DaemonSet is a Kubernetes beta resource that can ensure that exactly one pod is scheduled to a group of nodes. The group of nodes is all nodes by default, but can be limited to a subset using nodeSelector or the Alpha feature of node affinity/anti-affinity.
It seems that DaemonSet functionality can be achieved with replication controllers/replica sets with proper node affinity and anti-affinity.
Am I missing something? If that's correct should DaemonSet be deprecated before it even leaves Beta?
As you said, DaemonSet guarantees one pod per node for a subset of the nodes in the cluster. If you use ReplicaSet instead, you need to
use the node affinity/anti-affinity and/or node selector to control the set of nodes to run on (similar to how DaemonSet does it).
use inter-pod anti-affinity to spread the pods across the nodes.
make sure the number of pods > number of node in the set, so that every node has one pod scheduled.
However, ensuring (3) is a chore as the set of nodes can change over time. With DaemonSet, you don't have to worry about that, nor would you need to create extra, unschedulable pods. On top of that, DaemonSet does not rely on the scheduler to assign its pods, which makes it useful for cluster bootstrap (see How Daemon Pods are scheduled).
See the "Alternative to DaemonSet" section in the DaemonSet doc for more comparisons. DaemonSet is still the easiest way to run a per-node daemon without external tools.
Related
What is the easiest way to run a single Pod on every available worker node as part of the StatefulSet. So, a one to one mapping.
Am I right to say every Pod will run on a different Node by default with a StatefulSet? In which case is it sufficient to add x pods to the SS where x Worker nodes exist in the cluster?
Thanks.
Use DaemonSet instead.
A DaemonSet ensures that all (or some) Nodes run a copy of a Pod. As nodes are added to the cluster, Pods are added to them. As nodes are removed from the cluster, those Pods are garbage collected. Deleting a DaemonSet will clean up the Pods it created.
If you really want to use statefulSet, you can take a look at features like nodeSelector or Affinity and Anti-affinity.
Here is what I am working with.
I have 3 nodepools on GKE
n1s1 (3.75GB)
n1s2 (7.5GB)
n1s4 (15GB)
I have pods that will require any of the following memory requests. Assume limits are very close to requests.
1GB, 2GB, 4GB, 6GB, 8GB, 10GB, 12GB, 14GB
How best can I associate a pod to a nodepool for max efficiency?
So far I have 3 strategies.
For each pod config, determine the “rightful nodepool”. This is the smallest nodepool that can accommodate the pod config in an ideal world.
So for 2GB pod it's n1s1 but for 4GB pod it'd be n1s2.
Schedule a pod only on its rightful nodepool.
Schedule a pod only on its rightful nodepool or one nodepool higher than that.
Schedule a pod only on any nodepool where it can currently go.
Which of these or any other strategies will minimize wasting resources?
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Why would you have 3 pools like that in the first place? You generally want to use the largest instance type you can that gets you under 110 pods per node (which is the default hard cap). The job of the scheduler is to optimize the packing for you, and it's pretty good at that with the default settings.
I would use a mix of Taints and Tolerations and Node affinity.
Taints and tolerations work together to ensure that pods are not scheduled onto inappropriate nodes. One or more taints are applied to a node; this marks that the node should not accept any pods that do not tolerate the taints. Tolerations are applied to pods, and allow (but do not require) the pods to schedule onto nodes with matching taints.
You can set a taint on a node kubectl taint nodes node1 key=value:NoSchedule
The taint has key key, value value, and taint effect NoSchedule. This means that no pod will be able to schedule onto node1 unless it has a matching toleration.
While you are writing a pod yaml you can specify PodSpec and add toleration which will match the taint created on node1 which will allow pod with either toleration to be scheduled onto node1
tolerations:
- key: "key"
operator: "Equal"
value: "value"
effect: "NoSchedule"
or
tolerations:
- key: "key"
operator: "Exists"
effect: "NoSchedule"
Taints and tolerations are a flexible way to steer pods away from nodes or evict pods that shouldn’t be running. A few of the use cases are
Dedicated Nodes: If you want to dedicate a set of nodes for exclusive use by a particular set of users, you can add a taint to those nodes (say, kubectl taint nodes nodename dedicated=groupName:NoSchedule) and then add a corresponding toleration to their pods (this would be done most easily by writing a custom admission controller). The pods with the tolerations will then be allowed to use the tainted (dedicated) nodes as well as any other nodes in the cluster. If you want to dedicate the nodes to them and ensure they only use the dedicated nodes, then you should additionally add a label similar to the taint to the same set of nodes (e.g. dedicated=groupName), and the admission controller should additionally add a node affinity to require that the pods can only schedule onto nodes labeled with dedicated=groupName.
Nodes with Special Hardware: In a cluster where a small subset of nodes have specialized hardware (for example GPUs), it is desirable to keep pods that don’t need the specialized hardware off of those nodes, thus leaving room for later-arriving pods that do need the specialized hardware. This can be done by tainting the nodes that have the specialized hardware (e.g. kubectl taint nodes nodename special=true:NoSchedule or kubectl taint nodes nodename special=true:PreferNoSchedule) and adding a corresponding toleration to pods that use the special hardware. As in the dedicated nodes use case, it is probably easiest to apply the tolerations using a custom admission controller. For example, it is recommended to use Extended Resources to represent the special hardware, taint your special hardware nodes with the extended resource name and run the ExtendedResourceToleration admission controller. Now, because the nodes are tainted, no pods without the toleration will schedule on them. But when you submit a pod that requests the extended resource, the ExtendedResourceToleration admission controller will automatically add the correct toleration to the pod and that pod will schedule on the special hardware nodes. This will make sure that these special hardware nodes are dedicated for pods requesting such hardware and you don’t have to manually add tolerations to your pods.
Taint based Evictions: A per-pod-configurable eviction behavior when there are node problems, which is described in the next section.
As for node affinity:
is conceptually similar to nodeSelector – it allows you to constrain which nodes your pod is eligible to be scheduled on, based on labels on the node.
There are currently two types of node affinity, called requiredDuringSchedulingIgnoredDuringExecution and preferredDuringSchedulingIgnoredDuringExecution. You can think of them as “hard” and “soft” respectively, in the sense that the former specifies rules that must be met for a pod to be scheduled onto a node (just like nodeSelector but using a more expressive syntax), while the latter specifies preferences that the scheduler will try to enforce but will not guarantee. The “IgnoredDuringExecution” part of the names means that, similar to how nodeSelector works, if labels on a node change at runtime such that the affinity rules on a pod are no longer met, the pod will still continue to run on the node. In the future we plan to offer requiredDuringSchedulingRequiredDuringExecution which will be just like requiredDuringSchedulingIgnoredDuringExecution except that it will evict pods from nodes that cease to satisfy the pods’ node affinity requirements.
Thus an example of requiredDuringSchedulingIgnoredDuringExecution would be “only run the pod on nodes with Intel CPUs” and an example preferredDuringSchedulingIgnoredDuringExecution would be “try to run this set of pods in failure zone XYZ, but if it’s not possible, then allow some to run elsewhere”.
Node affinity is specified as field nodeAffinity of field affinity in the PodSpec.
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The new node affinity syntax supports the following operators: In, NotIn, Exists, DoesNotExist, Gt, Lt. You can use NotIn and DoesNotExist to achieve node anti-affinity behavior, or use node taints to repel pods from specific nodes.
If you specify both nodeSelector and nodeAffinity, both must be satisfied for the pod to be scheduled onto a candidate node.
If you specify multiple nodeSelectorTerms associated with nodeAffinity types, then the pod can be scheduled onto a node only if all nodeSelectorTerms can be satisfied.
If you specify multiple matchExpressions associated with nodeSelectorTerms, then the pod can be scheduled onto a node if one of the matchExpressions is satisfied.
We can use DaemonSet object to deploy one replica on each node. How can we deploy say 2 replicas or 3 replicas per node? How can we achieve that. please let us know
There is no way to force x pods per node the way a Daemonset does. However, with some planning, you can force a fairly even pod distribution across your nodes using pod anti affinity.
Let's say we have 10 nodes. The first thing is we need to have a ReplicaSet (deployment) with 30 pods (3 per node). Next, we want to set the pod anti affinity to use preferredDuringSchedulingIgnoredDuringExecution with a relatively high weight and match the deployment's labels. This will cause the scheduler to prefer not scheduling pods where the same pod already exists. Once there is 1 pod per node, the cycle starts over again. A node with 2 pods will be weighted lower than one with 1 pod so the next pod should try to go there.
Note this is not as precise as a DaemonSet and may run into some limitations when it comes time to scale up or down the cluster.
A more reliable way if scaling the cluster is to simply create multiple DaemonSets with different names, but identical in every other way. Since the DaemonSets will have the same labels, they can all be exposed through the same service.
By default, the kubernetes scheduler will prefer to schedule pods on different nodes.
The kubernetes scheduler will first determine all possible nodes where a pod can be deployed based on your affinity/anti-affinity/resource limits/etc.
Afterward, the scheduler will find the best node where the pod can be deployed. The scheduler will automatically schedule the pods to be on separate availability zones and on separate nodes if this is possible of course.
You can try this on your own. For example, if you have 3 nodes, try deploying 9 replicas of a pod. You will see that each node will have 2 pods running.
I have added podAntiAffinity to my DeploymentConfig template.
However, pods are being scheduled on nodes that I expected would be excluded by the rules.
How can I view logs of the kubernetes scheduler to understand why it chose the node it did for a given pod?
PodAntiAffinity has more to do with other pods than nodes specifically. That is, PodAntiAffinity specifies which nodes to exclude based on what pods are already scheduled on that node. And even here you can make it a requirement vs. just a preference. To directly pick the node on which a pod is/is not scheduled, you want to use NodeAffinity. The guide.
I have already deployed pods using Daemonsets with nodeselector. My requirements is I need to use kind Deployment but at the same time I would want to retain Daemonsets functionality
.I have nodeselector defined so that same pod should be installed in labelled node.
How to achieve your help is appreciated.
My requirements is pod should be placed automatically based on nodeselector but with kind Deployment
In otherwords
Using Replication controller when I schedule 2 (two) replicas of a pod I expect 1 (one) replica each in each Nodes (VMs). Instead I find both replicas are created in same node This will make 1 Node a single point of failure which I need to avoid.
I have labelled two nodes properly. And I could see both pods spawned on single node. How to achieve both pods always schedule on both nodes?
Look into affinity and anti-affinity, specifically, inter-pod affinity and anti-affinity.
From official documentation:
Inter-pod affinity and anti-affinity allow you to constrain which nodes your pod is eligible to be scheduled based on labels on pods that are already running on the node rather than based on labels on nodes. The rules are of the form “this pod should (or, in the case of anti-affinity, should not) run in an X if that X is already running one or more pods that meet rule Y”.