jiebi/CodeConvo huggingface dataset:
This issue is to discuss an e2e testing plan for Ephemeral Containers. /sig node
As a first step towards this issue, I am planning to create node-e2e tests (under ) to verify the following: With a running pod client adds a valid EphemeralContainer resource, and the ephemeral container starts from the ephemeral container, list processes of itself and other containers, and then signal other processes (if is ) from the ephemeral container, ping & curl other containers try to read/write to stdin/stdout of the ephemeral container verify resource request/limit values against calculated expectations check oom scores of processes in the ephemeral container verify volume mounts restarts according to policy, when process exits client tries to add EphemeralContainer resource with ports (should be rejected) client tries to add EphemeralContainer resource with probes (should be rejected) client tries to add EphemeralContainer resource with resources (should be rejected) client tries to add EphemeralContainer resource with securityContext (should be rejected) removes the ephemeral container, and the ephemeral container is stopped and deleted adds multiple ephemeral containers, should all startWith pod of non-running status (TBD)
Awesome, thanks! This is a great start, here are some things to look out for: Visibility should work, but I expect signaling to fail without which is not currently allowed. iirc was interested in how this would be tested for ephemeral containers. Can you point us towards any examples that would help us get started? Ephemeral containers will never restart (and the policy should always be never) These will be denied by the api server without ever making it to the kubelet, so they are better tested in the apiserver integration tests (and I think they are already). Removing ephemeral containers is not currently allowed. /assign
Validation of ephemeralContainers is well tested in Testing of removing ephemeral containers is in ,
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/reopen /assign /priority important-soon
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/reopen
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/reopen /remove-lifecycle rotten
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<!-- Please use this template while reporting a bug and provide as much info as possible. Not doing so may result in your bug not being addressed in a timely manner. Thanks! If the matter is security related, please disclose it privately via --What happened: Conformance test failed: xref What you expected to happen: I expected this conformance test to pass. How to reproduce it (as minimally and precisely as possible): We have configured docker's logging driver to use journald, and then ran sonobouy tests against a k8s 1.16.3 cluster. Anything else we need to know?: Environment: Kubernetes version (use ):Cloud provider or hardware configuration: OS (e.g: ): Red Hat Enterprise Linux Server release 7.7 (Maipo) Kernel (e.g. ): 5.3.11- SMP Tue Nov 12 14:45:59 EST 2019 x8664 x8664 x8664 GNU/Linux Install tools: Network plugin and version (if this is a network-related bug): Others:
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/assign
/sig cli
/assign
I have a working patch that fixes this, will push a PR shortly.
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/sig release
<!-- Please use this template while reporting a bug and provide as much info as possible. Not doing so may result in your bug not being addressed in a timely manner. Thanks!--What happened: I'm running a K8s 1.13 cluster and a partial netsplit happened due to a defective linecard dropping only TCP packets above a certain sizes between two masters, but the third could still communicate with both (so the fault made the network non-transitive). etcd was run on the physical network, but Kubernetes traffic runs on a Wireguard overlay for security and routing is provided by OSPF (so internal communication was not affected since it was tunelled over UDP). Etcd experienced some temporary issues due to the connections dropping only certain packets (above ~1000 bytes), but quickly dropped one host out of the quorum, at which point it was degraded but all the remaining hosts could communicate freely. Each master node has an etcd instance and an apiserver. All apiservers are anycasted inside the overlay to balance the load and provide redundancy and the announcement is coupled to the health check, so in case any apiserver fails no traffic will be routed to it. The problem was that the apiserver did not fail its healthcheck even though all etcd write requests timed out. The non-working apiserver was still accessible and got routed its share of the anycast traffic since that was routed over the UDP overlay. Thus ~1/3 of all workloads and nodes were stuck on a non-working apiserver until I nuked the non-working apiserver. TLDR: One etcd server started timing out all writes due to a non-transitive netsplit and the Kubernetes apiserver healthcheck did not fail, causing 1/3 of K8s clients to not have a working control plane until manual intervention. What you expected to happen: The health check should've failed (in my opinion, I'm happy to discuss if you think otherwise). How to reproduce it (as minimally and precisely as possible): Set up 3 etcds with corresponding apiservers, introduce a split between two (shouldn't have to be a packet size limit, a full disconnect should reproduce cleanly), observe that all three report healthy even though at least one of them no longer works. Anything else we need to know?
: Environment: Kubernetes version (use ): v1.13.0 Cloud provider or hardware configuration: Physical x86_64 servers OS (e.g. from /etc/os-release): Custom Kernel (e.g. ): Custom, based on 4.19. No patches affect this functionality. Install tools: Custom Others:
/sig api-machinery (maybe cluster-ops?)
cc
Looks like the etcdctl endpoint health command does a random read after fetching the list of members - - we should probably do the same?
Looks sane and would have prevented my issue. Should I PR this?
sure. thanks
/assign
In 1.8, we a new feature to expose PVC information through summary API so user can check the volume belongs to which PVC. We should add e2e test in under test/e2enode.
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/assign
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Can we please move this out of 1.8 milestone?
- I'm looking into adding this but yes we should move this out of 1.8. I don't have permissions to do that though.
ack Can we please move this out of 1.8?
Sorry am not sure why was this removed from 1.8 milestone. Are we not aiming to add the tests in 1.8? Or Are we disabling the feature or are we fine shipping it without any e2e tests in 1.8?
The test is in . I think we can keep this feature in 1.8
Is this a BUG REPORT or FEATURE REQUEST?: /kind feature What happened: We are starting to use the HPA (horizontal pod autoscaling) for one of our components. What is special about this particular component is that the warm up period is very long (sometimes even more than 30 minutes). During that time, the pod is in Running state but its containers are in Not Ready state. This pod is of course not able to receive any traffic so the metric we use in the HPA is ignoring those pods that are not Ready. The equation used by HPA to calculate the new number of replicas is as follows Our problem is that the equation used by HPA to calculate the new number of replicas is including in the "current replica count" the pods that are "Not Ready" so the value for "new replica count" is not correct. Since this warm up is so long we end up by having a lot of more instances of our services of what it should be necessary. They eventually get deleted once gradually the metrics start going down but that will take a lot of time and a big waste of resources without mentioning that with a changing traffic this HPA will never stabilize What you expected to happen: I would like to have a flag somewhere where I tell HPA to don't include in the "current replica count" the pods that are in "Not Ready" state How to reproduce it (as minimally and precisely as possible): Have a component that takes a lot of time to start and being able to receive traffic. Then define a metric that will ignore the pods that are Not Ready. Finally create a metric that will scale that component based on the mentioned metric Anything else we need to know?: Environment: Kubernetes version 1.7 Cloud provider or hardware configuration: Azure
/sig autoscaling
Hi - thanks for reaching out about this! It looks like this section of the , handles when pods aren't ready. I think you this behavior in , so I'm interested in your thoughts on whats best here. If we do end up adding a flag for this, I'd be happy to write the code :) Also, purely for my own curiosity, I'm curious what type of pod is taking 30 minutes to become ready. If your traffic pattern varies at a rate faster than 30 minutes, it may be difficult for auto-scaling to be helpful.
can you also share a bit more about your HPA?
What kind of metric type is it? Based on the description I'm suspecting that it is an Object metric, am I right?
So answer about why our process takes that much time to start. Basically this is a server that in order to do what he needs to do, he needs to load a lot of data in memory (several GBs) and then perform some warm up that are pretty intensive. The idea is that he doesn't need to grab (almost) anything from outside to attend a request so it's latency is extremely low (in the order of millisecond) By the way. Because of this we have another problem which is that there is not an specific flag per HPA object that we could tell k8s to don't try to scale up again passed some time so the new pods have enough time to get ready before the next scale happens. I know that there is a cluster wide flag but we will need to have a different one by resource that we scale. Now to answer You pointed another interesting topic. The metric we use is % Utilization. We are able to calculate the level of utilization of our pods and we want to keep this metric in an specific value. About the flag. I suggested the flag so it doesn't change the existing behavior by default but I honestly think that the default behavior should be to don't include the NonReady pods as part of the calculation
do you think we can add this change to 1.10? it's critical bug/feature for us and we are happy to help with code/testing.
It's really late for 1.10, especially since we don't have any code here, and I'd like to know more about what's going on here, and especially see if it reproduces on Kubernetes 1.9 (we've had HPA controller bugs in the past). FWIW, currently unready pods are treated as consuming 0% of their utilization on scale up, and it was intended that this should prevent such issues. That is, the formula is actually: If the pods are marked as not ready, I find it strange that they would be affecting the scaling that much, unless your other pods are under real heavy load (i.e. I would assume that the unready pods would skew the scaling back down so that the number of pods just stayed constant).
I believe there is different code branch here.
metric definition { "type": "Object", "object": { "target": { "kind": "Service", "name": "prometheus-exporter" }, "metricName": "PercentUtilization", "targetValue": "20" } } which should handle as and doesn't check Pod state.
I think proper handling of unready pods is a difficult problem that will require significant work. I suspect we may find out we need to start distinguishing pods that are booting up from pods that are 'broken' and it took us quite a while to solve analogous problem with nodes in Cluster Autoscaler (to be clear: I don't know if we will have to do that, that's just my intuition for now). That being said I agree the formula in GetObjectMetricReplicas will generally lead to scaling-up too much if the pod takes longer to initialize then backoff period in HPA (3 minutes). Assume it takes pod 4 minutes to boot up and we're currently running 2 pods. Metric value is 20 and target is 10. In this case: scales-up to 4 pods (20/10 utilization 2 pods). are starting up, they're not doing anything yet. 3 minutes later HPA scales-up to 8 pods (20/10 utilization 4 pods). If we only counted ready pods the scale-up in 3 would not occur, because the desired replicas would still be 4 (20/10 * 2). My intuition would be that this is the expected behavior of HPA. Also if there is no space in the cluster we will just keep scaling-up until we hit max replicas. The only case where current behavior would be more correct than filtering out unready pods in this formula is if someone uses a metric that is calculated based on total number of replicas. Do we advertise that as the right way to use HPA? If we add to Object metrics in 1.11 this usage pattern will stop making sense. To sum up - I think we will need to change the formula to only count ready pods at some point. I can see how current behavior could be a problem in any use-case except the one described in paragraph above. I'm inclined to agree with and provide some way to change the behavior in 1.10 (a flag or an annotation?), treating it as a bug fix.
WDYT
Ah, yeah, I missed that it was object metrics before. I'm not entirely convinced we need a flag here -- it might just make sense to bring Object metrics in line with all the other metrics types. On the other hand, there's a subtlety here -- the other metric types don't quite ignore unready pods -- they just ignore the metrics from unready pods, which causes a heavy bias in the direction of not scaling, but an overwhelming spike can still cause scaling. That behavior is not as achievable using object metrics. I'm inclined to just ignore that subtlety, though, and say that object metrics should ignore unready pods entirely.
I think this is what we should do long term. I was worried about breaking someone who relies on targetValue + averaged metric to get effect similar to targetAverageValue and though about providing something for transitional period, but if you're ok with it I'd rather just change it as you say.
I think it's safe to change as-is -- that feature's still in beta, and it's not an API change, so it shouldn't just hard-break anyone. Plus, we've change stuff like this before (when we did the initial resource metrics change).
As said, we are more than ready to help with the fix/test if required. This since this is a major blocking for us in the implementation of the HPA in our side
is anyone actively working on implementing this? If not, I'm happy to. I think I have a fairly clear picture of the change we need to make and will likely have time early this week to get it done.
(Also, from the conversation above, to we want to tag this as a release blocker for 1.10)?
I probably won't be able to work on this before the end of this week. Happy to do a review if you jump in and implement it.
I don't think this qualifies as a release blocking bug (but I'm willing to have someone try and convince me otherwise ;-)). There doesn't seem to be any behavior here that's explicitly broken -- it's just sub-optimal in some use cases. I'd definitely like to address this in 1.11, though.
it's not release blocker, but to share our experience ;).
without this feature we cannot use HPA as is and we need to figure out how to build/run fork (which can be challenging because everything inside controller-manager). also we are planning to upgrade to 1.10 quickly, but not sure for 1.11 (e.g. we are two versions behind now).
Sounds like a plan :) Will try and get this done tomorrow morning!
- I submitted a pr for this - would love your eyes on it so you can see if you think it meets your use case :)
/assign
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I checked the code and it seems good to me. About the comment about scaling down if the readyPods<CurrentPods, we should be fine since our metric is already not including the pods that are not ready.
Is there any advance in this ticket? I'm wondering if this made it to k8s 1.10 or if we need to wait for k8s 1.11?
The PR is lgtm-ed and it will merge once the code freeze is lifted. Meaning it will go to 1.11. To get it into 1.10 you would need to get approval for milestone from (sig-autoscaling lead).
Is this a BUG REPORT or FEATURE REQUEST?: Bug Report /kind bug What happened: Applying a file that has no changes compared to current will output something like , which muddles the output when using What you expected to happen: Using the -o flag should suppress the unchanged messages in stdout to prevent output from being muddled How to reproduce it (as minimally and precisely as possible): Given the file Will result in proper yaml being generated as expected Run again, and is outputted to stdout, and is invalid yaml Anything else we need to know?: I do need to check whether this still affects the most recent version kubectl Environment: Kubernetes version (use ): 1.8.6 Cloud provider or hardware configuration: OS (e.g. from /etc/os-release): Kernel (e.g. ): Darwin Install tools: Others:
confirmed on latest stable version (1.9.2)
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I have a solution to this, still waiting on my company to look over the agreement