Upgrade(metadata): Include Agent’s startup timestamp in host metadata payload

[misteriaud]

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What does this PR do?

Adding the Agent startup timestamp (Unix nanoseconds timestamp format) to the inventory hostMetadata payload.

Motivation

Useful value in the context of improving Fleet Automation. https://datadoghq.atlassian.net/browse/ASCII-1132

Additional Notes

Possible Drawbacks / Trade-offs

None that I know of.

Describe how to test/QA your changes

• The best way to test this PR would be to see how the datadog backend ingest the new inventory host metadata payload sent from the Agent, but for this we need to wait for the backend to implement upgrade to be able to process it.
• Until then, it is possible to print the newly agent_startup_time field value by running the following command: ./bin/agent/agent diagnose show-metadata inventory-host -c ./bin/agent/dist/datadog.yaml

[bits-bot]

All committers have signed the CLA.

[pr-commenter[bot]]

Bloop Bleep... Dogbot Here

Regression Detector Results

Run ID: 4acdf748-fc4a-4a21-b9b2-74f9af70e21a Baseline: c6b52ff2d46479df800861fe1d2991affc430d1a Comparison: 14bc61116d434bd6d77fea250ff458bf59f04d11

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	-0.48	[-7.03, +6.07]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	process_agent_standard_check_with_stats	memory utilization	+0.17	[+0.14, +0.20]
➖	process_agent_real_time_mode	memory utilization	+0.09	[+0.05, +0.12]
➖	trace_agent_json	ingress throughput	+0.02	[-0.01, +0.05]
➖	trace_agent_msgpack	ingress throughput	+0.02	[+0.01, +0.02]
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.20, +0.20]
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.00]
➖	otel_to_otel_logs	ingress throughput	-0.11	[-0.54, +0.32]
➖	file_tree	memory utilization	-0.17	[-0.23, -0.11]
➖	process_agent_standard_check	memory utilization	-0.19	[-0.23, -0.16]
➖	idle	memory utilization	-0.20	[-0.23, -0.16]
➖	basic_py_check	% cpu utilization	-0.24	[-2.40, +1.93]
➖	file_to_blackhole	% cpu utilization	-0.48	[-7.03, +6.07]
➖	tcp_syslog_to_blackhole	ingress throughput	-0.75	[-0.84, -0.65]
➖	pycheck_1000_100byte_tags	% cpu utilization	-0.84	[-6.04, +4.35]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-1.48	[-4.36, +1.40]

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".

[pr-commenter[bot]]

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv create-vm --pipeline-id=30269608 --os-family=ubuntu

[pr-commenter[bot]]

Regression Detector

Regression Detector Results

Run ID: e14ad468-f60a-49ab-b547-a2e3dfa4d23b Baseline: 28545c52d0ebf1b1039d95236c7a794f87165552 Comparison: ce220ead5e20a7702c05f6ebe495336406b8136e

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	+0.00	[-6.35, +6.36]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.72	[-2.02, +3.47]
➖	pycheck_1000_100byte_tags	% cpu utilization	+0.49	[-4.39, +5.37]
➖	basic_py_check	% cpu utilization	+0.03	[-2.46, +2.52]
➖	process_agent_real_time_mode	memory utilization	+0.02	[-0.02, +0.05]
➖	trace_agent_msgpack	ingress throughput	+0.02	[+0.01, +0.03]
➖	file_to_blackhole	% cpu utilization	+0.00	[-6.35, +6.36]
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.20, +0.20]
➖	trace_agent_json	ingress throughput	-0.01	[-0.03, +0.01]
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.02	[-0.06, +0.02]
➖	process_agent_standard_check	memory utilization	-0.04	[-0.07, -0.01]
➖	process_agent_standard_check_with_stats	memory utilization	-0.07	[-0.11, -0.04]
➖	otel_to_otel_logs	ingress throughput	-0.07	[-0.50, +0.35]
➖	file_tree	memory utilization	-0.11	[-0.21, -0.01]
➖	tcp_syslog_to_blackhole	ingress throughput	-0.23	[-0.32, -0.15]
➖	idle	memory utilization	-0.29	[-0.32, -0.25]

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".

[misteriaud]

/merge

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