I am fairly new to Kubernetes, and I think I understand the basics of provisioning nodes and setting memory limits for pods. Here's the problem I have: my application can require dramatically different amounts of memory, depending on the input (and there is no fool-proof way to predict it). Some jobs require 50MB, some require 50GB. How can I set up my K8s deployment to handle this situation?
I have one strategy that I'd like to try out, but I don't know how to do it: start with small instances (nodes with not a lot of memory), and if the job fails with out-of-memory, then automatically send it to increasingly bigger instances until it succeeds. How hard would this be to implement in Kubernetes?
Thanks!
Natively K8S supports horizontal autoscalling i.e. automatically deplying more replicas of a deployment basing on chosen metric like CPU usage, memory usage etc.: Horizontal Pod Autoscaling
What you are describing here though is vertical scaling. It is not supported out of the box, but there is a subproject that seems to be able to fulfill your requirements: vertical-pod-autoscaler
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We are migrating our infrastructure to kubernetes. I am talking about a part of it, that contains of an api for let's say customers (we have this case for many other resources). Let's consider we have a billion customers, each with some data etc. and we decided they deserve a specialized api just for them, with its own db, server, domain, etc.
Kubernetes has the notion of nodes and pods. So we said "ok, we dedicate node X with all its resources to this particular api". And now the question:
Why would I used multiple pods each of them containing the same nginx + fpm and code, and limit it to a part of traffic and resources, and add an internal lb, autoscale, etc., instead of having a single pod, with all node resources?
Since each pod adds a bit of extra memory consumption this seems like a waste to me. The only upside being the fact that if something fails only part of it goes down (so maybe 2 pods would be optimal in this case?).
Obviously, would scale the nodes when needed.
Note: I'm not talking about a case where you have multiple pods with different stuff, I'm talking about that particular case.
Note 2: The db already is outside this node, on it's own pod.
Google fails me on this topic. I find hundreds of post with "how to configure things, but 0 with WHY?".
Why would I used multiple pods each of them containing the same nginx + fpm and code, and limit it to a part of traffic and resources, and add an internal lb, autoscale, etc., instead of having a single pod, with all node resources?
Since each pod adds a bit of extra memory consumption this seems like a waste to me. The only upside being the fact that if something fails only part of it goes down (so maybe 2 pods would be optimal in this case?).
This comes down to the question, should I scale my app vertically (larger instance) or horizontally (more instances).
First, try to avoid using only a single instance since you probably want more redundancy if you e.g. upgrade a Node. A single instance may be a good option if you are OK with some downtime sometimes.
Scale app vertically
To scale an app vertically, by changing the instance to a bigger, is a viable alternative that sometimes is a good option. Especially when the app can not be scaled horizontally, e.g. an app that use leader election pattern - typically listen to a specific event and react. There is however a limit by how much you can scale an app vertically.
Scale app horizontally
For a stateless app, it is usually much easier and cheaper to scale an app horizontally by adding more instances. You typically want more than one instance anyway, since you want to tolerate that a Node goes down for maintenance. This is also possible to do for a large scale app to very many instances - and the cost scales linearly. However, not every app can scale horizontally, e.g. a distributed database (replicated) can typically not scale well horizontally unless you shard the data. You can even use Horizontal Pod Autoscaler to automatically adjust the number of instances depending on how busy the app is.
Trade offs
As described above, horizontal scaling is usually easier and preferred. But there are trade offs - you would probably not want to run thousands of instances when you have low traffic - an instance has some resource overhead costs, also in maintainability. For availability you should run at least 2 pods and make sure that they does not run on the same node, if you have a regional cluster, you want to make sure that they does not run on the same Availability Zone - for availability reasons. Consider 2-3 pods when your traffic is low, and use Horizontal Pod Autoscaler to automatically scale up to more instance when you need. In the end, this is a number game - resources cost money - but you want to provide a good service for your customers as well.
I would like to define a policy to dynamically assigns resource limits to pods and containers. For example, if there are 4 number of pods scheduled in a specific node, and the memory capacity is 100mi, each pod to be assigned with 25mi memory limit. In other words, the fair share of the node capacity.
So, is it necessary to change the codes in scheduler.go or I need to change other objects as well?
I do agree with Arslanbekov answer, it's contrary to the ideology of scalability used by kubernetes.
The principle is that you define what resources is needed by your application and the cluster do all it can to give this resource to the pod, scalling the resources (pod, nodes) depending on the global consumption of all apps.
What you are asking is the reverse, give resources to the pod depending on the node resources, this way could prove very difficult to allow automatic scallability of the nodes as it would be the resource aim to attain (I may be confusing in my explanation but that shows how difficult it could be).
One way to do what you want would be to size all your pod to the same size to use 80% of the nodes but this would prove wrong if an app need more resources.
I think this is contrary to the ideology of the kubernetes. In this approach, the new application will not be able to get to the node.
At each point in time for the scheduler will be the utilization of 100% each node.
I have a cluster w/ 3 nodes. Hurray! The cluster doesn't autoscale nodes.
These nodes run an amazing web app, yet most of the time do almost nothing.
I also have a background process that could use an infinite amount of CPU (the usefulness drops rapidly but remains useful).
I want these background pods to run on each Node and slowed down to leave a 20% CPU headroom on the Node. Or similar.
That's the shape of a DaemonSet.
Can I tell Kubernetes to deprioritize the DaemonSet Pods w/ a 20% headroom?
Can the DaemonSet Pods detect the Nodes CPU usage and deprioritize themselves (risky if buggy)?
QoS looks like it's for scheduling and evicting pods to make room for other pods, but they don't get 'niced'.
Priority also looks like it's for eviction.
You may achieve what you're looking for in many ways.
I imagine that you've already read this and that, based on the theory of this other.
Also RedHat has nice documentation about setting hardware limits via softwarre.
Here you can find how to restrict cpu usage, which may be set inside a container to achieve what you're looking for.
So, to recap: with K8S you can set requests and limits, and inside the container you can set even further restrictive limits.
Hope this gives you the solution or at least the path to follow in order to achieve what you want.
I want to optimally configure the CPU cores without over or under allocation. How can I measure the required CPU millicore for a given container? It also brings the question of how much traffic a proxy will send it to any given pod based on CPU consumption so we can optimally use the compute.
Currently I send requests and monitor with,
kubectl top pod
Is there any tool that can measure, Requests, CPU and Memory over the time and suggest the optimal CPU recommendation for the pods.
Monitoring over time and per Pod yes, there's suggestions at https://kubernetes.io/docs/tasks/debug-application-cluster/resource-usage-monitoring/ One of the more popular is the Prometheus-Grafana combination - https://grafana.com/dashboards/315
As for automatic suggestion of the request and limits, I don't think there is anything. Keep in mind Kubernetes already tries to balance giving each Pod what it needs without it taking too much. The limits and requests that you set are to help it do this more safely. There are limitations on automatically inference as an under-resourced Pod can still work but respond a bit slower - it is up to you to decide what level of slowness you would tolerate. It is also up to you to decide what level of resource consumption could be acceptable in peak load, as opposed to excessive consumption that might indicate a bug in your app or even an attack. There's a further limitation as the metric units are themselves an attempt to approximate resource power that can actually vary with types of hardware (memory and CPUs can differ in mode of operation as well as quantity) and so can vary across clusters or even nodes on a cluster if the hardware isn't all equal.
What you are doing with top seems to me a good way to get started. You'll want to monitor resource usage for the cluster anyway so keeping track of this and adjusting limits as you go is a good idea. If you can run the same app outside of kubernetes and read around to see what other apps using the same language do then that can help to indicate if there's anything you can do to improve utilisation (memory consumption on the JVM in containers for example famously requires some tweaking to get right).
We're moving all of our infrastructure to Google Kubernetes Engine (GKE) - we currently have 50+ AWS machines with lots of APIs, Services, Webapps, Database servers and more.
As we have already dockerized everything, it's time to start moving everything to GKE.
I have a question that may sound too basic, but I've been searching the Internet for a week and did not found any reasonable post about this
Straight to the point, which of the following approaches is better and why:
Having multiple node pools with multiple machine types and always specify in which pool each deployment should be done; or
Having a single pool with lots of machines and let Kubernetes scheduler do the job without worrying about where my deployments will be done; or
Having BIG machines (in multiple zones to improve clusters' availability and resilience) and let Kubernetes deploy everything there.
List of consideration to be taken merely as hints, I do not pretend to describe best practice.
Each pod you add brings with it some overhead, but you increase in terms of flexibility and availability making failure and maintenance of nodes to be less impacting the production.
Nodes too small would cause a big waste of resources since sometimes will be not possible to schedule a pod even if the total amount of free RAM or CPU across the nodes would be enough, you can see this issue similar to memory fragmentation.
I guess that the sizes of PODs and their memory and CPU request are not similar, but I do not see this as a big issue in principle and a reason to go for 1). I do not see why a big POD should run merely on big machines and a small one should be scheduled on small nodes. I would rather use 1) if you need a different memoryGB/CPUcores ratio to support different workloads.
I would advise you to run some test in the initial phase to understand which is the size of the biggest POD and the average size of the workload in order to properly chose the machine types. Consider that having 1 POD that exactly fit in one node and assign to it is not the right to proceed(virtual machine exist for this kind of scenario). Since fragmentation of resources would easily cause to impossibility to schedule a large node.
Consider that their size will likely increase in the future and to scale vertically is not always this immediate and you need to switch off machine and terminate pods, I would oversize a bit taking this issue into account and since scaling horizontally is way easier.
Talking about the machine type you can decide to go for a machine 5xsize the biggest POD you have (or 3x? or 10x?). Oversize a bit as well the numebr of nodes of the cluster to take into account overheads, fragmentation and in order to still have free resources.
Remember that you have an hard limit of 100 pods each node and 5000 nodes.
Remember that in GCP the network egress throughput cap is dependent on the number of vCPUs that a virtual machine instance has. Each vCPU has a 2 Gbps egress cap for peak performance. However each additional vCPU increases the network cap, up to a theoretical maximum of 16 Gbps for each virtual machine.
Regarding the prices of the virtual machines notice that there is no difference in price buying two machines with size x or one with size 2x. Avoid to customise the size of machines because rarely is convenient, if you feel like your workload needs more cpu or mem go for HighMem or HighCpu machine type.
P.S. Since you are going to build a pretty big Cluster, check the size of the DNS
I will add any consideration that it comes to my mind, consider in the future to update your question with the description of the path you chose and the issue you faced.
1) makes a lot of sense as if you want, you can still allow kube deployments treat it as one large pool (by not adding nodeSelector/NodeAffinity) but you can have different machines of different sizes, you can think about having a pool of spot instances, etc. And, after all, you can have pools that are tainted and so forth excluded from normal scheduling and available to only a particular set of workloads. It is in my opinion preferred to have some proficiency with this approach from the very beginning, yet in case of many provisioners it should be very easy to migrate from 2) to 1) anyway.
2) As explained above, it's effectively a subset of 1) so better to build up exp with 1) approach from day 1, but if you ensure your provisioning solution supports easy extension to 1) model then you can get away with starting with this simplified approach.
3) Big is nice, but "big" is relative. It depends on the requirements and amount of your workloads. Remember that while you need to plan for loss of a whole AZ anyway, it will be much more frequent to loose single nodes (reboots, decommissions of underlying hardware, updates etc.) so if you have more hosts, impact of loosing one will be smaller. Bottom line is that you need to find your own balance, that makes sense for your particular scale. Maybe 50 nodes is too much, would 15 cut it? Who knows but you :)