I previously wrote a blog post about getting started with Terraform for CDK and the benefits. At that time, the latest version was 0.3. Last week, version 0.5 was released. In this version, some new experimental features could make adopting CDK for Terraform exponentially easier.
Last week, AWS announced the public preview for SAM Pipelines. This feature expands the SAM CLI allowing users to create multi-account CI/CD pipelines for serverless applications quickly across several providers such as GitHub Actions, GitLab CI/CD, and Jenkins. Along with CDK Pipelines, the AWS tooling keeps making it easier to standardize with best practices.
Both AWS CDK and Terraform aim to solve a similar problem: alleviating some of the infrastructure management challenges with code. CDK supports several general-purpose languages, including C#, Python, and TypeScript, while Terraform uses its configuration language called HCL. While CDK can only create AWS resources, Terraform supports virtually every cloud provider, granting the ability to write code to deploy to multiple public clouds at once. Last year, Terraform and AWS announced a project called Terraform for CDK, aiming to grant the best of both worlds (i.e., support for GPLs, multi-cloud, etc.).
While infrastructure as code (IaC) has existed within the AWS ecosystem since 2011, adoption has exploded recently due to the ability to manage large amounts of infrastructure at scale and standardize design across an organization. There are almost too many options between CloudFormation (CFN), CDK, and Terraform for IaC and Serverless Application Model ( SAM) and Serverless Framework for development. This article from A Cloud Guru quickly sums up the pros and cons of each option. I choose this particular stack for some key reasons:
Per AWS, “Cloud9 is a cloud-based integrated development environment (IDE) that lets you write, run, and debug your code with just a browser. It includes a code editor, debugger, and terminal. Cloud9 comes prepackaged with essential tools like Docker and support for popular programming languages, including JavaScript, Python, PHP, and .NET.” The AWS, Serverless Application Model (SAM), and Cloud Development Kit (CDK) CLIs are pre-installed as well. Users are abstracted from credential management (i.e., there’s no need to provision an IAM user and run aws configure). Since the underlying compute is an EC2 instance, developers have a consistent experience across client devices.
I’ve been making a concerted effort lately to use infrastructure as code via CloudFormation for all of my personal AWS-hosted projects. Writing these templates can feel a bit tedious, even with editor tooling and plugins. I thought it would be awesome to generate CloudFormation templates for existing resources and first found CloudFormer. I found blog posts about CloudFormer from as far back as 2013, but it was never advertised much.
Update: Former2 is the de facto standard now that CloudFormer has been deprecated. I kept my notes on CloudFormer for posterity at the end of the post.
spr is a CLI tool for generating Markdown pages in the league Gatsby site for power rankings posts with Sleeper data injected. The source code is hosted on GitHub.
I use Azure DevOps for building CI/CD pipelines for professional projects. Given the GitHub integration and broader feature set, I’ve started using it instead of Travis CI for my open-source projects too. For most technology areas, there’s a wide set of pre-built tasks that can be leveraged to build robust pipelines quickly. There are several tasks for compiling and publishing iOS applications using Xcode on transient macOS virtual machines.
However, in the spirit of using Swift like a general-purpose language, I wanted to use a Linux build server, a more industry-standard approach for CI/CD. In my previous post, I described how I set up a Swift executable package to be more testable. This pipeline provides continuous integration for it. Azure Pipelines, which powers CI/CD in Azure DevOps, is scripted in YAML. It also supports integrating shell commands to be run on the virtual machine.
To better learn Swift, I’ve been trying to use it as a truly general-purpose programming language instead of purely iOS development. I’m currently building an iOS app that requires multiple versions of the same vector graphics (SVGs). I couldn’t find an open-source solution for my needs, so I decided to start scripting. Typically, I would have used Python or Node.js, but I powered through with Swift in the spirit of immersion.
Getting the initial project structure and unit tests set up took some research, so this quick guide will outline how I’ve been structuring my codebases for executable packages. Outside of iOS development, Swift’s documentation isn’t as robust as Python or Node.js, given the age difference. This blog post’s objective is to merge a lot of useful knowledge I found across forums.
I co-teach an advanced placement computer science class at Heights High School in Houston with the TEALS program. The curriculum utilizes the Java programming language and has an emphasis on object-oriented programming. From a school IT system perspective, we have limited options for the software we can distribute to the students’ laptops. During my first year of volunteering, we opted to use BlueJ for the first couple of months before exposing them to Eclipse, a more heavy-duty integrated development environment. Both editors have their challenges and limitations for novices, so we began to evaluate several other options, including Visual Studio Code. After considering numerous factors, including the complexity of installation, editor features, and accessibility, we opted to try a radically different option: Repl.it.