[corechecks/ksm] Support HPA v2beta2 again

[davidor]

What does this PR do?

Fixes the KSM check to support HPA v2beta2 again.

Support for HPA v2beta2 was broken in agent v7.44 when the kube-state-metrics dependency was upgraded (#15623)

The old kube-state-metrics version that we used only supported HPA v2beta2, so we added some custom code to also support HPA v2. The kube-state-metrics version that we're using now only support v2, so this time we need to add some custom code to support HPA v2beta2.

Fixes #17843

Describe how to test/QA your changes

Deploy the agent with the kubernetes state metrics check enabled. Deploy an HPA. Verify that the kubernetes_state.hpa.* metrics are reported both in a recent version of kubernetes (>= 1.23) with HPA v2 and an older version of kubernetes (< 1.23) with HPA v2beta2.

Reviewer's Checklist

• If known, an appropriate milestone has been selected; otherwise the Triage milestone is set.
• Use the major_change label if your change either has a major impact on the code base, is impacting multiple teams or is changing important well-established internals of the Agent. This label will be use during QA to make sure each team pay extra attention to the changed behavior. For any customer facing change use a releasenote.
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• If applicable, the k8s/<min-version> label, indicating the lowest Kubernetes version compatible with this feature.
• If applicable, the config template has been updated.

[clamoriniere]

should we also support v2beta1 ? and v1?

[davidor]

We could address that separately if needed. This PR only fixes the regression introduced in 7.44.

Just to clarify, metrics for HPA v2beta1 objects are still reported in clusters that support v2beta2.

[pr-commenter]

Bloop Bleep... Dogbot Here

Regression Detector Results

Run ID: f2630b46-5ad2-470d-a209-859d0a724b77
Baseline: 1ff721e
Comparison: ac454f4
Total datadog-agent CPUs: 7

Explanation

A regression test is an integrated performance test for datadog-agent in a repeatable rig, with varying configuration for datadog-agent. What follows is a statistical summary of a brief datadog-agent run for each configuration across SHAs given above. The goal of these tests are to determine quickly if datadog-agent performance is changed and to what degree by a pull request.

Because a target's optimization goal performance in each experiment will vary somewhat each time it is run, we can only estimate mean differences in optimization goal relative to the baseline target. We express these differences as a percentage change relative to the baseline target, denoted "Δ mean %". These estimates are made to a precision that balances accuracy and cost control. We represent this precision as a 90.00% confidence interval denoted "Δ mean % CI": there is a 90.00% chance that the true value of "Δ mean %" is in that interval.

We decide whether a change in performance is a "regression" -- a change worth investigating further -- if both of the following two criteria are true:

1. The estimated |Δ mean %| ≥ 5.00%. This criterion intends to answer the question "Does the estimated change in mean optimization goal performance have a meaningful impact on your customers?". We assume that when |Δ mean %| < 5.00%, the impact on your customers is not meaningful. We also assume that a performance change in optimization goal is worth investigating whether it is an increase or decrease, so long as the magnitude of the change is sufficiently large.

2. Zero is not in the 90.00% confidence interval "Δ mean % CI" about "Δ mean %". This statement is equivalent to saying that there is at least a 90.00% chance that the mean difference in optimization goal is not zero. This criterion intends to answer the question, "Is there a statistically significant difference in mean optimization goal performance?". It also means there is no more than a 10.00% chance this criterion reports a statistically significant difference when the true difference in mean optimization goal is zero -- a "false positive". We assume you are willing to accept a 10.00% chance of inaccurately detecting a change in performance when no true difference exists.

The table below, if present, lists those experiments that have experienced a statistically significant change in mean optimization goal performance between baseline and comparison SHAs with 90.00% confidence OR have been detected as newly erratic. Negative values of "Δ mean %" mean that baseline is faster, whereas positive values of "Δ mean %" mean that comparison is faster. Results that do not exhibit more than a ±5.00% change in their mean optimization goal are discarded. An experiment is erratic if its coefficient of variation is greater than 0.1. The abbreviated table will be omitted if no interesting change is observed.

No interesting changes in experiment optimization goals with confidence ≥ 90.00% and |Δ mean %| ≥ 5.00%.

Fine details of change detection per experiment.

experiment	goal	Δ mean %	Δ mean % CI	confidence
tcp_syslog_to_blackhole	ingress throughput	+0.55	[+0.46, +0.65]	100.00%
otel_to_otel_logs	ingress throughput	+0.26	[-0.33, +0.84]	42.56%
trace_agent_json	ingress throughput	+0.01	[-0.03, +0.05]	22.48%
file_to_blackhole	egress throughput	+0.00	[-0.95, +0.95]	0.00%
tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	2.38%
trace_agent_msgpack	ingress throughput	-0.04	[-0.08, +0.01]	72.83%
uds_dogstatsd_to_api	ingress throughput	-1.06	[-1.88, -0.25]	90.50%

[brett0000FF]

LGTM. Just two small capitalization suggestions for "Agent".