I m developing an application using SignalR to manage websockets and allow my clients to dialog between each other.
I m planning to host this back-office on an Azure worker role. As my SignalR requests carry data that is most of the time saved in the database, I m wondering if NoSQL's MongoDB instead of the classic SQL Server/Entity Framework couple should be a good approach.
Assuming that my application's data types will be strings for most of them, I think MongoDB will be a reliable and a performant solution, and it will allow me to get rid of Azure's SQL's database costs.
For information, the Azure worker role will be running on a machine with the following hardware: 1 core CPU, 3.5GB RAM and 50GB SSD storage.
Do you think I m on a good start with this architecture ?
Thanks
Do you think I m on a good start with this architecture?
In a word, no.
A user asked a similar question regarding running Redis on Worker Roles - Setting up Redis on Azure cloud service worker role - all of the content on that Q/A is relevant in the MongoDb context.
I'd suggest that you read my answer as it goes into more detail, but as an overview of why this is a bad architectural approach:
You cannot guarantee when a Worker Role will be restarted by the Azure Service Fabric.
In a real-world implementation of Mongo, you would run multiple nodes within a cluster, with a single Worker Role (as you have suggested in your question) this won't be possible.
You will need to manage your MongoDb installation within the Worker Role and they simply aren't designed for this.
If you are really fixed on using Mongo, I would suggest that you use a hosted solution such as MongoLabs (as suggested in earlier answers), or consider hosting it on Azure IaaS VM's.
If you are not fixed on using Mongo, I would sincerely suggest that you look at Azure DocumentDb (also suggested above), Microsoft's Azure NoSQL offering - I have used it in several production systems already and it is certainly a capable NoSQL solution; granted, it may not have all of the features available with MongoDb.
If you are looking at a NoSQL solution for caching of data (i.e. not long term storage), I would suggest you take a look at Azure Redis Cache, which is a very capable Redis offering.
Azure has its own native NoSQL Document database called DocumentDB, have you had a look at it? If I were you I would use DocumentDB unless there are some special requirements that you have that you have not mentioned, but from what little requirement info that you have posted DocumentDB would do just fine. I don't think that it is quite similar to MongoDB in terms of the basic functionality, see this article for a comparison between Azure DocumentDB and MongoDB.
Related
CONTEXT: I have been learning Kubernetes and trying to get some hands-on experience. I have been using AKS to abstract the complexity of having to deal with the control plane (and because I have a free student azure account). I am deploying a NodeJS app that connects to the MongoDB database. So far the deployment has been successful but I am using MongoDB Atlas and connecting to it.
Based on the little I have learned about Stateful sets, the MongoDB Atlas service seems a lot easier and more convenient but my question is, when would it be a better idea to consider deploying a stateful set with MongoDB database? (running on the pod) What's more cost-effective? More easily scalable?
I realize the questions might be a little bit vague but I am just getting started with Kubernetes..
disclaimer: This is not a production application, just something simple I am using to learn K8S
Official docs docs uses statefullset and that would make sense. Generally all DB kind of applications deployed as statefullset. Because there can be states that nodes are not sync with each other and that would create data inconsistencies between nodes(mongodb nodes not kubernetes).
You can deploy MongoDB as deployment. I have seen it deployed. But most clients use a connection string to connect(a string of multiple node addresses). And since kubernetes exposes statefullsets with headless services you should be okay.
For learning purpose, I advice you to deploy your MongoDB in a StatefulSet. Then you can learn how it works and what problem you could encounter with this Kubernetes object.
For production application, I advice to never deploy a database in a StatefulSet if you don't need it. In fact, StatefulSet will come with a lot of problematics that you might not need to manage.
Sometimes, companies rules restrict to host their data on external company storage.
To know if you need to put your database in a StatefulSet, the question I try to answer is:
Should my DB be hosted on premise (for privacy)?
Should my DB be scalable?
Should my DB be updated frequently?
You can find a list of pros/cons on the documentation.
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.
I am building my own webapp which requires a huge database. I want to build and manage my own Mongo database on AWS rather than using Mongo Atlas. Which will be more cost saving? And whether I should go for Mongo Atlas? What will be its advantage over my own database?
There are pros and cons for both approaches:
Running MongoDB on AWS
Pros:
Complete control over how you run the database and how resources are allocated on the server. This could even be together with an application server on the same EC2 instance depending on your traffic and load. This might help with cost saving if your database is huge but isn't likely to see much traffic.
Cons:
You will be responsible for ensuring database availability and applying security patches as and when they are available. You may also have to setup firewalls and protect the EC2 instance and database in other ways that would be trivial to do on a hosted service like Atlas.
Data sharding and clustering can be a real pain to manage by yourself.
Running on Atlas
Pros:
Completely managed service where you don't have to be concerned about performance optimization or scalability. You pay for the services and Mongodb takes care of the rest.
You can focus on building a great application instead of spending your time on administering the database and the EC2 instance on which the database runs.
Cons:
You will be constrained by the options offered by Atlas. For most use cases this should be fine, but if you really want a specific change, it would be difficult to implement it if Mongodb doesn't already support it as a part of Atlas.
Think running your application on EC2 vs buying a server on-premise and running your application on that.
Being a managed service, costs might also be higher if your database does not see much traffic.
HOSTING yourself: You can get one or more AWS ec2 instances(which are VMs) where you can install and run Mongo DB yourself and manage it like you wanted to, making sure that you spin up more instances when the workload becomes large and there are instances up and running at all times to enable high availability.
Cost (high) - Management responsibilities (lots) - Full MongoDB functionality
MongoDB Atlas is a managed service, you don't need to worry about management tasks like scaling of your database and high availability when a single/more instances die... You pay a very low cost for it - this is run by MongoDb themselves on AWS, Azure, Google cloud;
Cost (low) - Management responsibilities (some) - Full MongoDB functionality
Now AWS has its own Mongo compatible database called DocumentDB - this is also a managed database, so you don't need to worry about scalability, high availability etc. This is only available on AWS so super simple and convinient.
Cost (low) - Management responsibilities (minimal) - Limited MongoDB functionality
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.
Is MongoDB for Azure production ready ?
Can anyone share some experience with it ?
Looks like comfort is missing for using it for prod.
What do you think ?
Edit: Since there is a misunderstanding in my question i will try to redefine it.
The information i look into from the community is sharing an info of someone who is running mongo on windows azure to share experience from it.
What i mean by experience is not how to run it in the cloud(we already have the manual on 10gens faq) nor how many bugs it have(we can see that in mongo-azure jira).
What i am looking for is that how it is going with performance ?
Are there any problems(side effects) from running mongodb on azure ?
How does mongodb handle VM recycling ?
Does anyone tried sharding ?
In the end, is the mongo-azure worker role from 10gens stable for using it in production ?
Hope this clears out.
A bit of clarification here. MongoDB itself is production-ready. And MongoDB works just fine in Windows Azure, as long as you set up the scaffolding to get it to work in the environment. This typically entails setting up an Azure Drive, to give you durable storage. Alternatively, using a replicaset, you effectively have eventual consistency across the set members. Then, you could consider going with a standalone (or standalone with hot standby). Personally, I prefer a replicaset model, and that's typical guidance for production MongoDB systems.
As far as 10gen's support for Windows Azure: While the page #SyntaxC4 points to does clarify the wrapper is in a preview state, note that the wrapper is the scaffolding code that launches MongoDB. This scaffolding was initially released in December 2011, and has had a few tweaks since then. It uses the production MongoDB bits (and works just fine with version 2.0.5 which was published on May 9). One caveat is that the MongoDB replicaset roles are deployed alongside your application's roles, since the client app needs visibility to all replica set nodes (to properly build the set). To avoid this limitation, you'd need to run mongos and the entry point (and that's not part of 10gen's scaffolding solution).
Forgetting the preview scaffolding a moment: I have customers running MongoDB in production, with custom scaffolding. One of them is running a rather large deployment, with multiple shards, using a replicaset per shard.
So... does it work in Windows Azure? Yes. Should you take advantage of 10gen's supplied scaffolding? If you're just looking for a simple way to launch a replicaset, I think it's fine. If you want a standalone model, or a shard model, or if you need a separate deployment for MongoDB, you'd currently need to do this on your own (or modify the project 10gen published).
MongoLab is now offering Mongo as a service on Azure MongoLab Blog
Free Demo account is 0.5 GB storage are available in the Windows Azure Store
The warning message on their site says that it's a preview. This would mean that there would be no support for it at a product level in Windows Azure.
If you want to form your own opinion on a comfort level, you can take a look at their bug tracking system and get a feeling for what people are currently reporting as issues.