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
Related
Currently, when I create and run deployment, I only work on one node.
I want to work on one task at the same time using Kubernetes.
I want all nodes to work like one computer.
Kubernetes is about managing containers and scheduling them to run across a cluster, not about “jobs” per se. Have a look at MapReduce and Apache Spark.
First you need to understand more about Kubernetes and why your understanding might be a bit misleading for you concept. Kubernetes is an container orchestration tool that automates many of the manual processes involved in deploying, managing, and scaling containerized applications.
In other words, you can cluster together groups of hosts running Linux containers, and K8s helps you manage those clusters. To process some kind of job, data you will need a software that runs on kubernetes.
The next step that you might want to look into is distributed computing concept and distributed computing model called MapReduce.
MapReduce was introduce by Google to meet the demand of large set of users for its applications. Its used to write write scalable applications that can do parallel processing to process a large amount of data on a large cluster of commodity hardware servers. Hadoop is software that has adopted MapReduce and is capable of running it`s programs in various languages (Python, Ruby, C++).
Take a look on this medium article about distributed computing system based on MapReduce and Kubernetes.
Is there a way to enable caching between pods in Kubernetes cluster? For eg: Lets say we have more than 1 pods running on High availability mode.And we want to share some value between them using distributed caching between the pods.Is this something possible?
There are some experimental projects to let you reuse the etcd that powers the cluster, but I probably wouldn’t. Just run your own using etcd-operator or something. The specifics will massively depend on what your exact use case and software is, distributed databases are among the most complex things ever.
Been working on a web app with a simple database model that only needs CRUD operations, figured MongoDB would be perfect for it. The most important constraints of the project is that it be able to scale from a small amount of users to a large amount. I’ve been looking at the cloud launcher and I’ve noticed that the most popular MongoDB solution advertises a cost of ~$350/mo. This is a surprisingly large amount that makes me consider using cloud sql for my database instead. Is there a better way to deploy MongoDB to GCP that’s more fitted to my use case? I’ve been reading about automatic scaling with kubernetes but I can’t find anything about price. Any and all advice is greatly appreciated
I haven't used mongodb with kubernetes but we do use the cloud launcher solution at work. We use 2 nodes(n1-standard-1) and an arbiter(micro) + 100GB storage on each node which comes up around $100 a month. You would need a replicaset in a production environment so this seems to be a reasonable base cost.
Kubernetes does not provide a lot of advantages over the classic GCE deployment for mongodb compared to a webserver. Setting up a replicaset on kubernetes is a bit more work compared to GCE setup. https://medium.com/google-cloud/mongodb-replica-sets-with-kubernetes-d96606bd9474 and http://blog.kubernetes.io/2017/01/running-mongodb-on-kubernetes-with-statefulsets.html should serve as decent references but wouldn't lower your costs. Scaling nodes would be slightly easier though but does not strictly translate to scaling mongodb.
I have lately been working on a similar solution.
GCP announced that they don't charge for Kubernetes cluster management but only for resources used by it (instances, network ...):
https://cloud.google.com/kubernetes-engine/pricing
In general, databases are high maintenance (data mounts, backups, migrations...), so I would not start running Mongo on Kubernetes right away. You could get there but it will be more complicated than deploying your web app on Kubernetes.
Better to use MongoDB as a service that supports GCP (e.g. MongoDB Atlas), I have done so myself and see a few other companies do that.
If you scale gradually you should be able to control your costs.
The web app itself should be easy to deploy and maintain on Kubernetes.
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.
We are going to end up with dozens of these microservices (most are Akka-based), and I'm unsure how to best manage their deployment. Specifically, they are built to be independent of each other and as specialized and distributed as possible.
My question stems from the fact that all of them are too small for their own individual JVMs; even if we were to host them on AWS nano instances, we'll still end up with about 40 machines if you factor in redundancy, and such a high number is simply not needed. Three medium size instances could (and do) easily handle the entire workload.
Currently, I just group them into "container" applications, somewhat randomly, and then run these container applications on larger JVMs.
However, there has to be a better way. I am not aware of any application servers for Akka where you can just "deploy actors", so I wanted to get some insight on how others run Akka microservices in production (and specifically how to manage deployment).
This is probably not limited to Scala and Akka, but most other platforms have dedicated app servers where you deploy these things.
IMHO, the canonical way is to use a service orchestration tool, and that would indeed run them in individual processes, each with their own JVM.
That's the only way you get the decoupling, isolation, resilience you want with microservices, only this way you'll be able to deploy, update, stop, start them individually.
You're saying:
My question stems from the fact that all of them are too small for
their own individual JVMs; even if we were to host them on AWS nano
instances
You seem to treat JVM and Amazon VMs as equivalent, but that's not the case. You can have multiple JVM processes on a single virtual machine.
I suggest you have a look at service orchestration tools such as
Lightbend Production Suite / Service Orchestration
or Kubernetes
These are just examples, there are others. Note that this tool category will give you a lot of features you'll sooner or later need anyway, such as easy scaling, log consolidation, service lookup, health checks / service failure handling etc.