We have recently received a request from a customer, asking us to restrict the container registries that could be used to deploy images from in their OpenShift 4 cluster.

We could have added such configuration directly at node level, as explained in Red Hat’s documentation; it’s indeed possible to whitelist registries on repository and tag level, but that would have forced us to keep all those whitelists updated with those our Project Syn components regularly use.

We have instead chosen to use Kyverno for this task: it allows us to enforce the limitations on a per-namespace level, with much more flexibility and maintanability.

This is a ClusterPolicy object for Kyverno, adapted from the solution we provided to our customer, showing how we can restrict the limitation to some namespaces, so that containers can be pulled only from some specific registries.

apiVersion: kyverno.io/v1
kind: ClusterPolicy
metadata:
  name: restrict-registries
  annotations:
    policies.kyverno.io/title: Restrict Image Registries
    policies.kyverno.io/subject: Pod
    policies.kyverno.io/description: >-
      Restrict image pulling only to whitelisted registries
spec:
  validationFailureAction: enforce
  background: true
  rules:
  - name: validate-registries
    match:
      all:
      - resources:
          kinds:
          - Pod
          namespaces:
          - "namespace-wildcard-*"
    validate:
      message: "Image registry not whitelisted"
      pattern:
        spec:
          containers:
          - image: "registry.example.com/* | hub.docker.com/some-username/*"

As you know (and if you didn’t, now you do) GitLab has deprecated the certificate-based integration with Kubernetes in version 14.5, and it is expected that version 15 will disable it completely.

The official replacement to the (now legacy) certificate-based integration mechanism is the GitLab Agent, to be installed in your Kubernetes cluster, and providing a tighter integration between our two beloved platforms.

Well, hold on; things aren’t that easy. For example in our case, if we wanted to offer the GitLab Agent to our APPUiO Cloud users, we would run into two issues:

• On one side, installing the GitLab Agent is more complicated and expensive, because it would run as another pod in the same namespace, consuming resources.
• And on the other, we cannot install it cluster-wide, because of the admin permissions we have configured in this multi-tenant shared platform. Users would have to manage each and every GitLab Agent on their own, possibly having multiple agents deployed in several namespaces and GitLab repositories.

So, what’s a DevOps engineer to do? Well, here’s a simple, very simple solution; so simple that you might have never thought of it before: create your own KUBECONFIG variable in GitLab with a dedicated service account!

Here’s how it works using the OpenShift oc tool in your own system:

Create a service account in your OpenShift project:

oc create serviceaccount gitlab-ci

Add an elevated role to this service account so it can manage your deployments:

oc policy add-role-to-user admin -z gitlab-ci --rolebinding-name gitlab-ci

Create a local KUBECONFIG variable and login to your OpenShift cluster using the gitlab-ci service account:

TOKEN=$(oc sa get-token gitlab-ci)
export KUBECONFIG=gitlab-ci.kubeconfig
oc login --server=$OPENSHIFT_API_URL --token=$TOKEN
unset KUBECONFIG

You should now have a file named gitlab-ci.kubeconfig in your current working directory; copy its contents and create a variable named KUBECONFIG in the GitLab settings with the value of the file (that’s under “Settings” > “CI/CD” > “Variables” > “Expand” > “Add variable”). Remember to set the “environment” scope for the variable and to disable the old Kubernetes integration, as the KUBECONFIG variable might collide with this new variable.

Tada! There are a few advantages to this approach:

• It is certainly faster and simpler to setup for simple push-based deployments than by using the GitLab Agent.
• It is also easier to get rid of deprecated features without having to change the pipeline or migrating to the GitLab Agent.

But there are a few drawbacks as well:

• You don’t get all bells and whistles. It is reasonable to think that at some point the GitLab Agent will offer advanced features, such as access to pod logs from GitLab, monitoring alerts directly from the GitLab user interface, and many other things the old Kubernetes certificate-based integration could do. This approach does not provide anything like this.
• The cluster’s API endpoint has to be publicly accessible, not behind any firewall or VPN; conveniently enough, the GitLab Agent solves exactly this problem.

If you are interested in GitLab and Kubernetes, join us next week in our GitLab Switzerland Meetup for more information about the GitLab Agent for Kubernetes, and afterwards for a nice apéro with like-minded GitLab enthusiasts!

Oh, and a final tip; if you find yourself having to log in to OpenShift integrated registry but you don’t have yq installed in the job’s image, you can use sed to extract the token from the $KUBECONFIG file:

sed -n 's/^\s*token:\s*\(.*\)/\1/ p' "${KUBECONFIG}" | docker login -u gitlab-ci --password-stdin "${OPENSHIFT_REGISTRY}"

Back in 2019 we published a review of the most relevant serverless frameworks available for Kubernetes. That article became one of the most visited in our blog in the past two years, so we decided to return to this subject and provide an update for our readers.

TL;DR: Serverless in Kubernetes in 2022 means, to a large extent, Knative.

What’s in a Word

The “Serverless” word is polarizing.

Robert Scoble, one of the first tech influencers, uttered it for the first time fourteen years ago, as James Potter reported recently. In those times, “Serverless” just meant “not having servers and being an AWS EC2 customer”. Because, yes, companies used to have their own physical servers back then. Amazing, isn’t it?

Fast forward to 2022, and the CNCF Serverless Landscape has grown to such an extent that it can be very hard to figure out what “serverless” truly means.

Even though for some it just represents the eternal return of 1970s style batch computing, in the past five years the “Serverless” word has taken a different and very specific meaning.

The winning definition caters for the complete abstraction of the infrastructure required to run individual pieces of software, at scale, on a cloud infrastructure provider.

Or, in less buzzword-y terms, just upload your code, and let the platform figure out how to run it for you: the famous “FaaS”, also known as Function as a Service.

The IaaS Front

The three major Infrastructure as a Service providers in the world offer their own, more-or-less-proprietary version of the Serverless paradigm: AWS Lambda, Azure Functions, and Google Cloud Run (which complemented the previous Google Cloud Functions service). These are three different approaches to the subject of FaaS, each with its advantages and caveats.

Some companies, like A Cloud Guru, have successfully embraced the Serverless architecture from the very start (in this case, based on AWS Lambda), creating cost-effective solutions with incredible scalability.

But one of the aforementioned caveats, and not a small one for that matter, is platform lock-in. Portability has always been a major concern for enterprise computing: if building apps on AWS Lambda is an interesting prospect, could we move them to a different provider later on?

Well, we now have an answer to that question, thanks to our good old friend: the container.

The Debate Is Over

Almost exactly three years ago, Anthony Skipper wrote:

We will say it again… packaging code into containers should be considered a FaaS anti-pattern!

Containers or not? This was still a big debate at the time of our original article in 2019.

Some frameworks like Fission and IaaS services such as AWS Lambda and Google Cloud Functions did not require developers to package their apps as containers; just upload your code and watch it run. On the other hand, OpenFaaS and Knative-based offerings did require containers. Who would win this fight?

The world of Serverless solutions in 2022 has decided that wrapping functions in containers is the way to go. Even AWS Lambda started offering this option in December 2020. This is a huge move, allowing enterprises to run their code in whichever infrastructure they would like to.

In retrospect, the market has chosen wisely. Containers are now a common standard, allowing the same code to run unchanged, from a Raspberry Pi to your laptop to an IBM Mainframe. It is a natural choice, and it turned out that it was a matter of time before this happened.

Even better, with increased industry experience, container images got smaller and smaller, thanks to Alpine-based, scratch-based, and distroless-based images. Being lightweight allows containers to start and stop almost instantly, and makes scaling applications faster and easier than ever.

And this choice turned out to benefit one specific framework among all: Knative.

The Age of Knative

In the Kubernetes galaxy, Knative has slowly by steadily imposed its mark as the core infrastructure of Kubernetes serverless workloads.

In 2019, our article compared six different mechanisms to run serverless payloads on Kubernetes:

1. OpenFaaS
2. Fn Project
3. Fission
4. OpenWhisk
5. Kubeless
6. TriggerMesh

Of those, Fn Project and Kubeless have been simply abandoned. Other frameworks suffered the same fate: Riff has disappeared, just like Gestalt, including its parent company Galatic Fog. IronFunctions moved away from Kubernetes into its own PaaS product. Funktion has been sandboxed and abandoned; Leveros is abandoned too; BlueNimble does not show much activity.

On the other hand, new players have appeared in the serverless market: Rainbond, for example; or Nuclio, targeting the scientific computation market.

But many new contenders are based on Knative: apart from TriggerMesh, which we mentioned in 2019 already, we have now Kyma, Knix, and Red Hat’s OpenShift 4 serverless, all powered by Knative.

The interest in Knative is steadily growing these days: CERN uses it. IBM is talking about it. The Serverless Framework has a provider for it. Even Google Cloud Run is based on it! Which shouldn’t be surprising, knowing that Knative was originally created by Google, just like Kubernetes.

And now Knative has just been accepted as a CNCF incubating project!

Even though Knative is not exactly a FaaS per se, it deserves the top spot in our review of 2022 FaaS-on-K8s technologies, being the platform upon which other serverless services are built, receiving huge support from the major names of the cloud-native industry.

Getting Started with Knative

Want to see Knative in action? Getting started with Knative on your laptop now is easier than ever.

1. Install kind.
2. Run the following command on your terminal:

$ curl -sL install.konk.dev | bash

To work with Knative objects on your cluster, install the kn command-line tool. Once you have launched your new Knative-powered Kind cluster, create a file called knative-service.yaml with the contents below:

apiVersion: serving.knative.dev/v1
kind: Service
metadata:
  name: hello
spec:
  template:
    metadata:
      name: hello-world
    spec:
      containers:
        - image: gcr.io/knative-samples/helloworld-go
          ports:
            - containerPort: 8080
          env:
            - name: TARGET
              value: "World"

And then just apply it: kubectl apply -f knative-service.yaml.

The kn service list command should now display your “helloworld” service, which should become available after a few seconds. Once it’s ready, you can execute it simply with curl:

$ curl http://hello.default.127.0.0.1.sslip.io

(If you prefer to use Minikube, you can follow this tutorial instead.)

Thanks to Knative, developers can roll out new versions of their services (called “revisions” in Knative terminology) while the old ones are still running, and even distribute traffic among them. This can be very useful in A/B testing sessions, for example. Knative services can be triggered by a large array of events, with great flexibility.

A full introduction to Knative is outside of the scope of this review, so here are some resources we recommend to learn everything about Knative serving and eventing:

• The excellent and funny “Knative in Action” (2021) book by Jacques Chester, available for free courtesy of VMWare.
• A free, full introduction to Knative (July 2021) by Sebastian Goasguen, the founder of TriggerMesh; a video and its related source code are provided as well.
• And to top it off, the “Knative Cookbook” (April 2020) by Burr Suter and Kamesh Sampath, also available for free, courtesy of Red Hat.

Interested in Knative and Red Hat OpenShift Serverless? Get in touch with us and let us help you in your FaaS journey!

As part of our ongoing improvement process, we evaluate the requirements and new features of each version of Red Hat OpenShift, not only for our customers, but also for our internal use. Version 4.9 of Red Hat’s flagship container platform was announced October 18th, 2021 and it included some very important changes, some of which are potentially breaking ones.

In this article, targeted towards DevOps engineers and maintenance crews, I will provide a short overview of the most important points to take care before upgrading to this new version.

Kubernetes 1.22

The most important change in OpenShift 4.9 is the update to Kubernetes 1.22. This release of Kubernetes has completely removed APIs marked as v1beta1. The complete list is available in the Red Hat documentation website, but suffice to say that common objects such as Role or RoleBinding (rbac.authorization.k8s.io/v1beta1) and even Ingress (networking.k8s.io/v1beta1) are no more.

This is a major change, and it needs to be taken care of for all users of your clusters before an upgrade takes place, and all manifest files using those resources should be updated accordingly. Red Hat’s Knowledge Base includes a special article explaining all the steps required for upgrading to OpenShift 4.9.

And of course, if you need any help regarding the upgrade process towards OpenShift 4.9, just contact us, we will be glad to help you and your teams.

Mutual TLS Authentication

Mutual TLS is a strong way to secure an application running in the cloud, allowing a server application to authenticate any client connecting to it. It comes with some complexity to setup and manage, as it requires a certificate authority. But thanks to its inclusion as a feature in OpenShift 4.9, its usage is much simpler now.

Please check the release notes section related to Mutual TLS for more information.

Registry Multiple Logins

In previous versions of OpenShift, you could only list one repository from a given registry per project. OpenShift 4.9 includes multiple logins to the same registry, which allows pods to pull images from specific repositories in the same registry, each with different credentials. You can even define a registry with a specific namespace. The documentation contains examples of manifests to use this feature.

Changes to lastTriggeredImageID Field Update

And finally, here’s a change that can cause unforeseen headaches to your teams: OpenShift 4.9 does not update anymore the buildConfig.spec.triggers[].imageChange.lastTriggeredImageID field when the ImageStreamTag changes and references a new image. This subtle change in behavior is easy to overlook, and if your team depends on this feature, beware for trouble.

Learn More about OpenShift 4.9

If you’re interested in knowing what else changed in OpenShift 4.9, here are some selected resources published by Red Hat to help you:

• Red Hat OpenShift 4.9 Is Now Generally Available
• OpenShift Container Platform 4.9 release notes
• Time Is On Your Side: A Change to the OpenShift 4 Lifecycle
• How Customer Feedback Shaped OpenShift 4.9
• Ask an OpenShift Admin Office Hour – What’s new in OpenShift 4.9?
• Kuryr in OpenShift 4.9
• Introducing New Virtualization Features in OpenShift 4.9

Let’s take a somewhat tangential approach to the subject of the Microservices architecture. Most discussions about it are centered around technological aspects: which language to choose, how to create the most RESTful services, which service mesh is the most performant, etc.

However at VSHN we have long ago figured out that the most important factor of success for software projects is people. And the thesis of this article is that the choice of Microservices as an architectural pattern has more to do with the organizational structure of your organization, rather than the technological constraints and features of the final deliverable.

Or, put differently: your team has already chosen an architecture for your software, even if they are not fully aware of it. And lo and behold, Microservices might or might not be it.

Definition

First of all we must define the Microservices architecture. What is it?

“Microservices” is an architectural pattern in which the functionality of the whole system is decomposed into completely independent components, communicating with each other over the network.

The counterpart of the Microservices architecture is what is commonly referred to as the “Monolith”, which has been the most common approach for web applications and services in the past 25 years. In a Monolith, all functions of the application, from data management to the UI, are all contained within the same binary or package.

On the opposite side of the street we find the Microservices architecture, where each service is responsible of its own implementation and data storage.

By definition, Microservices are fine grained, and have a single purpose. They have strong cohesion, that is, the operations they encapsulate have a high degree of relatedness. They are an example of the “Single Responsibility Principle”. They are also deployed separately, with visibly bounded contexts, and they require DevOps approaches to their deployment and management, such as automation and CI/CD pipelines.

Very importantly, the Microservices architecture is a “share nothing architecture”, in which individual services never share common code through libraries, instead restricting all interaction and communication through the network connecting them.

And last but not least, Microservices (as the name implies) should be as small as possible, have low requirements of memory, and should be able to start and stop in seconds.

Given all of these characteristics, Microservices are, by far, the most complex architectural pattern ever created. It is difficult to plan, it can dramatically increase the complexity of projects, and for some teams, experience has shown that it was impossible to cope with.

History

This idea of “components sending messages to one another” is absolutely not new. Back in 1966, one of the greatest computer scientists of all time, Alan Kay, coined the term “Object Oriented Programming”. The industry co-opted and deformed his original definition, which was the following:

OOP to me means only messaging, local retention and protection and hiding of state-process, and extreme late-binding of all things.

Alan Kay, source.

This text is from 2003; the following is from 1998:

The big idea is “messaging” — that is what the kernal of Smalltalk/Squeak is all about (…) The key in making great and growable systems is much more to design how its modules communicate rather than what their internal properties and behaviors should be.

Alan Kay, source.

Alan Kay designed in the 1970s the Smalltalk programming language, based on these concepts. And after reading the texts above, it becomes clear that to a large extent, the Microservices architecture is an implementation of Alan Kay’s ideas of messaging, decoupling and abstraction, taken to the extreme.

To achieve the current state of Microservices, though, many other breakthroughs were required. During the 2000s, the emergence of XML, the SOAP protocol, and its related web services made the term “Service Oriented Architecture” a very common staple in architectural discussions. The rise of Agile methodologies made the industry pivot towards the REST approach instead of SOAP. During the last decade, the rise of DevOps and the rise of containerization through Docker and Kubernetes finally enabled teams to deploy thousands of containers as a microservice architecture, thanks to the whole catalog of Cloud Native technologies.

Pros and Cons

If Microservice architectures are so complex, why use them? It turns out, they can bring many benefits:

• Since each component can be completely isolated from the others, once teams agree on their interfaces they can be developed, documented, and tested thoroughly, 100% independently from others. This allows teams to move forward in parallel, implementing features that have 0% chance of collision with one another.
• Teams are also encouraged to choose the programming language that fits best the particular task that their microservice must fulfill.
• Since they are by definition “micro” services, they are designed to be quickly launched and dismissed, so that they only intervene when required, making the whole system more efficient and responsive.
• The size of microservices also allows for higher availability, since it is possible to have many of them behind a load balancer; should any instance of a microservice fail, it can be quickly dismissed and a new one instantiated in its place, without loss of availability.
• Systems can be updated progressively, with each team fixing bugs and adding functionality without disturbing the others. As long as interfaces are respected (and eventually versioned) there is no reason for the system to suffer from updates.

But there are many reasons not to choose the Microservices architectural approach; among the most important:

• Performance: a system built with Microservices must take into account the latency between those services; and in this respect, Monolithic applications are faster; a function call is always faster than a network call.
• Team maturity: Microservices demand a certain degree of experience and expertise from teams; those new to Microservices have a higher chance of project failures.
• Cost: creating a Microservices system will be costlier, if anything, because of the overhead created by each individual service.
• Feasibility: sometimes it is simply not possible to use Microservices, for example when dealing with legacy systems.
• Team structure: this is a decisive factor we will talk about extensively later.
• Complexity: it is not uncommon to end up with systems composed of thousands of concurrent services, and this creates challenges that we will also discuss later.

I would like to talk now about the last two points, which are in our experience the biggest issues in Microservice implementations: team structure and the perception and management of complexity.

Conway’s Law

One of the decisive factors that constrain teams’ ability to implement microservices is often invisible and overlooked: their own structure. Again, this phenomenon is not new. In 1968, Melvin A. Conway wrote an article for the Datamation magazine called “How Do Committees Invent?” in which the following idea stands out:

The basic thesis of this article is that organizations which design systems (…) are constrained to produce designs which are copies of the communication structures of these organizations.

Melvin Conway, source.

There is extensive evidence, both anecdotal and empirical, of this fact through research.

The corollary of this principle is the following: the choice of a Monolithic versus a Microservices architecture is already ingrained in the very hierarchical chart representing any organization.

One of the services we offer at VSHN tackles, precisely, this very issue. In our “DevOps enablement workshop” we evaluate the degree of agility of organizations, and the extent and improvements that DevOps could bring. Based on that information, we reverse engineer their structure through Conway’s Law in order to find a starting point for their digital transformation.

Complex vs. Complicated

Another important point is the distinction of “Complex” versus “Complicated”, as these two words can sometimes be confused with one another in everyday language, and to make things more difficult, the word “Simple” can be used as an antonym of both.

“Complex” is borrowed from the Latin complexus, meaning “made of intertwined elements”. Complexus is itself derived from plectere (“bend, intertwine”). This word has been used since the XVI century to qualify that which is made of heterogenous elements. It shares the same root (plectere) with the medical term “plexus” meaning “interlacing” and used since the 16th century as a medical term for “network of nerves or blood vessels”.

(Source: Dictionnaire historique de la langue française by Alan Rey)

On the other hand, “Complicated” has a similar origin but a different construction: it comes from the Latin complicare, literally meaning “to fold by rolling up”. Figuratively speaking this was taken as close to the notion of embarrassment or awkwardness. The word is composed of the word plicare which means “to fold”. Watches commonly known as “Complications” (such as the Patek Philippe Calibre 89, the Franck Muller Aeternitas Mega and the “Référence 57260” de Vacheron Constantin) are, well, complicated machines by definition.

In short, “Complex” and “Complicated” stem from slightly different roots: the Latin root plectere (“to intertwine”) in the former, and plicare (“to fold”) for the latter. Complex conveys the idea of a network of intertwined objects, whose state and behavior are continuously altered by the interaction with their peers in said network. The word complicated implies an intrinsic apparent “obscurity” through folding unto itself, inviting to an “unfolding” discovery process.

Or, put in another way: individual Microservices should not be complicated, but a Microservice architecture is complex by definition. Monoliths, on the other hand, tend to become very complicated as time passes. And of course, neither are simple.

History shows that software developers have a passionate relationship with complication; complicated systems are great to brag about on Hacker News, while maintainers also cry about them in private.

A “best practice” in this context has one and only one basic job: to help engineers translate the complicated into the complex. Most software-related disasters are caused by a simple fact: because of deadlines, organization, tooling, or just plain ignorance, software developers have a tendency to build complicated, instead of complex, systems.

This is another point we take care of in our DevOps Workshop, through the evaluation of the current assets (not only source code, but also current databases schemas, security requirements, network topologies, deployment procedures and rhythms, etc.)

Migrate or Rewrite? Equilibrium

The complication of Monoliths by itself is not problematic; it makes for tightly bound systems, which tend to be very fast, because as we said, a function call is faster than a network call. After all, we have been building very successful monoliths in the past. But experience shows that they tend to present problems regarding availability and scalability. Microservices represent a diametrally oposed approach, based on complexity rather than complication, but one that solves those issues.

There is a tension, then, between complexity and complication on one side, and organizational forces on the other. Put in other words, there is a tension between monolithic and microservices systems on one side, and more or less hierarchical structures on the other. Achieving equilibrium between these forces is, then, the engineering challenge faced by software architects these days.

Many teams face today the task, either requested internally (from their management) or externally (through customer demand or vendor requirements) of migrating their monoliths into Microservice-based architectures. Architects can thankfully apply a few techniques to find an equilibrium:

1. Start your migration path knowing that very often one does not need to migrate the whole application to Microservices. Some parts can and should remain monolithic, and in particular, proven older systems, even if written in COBOL or older technologies, can still deliver value, and can play a very important role in the success of the transition.
2. Identify components correctly, so that when isolated they will be neither only functionality-driven, nor only data-driven, nor only request-driven, but rather driven by these three factors (functionality, data, and request) at the same time. Pay attention to the organizational chart, and use that as a basis for the decomposition in microservices.
3. Remember that network bandwidth is not infinite. Some interfaces should be “chunky”, while others should be “chatty”. Plan for latency issues from the start.
4. Reduce inter-service communication as much as possible, which can be done in many ways:
   1. Consolidating services together
   2. Consolidating data domains (combining database schemas or using common caches)
   3. Using technologies such as GraphQL to reduce network bandwidth
   4. Using messaging queues, such as RabbitMQ.
5. Adopt Microservice-friendly technologies, such as Docker containers, Kubernetes, Knative, or Red Hat’s OpenShift and Quarkus.
6. Implement an automated testing strategy for each and every microservice.
7. Standardize technology stacks around containers & Kubernetes, and create common ground for a true microservice ecosystem within organizations.
8. Automate all of your work as much as possible, knowing that the effort for automation (DevOps, CI/CD) can be amortized over many services, and becomes thus a net investment in the long run.

As mentioned previously, we regularly help organizations in their digital transformation towards microservices, Kubernetes, OpenShift, DevOps, CI/CD, GitLab, and DevOps in general, to support your teams with the tooling they will need in the future. Borrowing Henny Portman’s Team Topologies concept, VSHN can support both as an “Enabling Team” (DevOps workshop, consulting) and a “Platform Team” (Kubernetes/OpenShift) to build microservices on, ensuring stability and peace of mind.

Conclusion

Going beyond the hype, Microservice architectures bring great benefits, but can become huge challenges to software teams.

The best way to tackle those challenges consists in reverse engineering Conway’s Law, and start by the analysis of the human organization of your teams first. Make them independent, agile, and free to choose the best tools for their job. Encourage them to negotiate with one another the interfaces of their respective components.

Let us create and run complex, not complicated, systems. We cannot get away from complexity; that is our job as engineers. But we can get rid of the complicated part.

As a closing thought, I will quote my former colleague and lifetime friend Adam Jones, an independent IT consultant from Geneva: in order to achieve success with the Microservice architecture, you must embed structure in your activities, and remove it from your hierarchy. It is not about making the structure go away; but instead, moving it to where it does the most good.

On the evening of August 25th, 1991, Linus Torvalds wrote a short message on the comp.os.minix Usenet newsgroup:

I’m doing a (free) operating system (just a hobby, won’t be big and professional like gnu) for 386(486) AT clones.

Fast-forward 30 years, and to make a long story short, well: Linux is now both big and professional. It’s nearly everywhere, hidden behind the shiny graphical user interface of your smartphone or tablet (well, except for iOS users), in your gaming box, in your car, and even on Mars.

I discovered Linux as a young boy still at school, in 1998. It was in a book store where I was attracted by a shiny package (I had no idea what Linux was) called “SuSE Linux 6.0” (nowadays available for download from archive.org), and since then I couldn’t stop working with it.

Over time, I got more and more into Linux, and it became an important hobby, with all my PCs running Linux, and also on my home server under my desk at home. Many years later, in 2011, I could finally start working full-time as a Linux administrator. My desktop computer has been only powered by Linux since around 2004, and I’ve been a big fan of KDE ever since.

Today, the Linux Kernel is more present, yet less visible than ever. We interact with it on containers and in VMs on the cloud, and it gets more and more abstracted away, deep down in serverless architectures, making the Kernel even more invisible than ever before. Albeit out of sight of most users, Linux has become much more solid, mature, and pervasive, and does its great job behind the scenes without interruption.

Linux empowers VSHN at all levels; not only it is the dominating technology we use every day, empowering Kubernetes, containers, and cloud VMs, but it is also the operating system that the majority of VSHNeers (around 66%, according to an internal poll) use for their daily job, the remaining third using macOS or Windows.

Some numbers: of those two thirds of VSHNeers that use Linux every day in their laptops, 61% chose Ubuntu (or one of its various derivatives); 17% use Arch, 11% Fedora, and others use Debian, Mint, and other distributions. Some even contemplate switching to Qubes OS soon! As for desktop environments, around 35% use GNOME, 25% use KDE, 20% use i3, and 6% use Cinnamon.

Each one of us VSHNeers has a unique feeling about Linux; here are some thoughts about what Linux means to us:

Before using Linux, I was primarily focused on how to use and work with computer systems. With the switch to Linux I started to understand how they actually work.

What I really appreciate about Linux is that it’s (still relatively) lightweight, powerful, transparent and adaptable. I do heavyweight gaming and video livestreaming on the same OS that runs my file servers and backup systems (not all on the same machine, don’t worry). Even my car and my television run Linux! This universality combined with the permissive licenses means that whenever one industry improves Linux (the kernel), every other industry profits.

I originally just wanted to play Minecraft with my friends. Suddenly I had to learn how to host this on a Linux server, which then turned into a fascination on how things work on the backstage. It’s the urge to see how our modern society works!

Linux is the operating system of our software-driven world.

On to the next 30 years of Linux!

One of the most difficult subjects in the world of Kubernetes is storage. In our day-to-day operations we’ve often had to choose the best storage solution for our customers, but in a changing landscape of requirements and technical offerings, such choice becomes a major task.

Faced to many options, we decided to benchmark storage solutions in real life conditions, to generate the data required for a proper decision. In this article we’re going to share with you our methodology, our results, and our final choice.

The chosen storage providers for this evaluation were:

• Ceph on Rook and OpenShift Data Foundation (previously known as OpenShift Container Storage)
• Longhorn
• Gluster (benchmarked on APPUiO Exoscale OpenShift 3.11)

All of these benchmarks (except Gluster) were run on an OpenShift 4.7 cluster on Exoscale VMs.

We benchmarked Ceph both with unencrypted and encrypted storage for the OSDs (object-storage daemons). We included Gluster in our evaluation for reference and comparison only, as that’s the solution we offered for storage on OpenShift Container Platform 3.x. We never intended to use Gluster as the storage engine for our new Kubernetes storage cluster product.

Methodology

We first created a custom Python script driving kubestr, which in turn orchestrates Fio. This script performed ten (10) iterations for each benchmark, each of which included the following operations in an isolated Fio run:

• Read iops
• Read bandwidth
• Write iops, with different frequencies of calls to fsync:
  • no fsync calls during each benchmark iteration
  • an fsync call after each operation (“fsync=1”)
  • an fsync call after every 32 operations (“fsync=32”)
  • an fsync call after every 128 operations (“fsync=128”)
• Write bandwidth, with different frequencies of calls to fsync:
  • no fsync calls during each benchmark iteration
  • an fsync call after each operation (“fsync=1”)
  • an fsync call after every 32 operations (“fsync=32”)
  • an fsync call after every 128 operations (“fsync=128”)

This is the Fio configuration used for benchmarking:

[global]
randrepeat=0
verify=0
ioengine=libaio
direct=1
gtod_reduce=1
[job]
name=JOB_NAME     (1)
bs=BLOCKSIZE      (2)
iodepth=64
size=2G
readwrite=OP      (3)
time_based
ramp_time=5s
runtime=30s
fsync=X           (4)

1. We generate a descriptive fio job name based on the benchmark we’re executing. The job name is generated by taking the operation (“read” or “write”) and the measurement (“bw” or “iops”) and concatenating them as “OP_MEASUREMENT”, for example “read_iops”.
2. The blocksize for each operation executed by fio. We use a blocksize of 4K (4kB) for IOPS benchmarks and 128K (128kB) for bandwidth benchmarks.
3. The IO pattern which fio uses for the benchmark. randread for read benchmarks. randwrite for write benchmarks.
4. The number of operations to batch between fsync calls. This parameter doesn’t have an influence on read benchmarks.

If writing to a file, issue an fsync(2) (or its equivalent) of the dirty data for every number of blocks given. For example, if you give 32 as a parameter, fio will sync the file after every 32 writes issued. If fio is using non-buffered I/O, we may not sync the file. The exception is the sg I/O engine, which synchronizes the disk cache anyway. Defaults to 0, which means fio does not periodically issue and wait for a sync to complete. Also see end_fsync and fsync_on_close.

Fio documentation

Results

The following graph, taken from the full dataset for our benchmark (available for download and study) shows the type of comparison performed across all considered solutions.

The table below gives an overview over the data gathered during our evaluation (each column shows mean ± standard deviation):

Unencrypted Rook/OCS numbers are from OCS, encrypted Rook/OCS numbers from vanilla Rook.

Conclusion

After careful evaluation of the results shown above, we chose Rook to implement our APPUiO Managed Storage Cluster product. Rook allows us to have a single product for all the Kubernetes distributions we’re offering at VSHN.

We have released the scripts on GitHub for everyone to verify our results, and published even more data in our Products documentation. Feel free to check the data, run these tests in your own infrastructure, and of course, your pull requests are more than welcome.

Welcome to the third and last installment of our “Networking Security” blog post series. If you didn’t read the previous ones yet, go ahead and read the first two parts:

• Part 1: What is TLS?
• Part 2: Verifying and Connecting
• Part 3: Trust and Certificate Chains

In this article we’ll talk about trust, and how it’s implemented thanks to certificate chains.

Chain of Trust

As explained in the previous posts, a server certificate can be signed by a CA certificate in order to confirm their identity. We trust that the CA has verified the contents of the server certificate before signing it, and hence we trust that the contents of the server certificate are valid.

Now, since a certificate that is used to sign other certificates, can in turn be signed by another certificate, we can build a whole “chain of trust”:

• At the top, there is the Root Certificate (also called the Anchor). It is signed by itself (self-signed).
• In between, there may be one or more Intermediate Certificates. They are signed by either other intermediates or the root certificate.
• On the bottom is our server (or client) certificate, called the Leaf Certificate.

In order to verify trust of a leaf certificate, we follow the “certificate chain” upwards until we reach a root certificate we already know and trust.

Root Certificates

In order to know which root certificates to trust, we must have a “trust store” containing all the root certificates. This store is usually provided by the operating systems, but some clients (most notably web browsers) have their own “trust store”.

In order for a CA to have its root certificate accepted into the trust store of an operating system or browser, they must meet certain quality criterias. They must prove that they take CSR validation seriously and that they have measures put in place to keep their private key confidential.

Having their private key stolen by an attacker is the absolute worst-case scenario for any CA. It would mean that the attacker could create and sign ANY certificate using the CA’s cert! And since the CA’s certificate is trusted by all operating systems, clients connecting to the attacker would be none the wiser!

The only way to react to a compromised key is to rotate it, that is, to create a new one using a new private key. This however would mean that the certificate also has to be replaced in all trust stores. Since this can take a long time for some operating systems or clients, CA’s usually only use Intermediate Certificates to sign your certificates, and store the root key somewhere safe (and by safe I mean printed out on a piece of paper and stored in an actual safe.)

Intermediate Certificates

As explained, CA’s usually use an Intermediate Certificate to sign your CSR’s. An Intermediate Certificate, in turn, can be signed by either the Root Certificate or another Intermediate Certificate. This means that, in theory, there could be any number of intermediate certificates between your server (leaf) certificate and the Root. In practice, however, there is usually only one or two intermediates.

CA Bundles

In order to verify a certificate, a client must possess the certificate that was used to sign it. So in order to verify the whole chain, it must have each intermediate certificate as well as the root certificate. The root certificate we already have in our trust store. But what about the intermediates?

When a CA has signed your CSR and sends you back the certificate, they will also send you all the intermediate certificates between your server cert and the Root. This is called the “CA bundle” or “CA chain”.

When we install our Server certificate in our web server, we also provide it the CA bundle. And when a client connects to the server, the server will send along the bundle with the certificate. This is safe because as long as one of the certificates in the chain is signed by a trusted root certificate, we know that we can trust the whole chain.

Note that the root certificate itself must NOT be part of the chain. It MUST come from the client’s trust store! Unfortunately some CA’s have the bad habit to include their root in the CA bundle, so make sure to remove it before configuring your webserver.

If you check your website against SSL Labs’ SSL Server Test, it will warn you if either intermediates are missing or the root (“anchor”) is included.

Closing Words

Congratulations, you’ve made it! You should now have some basic understanding about TLS/SSL certificates, what they do, and how they work!

Thanks to various browser vendors’ efforts, HTTPS has become the norm when surfing the web in recent years. And now that you’re armed with knowledge, there is no excuse anymore to serve your websites unencrypted!

Welcome to the second article of a series of three, about the subject of security in networking applications, TLS, certificates, keys, and more.

• Part 1: What is TLS?
• Part 2: Verifying and Connecting
• Part 3: Trust and Certificate Chains

In the first part, we learnt about TLS/SSL, certificates, CAs and Keys. If you haven’t read it yet, please do so now.

Now that we know all about the pieces of the puzzle, how do they all fit together? Let’s have a closer look!

Issuing Certificates

To begin with, we need to obtain a certificate. The process works something like this:

First, we need a public/private key pair. With it, we can now create a new CSR (short for “Certificate Signing Request”). The CSR contains all the details we want to include in the certificate (the “Subject”) as well as the public key. We then send the CSR to the CA (the “Certificate Authority”).

The CA then verifies the contents of our CSR, and signs it with their own certificate and private key. This results in a new certificate with the details from our CSR, its “Issuer” field set to the “Subject” of the CA’s certificate, and our public key embedded. The CA then sends the new certificate back to us.

And that’s it! We now have a certificate that is signed by the CA, and we have the accompanying private key. We can now install the certificate in our web server.

Establishing Connections

The whole purpose of certificates and keys is to create secure channels through which devices can exchange information, without the risk of snooping by third parties. In other words, security experts call this “being able to send secrets on a postcard”, and all things considered, the analogy is quite appropriate.

So, how do we establish a secure connection with SSL? The following things are required:

1. A (web) server with:
   • A CA certificate;
   • A server certificate;
   • And a server certificate key (with its corresponding passphrase).
2. And a client (usually a web browser) with a list of trusted CA certificates.

With all this in place, we can establish a trusted, secure connection between both:

1. The client opens a TLS connection to the server.
2. The server responds with its certificate.
3. The client verifies that the server certificate was indeed signed by a trusted CA.
4. The client verifies that the server certificate’s CN attribute (part of the “Subject”, remember?) matches the requested URL.
5. The client verifies that the server certificate is not expired.
6. The client can optionally check that the server’s certificate has not been revoked, either by using a CRL (a “Certificate Revocation List”), or a mechanism like OCSP (an “Online Certificate Status Protocol”).

After these steps, the client has established an encrypted connection, and verified that the server it is connected to is indeed the one it claims to be.

The client and the server can now exchange data securely with one another, without the risk of a third party being able to read their interactions, or even worse, manipulating them!

Client Certificates

There is however another use case for certificates, and that is Client Authentication. Remember how in the example above the server’s certificate was used to verify its identity? This works the other way around too!

If we have a client certificate that is signed by a CA known to the server, we can establish a mutually trusted, secured connection.

To achieve this, we need the following things:

1. A server with:
   • A CA certificate;
   • A server certificate;
   • And a server certificate key (with its corresponding passphrase).
2. A client with:
   • A CA certificate;
   • A client certificate;
   • And a client certificate key (again, with its corresponding passphrase).

Once all the things are in place, a mutually trusted, secured connection can be established:

1. The client opens a TLS connection to the server, sending its certificate.
2. The server responds with its own certificate.
3. The client verifies that the server certificate was indeed signed by a trusted root.
4. The client verifies that the server certificate’s CN attribute matches the requested URL.
5. The client verifies that the server certificate is not expired.
6. The client can optionally ensure the server’s certificate was not revoked.
7. The server verifies that the client certificate was indeed signed by a trusted root (our CA certificate in this case).
8. The server verifies that the client certificate is not expired.
9. The server can optionally ensure the client’s certificate was not revoked.

After these steps, both client and server have established an encrypted connection, one in which both parties are who they claim they are. The server can now read the client’s certificate details (mostly the Subject) to identify the client.

And just like previously, our connection is now fully encrypted and all communications flowing through it are confidential.

Coming Up

In the last part of this series, we are going to talk about some technical details about chains, roots, and intermediates. Stay tuned!

Tobias Brunner’s lightning talk at Crossplane Community Day Europe 2021.

Watch the recording

The Crossplane Service Broker exposes Crossplane primitives via the Open Service Broker API. Tobias will introduce the concepts behind the Crossplane Service Broker and demonstrate to the audience how it all works together. By leveraging the Open Service Broker API while coupling it with the powerful concept of Crossplane Compositions it’s very easy to enable users of a platform which exposes Open Service Broker API integration (like Kubernetes Service Catalog or Cloudfoundry) to provision services fully automated. In a demonstration a real use case will be shown how a Redis service can be provisioned using the Open Service Broker API, leveraging the Crossplane Compositions and the Helm provider.

Slides: https://speakerdeck.com/tobru/self-service-provisioning-with-the-crossplane-service-broker

Note: This talk was held at the Crossplane Community Day Europe 2021

Also see: Crossplane – The Control-Plane of the future