Repository: kubernetes
Issue: apiserver/cacher: adjust a watch's chanSize 
Labels: kind/bug,sig/scalability,sig/api-machinery,triage/accepted

### What happened?

Each watch request to the cacher is assigned a buffer for watch events. The buffer size is calculated before serving the actual watch request and remains unchanged for the duration of the request. The buffer size is constrained to be between 10 and 1000 slots, depending on the current size of the history window and whether the resource has an indexer or not.

When the buffer is full and there is a new event for the given watch, the watch is terminated. This event is recorded in the `terminated_watchers_total' metric`.

In general, at least three distinct factors can lead to a full buffer: a slow client that cannot consume incoming events in time, an overwhelmed server that doesn't have enough CPU capacity for sending incoming events to a client, or a surge of updates for the given resource that can fill the buffer quickly.

Now, it is important to note that a terminated watch will be resumed from the RV at which the termination happened. In other words the watch will make progress at the cost of establishing a new request.


However, when updates for a given request are frequent or constant, watch requests might be terminated frequently. For example, the number of events that have occurred when the watch was terminated is significant (initial events) to the point that before we can send all these events, the buffer might have already been filled with new events.

(If you've encountered different scenarios, please share them with us)

### What did you expect to happen?

The question is whether we could employ a better strategy for the scenario mentioned above. For instance, by taking into account the update rate and watch latency, we could dynamically adjust the buffer size to accommodate new events. However, it's important to note that a larger buffer might result in increased memory consumption, necessitating the imposition of limits.

Alternatively, we could consider redesigning the cacher so that the buffer is calculated and assigned after sending the initial events. In this case, the buffer size would need to accommodate events that have occurred while sending the initial events.

Another potential approach would be to redesign the cacher so that a buffer is unnecessary, and instead, we store pointers to the store that holds the entire history of changes.

Or we could also suggest that customers fix the components generating frequent updates.

### How can we reproduce it (as minimally and precisely as possible)?

1. write a controller that frequently updates a resource.
2. start a watch for the resource.

### Anything else we need to know?

_No response_

### Kubernetes version

<details>
N/A
```console
$ kubectl version
# paste output here
```

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### Cloud provider

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N/A
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### OS version

<details>

```console
# On Linux:
$ cat /etc/os-release
# paste output here
$ uname -a
# paste output here

# On Windows:
C:\> wmic os get Caption, Version, BuildNumber, OSArchitecture
# paste output here
```

</details>


### Install tools

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### Container runtime (CRI) and version (if applicable)

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### Related plugins (CNI, CSI, ...) and versions (if applicable)

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