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)
Related
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.
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
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.
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.
This is a question of integration:
I would like to run Jenkins on Google Compute Engine. I can do this, but I will quickly break my budget if I leave an 8-core virtual machine running at all times. As a solution, I think I can leave a micro instance with a low amount of memory powered on and acting as the jenkins master running at all times. It seems that I should be able to configure github to startup a jenkins slave (with 8 cores) whenever a push is performed. How do I connect github post-commit hooks to Google Compute Engine to achieve this? A complete answer is probably asking too much, but even just pointers to the relevant documentation would be helpful.
Alternatively, how would you solve my problem?
You can run an AppEngine instance and use the URL it provides as the target of your GitHub on-commit web hook. This way, you won't be charged unless the instance is actually running, which may even be cheaper than running a micro instance 24x7 on Compute Engine.
You can then start/stop instances on Compute Engine or trigger actions on them from your code running on App Engine.
Here's a related question which has an answer for how to authenticate to Compute Engine from code running on AppEngine.
I ended up using a preemptible instance that automatically gets restarted every few minutes. I had to setup the instance manager to perform this restart, and I had to use the API, since this is a bit of an advanced and peculiar use of the features.