Namespaced objects such as secrets and configmaps are a native way of storing data in Kubernetes. These are NOT cluster-scoped objects; meaning, they cannot be shared across multiple namespaces; logical boundaries in Kubernetes. Hence, if multiple namespaces need access to a common secret or a configmap, then they would need to be created in each namespace along with the corresponding RBAC rules to describe the permission.
For example, most of the companies that run Kubernetes in the production environment use a private container registry to store images & artifacts. For a pod to pull images or download artifacts during
pod:Init state, the pod will use the secret value stored in
imagePullSecret that will, in turn, have the credential info (username, password, URL) of the private registry. If there are 100 services deployed in dedicated namespaces, this secret needs to be created, rotated & managed 100 times (namespace x 100). This causes a lot of operational overhead, such as
Kubernetes doesn’t have a native solution to this problem yet. But they have operators. So I decided to write a Kubernetes operator to solve the above-listed problems; I call it** Agumbe** (named after a small town in coastal Karnataka). It is responsible for performing object replication (a.k.a. dupe).
perators are software extensions that make use of custom resources to manage applications & their components. Operators follow Kubernetes principles, notably the control loop. The Operator pattern aims to capture the key aim of a human operator who is managing a service or set of services & how it can be automated. Operators are clients of the Kubernetes API that act as controllers for a custom resource.
Fig2. Operators overview
The most common way to deploy an operator is to add the Custom Resource Definition (CRD) & its associated controller to your cluster. The controller will normally run outside of the control plane, much as you would run any containerized application.
gumbe is a highly scalable Kubernetes operator based on the Kopf project, and that introduces a custom resource called GlobalObject. When a user creates this CRD object, the Agumbe controller simply replicates the object to multiple namespaces while adopting the children objects it has created. The same applies to UPDATE, DELETE operations.
Now let’s take a look at a working example, where we need to replicate a secret from an admin namespace to THREE other service namespaces: proxy, app & database.
Without Agumbe, you would need to create the secret THREE times.
Fig3. Agumbe replicating Kubernetes secrets
Let’s take a look at the components involved,
Since we are dealing with transforming/replicating sensitive data, it’s important to run the Agumbe controller in isolation. An admin namespace that can be accessed only by cluster administrators is preferred
## NAMESPACE MANIFEST --- apiVersion: v1 kind: Namespace metadata: name: admin labels: env: DEV access: cluster-admins
2. Role-Based Access Control (RBAC)
The Agumbe controller pod needs to watch on specific events such as GlobalObjects _CREATE/UPDATE _events. It also needs the ability to CRUD secrets in all namespaces in the cluster. Hence the pod needs a ClusterRole privilege to be able to perform its operations.
## SERVICE-ACCOUNT MANIFEST --- apiVersion: v1 kind: ServiceAccount metadata: name: agumbe-global-object-controller namespace: admin labels: app: agumbe env: DEV ## CLUSTER-ROLE MANIFEST --- apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRole metadata: name: agumbe-global-object-controller labels: app: agumbe env: DEV rules: - apiGroups: [""] resources: ["secrets", "configmaps"] verbs: ["get", "create", "update", "list"] - apiGroups: [""] resources: ["namespaces"] verbs: ["get", "list"] - apiGroups: ["savilabs.io"] resources: ["globalobjects"] verbs: ["get", "list", "watch", "put", "post", "patch"] - apiGroups: ["events.k8s.io", ""] resources: ["events"] verbs: ["create"] ## CLUSTER-ROLE-BINDING MANIFEST --- apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRoleBinding metadata: name: agumbe-global-object-controller labels: app: agumbe env: DEV roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: agumbe-global-object-controller subjects: - kind: ServiceAccount name: agumbe-global-object-controller namespace: admin
#kubernetes #automation #devops #python #cloud-computing
Last year, we provided a list of Kubernetes tools that proved so popular we have decided to curate another list of some useful additions for working with the platform—among which are many tools that we personally use here at Caylent. Check out the original tools list here in case you missed it.
According to a recent survey done by Stackrox, the dominance Kubernetes enjoys in the market continues to be reinforced, with 86% of respondents using it for container orchestration.
And as you can see below, more and more companies are jumping into containerization for their apps. If you’re among them, here are some tools to aid you going forward as Kubernetes continues its rapid growth.
#blog #tools #amazon elastic kubernetes service #application security #aws kms #botkube #caylent #cli #container monitoring #container orchestration tools #container security #containers #continuous delivery #continuous deployment #continuous integration #contour #developers #development #developments #draft #eksctl #firewall #gcp #github #harbor #helm #helm charts #helm-2to3 #helm-aws-secret-plugin #helm-docs #helm-operator-get-started #helm-secrets #iam #json #k-rail #k3s #k3sup #k8s #keel.sh #keycloak #kiali #kiam #klum #knative #krew #ksniff #kube #kube-prod-runtime #kube-ps1 #kube-scan #kube-state-metrics #kube2iam #kubeapps #kubebuilder #kubeconfig #kubectl #kubectl-aws-secrets #kubefwd #kubernetes #kubernetes command line tool #kubernetes configuration #kubernetes deployment #kubernetes in development #kubernetes in production #kubernetes ingress #kubernetes interfaces #kubernetes monitoring #kubernetes networking #kubernetes observability #kubernetes plugins #kubernetes secrets #kubernetes security #kubernetes security best practices #kubernetes security vendors #kubernetes service discovery #kubernetic #kubesec #kubeterminal #kubeval #kudo #kuma #microsoft azure key vault #mozilla sops #octant #octarine #open source #palo alto kubernetes security #permission-manager #pgp #rafay #rakess #rancher #rook #secrets operations #serverless function #service mesh #shell-operator #snyk #snyk container #sonobuoy #strongdm #tcpdump #tenkai #testing #tigera #tilt #vert.x #wireshark #yaml
Automation is one of the fundamental components that makes Kubernetes so robust as a containerization engine. Even complex cloud infrastructure creation can be automated in order to simplify the process of managing cloud deployments. Despite the capability of leveraging so many resources and components to support an application, your cloud environment can still be fairly manageable.
Despite the many tools available on Kubernetes, the effort to make cloud infrastructure management more scalable and automated is ongoing. Kubernetes operator is one of the tools designed to push automation past its limits. You can do so much more without having to rely on manual inputs every time.
A Kubernetes operator, by definition, is an orchestration framework. It is a tool that lets you orchestrate and maintain cloud infrastructures with little to no human input. Kubernetes define operators as software extensions designed to utilize custom resources to manage applications and their components.
Kubernetes operators are not complex at all. Operators use controllers and the Kubernetes API to handle packaging, deployment, management, and maintenance of applications and the custom resources that they need. The whole process is fully automated, plus you can still rely on _kubectl _tooling for commands and operations.
In other words, an operator is basically a custom Kubernetes controller that integrates custom resources for management purposes. You can define parameters and configurations inside the custom resources directly, and then let the operators translate those parameters and run autonomously. Kubernetes operators’ continuous nature is their defining factor.
#blog #kubernetes #automation #kubernetes api #kubernetes deployment #kubernetes operators
Kubernetes is an open-source project that “containerizes” workloads and services and manages deployment and configurations. Released by Google in 2015, Kubernetes is now maintained by the Cloud Native Computing Foundation. Since its release, it has become a worldwide phenomenon. The majority of cloud-native companies use it, SaaS vendors offer commercial prebuilt versions, and there’s even an annual convention!
What has made Kubernetes become such a fundamental service? A major factor is its automation capabilities. Kubernetes can automatically make changes to the configuration of deployed containers or even deploy new containers based on metrics it tracks or requests made by engineers. Having Kubernetes handle these processes saves time, eliminates toil, and increases consistency.
If these benefits sound familiar, it might be because they overlap with the philosophies of SRE. But how do you incorporate the automation of Kubernetes into your SRE practices? In this blog post, we’ll explain the Kubernetes Operator—the Kubernetes function at the heart of customized automation—and discuss how it can evolve your SRE solution.
In Kubernetes Operators: Automating the Container Orchestration Platform, authors Jason Dobies and Joshua Wood describe an Operator as “an automated Site Reliability Engineer for its application.” Given an SRE’s multifaceted experience and diverse workload, this is a bold statement. So what exactly can the Operator do?
#tutorial #devops #kubernetes #site reliability engineering #site reliability #site reliability engineer #site reliability engineering tools #kubernetes operators #kubernetes operator
Kubernetes is a highly popular container orchestration platform. Multi cloud is a strategy that leverages cloud resources from multiple vendors. Multi cloud strategies have become popular because they help prevent vendor lock-in and enable you to leverage a wide variety of cloud resources. However, multi cloud ecosystems are notoriously difficult to configure and maintain.
This article explains how you can leverage Kubernetes to reduce multi cloud complexities and improve stability, scalability, and velocity.
Maintaining standardized application deployments becomes more challenging as your number of applications and the technologies they are based on increase. As environments, operating systems, and dependencies differ, management and operations require more effort and extensive documentation.
In the past, teams tried to get around these difficulties by creating isolated projects in the data center. Each project, including its configurations and requirements were managed independently. This required accurately predicting performance and the number of users before deployment and taking down applications to update operating systems or applications. There were many chances for error.
Kubernetes can provide an alternative to the old method, enabling teams to deploy applications independent of the environment in containers. This eliminates the need to create resource partitions and enables teams to operate infrastructure as a unified whole.
In particular, Kubernetes makes it easier to deploy a multi cloud strategy since it enables you to abstract away service differences. With Kubernetes deployments you can work from a consistent platform and optimize services and applications according to your business needs.
The Compelling Attributes of Multi Cloud Kubernetes
Multi cloud Kubernetes can provide multiple benefits beyond a single cloud deployment. Below are some of the most notable advantages.
In addition to the built-in scalability, fault tolerance, and auto-healing features of Kubernetes, multi cloud deployments can provide service redundancy. For example, you can mirror applications or split microservices across vendors. This reduces the risk of a vendor-related outage and enables you to create failovers.
#kubernetes #multicloud-strategy #kubernetes-cluster #kubernetes-top-story #kubernetes-cluster-install #kubernetes-explained #kubernetes-infrastructure #cloud
What is a ternary operator: The ternary operator is a conditional expression that means this is a comparison operator and results come on a true or false condition and it is the shortest way to writing an if-else statement. It is a condition in a single line replacing the multiline if-else code.
syntax : condition ? value_if_true : value_if_false
condition: A boolean expression evaluates true or false
value_if_true: a value to be assigned if the expression is evaluated to true.
value_if_false: A value to be assigned if the expression is evaluated to false.
How to use ternary operator in python here are some examples of Python ternary operator if-else.
Brief description of examples we have to take two variables a and b. The value of a is 10 and b is 20. find the minimum number using a ternary operator with one line of code. ( **min = a if a < b else b ) **. if a less than b then print a otherwise print b and second examples are the same as first and the third example is check number is even or odd.
#python #python ternary operator #ternary operator #ternary operator in if-else #ternary operator in python #ternary operator with dict #ternary operator with lambda