I am using elastic beanstalk to deploy the web services as Docker containers on AWS using ECS. Default load balancer available in EBS provides me network-level autoscaling to handle the load on EC2 instance.
How to handle auto-scaling in application level and use dockerization features using ECS like Kubernetes handling the dockers? I want to scale my individual dockers when it receives more memory or CPU usage. Please help me to achieve this.
You can have the ECS cluster managed by an auto scaling group . This would take care of the hardware underlying the docker images.
As far as the containers themselves are concerned, you can go the way of application auto scaling.
Since both these services can be configured to answer to CW alarms for both downscale and upscale, you can have a HIGH cpu/ram alarm policy to add 1 host and 2 container replicas for example and remove the same amount on a LOW alarm.
Hope this helps
You can use scaling of task when you are updating your service which is attached with a cloudwatch alarm to scale up/down and called as service autoscaling.
Ref: http://docs.aws.amazon.com/AmazonECS/latest/developerguide/service-auto-scaling.html
These are not the answers to this question. Using multicontainer docker environment on ELB service, we can specify (within the Dockerrun.aws.json) our docker services and their relationships, but we cannot specify the number of instances for individual services. This means that if we want to scale our ELB docker services, we can do that useing autoscale groups and CloudWatch alarms, but this means that our entire docker service environment will be affected: if we have 3 docker services, we can scale only all them together, not individual services.
The only think you can do is to use Elastic Container Service (ecs). Within this service you will create the cluster (similar to Kubernetes approach), then create individual services which will be able to run multiple tasks, and each task will be the specification for individual docker container. This way you will be able to run your services in desired way and scale them appropriately.
Related
Im new to docker and am wanting to accomplish something but I am unsure on how to Orchestrate my docker containers to do this.
What I want to do:
I have an API that in simple does a calculation from a requested file. It loads the file (around 80mb) from disk to memory then keep it in memory for 2 hours (caching).
Im wanting to have an architecture where for example when the container gets overwhelmed with requests a new one fires up, and when the original container frees its memory and the requests slow down then the container shuts down.
Is Memory and CPU Container Orchestration possible?
Thank You,
/Jeremy
Docker itself is not dedicated to the orchestration multiple containers. You need to use some container orchestration environment. The most popular are Kubernetes, Docker Swarm, and Apache Mesos. Or if you want to run in the Cloud, then some vendor-specific, like AWS ECS.
Here's a good list of container clustering toolkit.
In all these environments it's possible to configure what you described. If you're completely new to the topic, then I recommend installing Docker-for-Desktop which comes with built-in Kubernetes and play with that in your local.
For sure, container orchestration system is what you want to be able efficiently manage your docker containers.
You can find current complete list of solutions for production environment in this spreadsheet
Tools, like kubernetes will give you reach set of benefits eg
Provisioning and deployment of containers
Redundancy and availability of containers
Scaling up or removing containers to spread application load evenly
across host infrastructure
Allocation of resources between containers
Load balancing of service discovery between containers
Health monitoring of containers and hosts
In Kubernetes there is a Horizontal Pod Autoscaler, that
automatically scales the number of pods in a replication controller,
deployment, replica set or stateful set based on observed CPU
utilization (or, with custom metrics support, on some other
application-provided metrics). Note that Horizontal Pod Autoscaling
does not apply to objects that can’t be scaled, for example,
DaemonSets.
As for beginning I would recommend you start with minikube.
More advanced ways are setup manually cluster using kubeadm either look into the cloud providers
Please be aware that you will not have option to modify cloud based control plane. More info in my related answer
This question illustrates the theoretical differences between docker run and docker service.
What I don't understand is when would one need to use the exact same container replicated multiple times (as per the Docker documentation example)?
There, they run the same web app replicated 5 times.
Is deployment on Kubernetes (for example) a potential use case, where the developer does not want to centralize the app on one host, in order to make it more resilient, hence why 5 replicas are created?
To understand, can someone please please with an example use case, where the docker service is useful?
swarm is an orchestrator just like kubernetes. docker service deploys services to swarm just as you deploy your services to kubernetes using kubectl.
swarm is essentially built-in primitive orchestrator. One possible case for replicas is running a proxy that directs requests to proper containers. You could expose multiple machines and have one take place of another in case another fails. Or any other high availability case you could think of.
Your question could be rephrased as "What's the difference between running a single container and running containers in a cluster?", which would be another question altogether, but that rephrasing might help illustrate what docker service does.
If you want to scale your application, you can run multiple instances of it (horizontal scaling) or you beef up the machine(s) that it runs on (vertical scaling). For the first, you would have to put a load balancer in front of your application so that the traffic is evenly distributed between the different instances. The idea is that those instances run on different hosts, so if one goes down, your application is still up. Some controlling instance (a Kubernetes service, for example) will notice that one of your instances has gone south and won't direct any more traffic to it. Nowadays, with all the cloud stuff going on, this is typically the way to go.
You don't need Kubernetes for such a setup, but you're right, this would be a typical use case for it. At least if you run your application in a Docker container.
Once use case is running on Docker swarm which consists of n number of nodes in your swarm cluster. You can run replicas of your application on the swarm cluster with a load balancer/reverse proxy to load balance your setup. If any one of the nodes goes down the application can still run.
But the exact use case for running multiple instances is scalabilty. Suppose you know that one instance of your app can serve 10000 users (Assume Bank authentication) at a time.
If you want your application to serve 50K users just run 5 replicas(using docker service create) .
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.
I have been looking into Docker containerization for a while now but few things are still confusing to me. I understand that all the containers are grouped into a cluster and cluster management tools like Docker Swarm, DC/OS, Kubernetes or Rancher can be used to manage docker containers. I have been testing out Container cluster management with DC/OS and Kubernetes, but still a few questions remain unanswered to me.
How does auto scaling in container level help us in production servers? How does the application serve traffic from multiple containers?
Suppose we have deployed a web application using containers and they have auto scaled. How does the traffic flow to the containers? How are the sessions managed?
What metrics are calculated for autoscaling containers?
The autoscaling in DC/OS (note: Mesosphere is the company, DC/OS the open source project) the autoscaling is described in detail in the docs. Essentially the same as with Kubernetes, you can use either low-level metrics such as CPU utilization to decide when to increase the number of instances of an app or higher-level stuff like app throughput, for example using the Microscaling approach.
Regarding your question how the routing works (how are requests forwarded to an instance, that is a single container running): you need a load balancer and again, DC/OS provides you with this out of the box. And again, the options are detailed out in the docs, essentially: HAProxy-based North-South or IPtables-based, East-West (cluster internal) load balancers.
Kubernetes has concept called service. A Kubernetes Service is an abstraction which defines a logical set of Pods and a policy by which to access them. Kubernetes uses services to serve traffic from multiple containers. You can read more about services here.
AFAIK, Sessions are managed outside kubernetes, but Client-IP based session affinity can be selected by setting service.spec.sessionAffinity to "ClientIP". You can read more about Service and session affinity here
Multiple metrics like cpu and memory can be used for autoscaling containers. There is a good blog you can read about autoscaling, when and how.
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.