I'm aware that I can use k8s node affinity to select for nodes that have a value of a label gt than some value. However is it possible to make k8s prefer to schedule on nodes that have the greatest value? I.e. if one node has schedule-on-me: 5 and another has schedule-on-me: 6 it will prefer to schedule on the one with the higher value?
Not directly. You can tune weights in the scheduler but I don't think any those would help here so would need a custom scheduler extender webhook.
Related
I am having 3 nodepools, I want to enable autoscaling for my nodes want to make sure that not more than 3 pods should run in a single node. The minimum number is 10 pods per node. How can I fix this issue.?
Expecting solution, whether this can be achived or not. If yes, please provide and guide me how to achive it.
We run a Kubernetes-compatible (OKD 3.11) on-prem / private cloud cluster with backend apps communicating with low-latency Redis databases used as caches and K/V stores. The new architecture design is about to divide worker nodes equally between two geographically distributed data centers ("regions"). We can assume static pairing between node names and regions, an now we have added labeling of nodes with region names as well.
What would be the recommended approach to protect low-latency communication with the in-memory databases, making client apps stick to the same region as the database they are allowed to use? Spinning up additional replicas of the databases is feasible, but does not prevent round-robin routing between the two regions...
Related: Kubernetes node different region in single cluster
Posting this out of comments as community wiki for better visibility, feel free to edit and expand.
Best option to solve this question is to use istio - Locality Load Balancing. Major points from the link:
A locality defines the geographic location of a workload instance
within your mesh. The following triplet defines a locality:
Region: Represents a large geographic area, such as us-east. A region typically contains a number of availability zones. In
Kubernetes, the label topology.kubernetes.io/region determines a
node’s region.
Zone: A set of compute resources within a region. By running services in multiple zones within a region, failover can occur between
zones within the region while maintaining data locality with the
end-user. In Kubernetes, the label topology.kubernetes.io/zone
determines a node’s zone.
Sub-zone: Allows administrators to further subdivide zones for more fine-grained control, such as “same rack”. The sub-zone concept
doesn’t exist in Kubernetes. As a result, Istio introduced the custom
node label topology.istio.io/subzone to define a sub-zone.
That means that a pod running in zone bar of region foo is not
considered to be local to a pod running in zone bar of region baz.
Another option that can be considered with traffic balancing adjusting is suggested in comments:
use nodeAffinity to achieve consistency between scheduling pods and nodes in specific "regions".
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
(similar to 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 continues to run on the node. In the future we
plan to offer requiredDuringSchedulingRequiredDuringExecution which
will be identical to 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".
Update: based on #mirekphd comment, it will still not be fully functioning in a way it was asked to:
It turns out that in practice Kubernetes does not really let us switch
off secondary zone, as soon as we spin up a realistic number of pod
replicas (just a few is enough to see it)... they keep at least some
pods in the other zone/DC/region by design (which is clever when you
realize that it removes the dependency on the docker registry
survival, at least under default imagePullPolicy for tagged images),
GibHub issue #99630 - NodeAffinity preferredDuringSchedulingIgnoredDuringExecution doesn't work well
Please refer to #mirekphd's answer
So effective region-pinning solution is more complex than just using nodeAffinity in the "preferred" version. This alone will cause you a lot of unpredictable surprises due to the opinionated character of Kubernetes that has zone spreading hard-coded, as seen in this Github issue, where they clearly try to put at least some eggs in another basket and see zone selection as an antipattern.
In practice the usefulness of nodeAffinity alone is restricted to a scenario with a very limited number of pod replicas, because when the pods number exceeds the number of nodes in a region (i.e. typically for the 3rd replica in a 2-nodes / 2-regions setup), scheduler will start "correcting" or "fighting" with user preference weights (even as unbalanced of 100:1) very much in favor of spreading, placing at least one "representative" pod on every node in every region (including the non-preferred ones with minimum possible weights of 1).
But this default zone spreading issue can be overcome if you create a single-replica container that will act as a "master" or "anchor" (a natural example being a database). For this single-pod "master" nodeAffinity will still work correctly - of course in the HA variant, i.e. "preferred" not "required" version. As for the remaining multi-pod apps, you use something else - podAffinity (this time in the "required" version), which will make the "slave" pods follow their "master" between zones, because setting any pod-based spreading disables the default zone spreading. You can have as many replicas of the "slave" pods as you want and never run into a single misplaced pod (at least at schedule time), because of the "required" affinity used for "slaves". Note that the known limitation of nodeAffinity applies here as well: the number of "master" pod replicas must not exceed the number of nodes in a region, or else "zone spreading" will kick in.
And here's an example of how to label the "master" pod correctly for the benefit of podAffinity and using a deployment config YAML file: https://stackoverflow.com/a/70041308/9962007
Say I have some special nodes in my cluster, and I want to be able to identify with a pod label all pods running on these nodes.
Taints & tolerations are close to this, but afaik tolerations aren’t labels, they’re their own thing, and can’t be referenced in places where you expect a label selector, right? Is there any way to require all pods running on these nodes to identify themselves in a way that can then be queried via label selectors?
There are two parts of problem to solve:
1. Ensure that pods with specific labels are scheduled to specific set of nodes.
This can be done by using custom scheduler or a plugin for default scheduler (more here, here, here, here and here).
2. Prevent other schedulers to schedule pods to that nodes
This can by achieved by using nodeAffinity or node taints (more here and here)
A complete solution may require additional apiserver admission controller to set all those properties to a pod that suppose to have a specific label only.
Unfortunately, there is no easy built-in solution as of yet. Hopefuly the above will point you in the right direction.
I am wondering if there is a way to set up dynamically environment variables on a scale depending on the high load.
Let's imagine that we have
Kubernetes with service called Generic Consumer which at the beginning have 4 pods. First of all
I would like to set that 75% of pods should have env variable Gold and 25% Platinium. Is that possible? (% can be changed to static number for example 3 nodes Gold, 1 Platinium)
Second question:
If Platinium pod is having a high load is there a way to configure Kubernetes/charts to scale only the Platinium and then decrease it after higher load subsided
So far I came up with creating 2 separate YAML files with diff env variables and replicas numbers.
Obviously, the whole purpose of this is to prioritize some topics
I have used this as a reference https://www.confluent.io/blog/prioritize-messages-in-kafka.
So in the above example, Generic Consumer would be the Kafka consumer which would use env variable to get bucket config
configs.put(ConsumerConfig.PARTITION_ASSIGNMENT_STRATEGY_CONFIG,
BucketPriorityAssignor.class.getName());
configs.put(BucketPriorityConfig.TOPIC_CONFIG, "orders-per-bucket");
configs.put(BucketPriorityConfig.BUCKETS_CONFIG, "Platinum, Gold");
configs.put(BucketPriorityConfig.ALLOCATION_CONFIG, "70%, 30%");
configs.put(BucketPriorityConfig.BUCKET_CONFIG, "Platinum");
consumer = new KafkaConsumer<>(configs);
If you have any alternatives, please let me know!
As was mention in comment section, the most versitale option (and probably the best for your scenario with prioritization) is to keep two separate deployments with gold and platinium labels.
Regarding first question, like #David Maze pointed, pods from Deployment are identical and you cannot have few pods with one label and few others with different. Even if you would create manually (3 pods with gold and 1 with platiunuim) you won't be able to use HPA.
This option allows you to adjustments depends on the situation. For example you would be able to scale one deployment using HPA and another with VPA or both with HPA. Would help you maintain budget, i.e for gold users you might limit to have maximum 5 pods to run simultaneously and for platinium you could set this maximum to 10.
You could consider Istio Traffic Management to routing request, but in my opinion, method with two separated deployments is more suitable.
If I use a preferredDuringSchedulingIgnoredDuringExecution rule within a podAffinity, is the score of each node computed based on the number of pods on that node that match my rule? That is, would a node that has two pods that match my rule be preferred to another node which has only one pod matching my rule?
The podAffinity's design proposal algorithm suggests that it does not depend on the number of pods:
Repeat Step 1a except replace the last line with "foreach node W of {N} having label [key=H.TopologyKey, value=any K such that M[K]>0], Y[W]++"
Is there a way to make a podAffinity rule that DOES depend on the number of pods in each node matching my rule?
Kubernetes mechanism tries to run as many pods as possible on one node. It hasn't a mechanism to calculate the number of pods with the same labels, it relies on resources.