I need to install and configure all new system start with auto-scaling in aws as per the requirements , like if it is a app server install nodejs with respective git code for deployment using with Ansible.
How Ansible identify a new system came up and need to do this all configuration.
Here is a guide from ansible docs how to handle autoscaling with Ansible: https://docs.ansible.com/ansible/latest/scenario_guides/guide_aws.html#autoscaling-with-ansible-pull
The problem on this approch is, that you need the whole provisining prozess on startup. This takes much time and is error prone.
A common solution is to build a custom AMI with all infrastructure needed for your service and only deploy your current code to this maschine.
A good tool to build custom AMIs is Packer. A Guide for AWS is available here. https://www.packer.io/docs/builders/amazon.html
Related
I've bitbucket repository, bitbucket pipeline there and EC2 instance. EC2 have access to the repository ( can perform pull and docker build/run)
So it seems I only need to upload to EC2 some bash scripts and call it from bitbucket pipeline. How can I call it? Usually ssh connection is used to perform scripts on EC2, is it applicable from bitbucket pipeline? Is it a good solution?
two ways to solve this problem, I will leave it up to you.
I see you are using AWS, and AWS has a nice service called CodeDeploy. you can use that and create a few deployment scripts and then integrate it with your pipeline. Problem with it is that it is an agent that needs to be installed. so it will consume some resource not much but if u are looking at an agentless design then this solution wont work. you can check the example in the following answer https://stackoverflow.com/a/68933031/8248700
You can use something like Python Fabric (its a small gun) or Ansible (its a big canon) to achieve this. it is an agentless design works purely on SSH.
I'm using both the approaches for different scenarios. For AWS I use CodeDeploy and for any other cloud vendor I use Python Fabric. (We can use CodeDeploy on other than AWS but then it comes under on-premise pricing for which it charges for per deployment)
I hope this brings some clarity.
I have a single Java application. We developed the application in Eclipse. It is a Maven project. We already have a system for launching our application to AWS EC2. It works but is rudimentary and we would like to learn about the more common and modern approaches other teams use to launch their Java Maven apps to EC2. We have heard of Docker and I researched the tool yesterday. I understand the basics of building an image, tagging it and pushing to either Docker Hub or Amazon's ECS service. I have also read through a few tutorials describing how to pull a Docker image into an EC2 instance. However, I don't know if this is what we are trying to do, given that I am a bit confused about the role Docker can play in our situation to help make our dev ops more robust and efficient.
Currently, we are building our Maven app in Eclipse. When the build completes, we run a second Java file that uses the AWS JDK for Java to
launch an EC2 instance
copy the.jar artifact from the build into this instance
add the instance to a load balancer and
test the app
My understanding of how we can use Docker is as follows. We would Dockerize our application and push it to an online repository according to the steps in this video.
Then we would create an EC2 instance and pull the Docker image into this new instance according to the steps in this tutorial.
If this is the typical flow, then what is the purpose of using Docker here? What is the added benefit, when we are currently ...
creating the instance,
deploying the app directly to the instance and also
testing the running app
all using a simple single Java file and functions from the AWS SDK for Java?
#GNG what are your objectives for containerization?
Amazon ECS is the best method if you want to operate in only AWS environment.
Docker is effective in hybrid environments i.e., on physical servers and VMs.
the Docker image is portable and complete executable of your application: it delivers your jar, but it can also include property files, static resources, etc... You package everything you need and deploy to AWS, but you could decide also to deploy the same image on other platforms (or locally).
Another benefit is the image contains the whole runtime (OS, jdk) so you dont rely on what AWS provides ensuring also isolation from the underlying infrastructure.
Basically, I need to run a set of custom shell scripts on ec2 instances to provision some software. Is there any workflow manager like oozie or airflow with api access to schedule the same. I am asking for alternatives like oozie and airflow, as those are that of hadoop environment schedulers and my environment is not. I can ensure that there can be ssh access from the source machine that will run the workflow manager and the ec2 instance where want to install the software. Is there any such open source workflow schedulers?
I would recommend using Cadence Workflow for your use case. There are multiple provisioning solutions built on top of it. For example Banzai Cloud Pipeline Platform
See the presentation that goes over Cadence programming model.
I’m new to Ansible. I can create one VM using Ansible. I’d like to deploy multiple VMs at one go. I’d appreciate any references or guidelines to accomplish this.
Take a look through the cloud modules.
Which module applies to you depends on your infrastructure provider (i.e. vsphere, aws, gcp, libvirt, etc)
For example:
virt for libvirt
cloudformation for aws
vsphere_guest for vsphere
azure_rm_deployment for azure
...and many more
There are also tons of community developed roles at ansible-galaxy if the above don't quite meet your use case.
My organization's website is a Django app running on front end webservers + a few background processing servers in AWS.
We're currently using Ansible for both :
system configuration (from a bare OS image)
frequent manually-triggered code deployments.
The same Ansible playbook is able to provision either a local Vagrant dev VM, or a production EC2 instance from scratch.
We now want to implement autoscaling in EC2, and that requires some changes towards a "treat servers as cattle, not pets" philosophy.
The first prerequisite was to move from a statically managed Ansible inventory to a dynamic, EC2 API-based one, done.
The next big question is how to deploy in this new world where throwaway instances come up & down in the middle of the night. The options I can think of are :
Bake a new fully-deployed AMI for each deploy, create a new AS Launch config and update the AS group with that. Sounds very, very cumbersome, but also very reliable because of the clean slate approach, and will ensure that any system changes the code requires will be here. Also, no additional steps needed on instance bootup, so up & running more quickly.
Use a base AMI that doesn't change very often, automatically get the latest app code from git upon bootup, start webserver. Once it's up just do manual deploys as needed, like before. But what if the new code depends on a change in the system config (new package, permissions, etc) ? Looks like you have to start taking care of dependencies between code versions and system/AMI versions, whereas the "just do a full ansible run" approach was more integrated and more reliable. Is it more than just a potential headache in practice ?
Use Docker ? I have a strong hunch it can be useful, but I'm not sure yet how it would fit our picture. We're a relatively self-contained Django front-end app with just RabbitMQ + memcache as services, which we're never going to run on the same host anyway. So what benefits are there in building a Docker image using Ansible that contains system packages + latest code, rather than having Ansible just do it directly on an EC2 instance ?
How do you do it ? Any insights / best practices ?
Thanks !
This question is very opinion based. But just to give you my take, I would just go with prebaking the AMIs with Ansible and then use CloudFormation to deploy your stacks with Autoscaling, Monitoring and your pre-baked AMIs. The advantage of this is that if you have most of the application stack pre-baked into the AMI autoscaling UP will happen faster.
Docker is another approach but in my opinion it adds an extra layer in your application that you may not need if you are already using EC2. Docker can be really useful if you say want to containerize in a single server. Maybe you have some extra capacity in a server and Docker will allow you to run that extra application on the same server without interfering with existing ones.
Having said that some people find Docker useful not in the sort of way to optimize the resources in a single server but rather in a sort of way that it allows you to pre-bake your applications in containers. So when you do deploy a new version or new code all you have to do is copy/replicate these docker containers across your servers, then stop the old container versions and start the new container versions.
My two cents.
A hybrid solution may give you the desired result. Store the head docker image in S3, prebake the AMI with a simple fetch and run script on start (or pass it into a stock AMI with user-data). Version control by moving the head image to your latest stable version, you could probably also implement test stacks of new versions by making the fetch script smart enough to identify which docker version to fetch based on instance tags which are configurable at instance launch.
You can also use AWS CodeDeploy with AutoScaling and your build server. We use CodeDeploy plugin for Jenkins.
This setup allows you to:
perform your build in Jenkins
upload to S3 bucket
deploy to all the EC2s one by one which are part of the assigned AWS Auto-Scaling group.
All that with a push of a button!
Here is the AWS tutorial: Deploy an Application to an Auto Scaling Group Using AWS CodeDeploy