20 posts tagged with "Cloud"

Motivation and Background​

While I’ve used Kubernetes professionally in a few capacities (particularly in customer engagements while working at AWS), I wanted to cement my knowledge and increase my mastery with a systematic approach. I decided to prepare for the Certified Kubernetes Application Developer (CKAD) exam. I’ve taken and passed more than a dozen technology certification exams spanning AWS, Azure, HashiCorp, and more. This exam is unique in several ways. Namely, it’s all hands-on in a lab environment. Azure exams often have a coding, configuration, or CLI command component, but even these are typically multiple-choice questions. The CKAD presents you with a virtual desktop and several Kubernetes clusters, making you tackle 15-20 tasks with a strict two-hour time limit. I put together this repository and post for a few reasons:

• I wanted to document all of my hands-on preparation for when I have to recertify in two years
• I wanted to share my knowledge with others and offer a supplemental guide to a CKAD course
• Since the CKAD exam focuses on Kubernetes from a cloud-agnostic perspective, I wanted to fill in the gaps in my own knowledge of running Kubernetes in the AWS ecosystem (e.g., Karpenter, Container Insights, etc.)
• Many courses and guides leverage Microk8s or minikube to run Kubernetes locally, but I wanted to focus on cloud-based infrastructure, especially for things like EBS volumes created via PVCs, ELBs created via a Service, etc.

In summary, this material focuses on hands-on exercises for preparing for the exam and other tools in the cloud-agnostic and AWS ecosystems.

About​

This is a tool that I developed and open sourced at AWS. Find the latest in the GitHub repository here. It’s released under MIT-0.

AWS Billing Conductor (ABC) Overview​

AWS Billing Conductor is a priced service in the AWS billing suite designed to support showback and chargeback workflows for any AWS customer who needs to enforce visibility boundaries within their Organization or add custom rates unique to their business. This alternative version of the monthly bill is called a pro forma bill.

Solution Overview​

As I wrote about previously, AWS users are shifting left on costs using DevOps and automation. While tools like Infracost are powerful for estimating costs for Lambda and other services, they alone do not provide optimization or tuning feedback during the development lifecycle. This is where a tool like AWS Lambda Power Tuning assists:

AWS Lambda Power Tuning is an open-source tool that can help you visualize and fine-tune the memory and power configuration of Lambda functions. It runs in your own AWS account, powered by AWS Step Functions, and it supports three optimization strategies: cost, speed, and balanced.

Lambda pricing is determined by the number of invocations and the execution duration. There are several strategies for decreasing duration costs including using Graviton for 20% savings (which this solution does for both Lambda and CodeBuild), leveraging the latest runtime versions, taking advantage of execution reuse, etc. In addition to these, optimizing memory allocation is a key mechanism for efficiency. From the documentation:

The [duration] price depends on the amount of memory you allocate to your function. In the AWS Lambda resource model, you choose the amount of memory you want for your function and are allocated proportional CPU power and other resources. An increase in memory size triggers an equivalent increase in CPU available to your function.

Without running the Lambda function using different configurations, it is unclear what is the most optimal memory amount for cost and/or performance. This solution demonstrates AWS Lambda Tuning Tools integration with a CodeSuite CI/CD pipeline to bring Lambda tuning information to the pull request process and code review discussion. The source code is hosted on GitHub.

Overview​

I learn best by doing, so with every release cycle, I take the time to build fully functional examples and digest the blog posts and video content. Below are some of my favorite releases from re:Invent 2022. You can find all source code in this GitHub repository.

Solution Overview​

My current role focuses on every facet of AWS cost optimization. Much of this entails helping to remediate existing infrastructure and usage. Many customers ask how they can shift left on cloud costs, like they do with security. Ultimately, cost consciousness needs to be injected into every aspect of the engineering lifecycle: from the initial architecture design to implementation and upkeep.

One such aspect is providing developers visibility into the impact of their code changes. Infrastructure as code has made it easy to deploy cloud resources faster and at larger scale than ever before, but this means that cloud bills can also scale up quickly in parallel. This solution demonstrates how to integrate Infracost into a deployment pipeline to bring cost impact to the pull request process and code review discussion. The source code is hosted on GitHub.

The Course and Companion​

Rahul Nath recently released a course called AWS Lambda for the .NET Developer on Udemy and Gumroad. I had a ton of fun going through the exercises and highly recommend purchasing a copy. While working through the material, I implemented the solutions with infrastructure as code using AWS CDK in C# and .NET 6. I also containerized most of the Lambda functions and wrote unit tests for both the functions and infrastructure. You can find all my source code on GitHub.

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.

About AWS SAM Pipelines​

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.

Overview​

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.).

Deciding on Which Technology to Use​

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:

• CDK allows the infrastructure and the CI/CD pipeline to be described as C# instead of YAML, JSON, or HCL
• CDK provides the ability to inject more robust logic than intrinsic functions in CloudFormation and more modularity as well while still being a native AWS offering
• Docker ensures that the Lambda functions run consistently across local development, builds, and production environments and simplifies dependency management
• CDK Pipelines offer a higher level construct with much less configuration than CodePipeline and streamline management of multiple environments