I am evaluating a few "cloud monitoring" tools. What ready-made complex applications can I deploy to AWS, to evaluate these tools? I am looking for something which automatically creates a multi-node LAMP stack, for example, or a Hadoop/Spark cluster.
EDIT: as an example, I'm looking for something like CloudSuite, but that is meant to be deployed in a multi-host environment.
I am not aware of any premade complex setups that are turnkey and ready to go. They all take some configuration.
You can setup some preconfiured LAMP servers from BitNami:
LAMP powered by Bitnami
You can then cluster them via an Elastic Load Balance and connect them to a RDS instance for few hours and run tests.
You can also review the AWS Quick Start Reference Deployments
Another option would be to use CloudFormation Sample Templates to automate the setup of common services.
Related
Can we run an application that is configured to run on multi-node AWS EC2 K8s cluster using kops (project link) into local Kubernetes cluster (setup using kubeadm)?
My thinking is that if the application runs in k8s cluster based on AWS EC2 instances, it should also run in local k8s cluster as well. I am trying it locally for testing purposes.
Heres what I have tried so far but it is not working.
First I set up my local 2-node cluster using kubeadm
Then I modified the installation script of the project (link given above) by removing all the references to EC2 (as I am using local machines) and kops (particularly in their create_cluster.py script) state.
I have modified their application yaml files (app requirements) to meet my localsetup (2-node)
Unfortunately, although most of the application pods are created and in running state, some other application pods are unable to create and therefore, I am not being able to run the whole application on my local cluster.
I appreciate your help.
It is the beauty of Docker and Kubernetes. It helps to keep your development environment to match production. For simple applications, written without custom resources, you can deploy the same workload to any cluster running on any cloud provider.
However, the ability to deploy the same workload to different clusters depends on some factors, like,
How you manage authorization and authentication in your cluster? for example, IAM, IRSA..
Are you using any cloud native custom resources - ex, AWS ALBs used as LoadBalancer Services
Are you using any cloud native storage - ex, your pods rely on EFS/EBS volumes
Is your application cloud agonistic - ex using native technologies like Neptune
Can you mock cloud technologies in your local - ex. Using local stack to mock Kinesis, Dynamo
How you resolve DNS routes - ex, Say you are using RDS n AWS. You can access it using a route53 entry. In local you might be running a mysql instance and you need a DNS mechanism to discover that instance.
I did a google search and looked at the documentation of kOps. I could not find any info about how to deploy to local, and it only supports public cloud providers.
IMO, you need to figure out a way to set up your local EKS cluster, and if there are any usage of cloud native technologies, you need to figure out an alternative way about doing the same in your local.
The true answer, as Rajan Panneer Selvam said in his response, is that it depends, but I'd like to expand somewhat on his answer by saying that your application should run on any K8S cluster given that it provides the services that the application consumes. What you're doing is considered good practice to ensure that your application is portable, which is always a factor in non-trivial applications where simply upgrading a downstream service could be considered a change of environment/platform requiring portability (platform-independence).
To help you achieve this, you should be developing a 12-Factor Application (12-FA) or one of its more up-to-date derivatives (12-FA is getting a little dated now and many variations have been suggested, but mostly they're all good).
For example, if your application uses a database then it should use DB independent SQL or no-sql so that you can switch it out. In production, you may run on Oracle, but in your local environment you may use MySQL: your application should not care. The credentials and connection string should be passed to the application via the usual K8S techniques of secrets and config-maps to help you achieve this. And all logging should be sent to stdout (and stderr) so that you can use a log-shipping agent to send the logs somewhere more useful than a local filesystem.
If you run your app locally then you have to provide a surrogate for every 'platform' service that is provided in production, and this may mean switching out major components of what you consider to be your application but this is ok, it is meant to happen. You provide a platform that provides services to your application-layer. Switching from EC2 to local may mean reconfiguring the ingress controller to work without the ELB, or it may mean configuring kubernetes secrets to use local-storage for dev creds rather than AWS KMS. It may mean reconfiguring your persistent volume classes to use local storage rather than EBS. All of this is expected and right.
What you should not have to do is start editing microservices to work in the new environment. If you find yourself doing that then the application has made a factoring and layering error. Platform services should be provided to a set of microservices that use them, the microservices should not be aware of the implementation details of these services.
Of course, it is possible that you have some non-portable code in your system, for example, you may be using some Oracle-specific PL/SQL that can't be run elsewhere. This code should be extracted to config files and equivalents provided for each database you wish to run on. This isn't always possible, in which case you should abstract as much as possible into isolated services and you'll have to reimplement only those services on each new platform, which could still be time-consuming, but ultimately worth the effort for most non-trival systems.
Currently we have Jenkins that is running on-premise(VMware), planning to move into the cloud(aws). What would be the best approach to install Jenkins whether on ec2 or ECS?
Best way would be running on EC2. Make sure you have granular control over your instance Security Group and Network ACL's. I would recommend using terraform to build your environment as you can write code and also version control it. https://www.terraform.io/downloads.html
Have you previously containerized your Jenkins? On VMWare itself? If not, and if you are not having experience with containers, go for EC2. It will be as easy as running on any other VM. For reproducing the infrastructure, use Terraform or CloudFormartion.
I would recommend dockerize your on-premise Jenkins first. See how much efforts are required in implementation and administrating/scaling it. Then go for ECS.
Else, shift to EC2 and see how much admin overhead + costs you are billed. Then if required, go for ECS.
Another point you have to consider is how your Jenkins is architected. Are you using master-slave? Are you running builds contentiously so that VMs are never idle? Do you want easy scaling such that build environment is created and destroyed per build execution?
If you have no experience with running containers then create it on EC2. Before running on ECS make sure you really understand containers and container orchestration.
Just want to complement the other answers by providing link to official AWS white paper:
Jenkins on AWS
It might be of special interest as it discusses both options in detail: EC2 and ECS:
In this section we discuss two approaches to deploying Jenkinson AWS. First, you could use the traditional deployment on top of Amazon Elastic Compute Cloud (Amazon EC2). Second, you could use the containerized deployment that leverages Amazon EC2 Container Service (Amazon ECS).Both approaches are production-ready for an enterprise environment.
There is also AWS sample solution for Jenkins on AWS for ECS:
https://github.com/aws-samples/jenkins-on-aws:
This project will build and deploy an immutable, fault tolerant, and cost effective Jenkins environment in AWS using ECS. All Jenkins images are managed within the repository (pulled from upstream) and fully configurable as code. Plugin installation is automated, including versioning, as well as configured through the Configuration as Code plugin.
I have created a Spring cloud microservices based application with netflix APIs (Eureka, config, zuul etc). can some one explain me how to deploy that on AWS? I am very new to AWS. I have to deploy development instance of my application.
Do I need to integrate docker before that or I can go ahead without docker as well.
As long as your application is self-contained and you have externalised your configurations, you should not have any issue.
Go through this link which discusses what it takes to deploy an App to Cloud Beyond 15 factor
Use AWS BeanStalk to deploy and Manage your application. Dockerizing your app is not a predicament inorder to deploy your app to AWS.
If you use an EC2 instance then it's configuration is no different to what you do on your local machine/server. It's just a virtual machine. No need to dockerize or anything like that. And if you're new to AWS, I'd rather suggest to to just that. Once you get your head around, you can explore other options.
For example, AWS Beanstalk seems like a popular option. It provides a very secure and reliable configuration out of the box with no effort on your part. And yes, it does use docker under the hood, but you won't need to deal with it directly unless you choose to. Well, at least in most common cases. It supports few different ways of deployment which amazon calls "Application Environments". See here for details. Just choose the one you like and follow instructions. I'd like to warn you though that whilst Beanstalk is usually easier then EC2 to setup and use when dealing with a typical web application, your mileage might vary depending on your application's actual needs.
Amazon Elastic container Service / Elastic Kubernetes Service is also a good option to look into.
These services depend on the Docker Images of your application. Auto Scaling, Availability cross region replication will be taken care by the Cloud provider.
Hope this helps.
I'm developing a prototype IoT application which does the following
Receive/Store data from sensors.
Web application with a web-based IDE for users to deploy simple JavaScript/Python scripts which gets executed in Docker Containers.
Data from the sensors gets streamed to these containers.
User programs can use this data to do analytics, monitoring etc.
The logs of these programs are outputted to the user on the webapp
Current Architecture and Services
Using one AWS EC2 instance. I chose EC2 because I was trying to figure out the architecture.
Stack is Node.js, RabbitMQ, Express, MySQl, MongoDB and Docker
I'm not interested in using AWS IoT services like AWS IoT and Greengrass
I've ruled out Heroku since I'm using other AWS services.
Questions and Concerns
My goal is prototype development for a Beta release to a set of 50 users
(hopefully someone else will help/work on a production release)
As far as possible, I don't want to spend a lot of time migrating between services since developing the product is key. Should I stick with EC2 or move to Beanstalk?
If I stick with EC2, what is the best way to handle small-medium traffic? Use one large EC2 machine or many small micro instances?
What is a good way to manage containers? Is it worth it use swarm and do container management? What if I have to use multiple instances?
I also have small scripts which have status of information of sensors which are needed by web app and other services. If I move to multiple instances, how can I make these scripts available to multiple machines?
The above question also holds good for servers, message buses, databases etc.
My goal is certainly not production release. I want to complete the product, show I have users who are interested and of course, show that the product works!
Any help in this regard will be really appreciated!
If you want to manage docker containers with least hassle in AWS, you can use Amazon ECS service to deploy your containers or else go with Beanstalk. Also you don't need to use Swarm in AWS, ECS will work for you.
Its always better to scale out rather scale up, using small to medium size EC2 instances. However the challenge you will face here is managing and scaling underlying EC2's as well as your docker containers. This leads you to use Large EC2 instances to keep EC2 scaling aside and focus on docker scaling(Which will add additional costs for you)
Another alternative you can use for the Web Application part is to use, AWS Lambda and API Gateway stack with Serverless Framework, which needs least operational overhead and comes with DevOps tools.
You may keep your web app on Heroku and run your IoT server in AWS EC2 or AWS Lambda. Heroku is on AWS itself, so this split setup will not affect performance. You may heal that inconvenience of "sitting on two chairs" by writing a Terraform script which provisions both EC2 instance and Heroku app and ties them together.
Alternatively, you can use Dockhero add-on to run your IoT server in a Docker container alongside your Heroku app.
ps: I'm a Dockhero maintainer
I am working on a project using a microservices architecture.
Each service lives in its own docker container and has a separate git repository in order to ensure loose coupling.
It is my understanding that AWS recently announced support for Multi-Container Docker environments in ElasticBeanstalk. This is great for development because I can launch all services with a single command and test everything locally on my laptop. Just like Docker Compose.
However, it seems I only have the option to also deploy all services at once which I am afraid defies the initial purpose of having a micro services architecture.
I would like to be able to deploy/version each service independently to AWS. What would be the best way to achieve that while keeping infrastructure management to a minimum?
We are currently using Amazon ECS to accomplish exactly what you are talking about trying to achieve. You can define your Docker Container as a Task definition and then Create an ECS Service which will handle number of instances, scaling, etc.
One thing to note is Amazon mentions the word container a lot in the documentation. They may be talking about the EC2 instance used for the cluster for your docker instances/containers.