A Kubernetes Pod and an AWS ECS Task Definition both support multiple different container images: each instance of the pod / task will run all images as containers together.
Does CloudFoundry support a similar concept to allow apps that consist of multiple, separate processes?
Actually, CloudFoundry has a community project for container orchestration tools based on Kubernetes, so that will accept pods the same way Kubernetes does.
You can read more about it here
CloudFoundry also has a CF Application Runtime which is pretty much their PaaS that allows you to deploy applications Heroku style which under the hood run as 'containers'. It's not clear from the docs what type of containers, but I presume you could find out more reading the code, but that's not exposed to the users, neither it's exposed as Pods.
tl;dr
No. You can only run a single container per application instance.
Longer Answer
Most of the answers are quickly pointing you to PKS, however Cloud Foundry itself is outside of that.
Cloud Foundry runs each application via Diego. Each application runs as a standalone container on a diego-cell. This is different from Kubernetes which you think of Pods or groups of colocated containers.
Cloud Foundry allows you to run multiple instances of each container, but I believe this is different from what you are asking.
Workaround
You may not be able to run multiple containers, but you can run multiple processes. For an example of this, check out how CF-FaaS runs. It uses the CF-Space-Security processes in a collocated scheme.
Pivotal now provides PAS - Pivotal Application Service, which is the traditional PaaS.
As a developer, I cf push my archive, the platform creates the container, and the Diego Orchestrator run my application. And yes, I can run multiple instances of my app.
PKS - Pivotal Container Service (cool kids spell with 'K'), is Pivotal's implementation of Kubernetes. It is CaaS - Container as a Service. As a developer, I create my own container - a docker container, or a vendor provides me a container, and PKS runs the container in a POD, inside a PKS cluster.
Next one coming out, some time in next 3 - 6 months, from Pivotal is PFS - Pivotal Functional Service. It is Pivotal's implementation of Function as a Service. As a developer, I can create and deploy a function to PFS. I have to identify the triggers for this function, based on which PFS will spin up new instances of the function, and when done, destroy it.
How you use what, depends on your use case.
This deck is for the presentation at Dallas Cloud Native Meetup's last session. Parth did a great job simplifying and explaining the differences and how you choose. Hope you can access it. Take a look.
Related
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.
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.
I am trying to deploy my Docker images using Kubernetes orchestration tools.When I am reading about Kubernetes, I am seeing documentation and many YouTube video tutorial of working with Kubernetes. In there I only found that creation of pods, services and creation of that .yml files. Here I have doubts and I am adding below section,
When I am using Kubernetes, how I can create clusters and nodes ?
Can I deploy my current docker-compose build image directly using pods only? Why I need to create services yml file?
I new to containerizing, Docker and Kubernetes world.
My favorite way to create clusters is kubespray because I find ansible very easy to read and troubleshoot, unlike more monolithic "run this binary" mechanisms for creating clusters. The kubespray repo has a vagrant configuration file, so you can even try out a full cluster on your local machine, to see what it will do "for real"
But with the popularity of kubernetes, I'd bet if you ask 5 people you'll get 10 answers to that question, so ultimately pick the one you find easiest to reason about, because almost without fail you will need to debug those mechanisms when something inevitably goes wrong
The short version, as Hitesh said, is "yes," but the long version is that one will need to be careful because local docker containers and kubernetes clusters are trying to solve different problems, and (as a general rule) one could not easily swap one in place of the other.
As for the second part of your question, a Service in kubernetes is designed to decouple the current provider of some networked functionality from the long-lived "promise" that such functionality will exist and work. That's because in kubernetes, the Pods (and Nodes, for that matter) are disposable and subject to termination at almost any time. It would be severely problematic if the consumer of a networked service needed to constantly update its IP address/ports/etc to account for the coming-and-going of Pods. This is actually the exact same problem that AWS's Elastic Load Balancers are trying to solve, and kubernetes will cheerfully provision an ELB to represent a Service if you indicate that is what you would like (and similar behavior for other cloud providers)
If you are not yet comfortable with containers and docker as concepts, then I would strongly recommend starting with those topics, and moving on to understanding how kubernetes interacts with those two things after you have a solid foundation. Else, a lot of the terminology -- and even the problems kubernetes is trying to solve -- may continue to seem opaque
I have worked on Kubernetes and currently reading about Service Fabric, I know Service Fabric provides microservices framework models like stateful, stateless and actor but other than that it also provides GuestExecutables or Containers as well which is what Kubernetes also does manage/orchestrate containers. Can anyone explain a detailed difference between the two?
You can see in this project paolosalvatori/service-fabric-acs-kubernetes-multi-container-appthe same containers implemented both in Service Fabric, and in Kubernetes.
Their "service" (for external ingress access) is different, with Kubernetes being a bit more complete and diverse: see Services.
The reality is: there are "two slightly different offering" because of market pressure.
The Microsoft Azure platform, initially released in 2010, has implemented its own Microsoft Azure Fabric Controller, in order to ensure the services and environment do not fail if one or more of the servers fails within the Microsoft data center, and which also provides the management of the user's Web application such as memory allocation and load balancing.
But in order to attract other clients on their own Microsoft Data Center, they had to adapt to Kubernetes, released initially in 2014, which is now (2018) either adopted or closely considered by... pretty much everybody (as reported in late December)
(That does not mean one is "better" than the other,
only that the "other" is more "visible" than the first ;) )
So it is less about "a detailed difference between the two", and more about the ability to integrate Kubernetes-based system on Microsoft Data Centers.
This is in line (source: detailed here) with Microsoft continued its unprecedented shift toward an open (read: non-proprietary) staging platform for Azure (with Deis).
And Kubernetes orchestrator is available on Microsoft's Azure Container Service since February 2017.
You can see other differences in their architecture of a deployed application:
Service Fabric:
Vs. Kubernetes:
thieme mentions in the comments the article "Service Fabric and Kubernetes comparison, part 1 – Distributed Systems Architecture", from Marcin Kosieradzki.
Both are different. Kubernetes manages rkt or other containers.
Service Fabric is not for managing containers. In case it manages some, that does not make it its purpose. That does not enable it for a comparison with Kubernetes.
eg: When a pod dies Kubernetes puts it to other nodes immediately. The part of SF that manages containers does not do this, it is done by some other area of Service Fabric. And outside containers. And was not designed with containers in mind.