I started working in a project where we use multiple Jenkins instances, which are running as Docker in Docker (dind) across numerous VMs on premise. I'm not a big fan of it, but it is doing its job very good at the moment. As discussed in (Is it ok to run docker from inside docker?), it is not a good practice and there are better ways to handle this (mounting socket or sysbox).
Now we have the requirement to get rid of the on premise machines and migrate the whole setup to AWS. We listed different options and one of them is just lift and shift the setup to the cloud. I'm pretty new to AWS and wanted to ask, if some of you have experiences or concerns about this option.
Related
I want to confirm my approach to setting up a VPC using cloudformation/scepter and seeding instances with docker container is correct.
Create an aws ec2 instance.
Create a docker image on that instance
Create a cloudformation VPC template (.yaml )
-reference docker image in template?
Create a sceptre project using the template above and run script from ec2 instance
So as I understand if the majority of the work will be in the cloudformation template. Currently I'm stuck on sceptre errors, but I wanted to make sure I was approaching the problem correctly. Does this look like the right approach?
There are a lot of ways of doing what you want:
Run sceptre locally on your development machine
This is easier, but not best practice for important environments as
having a build server, gives a better trail of what was done when (especially in shared environments)
Use CodeBuild to save you having to do steps 1 & 2 yourself (AWS maintain a docker image with python installed)
It also avoids the chicken and egg problem of how you deploy the EC2 instance in the first place.
Configure Jobs on a build server such as Jenkins
CodeDeploy is good for simple setups, but a well configured build server, can have dashboards to track what is deployed where
as sceptre is just a way of generating/managing deploying templates across environments, there are lots of other ways of doing this including what you outlined.
p.s Apologies that the getting started documentation isn't great at the moment, it is something we are focusing on for release 2.0.
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 wondering what are benefits of using Docker to deploy my Spring Boot app on AWS EC2/ESB. Are there any?
This is a very broad question but in short the advantages are that if built right, Docker can fully automate your deploy process, allowing you to autoscale and resolve failures quickly (by destroying faulty containers and launching new ones instead - ideally using an automated process).
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.
I've been looking into Mesos, Marathon and Chronos combo to host a large number of websites. In my head I should be able to type a few commands into my laptop, and wait about 30 minutes for the thing to build and deploy.
My only issue, is that my resources are scattered across multiple data centers, numerous cloud accounts, and about 6 on premises places. I see no reason why I can't control them all from my laptop -- (I have serious power and control issues when it comes to my hardware!)
I'm thinking that my best approach is to build the brains in the cloud, (zoo keeper and at least one master), and then add on the separate data centers, but I am yet to see any examples of a distributed cluster, where not all the nodes can talk to each other.
Can anyone recommend a way of doing this?
I've got a setup like this, that i'd like to recommend:
Source code, deployment scripts and dockerfiles in GIT
Each webservice has its own directory and comes together with a dockerfile to containerize it
A build script (shell script running docker builds) builds all the docker containers, of which all images are pushed to a docker image repository
A ansible deploy deploys all the containers remotely to a set of VPSes. (You use your own deployment procedure, that fits mesos/marathon)
As part of the process, a activeMQ broker is deployed to the cloud (yep, in a container). While deploying, it supplies each node with the URL of the broker they need to connect to. In your setup you could instead use ZooKeeper or etcd for example.
I am also using jenkins to do automatic rebuilds and to run deploys whenever there has been GIT commits, but they can also be done manually.
Rebuilds are lightning fast, and deploys dont take much time either. I can replicate everything I have in my repository endlessly and have zero configuration.
To be able to do a new deploy, all I need is a set of VPSs with docker daemons, and some datastores for persistence. Im not sure if this is something that you can replace with mesos, but ansible will definitely be able to install a mesos cloud for you onto your hardware.
All logging is being done with logstash, to a central logging server.
i have setup a 3 master, 5 slave, 1 gateway mesos/marathon/docker setup and documented here
https://github.com/debianmaster/Notes/wiki/Mesos-marathon-Docker-cluster-setup-on-RHEL-7-with-three-master
this may help you in understanding the load balancing / scaling across different machines in your data center
1) masters can also be used as slaves
2) mesos haproxy bridge script can be used for service discovery of the newly created services in the cluster
3) gateway haproxy is updated every min with new services that are created
This documentation has
1) master/slave setup
2) setting up haproxy that automatically reloads
3) setting up dockers
4) example service program
You should use Terraform to orchestrate your infrastructure as code.
Terraform has a lot of providers that allows you to manage different resources accross multiples clouds services and/or bare-metal resources such as vSphere.
You can start with the Getting Started Guide.