[ASCII-1281] Ensure host metadata information information is sent first

[GustavoCaso]

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What does this PR do?

We add a new MetadataPriorityProvider. This allow us to ensure the host metadata information is sent synchronously at start time. This is a workaround to an issue in the backend regarding Metrics Tags Delay

Motivation

Workaround for incident https://app.datadoghq.com/incidents/25790

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

Ensure agent metrics have tags are present from the beginning. You can add system tags into the datadog configuration in the tags . We can add custom tags in the datadog.yaml file Ex. tags: ["foo:bar", "hello:world"]

To test that metrics do not have N/A tags. Run the agent with some custom tags.

1. Ensure your agent shows in the UI under the infrastructure list
2. Go to the metrics explored and validate that when querying for our custom tags we do not get nil values

💡 In the screenshot below we can compare two agent running. One with the fix the other running from main. We can see the one in main we see metrics with tags N/A

[pr-commenter[bot]]

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Regression Detector Results

Run ID: 7fb66c4d-f553-4455-803d-c2957b365dfb Baseline: d6a7e7927b96c58b1ca37d5a45453a1a457e3958 Comparison: 41f54110ca394dab4cc2c2d03462acbfe42dd7ac

Performance changes are noted in the perf column of each table:

• ✅ = significantly better comparison variant performance
• ❌ = significantly worse comparison variant performance
• ➖ = no significant change in performance

No significant changes in experiment optimization goals

Confidence level: 90.00% Effect size tolerance: |Δ mean %| ≥ 5.00%

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	file_to_blackhole	% cpu utilization	-2.69	[-9.16, +3.79]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	otel_to_otel_logs	ingress throughput	+1.08	[+0.42, +1.75]
➖	basic_py_check	% cpu utilization	+0.90	[-1.35, +3.15]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.61	[-0.80, +2.02]
➖	process_agent_standard_check_with_stats	memory utilization	+0.47	[+0.43, +0.52]
➖	idle	memory utilization	+0.46	[+0.41, +0.50]
➖	file_tree	memory utilization	+0.37	[+0.28, +0.46]
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.00, +0.00]
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.00, +0.00]
➖	trace_agent_json	ingress throughput	-0.00	[-0.03, +0.03]
➖	trace_agent_msgpack	ingress throughput	-0.00	[-0.01, +0.00]
➖	tcp_syslog_to_blackhole	ingress throughput	-0.01	[-0.07, +0.05]
➖	process_agent_real_time_mode	memory utilization	-0.44	[-0.49, -0.40]
➖	process_agent_standard_check	memory utilization	-1.14	[-1.19, -1.09]
➖	file_to_blackhole	% cpu utilization	-2.69	[-9.16, +3.79]

Explanation

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".

For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.

3. Its configuration does not mark it "erratic".

[GustavoCaso]

/merge

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