I want to setup my hyperledger blockchain application into kubernetes cluster.
I don't want to encourage questions like this but here are some steps that you could possibly help you:
Ensure your application runs correctly locally on Docker.
Construct your Kubernetes configuration files. What you will need:
A deployment or a statefulset for each of your peers.
A statefulset for the couchdb for each of your peers.
A deployment or a statefulset for each of your orderers.
One service per peer, orderer and couchdb (to allow them to communicate).
A job that creates and joins the channels.
A job that installs and instantiates the chaincode.
Generated crypto-material and network-artifacts.
Kubernetes Secrets or persistent volumes that hold your crypto-material and network-artifacts.
An image of your dockerized application (I assume you have some sort of server using an SDK to communicate with the peers) uploaded on a container registry.
A deployment that uses that image and a service for your application.
Create a Kubernetes cluster either locally or on a cloud provider and install the kubectl CLI on your computer.
Apply (e.g. kubectl apply -f peerDeployment.yaml) the configuration files on your cluster with this order:
Secrets
Peers, couchdb's, orderers (deployments, statefulsets and services)
Create channel jobs
Join channel jobs
Install and instantiate chaincode job
Your application's deployment and service
If everything was configured correctly, you should have a running HLF platform in your Kubernetes cluster. It goes without saying that you have to research each step to understand what you need to do. And to experiment, a lot.
Related
I have two K8S clusters setup, one on AWS EKS, the other is on GCP. I setup a rancher server which is used to manage this two clusters. I have an application (appA) which is packaged in a helm chart. The application is just a rest api server created by nodejs + express.
It is deployed to both clusters via Rancher. After deploy, appA are running in the two clusters separately.
I have another application (appB) (running outside of K8S) which is listening on a database stream and it needs to call appA (in the K8S clusters) to process the change events.
My question is how I can deploy an entry point, like nginx, which will forward the appB's requests to appA, one of the pod from the clusters should serve this request.
You can expose the appA Kubernetes service type as Loadbalancer.
You can run nginx outside of the k8s, create a upstream and add both EKS
and GKE loadbalancers urls as backend servers.
Follow the below link
https://docs.nginx.com/nginx/admin-guide/load-balancer/http-load-balancer/
I have recently been reading more about infrastructure as a service (IaaS) and platform as a service (PaaS) and had some questions. I see when we opt for a PaaS solution, it is generally very easy to create the infrastructure as the cloud providers handle that for us and we can even automate the deployment using an infrastructure as code solution like Terraform.
But if we use an IaaS solution or even a local on premise cluster, we lose a lot of the automation it seems that PaaS allows. So I was curious, are there any good tools out there for automating infrastructure deployment on a local cluster that is not in the cloud?
The best thing I could think of was to run a local Kubernetes cluster and then Dockerize each of the infrastructure components, but this seems difficult as each node in the cluster will need its own specific configuration files.
From my basic Googling, it seems like there is not a good solution to this.
Edit:
I was not clear enough with my original intentions. I have two problems I am trying to solve.
How do I automate infrastructure deployment locally? For example, suppose I wanted to create a Hadoop HDFS cluster. I would need to configure one node to be the namenode with an accessible IP, and the other nodes to be datanodes that are aware of the namenode's IP. At the moment, I have to do this manually by logging into each node, checking it's IP, and then configuring each one. How would I automate this? If I were to use a Kubernetes approach, how do I specify that one of the running pods needs to be the namenode and the others are datanodes? How do I find the pods' IPs and have them be aware of the namenode IP?
The next problem I have is very similar to the first, but a slight modification. How would I deploy specific configuration files to each node. For instance in Kafka, the configuration file for one node, requires the IPs of the Zookeeper nodes, as well as the IP it should listen on. This may be different for every node in the cluster. Is there a good way to make these config files pod specific, so that I do not have to do bash text processing to insert the correct contents into each pod's config files?
You can use Terraform for all of your on-premise Infra. Automation, and Ansible for configuration management.
Let's say you have three HPE servers, Install K8s or VMware on them using Ansible, then you can treat them as three Avvaliabilty zones in one region, same as AWS. from this you can start deploying dockerize apps, or helm charts using Terraform.
Summary:
Ansbile for installing and configuration K8s.
Terraform for provisioning K8s.
Helm for installing apps on K8s.
After this you gonna have a base automated on-premise Infra.
I am trying to create Kubernetes cluster having 1 master and 2 worker nodes by using the tool kubeadm in my on-premise machines. I am following the Kubernetes official documentation for forming the cluster from the following url:
https://kubernetes.io/docs/setup/production-environment/tools/kubeadm/high-availability/
After installing all the runtime and completing before begin pre-requistics steps, I found in the document as the first step of forming the cluster is Create load balancer for kube-apiserver.
My Doubt
When I created the single master 3 worker nodes cluster using kubespray tool, I did not created any separate load balancer for that. So here when I am following the kubeadm tool, Do I need to create the load balancer actually for forming ?
Why are both tools showing different way, Since I did not created load balancer by using kubespray tool. Now I am trying to create cluster with kubeadm tool.
Speaking of load balancers creation during Kubernetes deployment using Kubeadm it depends on your setup. It is not mandatory to setup load balancer. Your cluster will still work, but without load balancing, it's going to be hard to qualify this cluster as HA.
In a single master setup as it is in your case, the master node manages the etcd database, API server, controller manager and scheduler, along with the worker nodes. However, if that single master node fails, all the worker node fail as well and entire cluster will be lost.
Learn more here: kubernetes-ha-kubeadm.
Kubeadm covers the needs of a life-cycle management for Kubernetes clusters, including self-hosted layouts, dynamic discovery services, etc. Kubespray is more about generic configuration, initial clustering, and bootstrapping.
Kubespray is a good choice when you either are familiar with Ansible or seek a possibility to switch between multiple platforms. If your priority is tight integration with unique features offered by the supported clouds, and you plan to stick with your provider, kops may be a better option.
Deploying a loadbalancer is up to a user and is not covered by ansible roles in Kubespray. By default, it only configures a non-HA endpoint, which points to the access_ip or IP address of the first server node in the kube-master group. It can also configure clients to use endpoints for a given loadbalancer type. More information you can find here: kubespray-lb.
Here you have comparision of Kubernetes deployment tools: Kubernetes Deployment Tools.
I am implementing the continuous integration and continuous deployment by using Ansible, Docker, Jenkins and Kubernetes. I already created one Kubernetes cluster with 1 master and 2 worker nodes by using Ansible and kubespray deployment. And I have 30 - 40 number of micro service application. I need to create that much of service and deployments.
My Confusion
When I am using Kubernetes package manager Kubernetes Helm chart, then do I need to initiate my chart on master node or in my base machine from where I I deployed my kubernet cluster ?
If I am initiating inside master, then can I use kubectl to deploy using ssh on remote worker nodes?
If I am initiating outside the Kubernetes cluster nodes , then Can i use kubectl command to deploy in Kubernetes cluster ?
Your confusion seems to lie in the configuration and interactions of Helm components. This explanation provides a good graphics to represent the relationships.
If you are using the traditional Helm/Tiller configuration, Helm will be installed locally on your machine and, assuming you have the correct kubectl configuration, you can "initialize" your cluster by running helm init to install Tiller into your cluster. Tiller will run as a deployment in kube-system, and has the RBAC privileges to create/modify/delete/view the chart resources. Helm will automatically manage all the API objects for you, and the kube-scheduler will schedule the pods to all your nodes accordingly. You should not be directly interacting with your master and nodes via your console.
In either configuration, you would always be making the Helm deployment from your local machine with a kubectl access to your cluster.
Hope this helps!
If you look for the way for running helm client inside your Kubernetes cluster, please check the concept of Helm-Operator.
I would recommend you also to look around for term "GitOps" - set of practices which combines Git with Kubernetes, and sets Git as a source of truth for your declarative infrastructure and applications.
There are two great OSS projects out there, that implements GitOps best practices:
flux (uses Helm-Operator)
Jenkins-x (uses helm as a part of release pipeline, check out this session on YT to see it in action)
We run our stack on the Google Cloud Platform (hosted Kubernetes, GKE) and have a Consul cluster running outside of K8s (regular GCE instances).
Several services running in K8s use Consul, mostly for it's CP K/V Store and advanced locking, not so much for service discovery so far.
We recently ran into some issues with using the Consul service discovery from within K8s. Right now our apps talk directly to the Consul Servers to register and unregister services they provide.
This is not recommended best-practice, usually Consul clients (i.e. apps using Consul) should talk to the local Consul agent. In our setup there are no local Consul agents.
My Question: Should we run local Consul agents as sidekick containers in each pod?
IMHO this would be a huge waste of ressources, but it would match the Consul best-practies better.
I tried searching on Google, but all posts about Consul and Kubernetes talk about running Consul in K8s, which is not what I want to do.
As the official Consul Helm chart and the documentation suggests the standard approach is to run a DaemonSet of Consul clients and then use a connect-side-car injector to inject sidecars into your node simply by providing an annotation of the pod spec. This should handle all of the boilerplate and will be inline with best practices.
Consul: Connect Sidecar; https://www.consul.io/docs/platform/k8s/connect.html