How To

It’s hard to keep with Knative releases with a release every 6 weeks. I finally managed to update my Knative Tutorial for the latest Knative v0.12.0. In this blog post, I want to outline some of the differences I’ve observed.

Knative Serving

Knative Serving has been pretty stable in the recent releases and Knative Serving v0.12.0 is no exception. I didn’t need to update my tutorial specifically for this release.

Knative Eventing

Knative Eventing v0.12.0 changed the default yaml for Knative Eventing bundles. Now, they are under eventing.yaml (previously, it was release.yaml) and this is the yaml you need to point to install eventing. This makes sense as it’s more consistent with Knative Serving and its serving.yaml.

The default Knative Installation instructions for Google Kubernete Engine (GKE) is problematic (see bug 2266). In this post, I want to outline what the problem is, tell you what I do, and also provide you the scripts that work for me until a proper solution is implemented either in gcloud or Knative.

The problem

The default Knative Installation instructions tell you to create a GKE cluster as follows:

gcloud beta container clusters create $CLUSTER_NAME \
 --addons=HorizontalPodAutoscaling,HttpLoadBalancing,Istio \
 --machine-type=n1-standard-4 \
 --cluster-version=latest --zone=$CLUSTER_ZONE \
 --enable-stackdriver-kubernetes --enable-ip-alias \
 --enable-autoscaling --min-nodes=1 --max-nodes=10 \
 --enable-autorepair \
 --scopes cloud-platform

Notice the Istio add-on. This command creates a Kubernetes cluster with Istio already installed. This is good because Istio is a dependency of Knative but keep reading.

Many people who run Windows containers want to use a container management platform like Kubernetes for resiliency and scalability. In a previous post, we showed you how to run an IIS site inside a Windows container deployed to Windows Server 2019 running on Compute Engine. That’s a good start, but you can now also run Windows containers on Google Kubernetes Engine (GKE).

Support for Windows containers in Kubernetes was announced earlier in the year with version 1.14, followed by GKE announcement on the same. You can sign up for early access and start testing out Windows containers on GKE.

Last year, we published a blog post and demonstrated how to deploy a Windows container running Windows Server 2016 on Google Compute Engine. Since then, there have been a number of important developments. First, Microsoft announced the availability of Windows Server 2019. Second, Kubernetes 1.14 was released with support for Windows nodes and Windows containers.

Supporting Windows workloads and helping you modernize your apps using containers and Kubernetes is one of our top priorities at Google Cloud. Soon after the Microsoft and Kubernetes announcements, we added support for Windows Server 2019 in Compute Engine and Windows containers in Google Kubernetes Engine (GKE).

I use my Google Home all the time to check the weather before leaving home, set up alarms, listen to music, but I never considered writing an app for it. What does it take to write an app for the Google Home assistant? And can we make it smarter by leveraging Google Cloud? Those were the questions that my colleague Chris Bacon, and I were thinking about when we decided to build a demo for a conference talk.

Container virtualization is a rapidly evolving technology that can simplify how you deploy and manage distributed applications. When people discuss containers, they usually mean Linux-based containers. This makes sense, because native Linux kernel features like cgroups introduced the idea of resource isolation, eventually leading to containers as we know them today.

For a long time, you could only containerize Linux processes, but Microsoft introduced support for Windows-based containers in Windows Server 2016 and Windows 10. With this, you can now take an existing Windows application, containerize it using Docker, and run it as an isolated container on Windows. Microsoft supports two flavors of Windows containers: Windows Server and Hyper-V. You can build Windows containers on either the microsoft/windowsservercore and microsoft/nanoserver base images. You can read more about Windows containers in the Microsoft Windows containers documentation.

Google Cloud Dataflow provides a unified programming model for batch and stream data processing along with a managed service to execute parallel data processing pipelines on Google Cloud Platform.

Once a Dataflow pipeline is created, it can be tested locally using DirectPipelineRunner, and if everything looks good, it can be manually executed as a job in Dataflow Service by triggering DataflowPipelineRunner or BlockingDataflowPipelineRunner with Apache Maven or Dataflow Eclipse Plugin. You can monitor the progress of your submitted job with Dataflow Monitoring Interface from Cloud Platform Console or Dataflow Command-line Interface from gcloud.