Let’s continue our discovery of Goole Cloud Workflows!

Yesterday, we discovered the UI of Workflows. We created our first workflow. We started with a single step, returning a greeting message:

- sayHello:
    return: Hello from Cloud Workflows!

A workflow definition is made of steps. But not just one! You can create several steps. In YAML, the structure of your workflow will be something like:

- stepOne:
    # do something
- stepTwo:
    # do something else
- sayHello:
    return: Hello from Cloud Workflows!

By default, steps are executed in the order they appear, from top to bottom. The execution will finish when either you return a value, or you reach the final step. If there’s no return statement, a null value is returned as result of the workflow execution.

With more and more interconnected services, making sense of their interactions becomes critical. With Google Cloud Workflows, developers can orchestrate and automate such complex systems by creating serverless workflows.

In this series of articles, we will learn together how to use Goole Cloud Workflows, and get to know all its features, with short and easy to read tutorials. For our first day, we’ll discover and use the Workflows UI in the cloud console. We will create a simple “hello world” workflow, consisting of a simple step. Going further, in the coming days, we’ll learn more about advanced features. But first things first!

The trend toward splitting a monolith into fine-grained loosely-coupled microservices has its merits. It allows us to scale parts of an application more easily. Teams become more effective on their focused perimeter. However, in a chain or graph of services interacting with each other via message buses or other eventing mechanisms, it becomes difficult to understand when things start to break. Your business processes spanning those services are in limbo. Here starts detective work to find out how to get back on track.

Last week, I had the pleasure of presenting Micronaut in action on Google Cloud Platform, via a webinar organized by OCI. Particularly, I focused on the serverless compute options available: Cloud Functions, App Engine, and Cloud Run.

Here are the slides I presented. However, the real meat is in the demos which are not displayed on this deck! So let’s have a closer look at them, until the video is published online.

On Google Cloud Platform, you have three solutions when you want to deploy your code in a serverless fashion (ie. hassle-free infrastructure, automatic scaling, pays-as-you-go): 

For the 24 hours of talks by Google Cloud DevRel, I recorded my talk about the new Java 11 runtime for Google Cloud Functions. I wrote about this runtime in this article  showing for example how to run Apache Groovy functions, and I also wrote about it on the  GCP blog  and Google Developers blog as well.

In this video, I’m giving a quick explanations on the serverless approach, the various serverless options provided by Google Cloud, and then I dive into the various shapes Java functions can take (HTTP and background functions), the interfaces you have to implement when authoring a function. And I also do various demonstrations, deploying Java functions, Groovy functions, or Micronaut functions!

The Java programming language recently turned 25 years old, and it’s still one of the top-used languages powering today’s enterprise application customers. On Google Cloud, you can already run serverless Java microservices in App Engine and Cloud Run. Today we’re bringing Java 11 to Google Cloud Functions, an event-driven serverless compute platform that lets you run locally or in the cloud without having to provision servers. That means you can now write Cloud Functions using your favorite JVM languages (Java, Kotlin, Groovy, Scala, etc) with our Functions Framework for Java, and also with Spring Cloud Functions and Micronaut!

With the beta of the new Java 11 runtime for Google Cloud Functions, Java developers can now write their functions using the Java programming language (a language often used in enterprises) in addition to Node.js, Go, or Python. Cloud Functions allow you to run bits of code locally or in the cloud, without provisioning or managing servers: Deploy your code, and let the platform handle scaling up and down for you. Just focus on your code: handle incoming HTTP requests or respond to some cloud events, like messages coming from Cloud Pub/Sub or new files uploaded in Cloud Storage buckets.

Java celebrates its 25th anniversary!  Earlier this year, the Apache Groovy team released the big  3.0 version of the programming language.  GMavenPlus was published in version 1.9 (the Maven plugin for compiling Groovy code) which works with Java 14. And today, Google Cloud opens up the beta of the  Java 11 runtime for Cloud Functions. What about combining them all?

I’ve been working for a bit on the Java 11 runtime for Google Cloud Functions (that’s the Function-as-a-Service platform of Google Cloud, pay-as-you-go, hassle-free / transparent scaling), and in this article, I’d like to highlight that you can also write and deploy functions with alternative JVM languages  like Apache Groovy.

With my partner in crime, Mete Atamel, we ran two editions of our “Pic-a-Daily” serverless workshop. It’s an online, hands-on, workshop, where developers get their hands on the the serverless products  provided by Google Cloud Platform:

• Cloud Functions — to develop and run functions, small units of logic glue, to react to events of your cloud projects and services
• App Engine — to deploy web apps, for web frontends, or API backends
• Cloud Run — to deploy and scale containerised services

Hot on the heels of the announcement of the general availability of JDK 14, I couldn’t resist taking it for a spin. Without messing up my environment — I’ll confess I’m running 11 on my machine, but I’m still not even using everything that came past Java 8! — I decided to test this new edition within the comfy setting of a Docker container.

Minimal OpenJDK 14 image running JShell

Super easy to get started (assuming you have Docker installed on your machine), create a Dockerfile with the following content: