How to upsize volume of Terraformed EKS node - kubernetes

We have been using Terraform for almost a year now to manage all kinds of resources on AWS from bastion hosts to VPCs, RDS and also EKS.
We are sometimes really baffled by the EKS module. It could however be due to lack of understanding (and documentation), so here it goes:
Problem: Upsizing Disk (volume)
module "eks" {
source = "terraform-aws-modules/eks/aws"
version = "12.2.0"
cluster_name = local.cluster_name
cluster_version = "1.19"
subnets = module.vpc.private_subnets
#...
node_groups = {
first = {
desired_capacity = 1
max_capacity = 5
min_capacity = 1
instance_type = "m5.large"
}
}
I thought the default value for this (dev) k8s cluster's node can easily be the default 20GBs but it's filling up fast so I know want to change disk_size to let's say 40GBs.
=> I thought I could just add something like disk_size=40 and done.
terraform plan tells me I need to replace the node. This is a 1 node cluster, so not good. And even if it were I don't want to e.g. drain nodes. That's why I thought we are using managed k8s like EKS.
Expected behaviour: since these are elastic volumes I should be able to upsize but not downsize, why is that not possible? I can def. do so from the AWS UI.
Sure with a slightly scary warning:
Are you sure that you want to modify volume vol-xx?
It may take some time for performance changes to take full effect.
You may need to extend the OS file system on the volume to use any newly-allocated space
But I can work with the provided docs on that: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/recognize-expanded-volume-linux.html?icmpid=docs_ec2_console
Any guidelines on how to up the storage? If I do so with the UI but don't touch Terraform then my EKS state will be nuked/out of sync.

To my knowledge, there is currently no way to resize an EKS node volume without recreating the node using Terraform.
Fortunately, there is a workaround: As you also found out, you can directly change the node size via the AWS UI or API. To update your state file afterward, you can run terraform apply -refresh-only to download the latest data (e.g., the increased node volume size). After that, you can change the node size in your Terraform plan to keep both plan and state in sync.
For the future, you might want to look into moving to ephemeral nodes as (at least my) experience shows that you will have unforeseeable changes to clusters and nodes from time to time. Already planning with replaceable nodes in mind will make these changes substantially easier.

By using the terraform-aws-eks terraform module you are actually following the "ephemeral nodes" paradigm, because for both ways of creating instances (self-managed workers or managed node groups) the module is creating Autoscaling Groups that create EC2 instances out of a Launch Template.
ASG and Launch Templates are specifically designed so that you don't care anymore about specific nodes, and rather you just care about the number of nodes. This means that for updating the nodes, you just replace them with new ones, which will use the new updated launch template (with more GBs for example, or with a new updated AMI, or a new instance type).
This is called "rolling updates", and it can be done manually (adding new instances, then draining the node, then deleting the old node), with scripts (see: eks-rolling-update in github by Hellofresh), or it can be done automagically if you use the AWS managed nodes (the ones you are actually using when specifying "node_groups", that is why if you add more GB, it will replace the node automatically when you run apply).
And this paradigm is the most common when operating Kubernetes in the cloud (and also very common on-premise datacenters when using virtualization).
Option 1) Self Managed Workers
With self managed nodes, when you change a parameter like disk_size or instance_type, it will change the Launch Template. It will update the $latest version tag, which is commonly where the ASG is pointing to (although can be changed). This means that old instances will not see any change, but new ones will have the updated configuration.
If you want to change the existing instances, you actually want to replace them with new ones. That is what this ephemeral nodes paradigm is.
One by one you can drain the old instances while increasing the number of desired_instances on the ASG, or let the cluster autoscaler do the job. Alternatively, you can use an automated script which does this for you for each ASG: https://github.com/hellofresh/eks-rolling-update
In terraform_aws_eks module, you create self managed workers by either using worker_groups or worker_groups_launch_template (recommended) field
Option 2) Managed Nodes
Managed nodes is an EKS-specific feature. You configure them very similarly, but in reality, it is an abstraction, and AWS will create the actual underlying ASG.
You can specify a Launch Template to be used by the ASG and its version. Some config can be specified at the managed node level (i.e. AMI and instance_types) and at the Launch Template (if it wasn't specified in the former).
Any change on the node group level config, or on the Launch Template version, will trigger an automatic rolling update, which will replace all old instances.
You can delay the rolling update by just not pointing to the $latest version (or pointing to $default, and not updating the $default tag when changing the LT).
In terraform_aws_eks module, you create self managed workers by using the node_groups field. You can also play with these settings: create_launch_template=true and set_instance_types_on_lt=true if you want the module to create the LT for you (alternatively you can just not use it, or pass a reference to one); and to set the instance_type on such LT as specified above.
But behavior is similar to worker groups. In no case you will have your existing instances changed. You can only change them manually.
However, there is an alternative: The manual way
You can use the EKS module to create the control plane, but then use a regular EC2 resource in terraform (https://registry.terraform.io/providers/hashicorp/aws/latest/docs/resources/instance) to create one ore multiple (using count or for_each) instances.
If you create the instances using the aws_instance resource, then terraform will patch those instances (updated-in-place) when any change is allowed (i.e. increasing the root volue GB or the instance type; whereas changing the AMI will force a replacement).
The only tricky part, is that you need to configure the cloud-init script to make the instance join the cluster (something that is automatically done by the EKS module when using self/managed node groups).
However, it is very possible, and you can borrow the script from the module and plug it into the aws_instance's user_data field (https://registry.terraform.io/providers/hashicorp/aws/latest/docs/resources/instance#user_data)
In this case (when talking about disk_size), however, you still need to manually (either by SSH, or by running an hacky exec using terraform) to patch the XFS filesystem so it sees the increased disk space.
Another alternative: Consider Kubernetes storage
That said, there is also another alternative for certain use cases. If you want to increase the disk space of those instances because of one of your applications using a hostPath, then it might be the case that you can use a kubernetes built-in storage solution using the EBS CSI driver.
For example, I manage an ElasticSearch cluster in Kubernetes (and deploy it from terraform with the helm module), and it uses dynamic storage provisioning to request an EBS volume (note that performance is the same, because both root and this other volume are EBS volumes). EBS CSI driver supports volume expansion, so I can just increase this disk by changing a terraform variable.
To conclude, I would not recommend the aws_instance way, unless you understand it and are sure you really want it. It may make sense in certain cases, but definitely not common

Related

I want to stop/hibernate the cluster to save cost, any best approach/practice for it?

I have created A GKE cluster for a POC, later on, I want to stop/hibernate the cluster to save cost, any best approach/practice for it?
You can put all your node pool to 0 VM but be careful to data lost (according to your node pool configuration, if you delete all the VM you can loose data). However, you will continue to pay for the control plane.
Another approach is to backup your data and to use IaC (Infra as code, such as terraform) to detroy and rebuild your cluster as needed.
Both approach are valid, they depend on your use case and how long to you need to hibernate your cluster.
An alternative is to use GKE Autopilot if your workloads are compliant with this deployment mode.

GKE Node Upgrade "Out of Resources"

I had left my GKE cluster running 3 minor versions behind the latest and decided to finally upgrade. The master upgrade went well but then my node upgrades kept failing. I used the Cloud Shell console to manually start an upgrade and view the output, which said something along the lines of "Zone X is out of resources, try Y instead." Unfortunately,I can't just spin up a new node pool in a new zone and have my pipeline work because I am using GitLab's AutoDevOps pipeline and they make certain assumptions about node pool naming and such that I can't find any way to override. I also don't want to potentially lose the data stored in my persistent volumes if I end up needing to re-create everything in a new node-pool.
I just solved this issue but couldn't find any questions posed on this particular problem, so I wanted to post the answer here in case someone else comes looking for it.
My particular problem was that I had a non-autoscaling node pool with a single node. For my purposes, that's enough for the application stack to run smoothly and I don't want to incur unforeseen charges with additional nodes automatically being added to the pool. However, this meant that the upgrade had to apparently share resources with everything else running on that node to perform the upgrade, which it didn't have enough of. The solution was simple: add more nodes temporarily.
Because this is specifically GKE, I was able to use a beta feature called "surge upgrade", which allows you to set the maximum number of "surge" nodes to add when performing an upgrade. Once this was enabled, I started the upgrade process again and it temporarily added an extra node, performed the upgrade, and then scaled back down to a single node.
If you aren't on GKE, or don't wish to use a beta feature (or can't), then simply resize the node pool with the node(s) that needs upgrading. I would add a single node unless you are positive you need more.

Kubernetes different container args depending on number of pods in replica set

I want to scale an application with workers.
There could be 1 worker or 100, and I want to scale them seamlessly.
The idea is using replica set. However due to domain-specific reasons, the appropriate way to scale them is for each worker to know its: ID and the total number of workers.
For example, in case I have 3 workers, I'd have this:
id:0, num_workers:3
id:1, num_workers:3
id:2, num_workers:3
Is there a way of using kubernetes to do so?
I pass this information in command line arguments to the app, and I assume it would be fine having it in environment variables too.
It's ok on size changes for all workers to be killed and new ones spawned.
Before giving the kubernetes-specific answer, I wanted to point out that it seems like the problem is trying to push cluster-coordination down into the app, which is almost by definition harder than using a distributed system primitive designed for that task. For example, if every new worker identifies themselves in etcd, then they can watch keys to detect changes, meaning no one needs to destroy a running application just to update its list of peers, their contact information, their capacity, current workload, whatever interesting information you would enjoy having while building a distributed worker system.
But, on with the show:
If you want stable identifiers, then StatefulSets is the modern answer to that. Whether that is an exact fit for your situation depends on whether (for your problem domain) id:0 being "rebooted" still counts as id:0 or the fact that it has stopped and started now disqualifies it from being id:0.
The running list of cluster size is tricky. If you are willing to be flexible in the launch mechanism, then you can have a pre-launch binary populate the environment right before spawning the actual worker (that example is for reading from etcd directly, but the same principle holds for interacting with the kubernetes API, then launching).
You could do that same trick in a more static manner by having an initContainer write the current state of affairs to a file, which the app would then read in. Or, due to all Pod containers sharing networking, the app could contact a "sidecar" container on localhost to obtain that information via an API.
So far so good, except for the
on size changes for all workers to be killed and new one spawned
The best answer I have for that requirement is that if the app must know its peers at launch time, then I am pretty sure you have left the realm of "scale $foo --replicas=5" and entered into the "destroy the peers and start all afresh" realm, with kubectl delete pods -l some-label=of-my-pods; which is, thankfully, what updateStrategy: type: OnDelete does, when combined with the delete pods command.
In the end, I've tried something different. I've used kubernetes API to get the number of running pods with the same label. This is python code utilizing kubernetes python client.
import socket
from kubernetes import client
from kubernetes import config
config.load_incluster_config()
v1 = client.CoreV1Api()
with open(
'/var/run/secrets/kubernetes.io/serviceaccount/namespace',
'r'
) as f:
namespace = f.readline()
workers = []
for pod in v1.list_namespaced_pod(
namespace,
watch=False,
label_selector="app=worker"
).items:
workers.append(pod.metadata.name)
workers.sort()
num_workers = len(workers)
worker_id = workers.index(socket.gethostname())

What is the recommended way to upgrade a kubernetes cluster as new versions are released?

What is the recommended way to upgrade a kubernetes cluster as new versions are released?
I heard here it may be https://github.com/kubernetes/kubernetes/blob/master/cluster/kube-push.sh. If that is the case how does kube-push.sh relate to https://github.com/GoogleCloudPlatform/kubernetes/blob/master/cluster/gce/upgrade.sh?
I've also heard here that we should instead create a new cluster, copy/move the pods, replication controllers, and services from the first cluster to the new one and then turn off the first cluster.
I'm running my cluster on aws if that is relevant.
The second script you reference (gce/upgrade.sh) only works if your cluster is running on GCE. There isn't (yet) an equivalent script for AWS, but you could look at the script and follow the steps (or write them into a script) to get the same behavior.
The main different between upgrade.sh and kube-push.sh is that the former does a replacement upgrade (remove a node, create a new node to replace it) whereas the later does an "in place" upgrade.
Removing and replacing the master node only works if the persistent data (etcd database, server certificates, authorized bearer tokens, etc) reside on a persistent disk separate from the boot disk of the master (this is how it is configured by default in GCE). Remove and replacing nodes should be fine on AWS (but keep in mind that any pods not under a replication controller won't be restarted).
Doing an in-place upgrade doesn't require any special configuration, but that code path isn't as thoroughly tested as the remove and replace option.
You shouldn't need to entirely replace your cluster when upgrading to a new version, unless you are using pre-release versions (e.g. alpha or beta releases) which can sometimes have breaking changes between them.

Using Ansible to automatically configure AWS autoscaling group instances

I'm using Amazon Web Services to create an autoscaling group of application instances behind an Elastic Load Balancer. I'm using a CloudFormation template to create the autoscaling group + load balancer and have been using Ansible to configure other instances.
I'm having trouble wrapping my head around how to design things such that when new autoscaling instances come up, they can automatically be provisioned by Ansible (that is, without me needing to find out the new instance's hostname and run Ansible for it). I've looked into Ansible's ansible-pull feature but I'm not quite sure I understand how to use it. It requires a central git repository which it pulls from, but how do you deal with sensitive information which you wouldn't want to commit?
Also, the current way I'm using Ansible with AWS is to create the stack using a CloudFormation template, then I get the hostnames as output from the stack, and then generate a hosts file for Ansible to use. This doesn't feel quite right – is there "best practice" for this?
Yes, another way is just to simply run your playbooks locally once the instance starts. For example you can create an EC2 AMI for your deployment that in the rc.local file (Linux) calls ansible-playbook -i <inventory-only-with-localhost-file> <your-playbook>.yml. rc.local is almost the last script run at startup.
You could just store that sensitive information in your EC2 AMI, but this is a very wide topic and really depends on what kind of sensitive information it is. (You can also use private git repositories to store sensitive data).
If for example your playbooks get updated regularly you can create a cron entry in your AMI that runs every so often and that actually runs your playbook to make sure your instance configuration is always up to date. Thus avoiding having "push" from a remote workstation.
This is just one approach there could be many others and it depends on what kind of service you are running, what kind data you are using, etc.
I don't think you should use Ansible to configure new auto-scaled instances. Instead use Ansible to configure a new image, of which you will create an AMI (Amazon Machine Image), and order AWS autoscaling to launch from that instead.
On top of this, you should also use Ansible to easily update your existing running instances whenever you change your playbook.
Alternatives
There are a few ways to do this. First, I wanted to cover some alternative ways.
One option is to use Ansible Tower. This creates a dependency though: your Ansible Tower server needs to be up and running at the time autoscaling or similar happens.
The other option is to use something like packer.io and build fully-functioning server AMIs. You can install all your code into these using Ansible. This doesn't have any non-AWS dependencies, and has the advantage that it means servers start up fast. Generally speaking building AMIs is the recommended approach for autoscaling.
Ansible Config in S3 Buckets
The alternative route is a bit more complex, but has worked well for us when running a large site (millions of users). It's "serverless" and only depends on AWS services. It also supports multiple Availability Zones well, and doesn't depend on running any central server.
I've put together a GitHub repo that contains a fully-working example with Cloudformation. I also put together a presentation for the London Ansible meetup.
Overall, it works as follows:
Create S3 buckets for storing the pieces that you're going to need to bootstrap your servers.
Save your Ansible playbook and roles etc in one of those S3 buckets.
Have your Autoscaling process run a small shell script. This script fetches things from your S3 buckets and uses it to "bootstrap" Ansible.
Ansible then does everything else.
All secret values such as Database passwords are stored in CloudFormation Parameter values. The 'bootstrap' shell script copies these into an Ansible fact file.
So that you're not dependent on external services being up you also need to save any build dependencies (eg: any .deb files, package install files or similar) in an S3 bucket. You want this because you don't want to require ansible.com or similar to be up and running for your Autoscale bootstrap script to be able to run. Generally speaking I've tried to only depend on Amazon services like S3.
In our case, we then also use AWS CodeDeploy to actually install the Rails application itself.
The key bits of the config relating to the above are:
S3 Bucket Creation
Script that copies things to S3
Script to copy Bootstrap Ansible. This is the core of the process. This also writes the Ansible fact files based on the CloudFormation parameters.
Use the Facts in the template.