to your GitHub repository. Go to the Actions tab. Press Select Workflow.
Select ``CI``. Then, press "Run workflow" on your default branch.
2. You can also just commit new changes to your default branch.
Both ways work now and forever in the future! But this initial deployment will fail
since we didn't bring up an instance yet. Re-run your CI manually this time using the
GitHub actions tab, and voila a fully configured full-stack Django web server.
Credit and License
------------------
Repository is licensed under Apache 2.0; the license file can be found in
`LICENSE <./LICENSE>`_ in the root directory.
Repository created by `Andrew-Chen-Wang`_ sometime in July-August 2021. The original
motive was to set up `donate-anything.org `_ (which is
currently defunct as of August 2021). The original idea was to get this working in the
Spring/Summer of 2020, but it got way too complicated and I ended up writing my
`tutorial for ECS`_ instead since that was easier to work with (I mostly got stuck on
Parameter Store and the actual deployment from GitHub Actions. The CodeDeploy app either
just didn't or I just didn't comprehend the concepts of CI/CD, CodeDeploy, etc.; I
didn't understand the concepts very well since I'd only been on GitHub for...
6-ish months? So yea...).
.. _Andrew-Chen-Wang: https://github.com/Andrew-Chen-Wang
Additional Resources
--------------------
FAQ
^^^
**Is there a list of secrets that you added to GitHub and Parameter Store?**
Yup! Head down to the Additional Resources section [2]_ [3]_.
**How do I restart a deployment?**
You've got two options:
* Go to your application in CodeDeploy. Find the latest deployment for your application,
then re-run it.
* The other options involve Git/GitHub. You can go to your repository's Actions tab.
Select the workflow file including your deployment script. Then press Run workflow
and the branch should be your default branch. The other option is to simply commit
and push.
**How do I configure Celery and can I configure Celery on the same server?**
In `start_server`_ and `stop_server <./scripts/stop_server>`_,
you'll see I have some commands: two for starting and two for stopping. We run
everything in detached mode and stop it via its generated pidfile.
Here, I provide 4 methods for running Celery: 2 for doing everything on a single
instance/server and 2 for doing it on multiple instances/servers. If you can afford
it, the most recommended approach is to use the 3 EC2 autoscaling groups (meaning
a minimum of three servers which can be costly for some).
Yes, you can run Celery on one instance. For startups, having a single EC2 instance
is cost effective and more easily configurable; in fact, you have two options for
running Celery on one instance:
* Single server serving both Celery and your web server can be done either via Celery
itself or, if you need multiple nodes, supervisor. It comes in two parts (if you're
using Celery beat):
* Setup (celery beat only):
* For the single server solution: Celery beat should only be run on one instance, and
Blue/Green makes two (the original is not taken down until the new one is ready).
django-celery-beat is the preferred package over RedBeat's Redis scheduler and is
the one currently configured with cookiecutter-django. One way to work with this
is by doing a curl POST request to the following endpoint in our workflow file
**BEFORE** we use ``aws deploy`` to send a message to an endpoint. The endpoint:
.. code-block:: python
from django.http import HttpResponse
from config.celery_app import celery_app
def view(request):
celery_app.control.broadcast('shutdown')
return HttpResponse()
* Running Celery on the server:
* The systemd/init.d scripts Celery provides launches celery worker and beat using
detached and multi mode. In our script, we're going to use multi to run a single
Celery worker and beat. You can view this in our `start_server`_ script. If you're
curious, visit this doc for daemonization [4]_ and this GitHub issue thread [5]_
to get clarifications for confusing docs on deployment and ``multi`` usage.
* We have to install something called supervisord. We'll need to set up
that configuration and run ``supervisorctl restart project:*``. Our configuration
in this case allows us to run both Celery and our Django web app at the same time.
You don't have to have the website also running with supervisor. Note that to
properly shut down the celery workers, we need to manually send a signal.
* For those who require multiple servers, having a celery worker per server is overkill
and a waste of money/resources; I instead advise you to use a new EC2 Autoscaling
Group. This requires creating new launch templates since you don't want to attach
web security groups to your Celery based servers (you don't have to since the new
deployment groups don't have load balancer enabled, but just in case :P). If you can
afford it, I'd **REALLY** recommend three (one for celery beat since celery beat
can only be run on one server). But I'm assuming you can't; otherwise, why are you
reading this tutorial? Kidding. In the CI workflow file, you'll need to edit
``--target-instances ${{ secrets.AWS_AUTOSCALING_GROUP_NAME }}`` to have both your web
app's autoscaling group and your Celery worker autoscaling group deployed (in separate
commands). AWS provides an environment variable called ``DEPLOYMENT_GROUP_NAME``
where you can determine in your `start_server`_ script which command to run [6]_.
There are examples for both in `start_server`_ for how to do this.
* The next thing to understand is Celery beat can only be run on one server. A
solution is to use RedBeat, a Redis based beat scheduler in which case you can
ignore this bullet point. But if you want to use django-celery-beat, then you'll
need to have your autoscaling group for celery beat (if you're using the three
autoscaling groups method) or all of celery (the two autoscaling groups method)
be taken down first. This can be done via your CodeDeploy solution. When setting
up your CodeDeploy deployment groups, choose a Deployment type "In-place", choose
your celery based EC2 autoscaling groups, deployment settings should use whatever
percent based one you want (just don't do all at once), and disable the load
balancer.
For more background/daemonization docs for Celery:
https://docs.celeryproject.org/en/stable/userguide/daemonizing.html
**Can I run multiple websites like this?**
Yes. I, however, explicitly mentioned why not to (to have separate VPCs per project for
security) and to have different configurations. Also know that it'll be annoying when
one repository commits, all servers go down together. The method of setting this up is
the same as the celery bullet point above.
**Deployment is failing or website is not showing up**
Run the manual deployment a second or third time. Another method of troubleshooting
is to head to CodeDeploy, visit the latest deployment, then go to the bottom and
check the lifecycle hooks' logs.
**Can I run the shell in production?**
Yes! Just ssh into it via a random EC2 instance. Then run ``source venv/bin/activate``
to get the virtual environment and then run ``python manage.py shell``. I highly
recommend you install IPython via pip (stored in base.txt) and ``manage.py shell``
will use IPython automatically. To get autocomplete, press tab and jedi will appear.
**I'm getting an Internal Server Error**
Congrats on getting the website deployed! The server is running, but you may have
a view that's wrong or, commonly, the permissions for your S3 bucket is too secure.
For the first part, you should SSH into an EC2 instance. Run
``sudo su && chmod +x scripts/start_server``. Then run ``nano scripts/start_server``
and delete ``--daemon`` from gunicorn's command. Finally, run ``scripts/start_server``,
run the bad endpoint, and view the logs in gunicorn. For the S3 bucket permissions,
this is what you should have:
.. image:: docs/s3-perms.png
:width: 400
:alt: S3 bucket permissions
**Why is there still an instance after I deploy (double)?**
That's the reason for the 10 minutes termination. 10 minutes is more than enough time
to pip install and get everything ready. Traffic will slowly migrate to the new
instances. Doing it slowly makes sure our servers don't crash; additionally, it gives
us testing time to rollback a deployment if something goes awry. After a successful
deployment, the old instances and EC2 Autoscaling group will be destroyed.
Links
^^^^^
These are the additional resources that I used to create this tutorial
* Connecting CodeDeploy to GitHub: https://docs.aws.amazon.com/codedeploy/latest/userguide/deployments-create-cli-github.html
(`archived version of the integration docs `_)
* Creating the deployment: https://docs.aws.amazon.com/codedeploy/latest/userguide/deployments-create-cli.html
* Creating a subnet: https://docs.aws.amazon.com/vpc/latest/userguide/working-with-vpcs.html#AddaSubnet
* Internet Gateway and Route Table: https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Internet_Gateway.html#Add_IGW_Attach_Gateway
* Appspec "files" section: https://docs.aws.amazon.com/codedeploy/latest/userguide/reference-appspec-file-structure-files.html
* If you're curious what pricing and its price history for spot instances are, head to EC2/Spot Requests and find Pricing History at the top.
* Documentation for pricing for Spot Requests using Autoscaling: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-fleet-configuration-strategies.html
* Creating a CodeDeploy service role: https://docs.aws.amazon.com/codedeploy/latest/userguide/getting-started-create-service-role.html#getting-started-create-service-role-console
* Launch template support for service role: https://docs.aws.amazon.com/autoscaling/ec2/userguide/ec2-auto-scaling-launch-template-permissions.html#policy-example-create-launch-template
* Installing the CodeDeploy agent: https://docs.aws.amazon.com/codedeploy/latest/userguide/codedeploy-agent-operations-install-ubuntu.html
* Order of hooks: https://docs.aws.amazon.com/codedeploy/latest/userguide/reference-appspec-file-example.html#appspec-file-example-server
Thanks to this tutorial for helping me install the CodeDeploy agent in the right place
(turns out, someone else had already written a piece on this! Though using CodePipeline
might be a turn-off):
https://medium.com/cloudwithmore/blue-green-deployment-for-autoscaling-groups-with-codepipeline-codebuild-and-codedeploy-part-3-7-9d1d1d1824e7
Additional Notes
^^^^^^^^^^^^^^^^
.. [1] If you can't find the role, skip creating the User Group. Go back to IAM and go to
the Users section. Press Create User and select "Attach existing policies directly",
and finally press Create policy and copy the role below:
.. code-block:: json
{
"Version": "2012-10-17",
"Statement": [
{
"Action": [
"codedeploy:Batch*",
"codedeploy:CreateDeployment",
"codedeploy:Get*",
"codedeploy:List*",
"codedeploy:RegisterApplicationRevision"
],
"Effect": "Allow",
"Resource": "*"
},
{
"Sid": "CodeStarNotificationsReadWriteAccess",
"Effect": "Allow",
"Action": [
"codestar-notifications:CreateNotificationRule",
"codestar-notifications:DescribeNotificationRule",
"codestar-notifications:UpdateNotificationRule",
"codestar-notifications:Subscribe",
"codestar-notifications:Unsubscribe"
],
"Resource": "*",
"Condition": {
"StringLike": {
"codestar-notifications:NotificationsForResource": "arn:aws:codedeploy:*"
}
}
},
{
"Sid": "CodeStarNotificationsListAccess",
"Effect": "Allow",
"Action": [
"codestar-notifications:ListNotificationRules",
"codestar-notifications:ListTargets",
"codestar-notifications:ListTagsforResource",
"codestar-notifications:ListEventTypes"
],
"Resource": "*"
},
{
"Sid": "CodeStarNotificationsChatbotAccess",
"Effect": "Allow",
"Action": [
"chatbot:DescribeSlackChannelConfigurations"
],
"Resource": "*"
},
{
"Sid": "SNSTopicListAccess",
"Effect": "Allow",
"Action": [
"sns:ListTopics"
],
"Resource": "*"
}
]
}
.. [2] All my GitHub secret names and values
=============================== ==================================================
Secret Name Secret Value Description
=============================== ==================================================
AWS_ACCESS_KEY_ID The CodeDeploy access key ID we made in
`Setting up Credentials`_
AWS_SECRET_ACCESS_KEY The CodeDeploy secret access key we made in
`Setting up Credentials`_
AWS_CODEDEPLOY_REGION The region you created your CodeDeploy application
AWS_CODEDEPLOY_APPLICATION_NAME The CodeDeploy application name
AWS_CODEDEPLOY_DEPLOYMENT_GROUP The CodeDeploy deployment group name that we created in
`Creating a CodeDeploy Deployment Group`_
AWS_AUTOSCALING_GROUP_NAME The EC2 Autoscaling Group name we made in
`Setting up EC2 Auto Scaling Group`_
CI_CD_DEPLOYMENT_AUTH_TOKEN The authorization token to use for our single-execution
per-deployment script
=============================== ==================================================
.. _Setting up Credentials: #setting-up-credentials
.. _Creating a CodeDeploy Deployment Group: #creating-a-codedeploy-deployment-group
.. _Setting up EC2 Auto Scaling Group: #setting-up-ec2-auto-scaling-group
.. [3] All necessary Parameter Store names and values. Everything starts with your
prefix (in this case, it'll be "/p/" for "/project/" as that'll be my prefix). There
are plenty of other configuration/environment variables that you can find in the
`cookiecutter-django settings `_
================================= ======================================================
Parameter Names Parameter Value Description
================================= ======================================================
/p/DJANGO_SECRET_KEY Django secret key (same for Flask)
/p/DJANGO_SECURE_SSL_REDIRECT Tells Django to redirect HTTP to HTTPS. Should be False
since we're using ALB to do that.
/p/DATABASE_URL Database url from AWS RDS
/p/REDIS_URL Cache url from AWS ElastiCache/RabbitMQ (used mostly
for those using Celery). It can also be blank if you
don't choose to use Celery/a cache yet
/p/CELERY_BROKER_URL Same as REDIS_URL
/p/DJANGO_AWS_STORAGE_BUCKET_NAME The AWS S3 bucket where we store our media and static
files
/p/DJANGO_ADMIN_URL An extremely long, randomized path where our admin url
will be (e.g. ``base62-string/``)
/p/MAILGUN_API_KEY The email API key. For AWS infrastructure, it is
probably more wise to use AWS SES for cost purposes.
/p/MAILGUN_DOMAIN The email domain. For AWS infrastructure, it is
probably more wise to use AWS SES for cost purposes.
/p/SINGLE_CD_AUTHORIZATION_TOKEN The authorization token from our single-execution
per-deployment script
================================= ======================================================
.. [4] Celery Daemonization:
https://docs.celeryproject.org/en/stable/userguide/daemonizing.html
.. [5] Celery Beat daemonization GitHub issue thread:
https://github.com/celery/celery/issues/4304
.. [6] Environment variables for CodeDeploy:
https://docs.aws.amazon.com/codedeploy/latest/userguide/reference-appspec-file-structure-hooks.html#reference-appspec-file-structure-environment-variable-availability