With respect to java Microservices deployment since we use same kind of configurations to all the apps(micro services) does it make any difference using dockers, rather than deploying on multiple ports?Because at the end apps will be down if VM is down.
Docker is brilliantly suitable for microservice application deployment. You put each service into separate container and use docker-compose (docker-swarm, k8n or whatever) to launch your containers and link them into one isolated network (done automatically).
In such configuration you dont use port, but use hostnames. That means each container will have its own name inside network, and all requests are being done using that name. That is much more convenient comparing to use of different tcp ports.
Putting applications inside containers are becoming the defacto standard for deployment. Docker helps in creating container images which can be used to deploy inside your cluster like kubernetes cluster.
Related
I'm using Docker I have implemented a system to deploy environments (on a single server) based on Git branches using Traefik (*.dev.domain.com) and Docker Compose templates.
I like Kubernetes and I've never switched to it since I'm limited to one single server for my infrastructure. I've only used it using local installations (Docker for Windows).
So, my question is: does it make sense to run a Kubernetes "cluster" (master and nodes) on a single server to orchestrate and route containers (in place of Traefik/Rancher/Docker Compose)?
This use is for development and staging only for the moment, so high availability is not a prerequisite.
Thanks.
If it is not a production environment, it doesn't matter how many nodes you are using. So yes, it should be just fine in this case. But make sure all the k8s features you will need in production are available in test/dev, to keep things similar and portable.
AFAIU,
I do not see a requirement for kubernetes unless we are doing below at least for single host using native docker run or docker-compose or docker engine swarm mode -
Make sure there are enough(>=2) replicas of your app in a single server and you are balancing the load across those apps docker containers.
If you want to go bit advanced, we should be able to scale up & down dynamically (docker swarm mode supports this out of the box else use jwilder nginx proxy).
Your deployment should not cause a downtime. Make sure a single container is always healthy at any instant of time while deploying.
Container should auto heal(restart automatically) in case your HTTP or TCP health check fails.
Doing all of the above will certainly put you in a better place but single host is still a single source of failure which you got to deal with at regular intervals.
Preferred : if possible try to start with docker engine swarm mode or kubernetes single master or minikube. This will automatically take care of all the above scenarios out of the box and will also allow you to further scale up anytime by adding more nodes without changing much in your YML files for docker swarm or kubernetes.
Ref -
https://kubernetes.io/docs/setup/independent/create-cluster-kubeadm/
https://docs.docker.com/engine/swarm/
I would use single host k8s only if I managed clusters with the same project that I would like to deploy to the said host. This enables you to reuse manifests and all the automation you've created for your clusters.
Have I had single host environments only, I would probably stick to docker-compose.
If you're looking to try it out your easiest options are probably minikube (easy to run single-node cluster locally but without some features) or using one of the free trial accounts for a managed Kubernetes service from one of the big cloud providers (fully-featured and multi-node but limited use before you have to pay).
In our company ~7 projects, each based on Docker. Each project contain base services, like MySQL, Nginx, PHP. Some of projects communicate with other projects. Because of many services on same port, we make new docker host (docker-machine) for each project. From here few problems are coming:
VirtualBox assign random IP to each Docker host, depends on sequence of executing.
Hard to switch from project to project, need to set different shell envs all the time. Easy to make mistake.
Well, I'm searching for more enterprise solution to manage many docker machines. Or a some technique that can help me with current situation.
I had similar problems last summer.
First, I started to deploy my projects to swarm-cluster as services, instead of clustering several docker VMs. This enabled me to play around services with only the service IDs. It is important that how to separate projects into services, this part may be cumbersome depending on your project.
https://docs.docker.com/engine/swarm/swarm-tutorial/deploy-service/
Then, I build my configuration and monitoring software once on swarm-manager and use it. You can use your automation tools on docker-manager to control services.
A virtual machine consumes resources and it is better to avoid it if is not necessarily. Instead you could deploy the projects in the docker swarm on bare metals.
But because every project has an entry point that needs to be accesible from the outside world (i.e. https://site1.com and https://site2.com) you can't expose the same port (443 or 80) for all the frontend services in the same swarm. For this you can use a reverse proxy like HAProxy or Nginx that forwards the requests to the right service based on the hostname. The reverse proxy could be also a service in the swarm. In this situation you should not expose the projects' ports anymore.
A reverse proxy has many other advantages, like SSL termination (this makes the SSL certificate management a lot easier).
If you add the projects to the same custom network then the services from different projects could communicate securely and directly, using their docker service name and the internal port (i.e. 80).
I've been learning how to use Docker to setup dev environments, however, I'm
curious how these ideas translate to a production stack. As an example, I have a Laravel (Php) app, which uses MySQL, Redis, and Nginx
So in production, let's say I would normally have 2 application ec2 instances behind a load balancer on AWS. When setting up a similar production situation using Docker...
1) because I'd be using RDS and Elasticache, there would be no need for containers for those. So basically, id only need containers for PHP-Fpm and Nginx?
2) to have high availability, I would still have 2 (or least more than 1) ec2 instances behind the ELB. So I suppose each instance would run the above containers (PHP and Nginx). But that sounds no different than my previous VM setup, where each server runs what it needs to serve the application. Is that accurate?
3) with VMs, I would traditionally bake the code into an AMI and add those AMIs to a Launch Configuration and an Auto Scaling group, and that group would spin up instances as needed. So for deployment, I would tear down the old ec2 instances and spin up new ones. With Docker, Since these containers would be running on ec2 instances, wouldn't i still have to spin up / tear down the VMs, or would I just replace the containers and keep the VMs running?
Its reasonable to keep RDS, Elasticache and other fully managed services, outside of docker environment. Yes for high availability you need multiple EC2 instances having docker daemon running.
The real advantage is not coming with having two EC2 instances running two web server docker containers on each of them. Real advantages comes when you break down your application to microservices, where multiple containers in combination construct your web application providing the benefits of microservices.
Apart from that the DevOps flow would be different compared to traditional web application deployment in EC2 with autoscaling and load balancing and have many benefits. For example your source code will contain the container code as well, which will guarantee, the environment will work uniformly in your staging and production. Also you will be having images pointing to branches/tags in your source control, which allows to get new updates(delta downloads) for new releases.
If you are going to setup docker in AWS, its recommended to go with AWS ECS to reduce management overhead.
You're right, you will only need to run your code in a container and it will simply access the remote services. The only thing you'll have to consider is to ensure connectivity to them.
You're right again, you'll need to have everything you previously had in your VMs in the Docker container so that your code works as before. Anyway, with Docker containers it is possible to run multiple instances of your app on the same EC2 instance. Of course, your app will try to run on the same port, so some extra networking layer is needed for managing ports is necessary, but it's possible. All the EC2 instances needs to have is docker installed.
Instead of creating AMIs and closing and spinning up EC2 instances, you'll only have to pull the new Docker image and restart the container with the new image. This means just a few seconds compared to minutes in the EC2 instances flow. This is means you have a really quick way of reverting buggy deploys and opens the doors for a setup in which 0% downtime can be reached.
Good day
We have a development environment that consists of 6 virtual machines. Currently we are using Vagrant and Ansible with VirtualBox.
As you can imagine, hosting this environment is a maintenance nightmare particularly as versions of software/OS change. Not too mention resource load for developer machines.
We have started migrating some virtual machines to docker. But this itself poses problems around orchestration, correct configurations, communication etc. This led me to Kubernetes.
Would someone be so kind as to provide some reasoning as to whether Kubernetes would or wouldn't be the right tool for the job? That is managing and orchestrating 'development' docker containers.
Thanks
This is quite complex topic and many things have to be considered if it's worth to use k8s as local dev environment. Especially I used it when I wanted to have my local developer environment very close to production one which was running on Kubernetes. This helped to avoid many configuration bugs.
In my opinion Kubernetes(k8s) will provide you all you need for a development environment.
It gives you much flexibility and does much configuration itself. Few examples:
An easy way to deploy new version into local kubernetes stack
You prepare k8s replication controller files for each of your application module (keep in mind that they need to be stateless modules)
In replication controller you specify the docker image and that's it.
Using this approach you can push new docker images to local docker_registry and then using kubectl control the lifecycle of your application.
Easy way to scale your application modules
For example:
kubectl scale rc your_application_service --replicas=3
This way k8s will check how many pods you have running for your service and if it recognises that the number is smaller then the replicas value it will create new to satisfy the replicas number.
It's endless topic and many other things come to my mind, but I would suggest you to try it out.
There is a https://github.com/kubernetes/kubernetes/blob/master/docs/devel/developer-guides/vagrant.md project for running the k8s cluster in vagrant.
Of course you have to remember that if you have many services all of them have to be pushed to local repository and run by k8s. This will require some time but if you automate local deploy with some custom scripts you won't regret.
As wsl mentioned before, it is a quite complex topic. But i'm doing this as well at the moment. So let me summaries some things for you:
With Kubernetes (k8s) you're going to orchestrate your SaaS Application. In best case, it is a Cloud-native Application. The properties/requirements for a Cloud-native Application are formulated by the Cloud Native Computing Foundation (CNCF), which basically were formed around k8s, after Google donates it to the Linux Foundation.
So the properties/requirements for a Cloud-native Application are: Container packaged, Dynamically managed and Micro-services oriented (cncf.io/about/charter). You will benefit mostly from k8s, if your applications are micro-service based and every service has a separate container.
With micro-service based applications, every service can be developed independently. The developer only needs to follow the 12Factor Method (12factor.net) for example (use env var instead of hard coded IP addresses, etc).
In the next step the developer build the container for a service and pushes it the a container registry. For a local develop environment, you may need to run a container registry inside the cluster as well, so the developer can push and test his code locally.
Then you're able to define your k8s replication-controllers, services, PetSets, etc. with Ports, Port-mapping, env vars, Container Images... and create and run it inside the cluster.
The k8s-documentation recommend Minikube for running k8s locally (kubernetes.io/docs/getting-started-guides/minikube/). With Minikube you got features like DNS, NodePorts, ConfigMaps and Secrets
Dashboards.
But I choose the multi node CoreOS Kubernetes with Vagrant Cluster for my Development Environment as Puja Abbassi mentioned in the Blog "Finding The Right Local Kubernetes Development Environment" (https://deis.com/blog/2016/local-kubernetes-development-environment/), it is closer to the my production environment (12Factor: 10 - Dev/prod parity).
With the Vagrant Environment you got features like:
Networking with flannel
Service Discovery with etcd
DNS names for a set of containers with SkyDNS
internal load balancing
If you want to know, how everything works look inside this Github repo github.com/coreos/coreos-kubernetes/tree/master/multi-node (vagrant and generic folder).
So you have to ask yourself, if you or your developers really need to run a complete "cloud environment" locally. In many cases a developer can develop a service (based on micro-services and containers) independently.
But sometimes it is necessary to have multiple or all services run on your local machine as a dev-environment.
I’m using Amazon ECS to auto deploy my containers on uat/production.
What is the best way to do that?
I have a REST api with a several front-end clients
Should I package my api container with nginx in the same container?
And do the same thing with the others front end clients.
Or I have to write a big task definition to bring together all my containers(db, nginx, php, api, clients) :(, but that's mean that I should redeploy all my infrastructure at each push uat/prod
I'm very confusing.
I would avoid including too much in a single container. Try and distill your containers down to one process doing one thing. If all you're doing is serving up a REST API for consumption by your front end, just put the essential pieces in for that and no more.
In my experience you also want your ECS tasks to be able to handle failure gracefully and restart, and the more complicated your containers are the harder this is to get right.
Depending on your requirements I would look into using ELB instead of nginx, you can have your ECS cluster point at an ELB and not have to deal with that piece at all.
Do not use ECS - it's too crude. I was using it as a platform for our staging/production environments and had odd problems during deployments - sometimes it worked well, sometimes - not (with the same Docker images). ECS provides not clear model of container deployment and maintenance.
There is another good, stable and predictive option - Docker Cloud service. It's new tool (a.k.a. Tutum) that was acquired by Docker. I switched the CI/CD to use it and we're happy with it.
Bind Amazon user credentials to Docker Cloud account. Docker Cloud uses AWS (or other provider) API for creating appropriate computer instances.
Create Node. Select Amazon EC2 instance type and parameters of storage, security group and so on. New instance will contain installed docker software and managing container that handles messages from Docker Cloud (deploy, destroy and others).
Create Stackfile, see https://docs.docker.com/docker-cloud/apps/stack-yaml-reference/. Stackfile is a definition of container group you required. You can define different scaling/distribution models for your containers using specific Stackfile options like deployment strategy, see https://docs.docker.com/docker-cloud/apps/stack-yaml-reference/#deployment-strategy-1.
Define ELB configurations in AWS for your new instances.
P.S. I'm not a member of Docker team and I like other AWS services :).
Here is my two cents on the topic, the question is not really related to ecs, it applies to any body deploying their apps on docker.
I would suggest separating the containers, one for nginx and one for API.
if they need to be co-located on the same instance, on ECS you can define them as part of the same task and on kubernetes you can make them part of same pod.
Define a docker link between the nginx and the api container. This will allow the nginx process to talk to api container without the api container exposing its ports to the host.
One advantage of using the container running platforms such as kubernetes and ecs is that they ensure each of the container run all the time and dynamically restart if one of the processes/containers go down.
Separating the containers will allow these platforms to monitor both the processes separately. When you combine the two into one container the docker container can only run with one of the processes in foreground, so you will loose the advantage of auto-healing for one of the processes.
Also moving from nginx to ELB is not a straightforward solution, you may have redirections and other things configured on the nginx, which are not available on ELB(As of date).
If you also need the ELB, there is no harm in forwarding the requests from the ELB to the nginx port.