๐ฆ๐ Launching MVP Start-Up Application ๐ฆ๐
This week blog post, I dove deep into the world of infrastructure automation for launching a Minimum Viable Product Application, specifically focusing on building custom AMIs with Packer. I successfully automated the release of new versions by applying semantic versioning and also automated the configuration of my AMIs with pre-installed softwares such as Nginx, Gunicorn, PostgreSQL and Python Virtual Environment for Django Framework using Bash scripting, streamlining my infrastructure setup process.
Building Custom AMIs with Hashicorp Packer
Leveraging HashiCorp Packer, I created custom AMIs using “Image as Code” principles, enabling consistent multi-platform virtual machine images.
Initially, I faced IAM permission challenges during build processes. By carefully analyzing error messages, I identified and added critical missing IAM actions: “autoscaling:DescribeLaunchConfigurations”, “ec2:DescribeLaunchTemplates”, and “ec2:DeleteLaunchTemplate”.
These additions resolved build failures and successfully enabled AMI creation.
Launching EC2 Instances with Custom AMIs
Once the AMI was ready, the next step was to launch EC2 instances using it. I implemented a Github Action workflow dispatch mechanism to select the desired AMI release version . This approach provides flexibility for launching instances from different AMI versions and rolling back to stable versions if needed.
I faced a few challenges during the launch process. Initially, filtering AMIs by name didn’t work as expected. I resolved this by appending a wildcard to the filter. Additionally, I encountered issues with passing variables to the Terraform script. I eventually found a workaround by using environment variables.
Connecting to EC2 Instances with SSM
To streamline instance access, I configured AWS Systems Manager (SSM) by creating instance profile and attaching it to the running instance with the required role, permission,SSM core policy and integrating it directly into my Terraform script.
This approach enabled seamless, secure connectivity to EC2 instances without traditional SSH methods, enhancing both operational efficiency and security.
Configuring AMIs with Bash Scripting for Automated Set-up
The final step in my infrastructure automation involved creating a Bash script to pre-install critical web application components. My goal was to streamline the AMI configuration by integrating Django Framework, PostgreSQL, Nginx, Gunicorn and a Python virtual environment into a cohesive deployment ecosystem. My start-up web application stack integrates multiple technologies seamlessly: Django serves as the web framework, powered by Gunicorn as the WSGI HTTP server for efficient Python web application deployment. Nginx acts as a robust reverse proxy, handling client requests and directing traffic to Gunicorn, while PostgreSQL provides a powerful, relational database backend. The entire ecosystem is encapsulated within a Python virtual environment, ensuring clean dependency management and isolation. During the setup, I encountered a significant challenge with database configuration. Specifically, I faced an issue with generated Open SSL password escaping some characters with the sed command in my script that prevented correct loading of database credentials from the environment variable. Through careful debugging, I identified and resolved the problem by implementing proper escaping techniques with pipes and backslash, ensuring secure and reliable database connectivity.
Key Takeaways
Key learnings from infrastructure automation and cloud deployment:
- IAM Permissions: Critical understanding of specific permissions required for infrastructure tasks
- Troubleshooting: Methodical debugging approach for resolving complex technical challenges
- Automation: Strategic use of tools like Packer and Terraform to streamline infrastructure provisioning
- Best Practices: Maintaining rigorous standards for security, configuration management, and version control