I'm currently looking into GCP app engine and I was figuring out how I would deploy a very large application with multiple services. I also wanted to use mongodb. GCP docs say that app engine allows dockerfiles and images. What would happen if I used the mongo docker image as a service on app engine? How would it scale it's instances? What will happen to consistency? I'm aware GCP have a third party solution for mongo, but since they allow docker images, what stops me from using it?
App Engine routinely tears down and creates new instances. If your instance is running MongoDB, then all the data stored in that instance will be lost.
This is why Google Cloud offers other, permanent places to store state, like Datastore and CloudSQL. You can also run MongoDb yourself on Google Compute Engine.
What would happen if I used the mongo docker image as a service on app engine?
Flexible App Engine allows you to use docker images to build your own application, as per is mentioned on this document [1]: "App Engine flexible environment instances are Compute Engine virtual machines, which means that you can take advantage of custom libraries, use SSH for debugging, and deploy your own Docker containers."
So there is no problem to use your own docker image in flexible app Engine.
How would it scale it's instances?
Each active version in App Engine must have at least one instance to handle requests, there are two ways to scale the instance in App Engine: automatic and manual.
As per is mentioned on the document[2]:
Automatic scaling creates instances based on request rate, response latencies, and other application metrics. You can specify thresholds for each of these metrics, as well as a minimum number instances to keep running at all times.
Manual scaling specifies the number of instances that continuously run regardless of the load level. This allows tasks such as complex initializations and applications that rely on the state of the memory over time.
The way you can configure these features is through the app.yaml file, I suggest you read this document[3]
What will happen to consistency?
Since App Engine scaling can be configured depending on its load, this allows for good performance in service execution and provides consistency in operations and optimization of resources.
[1] https://cloud.google.com/appengine/docs/flexible#features
[2] https://cloud.google.com/appengine/docs/flexible/go/how-instances-are-managed#instance_scaling
[3] https://cloud.google.com/appengine/docs/flexible/go/configuring-your-app-with-app-yaml
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
I'm working on trying to get a MongoDB replicaset deployed into Kubernetes with a default set of collections and data. The Kubernetes piece isn't too pertinent but I wanted to provide that for background.
Essentially in our environment we have a set of collections and data in the form of .js scripts that we currently build into our MongoDB image by copying them into /docker-entrypoint-initdb.d/. This works well in our current use case where we're only deploying MongoDB as a single container using Docker. Along with revamping our entire deployment process to deploy our application into Kubernetes, I need to get MongoDB deployed in a replicaset (with persistent storage) for obvious reasons such as failover.
The issue I've run into and found recognized elsewhere such as this issue https://github.com/docker-library/mongo/issues/339 is that scripts in /docker-entrypoint-initdb.d/ do not run in the same manner when configuring a replicaset. I've attempted a few other things such as running a seed container after the mongo replicaset is initialized, building our image with the collections and data on a different volume (such as /data/db2) so that it persists once the build is finished, and a variety of scripts such as those in the github link above. All of these either don't work or feel very "hacky" and I don't particularly feel comfortable deploying these to customer environments.
Unfortunately I'm a bit limited with toolsets and have not been approved to use a cloud offering like MongoDB Atlas or tooling such as the Enterprise Kubernetes Operator. Is there any real supported method for this use case or is the supported method to use a cloud offering or one of the MondoDB operators?
Thanks in advance!
I want to deploy Strapi on GKE (Kubernetes), I have a docker-compose file, and I think I can use kcompose to create the deployment.
My questions is, has anyone used Mongodb Atlas + GKE or should I deploy Mongo on my own?
The question is more opinion based. It all depends on your needs.
If your needs match one of below you should stay with MongoDB:
Your app runs on-prem and contracts or privacy statements dont allow you to store data with a 3rd party.
You need large storage but not much query power.
There is other privacy/compliance issues.
Your app does not have internet access (firewalls, isolated environments)
You are running 3rd party applications that require a very old version of MongoDB
Here are some MongoDB Altas advantages:
Easily deploy, modify, and elastically scale their database clusters with a few clicks or an API call
Gain complete visibility into the performance the database and the underlying instances
Focus more on development, with built-in operational and security best practices such as geographically distributed, auto-healing clusters, and always-on authentication and encryption.
The best way would be if you will check how work with MongoDB Atlas on GCP looks alike. You can check this tutorial.
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.