GCS Default OVA Publishing & Full Builder Run

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GCS Default OVA Publishing & Full Builder Run

Hey folks! 👋 Let's dive into making Google Cloud Storage (GCS) the go-to spot for our build artifacts. We're also gonna run a full-blown builder run to make sure everything's shipshape. This is a big step, so buckle up!

1. Setting the Stage: Docs, Configs, and the Plan 📜

Alright, first things first: let's get our ducks in a row with the documentation and configuration. We're talking about updating the key documents that guide our build process. We'll be updating docs/build/BUILD_GUIDE.md, docs/build/GCP_BUILDER.md, and docs/RELEASE_PROCESS.md to reflect the change. Think of these as our instruction manuals. The main focus will be making GCS the star of the show for storing our build artifacts. Instead of AWS/S3, the shared bucket on GCS will be the primary destination. We're looking at something like gs://hedgehog-lab-artifacts-teched-473722/.

But hey, we're not ditching AWS/S3 altogether! We'll keep the instructions for using AWS/S3, but we're going to mark them as an alternative, something for future marketplace work. This way, we're prepared for the future, but we're keeping things simple and streamlined for now. It's all about making life easier for our contributors, you know?

Then there's the .env.gcp.example file. This is where we lay out the environment variables that our scripts will use. We'll make sure this file clearly documents the necessary bucket variables and that the scripts know how to load them. This is the foundation upon which our build automation rests. Everything has to be set up correctly, or the whole thing falls apart. The .env.gcp.example file will be the key to avoiding those kinds of issues. We're aiming for a seamless transition, so proper documentation and configuration are super important here. This way, any new contributor, or anyone revisiting the process after a while, can get up to speed quickly.

Why This Matters

This first step is all about making sure everyone is on the same page. Clear documentation and proper configuration mean fewer headaches down the road. It ensures that the build process is transparent, maintainable, and easy to understand. Plus, it's essential for getting our first full builder run off the ground without a hitch. This is the kind of detail that separates a smooth operation from a chaotic one, and that's precisely what we're aiming for. Setting up the documentation and the configuration properly will prevent confusion later on, and streamline the whole process, enabling smooth automation!

2. Automating the Magic: Script and Target Updates 💻

Now, let's talk automation. We need to tweak scripts/launch-gcp-build.sh (or maybe even some Make targets – we'll see what works best) to automatically upload our .ova and .sha256 files to GCS after a successful build. This is where the real magic happens, guys! The build process creates our precious artifacts (the .ova file and its checksum), and we want them safely tucked away in GCS.

So, what does this mean? Basically, after the build completes without any errors, the script needs to:

  1. Identify the Artifacts: Find the .ova and .sha256 files that were just created.
  2. Authenticate with GCS: Make sure the script has the correct permissions to upload files to the specified GCS bucket. This usually involves some form of authentication, like using a service account.
  3. Upload the Files: Use the gsutil command-line tool (or its equivalent in the script) to upload the files to the designated GCS bucket.
  4. Log the Process: Log each step of the upload process. This is super important because it provides an audit trail. If something goes wrong, we'll know exactly where to look.

The Upload Helper

We'll also create a helper target (e.g., make publish-gcs) that can re-upload artifacts if we need to. This is really handy for troubleshooting or if we need to manually update something in GCS. Think of it as a backup plan, so we can always make sure our artifacts are where they need to be. It's like having a safety net.

Logging is Key

We need to log every gsutil upload step. This is crucial for several reasons: It allows us to keep track of the upload process, it provides an audit trail for debugging purposes, and it helps to ensure that we can verify the integrity of the uploaded artifacts. The logs should include timestamps, the files uploaded, and any error messages that occur. Good logging practices will save us tons of time and headaches later on.

Why automate?

Automating the upload process ensures that our build artifacts are stored in GCS automatically and efficiently. This reduces the need for manual intervention, which in turn reduces the risk of human error. It also streamlines the workflow. This will also make the process more reliable and less prone to errors. With automation, the artifacts are available immediately after a successful build.

3. The Grand Finale: First Full Builder Run 🚀

It's showtime, folks! We're finally going to execute a real scripts/launch-gcp-build.sh run. This is where we put everything to the test. We'll use the shared bucket, as we planned, and see how the whole process unfolds. We'll verify that everything works as expected.

Verifying the Build

Here’s what we'll be checking during this run:

  • Nested KVM: We'll ensure that nested KVM (Kernel-based Virtual Machine) is working correctly inside the VM. This is crucial for running our builds, as it allows us to create and manage virtual machines within the virtual machine that our builder runs on.
  • Packer Success: We will be checking that Packer, our tool for creating machine images, completes its tasks successfully. If Packer fails, it will indicate a problem with the configuration or the environment.
  • Artifact Checksum: We'll verify the artifact checksum to make sure the .ova file hasn't been corrupted during the build or upload process. This ensures data integrity.
  • Bucket Contents: We'll use gsutil ls gs://... to check the contents of our GCS bucket. We'll make sure the .ova file and checksum are there, confirming that the upload was successful.

Recording the Data

During the run, we'll be collecting some important data:

  • Build Duration: How long does the entire build process take? This helps us to assess efficiency and identify any bottlenecks.
  • Instance Size: What instance size did we use? This will allow us to assess the resources our build process needs.
  • Disk Usage: How much disk space did the build process use? This helps us determine if our disks are properly sized.
  • Cost: Estimate the cost of the run based on gcloud compute instances describe / billing estimate. This is important to ensure the build process is cost-effective.

Sharing the Results

We'll attach or paste the build log summary and gsutil output to this issue. This will give us a complete record of the build process and will be extremely helpful for future debugging, analysis, and improvements.

4. After the Dust Settles: The Post-Run Checklist ✅

Alright, the builder run is done. Time to analyze the results and make sure we learned some lessons.

Lessons Learned

We will update docs/build/GCP_BUILDER.md with lessons learned. We will cover things like:

  • Throughput: Was the upload speed as expected? This can help us to optimize the upload process.
  • Quotas: Did we run into any quota limitations from GCS? We need to keep this in mind.
  • Cost Observations: Were our cost estimates accurate, and did we find any unexpected costs? We will keep an eye on our spending.

Edge Cases and Mitigation

If the upload helper exposes any edge cases, like problems with resumable uploads, we'll document the mitigation steps. We'll cover any issues we find during the run.

Follow-Up Issues

If we discover any problems during this run, we'll open follow-up issues, referencing this ticket. This will ensure that nothing slips through the cracks and the process gets refined. This follow-up work will help us optimize the build process.

Wrapping Up

This entire process is about optimizing our build pipeline. By making GCS the default destination, automating uploads, and rigorously testing the process, we're making our builds more efficient, reliable, and cost-effective. Each step we take brings us closer to a more streamlined and automated build process. It's a continuous journey of improvement, and you are all a part of it! By following the post-run checklist, we are building a more sustainable and robust build system.