Deploying a platform (Spring Cloud Data Flow) to Azure Kubernetes Service

Platforms should run on Kubernetes, apps should run on PaaS. That simple heuristic seems to resonate with the companies I talk to. When you have access to both environments, it makes sense to figure out what runs where. PaaS is ideal when you have custom code and want an app-aware environment that wires everything together. It’s about velocity, and straightforward Day 2 management. Kubernetes is a great choice when you have closely coordinated, distributed components with multiple exposed network ports and a need to access to infrastructure primitives. You know, a platform! Things like databases, message brokers, and hey, integration platforms. In this post, I see what it takes to get a platform up and running on Azure’s new Kubernetes service.

While Kubernetes itself is getting to be a fairly standard component, each public cloud offers it up in a slightly different fashion. Some clouds manage the full control plane, others don’t. Some are on the latest version of Kubernetes, others aren’t. When you want a consistent Kubernetes experience in every infrastructure pool, you typically use an installable product like Pivotal Container Service (PKS).  But I’ll be cloud-specific in this demo, since I wanted to take Azure Kubernetes Service (AKS) for a spin. And we’ll use Spring Cloud Data Flow as our “platform” to install on AKS.

To start with, I went to the Azure Portal and chose to add a new instance of AKS. I was first asked to name my cluster, choose a location, pick a Kubernetes version, and set my initial cluster size.

For my networking configuration, I turned on “HTTP application routing” which gives me a basic (non-production grade) ingress controller. Since my Spring Cloud Data Flow is routable and this is a basic demo, it’ll work fine.

After about eleven minutes, I had a fully operational Kubernetes cluster.

Now, this is a “managed” service from Microsoft, but they definitely show you all the guts of what’s stood up to support it. When I checked out the Azure Resource Group that AKS created, it was … full. So, this is apparently the hooves and snouts of the AKS sausage. It’s there, but I don’t want to know about it.

The Azure Cloud Shell is a hidden gem of the Microsoft cloud. It’s a browser-based shell that’s stuffed with powerful components. Instead of prepping my local machine to talk to AKS, I just used this. From the Azure Portal, I spun up the Shell, loaded my credentials to the AKS cluster, and used the kubectl command to check out my nodes.

Groovy. Let’s install stuff. Spring Cloud Data Flow (SCDF) makes it easy to build data pipelines. These pipelines are really just standalone apps that get stitched together to form a sequential data processing pipeline. SCDF is a platform itself; it’s made up of a server, Redis node, MySQL node, and messaging broker (RabbitMQ, Apache Kafka, etc). It runs atop a number of different engines, including Cloud Foundry or Kubernetes. Spring Cloud Data Flow for Kubernetes has simple instructions for installing it via Helm.

I issued a Helm command from the Azure Cloud Shell (as Helm is pre-installed there) and in moments, had SCDF deployed.

When it finished, I saw that I had new Kubernetes pods running, and a load balancer service for routing traffic to the Data Flow server.

SCDF offers up a handful of pre-built “apps” to bake into pipelines, but the real power comes from building your own apps. I showed that off a few weeks ago, so for this demo, I’ll keep it simple. This streaming pipeline simply takes in an HTTP request, and drop the payload into a log file. THRILLING!

The power of a platform like SCDF comes out during deployment of a pipeline. See here that I chose Kubernetes as my underlying engine, created a load balancer service (to make my HTTP component routable) via a property setting, and could have optionally chose different instance counts for each component in the pipeline. Love that.

If you have GUI-fatique, you can always set these deploy-time properties via free text. I won’t judge you.

After deploying my streaming pipeline, I saw new pods shows up in AKS: one pod for each component of my pipeline.

I ran the kubectl get services command to confirm that SCDF built out a load balancer service for the HTTP app and assigned a public IP.

SCDF reads runtime information from the underlying engine (AKS, in this case) and showed me that my HTTP app was running, and its URL.

I spun up Postman and sent a bunch of JSON payloads to the first component of the SCDF pipeline running on AKS. 

I then ran a kubectl logs [log app’s pod name] command to check the logs of the pipeline component that’s supposed to write logs.

And that’s it. In a very short period of time, I stood up a Kubernetes cluster, deployed a platform on top of it, and tested it out. AKS makes this fairly easy, and the fact that it’s vanilla Kubernetes is nice. When using public cloud container-as-a-service products or installable software that runs everywhere, consider Kubernetes a great choice for running platforms. 

Author: Richard Seroter

Richard Seroter is Director of Outbound Product Management at Google Cloud, with a master’s degree in Engineering from the University of Colorado. He’s also an instructor at Pluralsight, the lead editor for cloud computing, a frequent public speaker, the author of multiple books on software design and development, and a former 12-time Microsoft MVP for cloud. As Director of Outbound Product Management at Google Cloud, Richard leads a team focused on products and customer success for app modernization (e.g. Anthos). Richard maintains a regularly updated blog on topics of architecture and solution design and can be found on Twitter as @rseroter.

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