Category Archives: Kubernetes

Kicking the tires of the Brigade service for Kubernetes

Recently, the Azure team announced a new open source service called Brigade. The point of this service is to provide a neat way to execute pipelines of job executions via containers within a Kubernetes system, in response to events from the likes of GitHub or Dockerhub. I thought I’d take it for a test drive on the IBM Cloud Containers Service, which exposes Kubernetes.

Background Knowledge

This post assumes some level of understanding of Kubernetes, (Docker) Containers, and GitHub pull requests.

What’s the point?

Why would somebody want a Brigade like service? There are many reasons, but for me the reason that stood out was an easy way to set up a Continuous Integration and Continuous Delivery pipeline. That is, I wanted proposed changes to code I have hosted on GitHub to be tested before I merge it, and once merged, to be deployed somewhere. There are many options available to provide CI/CD services in the (growing) GitHub Marketplace, however being in control of your own service and infrastructure, as well as being able to potentially tie into other event streams, is an appealing thought. Full disclosure, I was recently working on a project to add another option to the GitHub Marketplace for CI/CD, based on the open source Zuul project from OpenStack.

Getting Started

The installation process for Brigade was very easy, which is to be expected from the same team that created Helm. It should be no surprise that the installation is managed via Helm. Pretty much any Kubernetes system will suffice, and for my experimentation I made use of the IBM Containers Service. I could have just as easily used Minikube, however one aspect of the Brigade service is ingesting GitHub webhook events. Having a Kubernetes cluster available that supported LoadBalancer services makes things a bit easier.

Installing the Brigade Service

The installation documentation from Brigade is very straight forward. All I had to do was make sure I had Helm installed on my laptop and my cluster, and that my kubectl client was properly configured to reach my cluster. There are two Helm charts to deal with. One chart defines a project to interact with, such as a GitHub repository. This chart is expected to be used multiple times, once for each repository to be interacted with. As such, there is an example set of values that need to be copied and defined specifically for the repository. In my case, I populated the myvalues.js file with details for a GitHub repository I specifically created for this experiment, j2sol/demobrigade. I defined a secret phrase that will be used to validate incoming events as having come from GitHub, and supplied a GitHub personal access token so that the Brigade install can act on my behalf when interfacing with the GitHub API. I left the rest of the values alone, and installed the chart:

The other Helm chart provided in the Brigade project repository is the chart for the Brigade services themselves. This is expected to be installed only once. (They advocate one Brigade install per tenant, where a tenant is one or more people responsible for one or more associated repositories. I’d take that a bit further and advocate for independent Kubernetes clusters per Tenant.) For my installation, I needed to tweak one feature, which is the rbac feature. IBM Cloud Container Service’s Kubernetes supports Role Base Access Control, and thus my Brigade install needed to account for that. I did not need to edit any files to account for this, I just used an extra option during the helm install:

At this point, all the Brigade pods had been created and the services had also been created. For the next step I needed information about the Brigade Gateway service, easily obtainable from the above output, or by using kubectl:

The information we need is the EXTERNAL-IP and the external port, which in this case is 169.48.217.138 32586. With this information I can moved on to the GitHub repository setup.

Configuring GitHub

My running Brigade service is rather useless without events to act upon. The events I wanted to react to come from GitHub, in the form of webhooks. As certain things happen to a repository on GitHub, it can send data about those events to a set URL. Setting this up requires administrative rights to the repository within GitHub. To set this up, I followed the provided instructions, replacing the Payload URL with data discovered in the above section, and the Secret with the string I added to myvalues.yaml earlier. Since my demo repository is public, I did not go through the steps to set up a private repository.

Upon completing the webhook configuration, GitHub will send an event to the webhook, a ping event. This is visible in the Recent Deliveries section of the webhook configuration page. It should have a little green checkmark next to the UUID of the delivery. This lets me know that my GitHub project is able to communicate with my Brigade service as expected.

Create a brigade.js file

With my Brigade install reacting to GitHub events, it’s time to direct it to do something when those events occur. The Brigade architecture relies on a special file within the git repository in question to inform the system what to do. This file is expected to be JavaScript. To start with, a simple statement to log a can be used:

I created this file and pushed it to a branch. Because I pushed this branch to the repository that is generating webhooks, Brigade got events for the branch creation and push of a commit to the branch. I was able to see this by checking the logs of the gateway pod’s container:

I can also look at the logs of the Brigade controller to get some more information:

If I follow the clues, I can get the logs from the worker pod that was started:

With this output I can see the string I asked to be logged to the console, hello from Brigade. All the basics are set up and functioning as expected!

Beyond the basics

Simply logging words to the console of a container isn’t very useful. One of the basic capabilities of Brigade is to launch containers to do actions upon certain events. For instance, I’d like to validate that my demoapp code passes pycodestyle. To achieve this, I can follow the scripting guide in order to update my brigade.js file to run pycodestyle within a container using the python:alpine image on pull_request events. I’ve created such a script and opened a pull request. The content of brigade.js is:

Don’t worry if you don’t know JavaScript, I don’t either. I’m just using details from the scripting guide to create my file content. I’m making use of the /src/ directory, as that’s where Brigade has made the code from my repository available. The version of code that is checked out is the tip of the pull request. Once again, I can log the controller pod to discover the worker pod that was created in reaction to the pull request event:

The logs for the worker pod show something interesting:

It appears that the pod created for my job has failed. I’ll look at the logs of the failed pod:

Looks like my code does not currently pass pycodestyle! Normally I’d fix those as part of the pull request to add the testing, but I want to leave this pull request open for the sake of the blog. I’ll open a new one that has the fixed contents on a new branch. This time the worker pod logs show a successful run:

Pipelines

Reacting to events to run tests is a pretty common thing. There’s nothing special about it. What really makes Brigade interesting is the ability to run multiple jobs in multiple containers, and to create groups of those jobs which can either run in parallel or serially. There is also the ability to use content and output generated as part of a job in a later job.

For my simple demo repository, I want to set up a functional test of my application, and alter the brigade.js file to run the functional test and the style test in parallel via two containers. Another pull request shows this change. The content of the brigade.js file is now:

Viewing the logs from the worker pod that was created for the pull_request event shows the new job names and the pods used. The output also shows how they’re running in parallel:

I can imagine creating complex sets of jobs within groups to run some tests in parallel, some in serial, more in parallel, while sharing content and output between the sets.

Conclusion

Brigade is an interesting project. It has the capability to create complex workflows in reaction to specific events, and the set up is pretty easy. It may be a better solution than creating your own webhook handler and launching native Kubernetes Jobs. As a project, I think it’s in the early stages of development. I ran into a few issues as I was playing around. The project developers have been quite responsive and eager for input. I wouldn’t recommend taking Brigade into production just yet, but I would encourage exploring the capability and thinking about potential use cases. I’m sure the developers would love more input on how people would like to use Brigade. The project is open source, so if you want to get your hands dirty I’m sure they’d welcome the help too!

Interactive debugging python code in (mini) Kubernetes

Lately I’ve been playing around with Kubernetes. If you don’t know what Kubernetes (k8s) is, then the rest of this post is going to be very confusing to you.

Needed concepts

I’m going to talk about a few things. Here are some links to places to get up to speed should any of this not make any sense:

Background

I’m somewhat late to the k8s game, and I’m still trying to get my bearings. One task I set out to figure out is how I can replicate my development workflow I had built up with Docker Compose to launch containers of my application locally for testing. The application I’ve been developing on consists of a Zookeeper service and three Python services. I have the Python services broken out into three separate containers based on the same image, but with different launch commands. When testing things locally, I often want to introduce code that I haven’t yet committed to a repository, and instead of building new images every time, I make use of a volume mount to bring my code into the container at runtime. This works well with Python thanks to a feature in pip, the tool for installing python packages. I can tell pip to perform an editable install. This type of install makes use of a symlink in the installation target path which links to the source directory of the install. In my Dockerfile I clone the source code to /zuul, and then perform the pip install from there. What this means is that after the install, I could simply alter the files in the original checkout in /zuul and restart the process and the changes will take effect. To expand on that further, this gives me the ability to attach a volume mount from my laptop’s zuul checkout directory (where I have edited files) to the /zuul path within the container at start time, so that I can make use of edited files without rebuilding the image.

On to Kubernetes!

My workflow worked great in docker-compose, but now I want to do this with k8s. To demonstrate how this works, I’ve created a simple Python web server in a demo app. I’ve put the source for this on GitHub for convenience.

First, I need to install and run minikube, a tool to run a Kubernetes cluster locally on my laptop. Minikube will download some data and launch a virtual machine in which to run the k8s services, including its own Docker daemon.

Build Docker image

With minikube running, I next need to configure my Docker client to make use of the Docker Engine within minikube. A simple command eval $(minikube docker-env) will set up my client. Now I can build my Docker image so that it’ll be available for use within k8s. I’ve cloned my repository into a src/derpops/demoapp directory relative to my homer. From there I just need a simple Docker build command to build my image. NOTE! I’m using a tag other than latest so that Kubernetes will not try to pull the latest version of my image. My image will only exist locally, so a pull would fail.

Creating a Deployment

With the image in place, I can now create a k8s Deployment. The Deployment lets me define a container to launch, with the image I built above, and a command to run within the container. I can do this with a simple kubectl command:

If my deployment is created successfully, a new pod will show up, which will be running my code.

Creating a Service

To see if my code is working properly, I need to be able to reach the web server. A k8s Service is necessary to set up the networks appropriately. Just like with the Deployment, a simple kubectl command will suffice to create the service:

This command created a service that will allow me to reach port 8000 of the container running the application. My code specifically tells the python library to use port 8000 and to listen on all addresses. This type of service is a nodeport, which makes this port reachable from every node. However, since this is minikube, I only have one node, and I can ask minikube to tell me what the IP address of the node is with the service command. I’ll ask it to just display the URL, instead of opening the URL in my browser, and then use curl to access the URL:

Injecting new code

My application works, but now I want to alter the code. To get new code into my container, I need to add a volume mount to my deployment.

VolumeMounts are used to expose content into a container. A VolumeMount definition combines the name of a volume and a path to mount it within the container. The name matches a defined Volume, of which many types are supported. The type we’re interested in, the hostPath type, exposes a file or a directory from the node (the machine a container is running on). The use of minikube automatically exposes a folder from the host machine minikube runs on into the VM where k8s is running, which is the node. In my case, the /Users directory on my laptop is exposed as /Users on the node, and thus I can make use of this in a hostPath volume.

To add a volume to my Deployment, I’ll need to create a yaml file describing my Deployment spec. I can do this quickly by repeating the earlier command but adding arguments to print out YAML:

I saved this output to a new file,  demoapp-deployment.yaml, where I can make adjustments as needed. I need to alter the containers spec for the demoapp container to define a volumeMount:

This references a volume by the name of demosource which I also need to define in the spec as a new key:

These additions will cause the directory /Users/jkeating/src/derpops/demoapp/demoapp to be mounted to the path /demoapp/demoapp within the container when it launches. This will overlay the version of code on my laptop on top of the code that already exists in the container.

To get the new definition of my Deployment in use, I’ll delete the existing Deployment and create a new one from the YAML file:

The existing pod is being terminated while a new pod is running. If all went well, I should still be able to use curl to reach the web server:

The output is the same as earlier, as I haven’t changed any code. But what if I change the value of the variable RESPONSE to something new by editing the file demoapp/__init__.py on my laptop.

To get my new code in use, I can simply delete the Pod my Deployment created. A Deployment uses a ReplicaSet to control how many Pods are active. Deleting the pod will trigger the creation of a new one, which should pick up my new code. To determine which pod to delete, I’ll use the get pods command via kubectl. This will list all the running pods. Then I’ll use the delete pod command to delete the demoapp pod, and then make sure a new one is created:

The new pod should have my new code, which I’ll verify with curl once more:

Interactive debugging

Getting new code used in the container is fun, but what is even more useful is being able to interactively debug this new code. Python developers should be familiar with the use of pdb, the python debugger. This utility can be used to insert a break point into the source code in order to interactively debug the code at that point in the execution. Pdb is fantastic when you are able to execute the code directly, but requires being attached to the tty that started the python process. That’s a difficult feat inside of a system like k8s. Thankfully there is a wrapper around pdb specifically for connecting to remote python processes, called rpdb. When using rpdb, the wrapper will redirect stdout/stdin to a socket handler, which can be accessed over TCP. This will allow me to define a breakpoint in the code and then connect to the socket remotely in order to attach to the debugger to interact with the process. (One downside of rpdb is that it is not part of the standard library, and thus the library will need to be explicitly installed into the python environment. I’ve done this in my Dockerfile.)

To debug my code, I first have to add the breakpoint to one of my source files. At the appropriate line, I need to insert import rpdb; rpdb.set_trace("0.0.0.0"). I specifically need to tell rpdb to listen on 0.0.0.0 instead of the default 127.0.0.0. This will make rpdb listen on all addresses rather than just localhost. This is less secure, but required in order to reach it through k8s. Once again, I’ll edit the demoapp/__init__.py file on my laptop:

I also need to update my k8s Service to expose the new port. Rpdb will listen on port 4444 by default, so I’ll use that in my service. First I’ll delete the existing demoapp service and then re-create it adding a second port:

I can test that the new service works by using curl to the new port:

Now that the code is edited and the service is created to forward ports through, I can restart the container. Once again I’ll delete the running pod to trigger the creation of a replacement.

Since my breakpoint is inside the do_GET function, I’ll need to use curl to initiate a GET request. This will seem to hang in the terminal, as the break point has been reached and execution is waiting. At this point, I should be able to connect to the waiting debugger,  using the Service information to determine which port to connect to. In another terminal I can use nc to connect to the debugger! From this point on, it’s debugging as usual.

From here I can change the value of RESPONSE once more, and then continue execution, which should cause my curl command to return with my new message:

Conclusion

Kuberetes is a pretty huge leap forward in container orchestration. With that advancement comes some complexity, and a whole lot of new concepts to learn. However, the basic building blocks are there to continue using workflows that have been useful in the past. This workflow to debug code live is just a small example of what is possible with k8s, minikube, and containers in general.

I’ve added a complete demoapp-deployment.yaml file to the git repository, including a Service definition. Hopefully this example will be useful! As always, comment here or on Twitter should you have any thoughts to share.

Happy kubing!