I was thinking of testing out Google's Cloud Run for a simple app when all of a sudden I got thinking as to whether Cloud Run is basically a managed K8s cluster. I really wanted to know as to when using Cloud Run would be preferred over traditional K8s clusters and why we should prefer it?
Thanks.
Technology wise, cloud Run is a managed Kubernetes cluster with Knative to run the containers on top of it.
However Cloud Run brings an additional advantages when you run fully managed: you only pay for used resources. In other words, Cloud Run can do scale down to zero cost, rather than bottoming out at the cost of keeping a minimum sized cluster running.
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I'm trying to run open-source with minimal costs on the cloud and would love to run it on k8s without the hassle of managing it (managed k8s cluster). Is there a free tier option for a small-scale project in any cloud provider?
If there is one, which parameters should I choose to get the free tier?
You can use IBM cloud which provides a single worker node Kubernetes cluster along with container registry like other cloud providers. This is more than enough for a beginner to try the concepts of Kubernetes.
You can also use Tryk8s which provides a playground for trying Kubernetes for free. Play with Kubernetes is a labs site provided by Docker and created by Tutorius. Play with Kubernetes is a playground which allows users to run K8s clusters in a matter of seconds. It gives the experience of having a free Alpine Linux Virtual Machine in the browser. Under the hood Docker-in-Docker (DinD) is used to give the effect of multiple VMs/PCs.
If you want to use more services and resources, based on your use case you can try other cloud providers, they may not provide an indefinitely free trial but have no restriction on the resources.
For Example, Google Kubernetes engine(GKE) provides $300 credit to fully explore and conduct an assessment of Google Cloud. You won’t be charged until you upgrade which can be used for a 3 month period from the account creation. There is no restriction on the resources and the number of nodes for creating a cluster. You can add Istio and Try Cloud Run (Knative) also.
Refer Free Kubernetes which Lists the free Trials/Credit for Managed Kubernetes Services.
I'm developing a Kubernetes scheduler and I want to test its performance when nodes join and leave a cluster, as well as how it handles node failures.
What is the best way to test this locally on Windows 10?
Thanks in advance!
Unfortunately, you can't add nodes to Docker Desktop with Kubernetes enabled. Docker Desktop is single-node only.
I can think of two possible solutions, off the top of my head:
You could use any of the cloud providers. Major (AWS, GCP, Azure) ones have some kind of free tier (under certain usage, or timed). Adding nodes in those environments is trivial.
Create local VM for each node. This is less than perfect solution - very resource intesive. To make adding nodes easier, you could use kubeadm to provision your cluster.
I would like to know if it is possible for multiple pods in the same Kubernetes cluster to access a database which is configured using persistent volumes on a Google cloud persistent disk.
Currently I am building a microservices achitecture web app which has 3 node apis in different pods all accessing the same database. So how do I achieve this with kubernetes.
Kindly let me know if my architecture is right as well
You can certainly connect multiple node-based app pods to the same database. It is sometimes said that microservices shouldn't share a database but this depends on what your apps are doing, the project history and the extent to which you want the parts to be worked on separately.
There are questions you have to answer about running databases at scale, such as your future load and whether you want to use relational databases if you're going to try to span availability zones. And there are
some specific to kubernetes, especially around how you associate DB Pods to data. See https://stackoverflow.com/a/53980021/9705485. Another popular option is to use a managed DB service from a cloud provider. If you do run the DB in k8s then I'd suggest looking for a helm chart or looking at an operator, such as the kubeDB operator, to avoid crafting the kubernetes descriptors yourself and to get more guidance on running the DB and setting it up.
If it's a new project and you've not used k8s before then you'll also have to decide where to host your code, your docker images and your deployment descriptors and how to setup your CI pipelines. If you've not got answers to these questions already then I'd suggest looking at Jenkins-X as it will provide you with out of the box defaults for a whole cluster and CI setup and a template ('build pack') for building node apps and deploying them to staging and prod environments through a pipeline.
We have a 5-node Azure Service Fabric Cluster as our main Production microservices hub. Up until now, for testing purposes, we've just been pushing out separate versions of our applications (the production application with ".Test" appended to the name) to that production SFC.
We're looking for a better approach, namely a separate test Service Fabric Cluster. But the issue comes down to costs. The smallest SFC you can create in Azure is 3 nodes. Further, you can't shutdown a SFC when it's not being used, which we would also need to do to save on costs.
So now I'm looking at just spinning up a plain Windows VM in Azure and installing the local Service Fabric Cluster app (which allows just one-node setup). Is it possible to do this and be able to communicate with the cluster from outside the VM?
What you are trying to accomplish is setup a standalone cluster. The steps to do it is documented in this docs.
Yes, you can access the cluster from outside the VM, In simple terms enable access to the network and open the firewall ports.
Technically both deployments(Guide and DevCluster) are very similar, the main difference is that you have better control on the templates following the standalone guide, using the development setup you don't have much options and all the process is automated.
PS: I would highly recommend you have a UAT\Staging cluster with the
exact same specs as the production version, the approach you used
could be a good idea for staging environment. Having different
environments increase the risk of issues, mainly related to
configuration and concurrency.
We have an application to be deployed on cloud, our application creates a database for every company you create. Is it advisable to go for Amazon EC2 hosting for the same or its better we go for some private hosting and configure our application server and database server separately and handling the cluster instances on my own? Please suggest, as are looking for a highly scalable deployment starting with a normal configuration initially.
This is off-topic but if you want to start small go with EC2/RDS (database) and then as you grow you can use their other services to scale up. Services like Autoscale, ELB, Cloudformation, DynamoDB, etc.
You can also start small with their Elastic Beanstalk service assuming that your application is in one of the supported stacks. Once you deploy to it, AWS takes care of all the scalability.
Another alternative is to use a service like Heroku which autoscales as your needs (and happens to run on top of AWS)
Finally, you can also look at other hosting places like Rackspace, Digital Ocean, Joyent among many others.