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.

I missed this talk from Alexandre Dubreuil, when attending Devoxx Belgium 2019, but I had the chance to watch while doing my elliptical bike run, confined at home. It’s about applying Machine Learning to music generation, thanks to the Magenta project, which is based on Tensorflow.

I like playing music (a bit of piano & guitar) once in a while, so as a geek, I’ve also always been interested in computer generated music. And it’s hard to generate music that actually sounds pleasant to the ear! Alexandre explains that it’s hard to encode the rules a computer could follow to play music, but that machine learning is pretty interesting, as it’s able to learn complex functions, thus understanding what does sound good.

We all know about HTML 5, right? Well, I knew about some of the new semantic tags, like header / nav / main / article / aside / footer, but I’m still falling down to using tons of divs and spans instead. So as I want to refresh that blog at some point, it was time I revise those semantic tags. Let’s take the little time we have during confinement to learn something!

Next in my series of videos while doing sports at home, I watched this video from my colleague Tom Greenaway! It’s about modern web game development, and was recorded last year at Google I/O.

There are big gaming platforms, like Sony’s PlayStation, Microsoft’s XBox, Nintendo Switch, as well as plenty of mobile games on Android and iOS. But the Web itself, within your browser, is also a great platform for developing and publishing games! There’s all that’s needed for good games!

Doing sport at home on a treadmill or an elliptical bike is pretty boring, when you’re confined, so to make things more interesting, I’m watching some videos to learn something new while exercising. This time, I found this old video about how to decode QR codes… by hand! Have you ever thought how these were encoded?

This video comes from the Robomatics YouTube channel. You recognise easily QR codes thanks to the 3 big squares with the inner white line. I knew about the purple dotted lines that was fixed in those patterns. What I didn’t know however was that there’s a mask that is applied to the data, to avoid QR codes to potentially look all white or black. It was interesting to see also how the bytes were encoded: how they follow a path through out the matrix.

Confined at home, because of the corona-virus pandemic, I’m also doing sport at home. I have a small treadmill for light walks (mostly during conf calls!) and also an elliptical bike. I’d much rather run outside though, but I have to use what I have, even if I hate that stationary elliptical bike in my basement. It’s so boring! So to avoid feeling like wasting my time, I decided to watch videos during my sessions! Not necessarily series on Netflix. No! But interesting technical videos. So today, I’d like to share with you a series of posts on those interesting videos I’m watching while exercising.

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:

Requirements:

• an existing Google Cloud Platform account and project
• Cloud Functions should be enabled for that project

Compared to the previous tip when using Exress’ request path attribute, we can take advantage of Express routing.

So to support the following paths:

https://us-central1-myproject.cloudfunctions.net/api/customers
https://us-central1-myproject.cloudfunctions.net/api/customers/32
https://us-central1-myproject.cloudfunctions.net/api/customers/32/address

We can have our functions require Express by adding Express in package.json:

{
  "name": "mini-api-router",
  "version": "0.0.1",
  "dependencies": {
    "express": "^4.17.1"
  }
}

Then we can require that dependency in our new functions script: