Assign mapped hostname from OTLP logs in logs agent pipeline

[liustanley]

What does this PR do?

This PR explicitly assigns the mapped hostname from our OTLP logs mapping to the logs agent Message object. This way, the logs agent pipeline will recognize the correct hostname for logs instead of using the Agent hostname:

datadog-agent/pkg/logs/processor/processor.go

Lines 291 to 311 in 5d954e9

	// GetHostname returns the hostname to applied the given log message
	func (p *Processor) GetHostname(msg *message.Message) string {
	if msg.Hostname != "" {
	return msg.Hostname
	}
	if msg.Lambda != nil {
	return msg.Lambda.ARN
	}
	if p.hostname == nil {
	return "unknown"
	}
	hname, err := p.hostname.Get(context.TODO())
	if err != nil {
	// this scenario is not likely to happen since
	// the agent cannot start without a hostname
	hname = "unknown"
	}
	return hname
	}

Motivation

Additional Notes

This doesn't change anything for Agent OTLP ingest or the Converged Agent because when the Agent is deployed as a Daemonset, the agent hostname is the same as the hostname obtained from the mapping. This will change how log hostnames are set with the OTel Collector when deployed in a Gateway model, so we'll add a release note in the upstream PR.

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

Tested locally and in e2e test suite. Will add an e2e test for this change in this PR #28866.

[pr-commenter]

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=43654217 --os-family=ubuntu

Note: This applies to commit 0a66651

[pr-commenter]

Regression Detector

Regression Detector Results

Run ID: 52690e95-9de1-49de-a5fb-0708d8abca53 Metrics dashboard Target profiles

Baseline: 9185aee
Comparison: 0a66651

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.

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	pycheck_lots_of_tags	% cpu utilization	+2.11	[-0.39, +4.60]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+1.88	[+1.09, +2.67]	1	Logs
➖	idle	memory utilization	+0.29	[+0.26, +0.33]	1	Logs
➖	file_tree	memory utilization	+0.28	[+0.23, +0.32]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.01, +0.01]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	1	Logs
➖	basic_py_check	% cpu utilization	-0.04	[-2.72, +2.65]	1	Logs
➖	otel_to_otel_logs	ingress throughput	-0.59	[-1.40, +0.22]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-5.69	[-18.19, +6.81]	1	Logs

Bounds Checks

perf	experiment	bounds_check_name	replicates_passed
✅	idle	memory_usage	10/10

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".

[mackjmr]

Does this still warrant QA instructions for a manual local test, or are we deferring to the E2E tests for QA ?

[liustanley]

Does this still warrant QA instructions for a manual local test, or are we deferring to the E2E tests for QA ?

I completed a manual local test, and will also add an explicit E2E test in #29091

[liustanley]

/merge

[dd-devflow]

🚂 MergeQueue: pull request added to the queue

The median merge time in main is 23m.

Use /merge -c to cancel this operation!