Define PreviewEnvironment CRD Schema & Types: A Comprehensive Guide

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Define PreviewEnvironment CRD Schema & Types: A Comprehensive Guide

Hey everyone! Today, we're diving deep into the heart of our operator by defining the PreviewEnvironment Custom Resource Definition (CRD) schema. This is super crucial because it lays the groundwork for how we manage preview environments within our Kubernetes cluster. Think of it as setting the rules of the game, ensuring everyone plays nice and our system remains robust and extensible. Let's get started!

Understanding the Importance of CRDs

Before we jump into the specifics, let's quickly recap why CRDs are so important. In Kubernetes, CRDs allow us to extend the Kubernetes API by defining our own custom resources. This means we can create resources that are tailored to our specific needs, in this case, managing preview environments. A well-defined CRD acts as the contract between users and the system, specifying what information is required to create a preview environment, how it should be validated, and what status information is available. This leads to a more declarative and manageable system, making it easier to reason about and operate.

The key here is to build a robust foundation. The CRD will define the API contract for preview environments, and we want to ensure it supports all the features we've planned, as well as allowing room for future enhancements. It’s like building a house – a strong foundation ensures everything else can be built on top without collapsing!

Diving into the Technical Details

Based on our DATA_MODEL.md (you should totally check that out if you haven't already!), we're going to implement the following spec and status fields. These fields are the nuts and bolts of our CRD, defining what data we need and how it's structured. Think of them as the ingredients in a recipe – each one plays a specific role in the final dish.

Spec Fields: Defining the Desired State

The spec section of our CRD is where users define the desired state of their preview environment. It’s like telling the system, “Hey, I want a preview environment that looks like this!” Here are the fields we'll be including:

  • repository (string, pattern validated): This field specifies the repository where our code lives. We'll use pattern validation to ensure the repository is in the correct format (e.g., company/monorepo). It’s like specifying the exact location of the source code, so we know where to fetch the latest changes.
  • prNumber (int, min: 1): This is the pull request number associated with the preview environment. We'll enforce a minimum value of 1, because, well, a PR number less than 1 doesn't make much sense, right? It helps us tie the preview environment to a specific pull request, making it easy to track changes.
  • headSHA (string, 40 chars): This is the SHA (Secure Hash Algorithm) of the head commit in the pull request. It's a unique identifier for a specific version of the code. We'll ensure it's exactly 40 characters long, which is the standard length for a SHA-1 hash. This is like taking a snapshot of the code at a particular point in time, ensuring we're previewing the exact code we expect.
  • baseBranch (string): This is the base branch the pull request is targeting (e.g., main). It helps us understand the context of the changes in the pull request. It’s like knowing the foundation the changes are built upon.
  • headBranch (string): This is the branch associated with the pull request (e.g., feature/test). It tells us which branch contains the changes we're previewing. It’s the specific branch we’re trying out in our preview environment.
  • services ([]string, optional): This is an optional list of services to include in the preview environment. If not specified, we'll include all services by default. This allows us to cherry-pick the services we want to include in our preview, giving us more control and flexibility.
  • ttl (duration, default: "4h"): This specifies the time-to-live for the preview environment. After this duration, the environment will be automatically deleted. We'll set a default of 4 hours, but users can override this. It’s like setting an expiration date for our preview environment, ensuring we don't clutter our system with stale previews.
  • resources (ResourceRequirements, optional): This allows users to specify resource requirements (CPU, memory, etc.) for the preview environment. It’s like telling the system how much computing power our preview environment needs.
  • config (PreviewConfig, optional): This allows users to specify custom configuration for the preview environment. It's like adding extra options and tweaks to our preview setup.

Status Fields: Reflecting the Current State

The status section of our CRD reflects the current state of the preview environment. It’s like the system telling us, “Hey, this is what your preview environment looks like right now!” Here are the fields we'll be including:

  • conditions ([]metav1.Condition): This is a list of conditions representing the current state of the preview environment (e.g., Ready, Failed). It provides a detailed view of what's happening behind the scenes.
  • phase (enum: Pending|Creating|Ready|Updating|Deleting|Failed): This is a high-level phase indicating the overall state of the preview environment. It gives us a quick snapshot of the current status, like a progress bar.
  • url (string): This is the URL for accessing the preview environment. It's the magic link that takes us to our live preview.
  • namespace (string): This is the Kubernetes namespace where the preview environment is deployed. It helps us isolate our preview environments within the cluster.
  • services ([]ServiceStatus): This is a list of statuses for each service in the preview environment. It gives us a granular view of each service's health and status.
  • *costEstimate (CostEstimate): This is an estimate of the cost associated with running the preview environment. It helps us keep track of our spending.
  • *createdAt, expiresAt, lastSyncedAt (metav1.Time): These timestamps track when the environment was created, when it will expire, and when it was last synced. They provide a timeline of our preview environment's lifecycle.
  • observedGeneration (int64): This is the generation of the spec that was last observed by the controller. It helps us ensure our controller is working with the latest version of the spec.

Kubebuilder Markers: Adding Validation and Metadata

To make our CRD robust and user-friendly, we'll be using Kubebuilder markers. These markers add metadata and validation rules to our fields. Think of them as guardrails, preventing users from entering invalid data and making our CRD more self-documenting.

  • Validation rules (regex patterns, min/max, enums): We'll use these to enforce constraints on our fields, such as ensuring the repository is in the correct format or the PR number is a positive integer. It’s like having a built-in spell checker for our data!
  • Print columns for kubectl output: These markers allow us to define custom columns when users run kubectl get previews. This makes it easier to view important information about our preview environments at a glance. It’s like customizing our view to show the most relevant information.
  • Subresource for status updates: This marker allows us to update the status subresource independently of the spec. This is important for performance and consistency. It’s like having a dedicated channel for status updates.
  • Short names (preview, previews): These markers define short names for our CRD, making it easier to use with kubectl (e.g., kubectl get preview). It’s like giving our CRD a nickname for convenience.

Acceptance Criteria: Ensuring Quality

To ensure our CRD is working as expected, we've defined a set of acceptance criteria using Gherkin syntax. These scenarios will be translated into unit tests, giving us confidence that our CRD is rock solid. Think of these as our quality control checklist.

Here are a few examples of the scenarios we'll be testing:

  • CRD is installed to cluster: We'll verify that our CRD can be successfully installed in a Kubernetes cluster.
  • Create PreviewEnvironment with valid spec: We'll create a preview environment with a valid spec and ensure it's created successfully.
  • Validation rejects invalid repository format: We'll try to create a preview environment with an invalid repository format and ensure the creation fails with a validation error.
  • Default TTL is applied: We'll create a preview environment without specifying a TTL and ensure it defaults to 4 hours.

Definition of Done: Marking Our Progress

To keep us on track, we've defined a clear definition of done. This outlines the tasks that need to be completed before we can consider this work finished. Think of it as our finish line.

Here's what we need to do:

  • [ ] CRD defined in api/v1alpha1/previewenvironment_types.go
  • [ ] All kubebuilder validation markers added
  • [ ] controller-gen successfully generates CRD YAML
  • [ ] CRD can be applied to a cluster (make install)
  • [ ] kubectl get previews shows custom columns
  • [ ] All validation scenarios pass (unit tests)
  • [ ] DeepCopy methods generated
  • [ ] Documentation updated (godoc comments)

Dependencies and Estimated Size

The good news is that we don't have any dependencies for this task – this is the first step! We've estimated the size of this task as Medium (M) because it involves core data model design and requires careful attention to detail. It’s like laying the first brick in a building, you want to make sure it’s perfectly placed.

References: Guiding Our Way

We have a few references to help us along the way:

  • DATA_MODEL.md: This document defines our data model and provides the foundation for our CRD.
  • COMPONENT_DESIGN.md: This document outlines the design of our components and how they interact.
  • Kubebuilder CRD Tutorial: This tutorial provides a step-by-step guide to creating CRDs with Kubebuilder.

Conclusion: Building a Solid Foundation

Defining the PreviewEnvironment CRD schema and types is a critical step in building our operator. It sets the stage for managing preview environments in a robust, scalable, and user-friendly way. By carefully defining our spec and status fields, adding validation rules, and writing comprehensive tests, we can ensure our CRD is a solid foundation for the rest of our project. So, let's roll up our sleeves and get coding, guys! We're building something awesome here.

I hope this comprehensive guide has been helpful. Stay tuned for more updates as we continue to build our operator! Feel free to drop any questions or thoughts in the comments below. Let's build this together!