Service Fabric looks great but right now, I do not have enough demand to hire 5 machines (I think it is the minimum number of nodes of a cluster).
I was thinking to install Service Fabric SDK on a single Azure Virtual Machine.
I know that I will not have the main benefits of a service fabric application: reliability and scalability, but I will be developing in a framework that I can easily can hire more machines and to scale if it is necessary in the future without changing anything.
Right now, I have 15 microservices and I plan to add 10 more. At the present I am using IIS and deployment and maintenance is not too fast. It seems that Service Fabric could solve it, plus it would be easily scalabe
Does it make sense to use Service Fabric in a single machine? or better to keep under IIS.
Technically it is possible, though it doesn't make much sense. The one node cluster, runs with a special configuration and so, scale out of that cluster is not supported. You can use a single node cluster for testing and then create another one for production use.
Related
Say you have 3 or more services that communicate with each other constantly, if they are deployed remotely to the same cluster all is good cause they can see each other.
However, I was wondering how could I deploy one of those locally, using minikube for instance, in a way that they are still able to talk to each other.
I am aware that I can port-forward the other two so that the one I have locally deployed can send calls to the others but I am not sure how I could make it work for the other two also be able to send calls to the local one.
TL;DR Yes, it is possible but not recommended, it is difficult and comes with a security risk.
Charlie wrote very well in the comment and is absolutely right:
Your local service will not be discoverable by a remote service unless you have a direct IP. One other way is to establish RTC or Web socket connection between your local and remote services using an external server.
As you can see, it is possible, but also not recommended. Generally, both containerization and the use of kubernetes tend to isolate environments. If you want your services to communicate with each other anyway being in completely different clusters on different machines, you need to configure the appropriate network connections over the public internet. It also may come with a security risk.
If you want to set up the environment locally, it will be a much better idea to run these 3 services as an independent whole. Also take into account that the Minikube is mainly designed for learning and testing certain solutions and is not entirely suitable for production solutions.
I deploying a python + tensorflow + flask application using a fully managed Google Cloud Run Service (1 vCPUs and 4 GB Ram).
System works fine but it is really slow, so I am evaluating ways of making it fast (it needs to run 20-30 times faster than what is doing now)
What would be the best approach?
To use a Kubernetes Cluster with one or two powerful machines
To use a Kubernetes Cluster with 3-5 weaker machines
To forget about Kubernets/Docker and run everything on single powerfull VM
Something else maybe?
For now I don't expect to have more than 10 users at a time but I want to be able to scale it up eventually.
You might want to evaluate according to your use case
Per this article, Fully managed Cloud Run is an ideal serverless platform for stateless containerized microservices that don’t require Kubernetes features like namespaces, co-location of containers in pods (sidecars) or node allocation and management.
GKE is a great choice if you are looking for a container orchestration platform that offers advanced scalability and configuration flexibility.
You mentioned you are looking the cheaper/easier method to develop, but this will probably not be as scalable, efficient or manageable, you might want to take a closer look at all cloud compute options in GCP to see what could benefit your use case the most.
You mentioned your use case is CPU intensive, so you might want to leverage the high CPU machine types, these might be used directly by creating a VM, creating an instance group or using them in other services like GKE or App Engine
Is there any advantage if I use Cloud Run instead of deploying a normal service/container in GKE?
I will try to add my perspective.
This answer does not cover running containers in Google Cloud Run Kubernetes. The reason is that we wanted an almost zero cost solution for a legacy PHP website. Cloud Run fit perfectly and we had an easy time both porting the code and learning Cloud Run.
We needed to do something with a legacy PHP website. This website was running on Windows Server 2012, IIS and PHP 7.0x. The cost was over $100.00 per month - mostly for Windows licensing fees for a VM in the cloud. The site was not accessed very much but was needed for various business reasons.
A decision was made Thursday (4/18/2019) was that we needed to learn Google Cloud Run, so we decided to port this site to a container and try to run the container in Google Cloud. Nothing like a real world example to learn the details.
Friday, we ported the PHP code to Apache. Very easy process. We did not worry about SSL as we intend to use Cloud Run SSL.
Saturday we started to learn Cloud Run. Within an hour we had the Hello World PHP example running. Link.
Within two hours we had the containerized website running in Cloud Run. Again, very simple.
Then we learned how to configure Cloud Run SSL with our DNS server.
End result:
Almost zero cost for a PHP website running in Cloud Run.
Approximately 1.5 days of effort to port the legacy code and learn Cloud Run.
Savings of about $100.00 per month (no Windows IIS server).
We do not have to worry about SSL certificates from now on for this site.
For small websites that are static, Cloud Run is a killer product. The learning curve is very small even if you do not know Google Cloud. You just need to configure gcloud for container builds and deployment. This means developers can be independant of needing to master GCP.
There are many distinctions in using Cloud Run to expose a service as compared to running it natively in GKE. The primary of these is that Cloud Run provides more of a serverless infrastructure. Basically you declare that you want to expose a service and then let GCP do the rest. Contrast this with creating a Kubernetes cluster and then defining your service in pods. With a manually created GKE cluster, the nodes and environment are always on which means that you are billed for them regardless of utilization. With Cloud Run, your service is merely available and you are only billed for actual consumption. If your service not being called, your costs are zero. Another advantage is that you don't have to predict your utilization needs and allocate sufficient nodes. Scaling happens automatically for you.
See also these presentations from Google Next 19:
Migrating from a Monolith to Microservices (Cloud Next '19)
What's New in Serverless Compute? (Cloud Next '19)
Run Containers on GCP's Serverless Infrastructure (Cloud Next '19)
Run Cloud Functions Everywhere (Cloud Next '19)
Container Once, Serverless Anywhere (Cloud Next '19)
If I've understood the docs properly, azure service fabric-based apps/microservices cannot be installed together with their service-fabric operational environment in one "packaged installer" step. For example, if I want to deploy a set of microservices on premises at a company that is running a typical windows server 2012 or VMWare IT center, then I'm out of luck? I'd have to require the company to first commit to (and execute) an installation of an azure app service fabric on several machines.
If this is the case, then the Azure Service Fabric is only an option for pure cloud operations where the service fabric cluster can be created on-demand by the provider or for companies that have already committed to azure service fabric. This means that a provider of classical "installer-based" software cannot evolve to the azure service fabric advantages since the datacenter policies of the potential customers is unknown.
What have I missed?
Yes, you always have to have a cluster to run Service Fabric Applications and Microservices. It is however not any more limited to a pure cloud environment, as of September last year the on-premise version of Azure Service Fabric for Windows Server went GA (https://azure.microsoft.com/en-us/blog/azure-service-fabric-for-windows-server-now-ga/) and that lets you run your own cluster on your own machines (whether physical or virtual, doesn't matter) or in another data center (or even at another cloud provider).
Of course, as you say, this requires your customer company to either have their own cluster or that you set one up for them (https://learn.microsoft.com/en-us/azure/service-fabric/service-fabric-cluster-creation-for-windows-server). They will also need to have the competence to manage that cluster over time. It could be argued though that this shouldn't be much more difficult than managing a VMWare farm or setting up and managing say a Docker container host(s).
For the traditional 'shrink-wrapped-DVD-installer-type' of software vendor this might not be as easy as just supplying an .exe and some system requirements, i agree with you on that. If the customer can't or don't wan't to run their own cluster and cloud is not an option then it definitely adds additional complexity to selling and delivering your solution.
The fact that you can run your own cluster on any Windows Server environment means that there is no real lock-in to Azure as a cloud platform, I think that this is a big pro for SF as a framework. Once you have a cluster to receive your applications then you can focus on developing that, this cannot be said of most other cloud-based PaaS frameworks/services.
I'm trying to learn a deployment process that can guarantee a headackeless scaling of a meanjs application (not in the level that people do it in big companies, but also not at a hobby level).
So as long as I understood, this could be a solution to work on:
Having mongodb on digitalocean on Ubuntu
Having the meanjs application (all other than mongodb) in a docker
Then one can scale! Because mongodb could be clustered separately and docker keeps the scaling of the application easy.
Well, I know it sounds trivial and that's why I'm asking here: I just want to go and learn docker and want to be sure before investing time on the above assumed solution!
Do you think this guarantee an easy scaling, say, for a simple online multiplayer game on meanjs please? Thank you.
UPDATE 31/07/2018
Digital Ocean introducing Kubernates which does all the orchestration they have also released load balancer which I think will work well with kubernates
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There is no off the shelf solution.
You can use docker with swamp but for small deployment it brings additional issues of monitoring and networking.
So here is what I did:
Create a script to generate HAProxy config when you start/stop instance
Have mongo in a cluster or replica or whatever. Database usually does not need to be scaled dynamically. You just have single mongo server then you scale it up and when you can't scale it vertically anymore you scale it horizontally by creating replica set and then scale it up until you can't then you do sharding.
So have HAProxy as load balancer that accepts connections on port 80 and forwards to your droples oven private network.
You can also write scripts to use DO API to create an image with your deployment and fire it up once you have more traffic either dynamically by detecting response time or cpu load or whatever other metric you have or statically.
I hope this helps.