Time to do something pretty handy: calling an HTTP endpoint, from your Google Cloud Workflows definitions. Whether calling GCP specific APIs such as the ML APIs, REST APIs of other products like Cloud Firestore, or when calling your own services, third-party, external APIs, this capability lets you plug your business processes to the external world!

Let’s see calling HTTP endpoints in action in the following video, before diving into the details below:

All the workflow definitions we’ve seen so far, in this series, were self-contained. They were not parameterized. But we often need our business processes to take arguments (the ID of an order, the details of the order, etc.), so that we can treat those input values and do something about them. That’s where workflow input parameters become useful!

Let’s start with a simple greeting message that we want to customize with a firstname and lastname. We’d like our workflow to look something like this:

So far, in this series of articles on Cloud Workflows, we have used simple data types, like strings, numbers and boolean values. However, it’s possible to use more complex data structures, like arrays and dictionaries. In this new episode, we’re going to use those new structures.

Arrays can be defined inline (like anArray) or spanning over several lines (like anotherArray):

- assignment:
    assign:
        - anArray:  ["a",  "b",  "c"]
        - anotherArray:
            -  one
            -  two
- output:
    return:  ${anArray[0] + anotherArray[1]}

The output step will return the string "atwo".

So far, in our Cloud Workflows series, we have seen some of the YAML syntax for defining workflows. However, steps are defined after each other, as a series of step definitions, but in spite of the jump instructions, the conditionals, you don’t really see visually what is going to be the next potential step in a workflow execution.

Fortunately, a new UI enhancement has landed in the Google Cloud Console: the ability to visualize a workflow definition with a graph, when you’re editing the definition. Furthermore, the graph is updated in quasi real-time as you make updates to the definition.

In the previous articles about Google Cloud Workflows, we talked about how to assign variables, create expressions, and also how to jump from a step to another. It’s time to combine both aspects to understand how we can do conditional jumps, thanks to the switch instruction.

Let’s start with a first step defining a variable, whose value we’ll use in our switch condition:

- assignement:
    assign:
        - number:  42

Then we’re going to create our second step that will use a switch instruction, with an expression:

Now that we have multiple steps in our workflow definition, let’s see how we can pass data around, from a step to another.

In a step, you can assign values to variables. Those values can be ints, doubles, strings, or booleans (and also null). Use the assign keyword as follows:

- assignments:
    assign:
        - two:  2
        - pi:  3.14
        - message:  "Hello"
        - bool:  True

Those variables are available in the whole scope of the workflow, and can be accessed in other steps. So let’s see how we can do something with those variables. Let’s add a second step to our workflow definition:

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):