Workflows allows you to call APIs, whether from or hosted on Google Cloud, or any external API in the wild. A few days ago, for example, we saw an example on how to use the SendGrid API to send emails from a workflow. However, in that article, I had the API key hard-coded into my workflow, which is a bad practice. Instead, we can store secrets in Secret Manager. Workflows has a specific connector for Secret Manager, and a useful method to access secrets.

Following up the article on writing and reading JSON files in cloud storage buckets (GCS), we saw that we could access the data of the JSON file, and use it in our workflow. Let’s have a look at a concrete use of this.

Today, we’ll take advantage of this mechanism to avoid hard-coding the URLs of the APIs we call from our workflow. That way, it makes the workflow more portable across environments.

Following up the article on writing and reading JSON files in cloud storage buckets, we saw that we could access the data of the JSON file, and use it in our workflow. Let’s have a look at a concrete use of this.

Today, we’ll take advantage of this mechanism to avoid hard-coding the URLs of the APIs we call from our workflow. That way, it makes the workflow more portable across environments.

At enterprises across industries, documents are at the center of core business processes. Documents store a treasure trove of valuable information whether it’s a company’s invoices, HR documents, tax forms and much more. However, the unstructured nature of documents make them difficult to work with as a data source. We call this “dark data” or unstructured data that businesses collect, process and store but do not utilize for purposes such as analytics, monetization, etc. These documents in pdf or image formats, often trigger complex processes that have historically relied on fragmented technology and manual steps. With compute solutions on Google Cloud and Document AI, you can create seamless integrations and easy to use applications for your users. Document AI is a platform and a family of solutions that help businesses to transform documents into structured data backed by machine learning. In this blog post we’ll walk you through how to use Serverless technology to process documents with Cloud Functions, and with workflows of business processes orchestrating microservices, API calls, and functions, thanks to Workflows.

One year after Google App Engine was released in 2008, Java became the second language runtime available on the platform. Java developers were able to deploy and scale their servlet-based web applications easily, without worrying about infrastructure management. Not only Java was able to run then, but alternative JVM languages, like Apache Groovy, and Kotlin are also part of the game. Fast forward to today, we’re pleased to announce that the Java Runtime for App Engine is now available as open source, in the GoogleCloudPlatform/appengine-java-standard repository on Github.

Workflows provides several connectors for interacting with various Google Cloud APIs and services. In the past, I’ve used for example the Document AI connector to parse documents like expense receipts, or the Secret Manager connector to store and access secrets like passwords. Another useful connector I was interested in using today was the Google Cloud Storage connector, to store and read files stored in storage buckets.

Those connectors are auto-generated from their API discovery descriptors, but there are some limitations currently that prevent, for example, to download the content of a file. So instead of using the connector, I looked at the JSON API for cloud storage to see what it offered (insert and get methods).

As I was working with my colleague Sara Ford on testing the Cloud Functions runtimes for the upcoming “second generation” of the product, rebased on the Cloud Run platform, I wrote a few simple functions for the Java runtime. In one of those Java functions, I wanted to use Google Cloud Storage, to download a file from a bucket. I took a look at the existing sample to download an object:

Storage storage = StorageOptions.newBuilder()
    .setProjectId(projectId)
    .build()
    .getService();

Blob blob = storage.get(BlobId.of(bucketName, objectName));
blob.downloadTo(Paths.get(destFilePath));

I know the name of the bucket, the name of the file, I’m going to store the file in the local file system. So I have all the information needed… except the project ID within which I deployed my Java cloud function. So how do I get the project ID, in Java, inside the Cloud Functions environment?

With Workflows, developers can easily orchestrate various services together, on Google Cloud or third-party APIs. Workflows connectors handle long-running operations of Google Cloud services till completion. And Workflow executions can also wait for time to pass with the built-in sys.sleep function, till some computation finishes, or some event takes place. 

But what if you need some user input or some approval in the middle of the workflow execution, like validating automatic text translation? Or an external system like a fulfillment center or an inventory system that is going to notify that products are back in stock? Instead of using a combination of “sleep” instructions and API polling, now you’ll be able to use Workflows callbacks! 

Workflows is a service to orchestrate not only Google Cloud services, such as Cloud Functions,  Cloud Run, or machine learning APIs, but also external services. As you might expect from an orchestrator, Workflows allows you to define the flow of your business logic, as steps, in a YAML or JSON definition language, and provides an execution API and UI to trigger workflow executions. You can read more about the benefits of Workflows in our previous article.

Over the past year, we (Mete and Guillaume) have developed a picture sharing application, named Pic-a-Daily, to showcase Google Cloud serverless technologies such as Cloud Functions, App Engine, and Cloud Run. Into the mix, we’ve thrown a pinch of Pub/Sub for interservice communication, a zest of Firestore for storing picture metadata, and a touch of machine learning for a little bit of magic.

We also created a hands-on workshop to build the application, and slides with explanations of the technologies used. The workshop consists of codelabs that you can complete at your own pace. All the code is open source and available in a GitHub repository.