Python’s Protocol Buffers code-generation using protoc has had significant changes that can cause developers… “challenges”. This post summarizes my experience of these mostly to save me from repreatedly recreating this history for myself when I forget it. Version change Generated code change Implementation Backends I’ll use this summarized table of proto and the Pypi library’s history in this post. protoc refers to the compiler that supports code-generation in multiple languages. protobuf refers to the corresponding Python (runtime) library on Pypi:

Google announced Firestore … integration with Eventarc. Ackal uses Firestore to persist Customer and Check information and it uses Google Cloud Firestore Triggers to handle events on these document types. Eventarc feels like the strategic future of eventing in Google Cloud and I’ve been concerned since adopting the technology that Google would abandon Google Cloud Firestore Triggers. For this reason, when I saw last week’s announcement, I thought I should evaluate the mechanism and this blog post is a summary of that work.

I’ve been running a DigitalOcean Apps static site for Hugo using the Hugo Buildpack. I’ve migrated a set of Hugo sites to use Hugo Modules which includes the addition of go.mod (and go.sum) files to the Hugo project in order to manage e.g. themes. Unfortunately, the Hugo Buildpack used by DigitalOcean Apps does not support Hugo Modules. DigitalOcean support recommended that I switch to use a build with a Dockerfile. Unfortunately (!

I’ve been spending time recently optimizing Ackal’s use of Google Cloud Logging and Cloud Monitoring in posts: Filtering metrics w/ Google Managed Prometheus Kubernetes metrics, metrics everywhere Google Metric Diagnostics and Metric Data Ingested Yesterday, I read that Robusta has a new open source project Kubernetes Resource Recommendations (KRR) so I took some time to evaluate it. This post describes the changes I had to make to get KRR working with Google Managed Prometheus (GMP):

I’ve been on an efficiency drive with Cloud Logging and Cloud Monitoring. With regards Cloud Logging, I’m contemplating (!) eliminating almost all log storage. As it is I’ve buzz cut log storage with a _Default sink that has comprehensive sets of NOT LOG_ID(X) inclusion and exclusion filters. As I was doing so, I began to wonder why I need to pay for the storage of much logging. There’s the comfort from knowing that everything you may ever need is being logged (at least for 30 days) but there’s also the costs that that entails.

I’ve written Prometheus Exporters for various cloud platforms. My motivation for writing these Exporters is that I want a unified mechanism to track my usage of these platform’s services. It’s easy to deploy a service on a platform and inadvertently leave it running (up a bill). The set of exporters is: Prometheus Exporter for Azure Prometheus Exporter for Fly.io Prometheus Exporter for GCP Prometheus Exporter for Linode Prometheus Exporter for Vultr This post describes the recently-added Azure Exporter which only provides metrics for Container Apps and Resource Groups.

I’ve been tinkering with ways to “unit-test” my assumptions when using cloud platforms. I recently wrote about good posts by Google describing achieving cost savings with Cloud Monitoring and Cloud Logging: How to identify and reduce costs of your Google Cloud observability in Cloud Monitoring Cloud Logging pricing for Cloud Admins: How to approach it & save cost With Cloud Monitoring, I’ve restricted the prometheus.googleapis.com metrics that are being ingested but realized I wanted to track the number of Pods (and Containers) deployed to a GKE cluster.

Google has published two, very good blog posts on cost management: How to identify and reduce costs of your Google Cloud observability in Cloud Monitoring Cloud Logging pricing for Cloud Admins: How to approach it & save cost This post is about my application cost reductions for Cloud Monitoring for Ackal. I’m pleased with Google Cloud Managed Service for Prometheus (hereinafter GMP). I’ve a strong preference for letting service providers run components of Ackal that I consider important but non-differentiating.

The majority of Ackal’s components are deployed to Google Cloud. However, by its nature, Ackal benefits from deployments that span cloud platforms. I’ve deployed Ackal’s gRPC health checks to Fly, and managed Kubernetes services on Linode and Vultr. Today, I decided to revisit¹ Azure. Ackal uses Azure (Active Directory) for one of its OAuth providers. This time, I wanted to deploy a containerized gRPC service. Azure provides several container-oriented services. I decided to use Azure Container Apps and, in hindsight, find it analogous to Google Cloud Run.

I’m building a Google Sheet that interacts with Google Cloud Storage (GCS) objects using Apps Script. I Googled but found few examples of such integrations beyond out-of-band solutions (e.g. Python solutions) that interact with Google services and program Google Sheets using its library. In my case, I’m going to bind a Google Sheet to a specific Google Cloud project and my Google (User) account has owner access to the Google Cloud Storage bucket and its objects.