Author: Daniel Smith (Google)
Server-side apply (SSA) has now been GA for a few releases, and I have found myself in a number of conversations, recommending that people / teams in various situations use it. So I’d like to write down some of those reasons.
A list of improvements / niceties you get from switching from various things to Server-side apply!
- Versus client-side-apply (that is, plain
- The system gives you conflicts when you accidentally fight with another actor over the value of a field!
- When combined with
--dry-run, there’s no chance of accidentally running a client-side dry run instead of a server side dry run.
- Versus hand-rolling patches:
- The SSA patch format is extremely natural to write, with no weird syntax. It’s just a regular object, but you can (and should) omit any field you don’t care about.
- The old patch format (“strategic merge patch”) was ad-hoc and still has some bugs; JSON-patch and JSON merge-patch fail to handle some cases that are common in the Kubernetes API, namely lists with items that should be recursively merged based on a “name” or other identifying field.
- There’s also now great go-language library supportfor building apply calls programmatically!
- You can use SSA to explicitly delete fields you don’t “own” by setting them to
null, which makes it a feature-complete replacement for all of the old patch formats.
- Versus shelling out to kubectl:
- You can use the apply API call from any language without shelling out to kubectl!
- As stated above, the Go library has dedicated mechanismsto make this easy now.
- Versus GET-modify-PUT:
- (This one is more complicated and you can skip it if you've never written a controller!)
- To use GET-modify-PUT correctly, you have to handle and retry a write failure in the case that someone else has modified the object in any way between your GET and PUT. This is an “optimistic concurrency failure” when it happens.
- SSA offloads this task to the server– you only have to retry if there’s a conflict, and the conflicts you can get are all meaningful, like when you’re actually trying to take a field away from another actor in the system.
- To put it another way, if 10 actors do a GET-modify-PUT cycle at the same time, 9 will get an optimistic concurrency failure and have to retry, then 8, etc, for up to 50 total GET-PUT attempts in the worst case (that’s .5N^2 GET and PUT calls for N actors making simultaneous changes). If the actors are using SSA instead, and the changes don’t actually conflict over specific fields, then all the changes can go in in any order. Additionally, SSA changes can often be done without a GET call at all. That’s only N apply requests for N actors, which is a drastic improvement!
kubectl apply --server-side! Soon we (SIG API Machinery) hope to make this the default and remove the “client side” apply completely!
There’s two main categories here, but for both of them, you should probably_force conflicts_ when using SSA. This is because your controller probably doesn’t know what to do when some other entity in the system has a different desire than your controller about a particular field. (See the CI/CD section, though!)
This kind of controller GETs an object (possibly from awatch), modifies it, and then PUTs it back to write its changes. Sometimes it constructs a custom PATCH, but the semantics are the same. Most existing controllers (especially those in-tree) work like this.
If your controller is perfect, great! You don’t need to change it. But if you do want to change it, you can take advantage of the new client library’s _extract_workflow– that is, get the existing object, extract your existing desires, make modifications, and re- apply. For many controllers that were computing the smallest API changes possible, this will be a minor update to the existing implementation.
This workflow avoids the failure mode of accidentally trying to own every field in the object, which is what happens if you just GET the object, make changes, and then apply. (Note that the server will notice you did this and reject your change!)
This kind of controller wasn't really possible prior to SSA. The idea here is to (whenever something changes etc) reconstruct from scratch the fields of the object as the controller wishes them to be, and then apply the change to the server, letting it figure out the result. I now recommend that new controllers start out this way–it's less fiddly to say what you want an object to look like than it is to say how you want it to change.
The client library supports this method of operation by default.
The only downside is that you may end up sending unneeded apply requests to the API server, even if actually the object already matches your controller’s desires. This doesn't matter if it happens once in a while, but for extremely high-throughput controllers, it might cause a performance problem for the cluster–specifically, the API server. No-op writes are not written to storage (etcd) or broadcast to any watchers, so it’s not really that big of a deal. If you’re worried about this anyway, today you could use the method explained in the previous section, or you could still do it this way for now, and wait for an additional client-side mechanism to suppress zero-change applies.
To get around this downside, why not GET the object and only send your apply if the object needs it? Surprisingly, it doesn't help much – a no-op apply is not very much more work for the API server than an extra GET; and an apply that changes things is cheaper than that same apply with a preceding GET. Worse, since it is a distributed system, something could change between your GET and apply , invalidating your computation. Instead, you can use this optimization on an object retrieved from a cache–then it legitimately will reduce load on the system (at the cost of a delay when a change is needed and the cache is a bit behind).
Continuous integration (CI) and/or continuous deployment (CD) systems are a special kind of controller which is doing something like reading manifests from source control (such as a Git repo) and automatically pushing them into the cluster. Perhaps the CI / CD process first generates manifests from a template, then runs some tests, and then deploys a change. Typically, users are the entities pushing changes into source control, although that’s not necessarily always the case.
Some systems like this continuously reconcile with the cluster, others may only operate when a change is pushed to the source control system. The following considerations are important for both, but more so for the continuously reconciling kind.
CI/CD systems are literally controllers, but for the purpose of apply , they are more like users, and unlike other controllers, they need to pay attention to conflicts. Reasoning:
- Abstractly, CI/CD systems can change anything, which means they could conflict with any controller out there. The recommendation that controllers force conflicts is assuming that controllers change a limited number of things and you can be reasonably sure that they won’t fight with other controllers about those things; that’s clearly not the case for CI/CD controllers.
- Concrete example: imagine the CI/CD system wants
.spec.replicasfor some Deployment to be 3, because that is the value that is checked into source code; however there is also a HorizontalPodAutoscaler (HPA) that targets the same deployment. The HPA computes a target scale and decides that there should be 10 replicas. Which should win? I just said that most controllers–including the HPA–should ignore conflicts. The HPA has no idea if it has been enabled incorrectly, and the HPA has no convenient way of informing users of errors.
- The other common cause of a CI/CD system getting a conflict is probably when it is trying to overwrite a hot-fix (hand-rolled patch) placed there by a system admin / SRE / dev-on-call. You almost certainly don’t want to override that automatically.
- Of course, sometimes SRE makes an accidental change, or a dev makes an unauthorized change – those you do want to notice and overwrite; however, the CI/CD system can’t tell the difference between these last two cases.
Hopefully this convinces you that CI/CD systems need error paths–a way to back-propagate these conflict errors to humans; in fact, they should have this already, certainly continuous integration systems need some way to report that tests are failing. But maybe I can also say something about how humans can deal with errors:
Reject the hotfix: the (human) administrator of the CI/CD system observes the error, and manually force-applies the manifest in question. Then the CI/CD system will be able to apply the manifest successfully and become a co-owner.
Accept the hotfix: the author of the change in question sees the conflict, and edits their change to accept the value running in production.
Accept then reject: as in the accept option, but after that manifest is applied, and the CI/CD queue owns everything again (so no conflicts), re-apply the original manifest.
I can also imagine the CI/CD system permitting you to mark a manifest as “force conflicts” somehow– if there’s demand for this we could consider making a more standardized way to do this. A rigorous version of this which lets you declare exactly which conflicts you intend to force would require support from the API server; in lieu of that, you can make a second manifest with only that subset of fields.
Future work: we could imagine an especially advanced CI/CD system that could parse
metadata.managedFieldsdata to see who or what they are conflicting with, over what fields, and decide whether or not to ignore the conflict. In fact, this information is also presented in any conflict errors, though perhaps not in an easily machine-parseable format. We (SIG API Machinery) mostly didn't expect that people would want to take this approach — so we would love to know if in fact people want/need the features implied by this approach, such as the ability, when apply ing to request to override certain conflicts but not others.
Happy apply ing!