Cloud Run

Cloud Code is a set of IDE plugins for popular IDEs that make it easier to create, deploy and integrate applications with Google Cloud. Cloud Code provides an excellent extension mechanism through custom templates. In this post, I show you how you can create and use your own custom templates to add some features beyond those supported natively in Cloud Code, such as .NET functions, event triggered functions and more. As a recap, in my Introducing Cloud Functions support in Cloud Code post, I pointed out some limitations of the current Cloud Functions support in Cloud Code: Read More →

.NET 7 was released a few days ago with new features and performance improvements and it’s already supported on Cloud Run on Google Cloud! In this short blog post, I show you how to deploy a .NET 7 web app to Cloud Run. Create a .NET 7 web app First, make sure you’re on .NET 7: dotnet --version 7.0.100 Create a simple web app: dotnet new web -o helloworld-dotnet7 This creates the helloworld-dotnet7 folder with a project file. Read More →

At Google Cloud I/O, we announced the public preview of Cloud Run jobs. Unlike Cloud Run services that run continuously to respond to web requests or events, Cloud Run jobs run code that performs some work and quits when the work is done. Cloud Run jobs are a good fit for administrative tasks such as database migration, scheduled work like nightly reports, or doing batch data transformation. In this post, I show you a fully serverless, event-driven application to take screenshots of web pages, powered by Cloud Run jobs, Workflows, and Eventarc. Read More ↗︎

Until recently, this is how you deployed code to Cloud Run: Define your container-based app with a Dockerfile. Build the container image and push it to the Container Registry (typically with Cloud Build). Deploy the container image to Cloud Run. Back in December, we announced the beta release of source-based deployments for Cloud Run. This combines steps 2 and 3 above into a single command. Perhaps more importantly, it also eliminates the need for a Dockerfile for supported language versions. Read More ↗︎

Cloud Run and Eventarc is a great combination for building event-driven services with different event routing options. There are two trigger types (Audit Logs and Pub/Sub) to choose from in Eventarc. Eventarc uses Pub/Sub as its underlying transport layer and provides convenience and standardization on top of it. If you wanted to, you could skip Eventarc and read messages directly from Pub/Sub in Cloud Run. This blog post details three ways of receiving events in Cloud Run and provides a decision framework on how to choose. Read More ↗︎

Introduction We recently announced a new feature, Events for Cloud Run for Anthos, to build event driven systems on Google Kubernetes Engine (GKE). In the announcement, we also stated that the solution is based on open-source Knative. In this blog post, I want to further explain the relationship between this new feature and Knative. I also want to convince you that our solution is an easier way to deploy Knative compliant event consuming services on Google Cloud. Read More →

Building microservices on Google Kubernetes Engine (GKE) provides you with maximum flexibility to build your applications, while still benefiting from the scale and toolset that Google Cloud has to offer. But with great flexibility comes great responsibility. Orchestrating microservices can be difficult, requiring non-trivial implementation, customization, and maintenance of messaging systems. Cloud Run for Anthos now includes an events feature that allows you to easily build event-driven systems on Google Cloud. Read More ↗︎

Challenges in connecting services When I think about my recent projects, I probably spent half of my time coding new services and the other half in connecting services. Service A calling Service B, or Service C calling an external service and using the result to feed into another Service D. Connecting services is one of those things that ‘should be easy’ but in reality, it takes a lot of time and effort. Read More →

My colleague Felipe Hoffa recently published a blog post titled Get started with BigQuery and dbt, the easy way. More specifically, he showed how to install dbt in Google Cloud Shell, configure it and manually run it to create a temporary dataset in BigQuery. This is great for testing dbt + BigQuery but how do you run this kind of setup in production? dbt documentation states that Running dbt in production simply means setting up a system to run a dbt job on a schedule, rather than running dbt commands manually from the command line. Read More →