Deploying and Update Process of On Premise Kubernetes Environment Application - kubernetes

We are developing a microservice based system that is orchestrated using Kubernetes. Part of our use case is supplying our clients an On-Premise installation where they receive an Image (VMDK / QCOW2) with all the system deployed.
One of our main challenges is handling the update process of such system, currently the plan is to have an API endpoint that will receive an encrypted and signed package that will contain all the images and a certain update shell script. The API endpoint will start an asynchronous process that will extract the images and execute the shell script that eventually should call the Kubernetes to update all the images with the new code.
The question is where this API endpoint should be defined?
Be in a special "Maintenance" service that will be outside of the Kubernetes and control it, this service will be updated last in case it's code should be also updated.
Be part of one of the microservices containers that run inside Kubernetes - but then this image can be part of the updated images so any API that should return the update status can be un-available
What is the common way to export an interface to System Update or System Deployment wizard processes?
Thanks!

Related

Possible to deploy or use several containers as one service in Google Cloud Run?

I am testing Google Cloud Run by following the official instruction:
https://cloud.google.com/run/docs/quickstarts/build-and-deploy
Is it possible to deploy or use several containers as one service in Google Cloud Run? For example: DB server container, Web server container, etc.
Short Answer NO. You can't deploy several container on the same service (as you could do with a Pod on K8S).
However, you can run several binaries in parallel on the same container -> This article has been written by a Googler that work on Cloud Run.
In addition, keep in mind
Cloud Run is a serverless product. It scales up and down (to 0) as it wants (but especially according with the traffic). If the startup duration is long and a new instance of your service is created, the query will take time to be served (and your use will wait)
You pay as you use, I means, you are billed only when HTTP requests are processed. Out of processing period, the CPU allocated to the instance is close to 0.
That implies that Cloud Run serves container that handle HTTP requests. You can't run a batch processing out of any HTTP request, in background.
Cloud Run is stateless. You have an ephemeral and in memory writable directory (/tmp) but when the instance goes down, all the data goes down. You can't run a DB server container that store data. You can interact with external services (Cloud SQL, Cloud Storage,...) but store only transient file locally
To answer your question directly, I do not think it is possible to deploy a service that has two different containers: DB server container, and Web server container. This does not include scaling (service is automatically scaled to a certain number of container instances).
However, you can deploy a container (a service) that contains multiple processes, although it might not be considered as best practices, as mentioned in this article.
Cloud Run takes a user's container and executes it on Google infrastructure, and handles the instantiation of instances (scaling) of that container, seamlessly based on parameters specified by the user.
To deploy to Cloud Run, you need to provide a container image. As the documentation points out:
A container image is a packaging format that includes your code, its packages, any needed binary dependencies, the operating system to use, and anything else needed to run your service.
In response to incoming requests, a service is automatically scaled to a certain number of container instances, each of which runs the deployed container image. Services are the main resources of Cloud Run.
Each service has a unique and permanent URL that will not change over time as you deploy new revisions to it. You can refer to the documentation for more details about the container runtime contract.
As a result of the above, Cloud Run is primarily designed to run web applications. If you are after a microservice architecture, which consists of different servers running each in unique containers, you will need to deploy multiple services. I understand that you want to use Cloud Run as database server, but perhaps you may be interested in Google's database solutions, like Cloud SQL, Datastore, BigTable or Spanner.

Kubernetes: Policy check before container execution

I am new to Kubernetes, I am looking to see if its possible to hook into the container execution life cycle events in the orchestration process so that I can call an API to pass the details of the container and see if its allowed to execute this container in the given environment, location etc.
An example check could be: container can only be run in a Europe or US data centers. so before someone tries to execute this container, outside this region data centers, it should not be allowed.
Is this possible and what is the best way to achieve this?
You can possibly set up an ImagePolicy admission controller in the clusters, were you describes from what registers it is allowed to pull images.
kube-image-bouncer is an example of an ImagePolicy admission controller
A simple webhook endpoint server that can be used to validate the images being created inside of the kubernetes cluster.
If you don't want to start from scratch...there is a Cloud Native Computing Foundation (incubating) project - Open Policy Agent with support for Kubernetes that seems to offer what you want. (I am not affiliated with the project)

Spring cloud data flow deployment

I wanna deploy the Spring-cloud-data-flow on several hosts.
I will deploy the server of Spring-cloud-data-flow on one host-A, and deploy the agents on the other hosts(These hosts are in charge of executing the tasks).
Except the host-A, all the other hosts run the same tasks.
Shall I modify on the basis of the Spring Cloud Data Flow Local Server or on the Spring Cloud Data Flow Apache Yarn Server or other better choice?
Do you mean how the apps are deployed on several hosts? If so, the apps are deployed using the underlying deployer implementation. For instance, if it is local deployer then, each app is deployed by spawning a new process. You can scale out the number apps deployment using the count property during the stream deploy. I am not sure what do you mean by the agents here.

How can we route a request to every pod under a kubernetes service on Openshift?

We are building a Jboss BRMS application with two microservices in spring-boot, one for rule generation (SRV1) and one for rule execution (SRV2).
The idea is to generate the rules using the generation microservice (SRV1) and persist them in the database with versioning. The next part of the process is having the execution microservice load these persisted rules into each pods memory by querying the information from the shared database.
There are two following scenarios when this should happen :
When the rule execution service pod/pods starts up, it queries the db for the lastest version and every pod running the execution application loads those rules from the shared db.
The second senario is we manually want to trigger the loading of a specific version of rules on every pod running the execution application preferably via a rest call.
Which is where the problem lies!
Whenever we try and issue a rest request to the api, since it is load balanced under a kubernetes service, the request hits only one of the pods and the rest of them do not load the specific rules.
Is there a programatic or design change that may help us achieve that or is there any other way we construct our application to achieve a capability to load a certain version of rules on all pods serving the execution microservice.
The second senario is we manually want to trigger the loading of a specific version of rules on every pod running the execution application preferably via a rest call.
What about using Rolling Updates? When you want to change the version of rules to be fetched within all execution pods, tell OpenShift to do rolling update which kills/starts all your pods one by one until all pods are on the new version, thus, they fetch the specific version of rules at the startup. The trigger of Rolling Updates and the way you define the version resolution is up to you. For instance: Have an ENV var within a pod that defines the version of rules that are going to be fetched from db, then change the ENV var to a new value and perform Rollling Updates. At the end, you should end up with new set of pods, all of them fetching the version rules based on the new value of the ENV var you set.

How do micro services in Cloud Foundry communicate?

I'm a newbie in Cloud Foundry. In following the reference application provided by Predix (https://www.predix.io/resources/tutorials/tutorial-details.html?tutorial_id=1473&tag=1610&journey=Connect%20devices%20using%20the%20Reference%20App&resources=1592,1473,1600), the application consisted of several modules and each module is implemented as micro service.
My question is, how do these micro services talk to each other? I understand they must be using some sort of REST calls but the problem is:
service registry: Say I have services A, B, C. How do these components 'discover' the REST URLs of other components? As the component URL is only known after the service is pushed to cloud foundry.
How does cloud foundry controls the components dependency during service startup and service shutdown? Say A cannot start until B is started. B needs to be shutdown if A is shutdown.
The ref-app 'application' consists of several 'apps' and Predix 'services'. An app is bound to the service via an entry in the manifest.yml. Thus, it gets the service endpoint and other important configuration information via this binding. When an app is bound to a service, the 'cf env ' command returns the needed info.
There might still be some Service endpoint info in a property file, but that's something that will be refactored out over time.
The individual apps of the ref-app application are put in separate microservices, since they get used as components of other applications. Hence, the microservices approach. If there were startup dependencies across apps, the CI/CD pipeline that pushes the apps to the cloud would need to manage these dependencies. The dependencies in ref-app are simply the obvious ones, read-on.
While it's true that coupling of microservices is not in the design. There are some obvious reasons this might happen. Language and function. If you have a "back-end" microservice written in Java used by a "front-end" UI microservice written in Javascript on NodeJS then these are pushed as two separate apps. Theoretically the UI won't work too well without the back-end, but there is a plan to actually make that happen with some canned JSON. Still there is some logical coupling there.
The nice things you get from microservices is that they might need to scale differently and cloud foundry makes that quite easy with the 'cf scale' command. They might be used by multiple other microservices, hence creating new scale requirements. So, thinking about what needs to scale and also the release cycle of the functionality helps in deciding what comprises a microservice.
As for ordering, for example, the Google Maps api might be required by your application so it could be said that it should be launched first and your application second. But in reality, your application should take in to account that the maps api might be down. Your goal should be that your app behaves well when a dependent microservice is not available.
The 'apps' of the 'application' know about each due to their name and the URL that the cloud gives it. There are actually many copies of the reference app running in various clouds and spaces. They are prefaced with things like Dev or QA or Integration, etc. Could we get the Dev front end talking to the QA back-end microservice, sure, it's just a URL.
In addition to the aforementioned, etcd (which I haven't tried yet), you can also create a CUPS service 'definition'. This is also a set of key/value pairs. Which you can tie to the Space (dev/qa/stage/prod) and bind them via the manifest. This way you get the props from the environment.
If micro-services do need to talk to each other, generally its via REST as you have noticed.However microservice purists may be against such dependencies. That apart, service discovery is enabled by publishing available endpoints on to a service registry - etcd in case of CloudFoundry. Once endpoint is registered, various instances of a given service can register themselves to the registry using a POST operation. Client will need to know only about the published end point and not the individual service instance's end point. This is self-registration. Client will either communicate to a load balancer such as ELB, which looks up service registry or client should be aware of the service registry.
For (2), there should not be such a hard dependency between micro-services as per micro-service definition, if one is designing such a coupled set of services that indicates some imminent issues such as orchestrating and synchronizing. If such dependencies do emerge, you will have rely on service registries, health-checks and circuit-breakers for fall-back.