[Backport 7.61.x] Fix converter

[agent-platform-auto-pr]

Backport 0200b9d from #31487.

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What does this PR do?

This PR brings fixes to the converter component. Fixes included are:

Setting col config from core config

• Previously, the logic to set col config from core config would look for exporter datadog in the exporter list. This is incorrect in two ways: the name is not necessarely datadog (can be datadog/anything), and there can be multiple datadog exporters. Now the updated logic checks for prefix datadog, and will also apply the logic if there are multiple datadog exporters.
• Previously, the logic would not be applied for site and or key if those sections were there but the value was unset. If the sections are unset, this is reason more to apply the settings from the core agent. Now the updated logic will add the site and key from core configs even if the section is not present in the config.

Prometheus

Previously, the prometheus logic would check if prometheus receiver was in same pipeline as DD exporter. If so, it wouldn't add it. It would exit out at first DD exporter. Now, this logic gets added to all DD exporters.

Motivation

Describe how to test/QA your changes

API Key

Unset:
Set API key in agent config:

api_key:12345

but not in col config:

exporters:
  datadog:
    api:
      key:

Ensure API Key is added

Missing section:
Set API key in agent config:

api_key:12345

but not in col config:

exporters:
  datadog:

Ensure API Key is added

Empty string:
Set API key in agent config:

api_key:12345

but not in col config:

exporters:
  datadog:
    api:
      key: ""

Ensure API Key is added

Site

Do the same as above but with site

Overrides

Set api key and site in agent config, and set api key and site in col config to different values. Ensure the col config is not overriden.

Prometheus

Configure collector with multiple DD exporters and prometheus receiver, and ensure each DD exporter is in a pipeline with DD exporter :

    prometheus/user-defined:
      config:
        scrape_configs:
          - job_name: 'datadog-agent'
            scrape_interval: 10s
            static_configs:
              - targets: ['0.0.0.0:8888']

exporters:
    datadog:
      api:
        key: 12345
    datadog/2:
      api:
        key: 12345

        metrics:
            receivers: [nop, prometheus/user-defined]
            processors: [infraattributes/user-defined]
            exporters: [datadog]
        metrics/2:
            receivers: [prometheus/user-defined]
            processors: [infraattributes/user-defined]
            exporters: [datadog/2]

Ensure that no additional prom receivers/ pipelines are added to your config.

In order to validate the used config, you can go to localhost:7777 and look for the enhanced config.

Possible Drawbacks / Trade-offs

Additional Notes

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• Anything else we should know when reviewing?
• Include benchmarking information here whenever possible.
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[agent-platform-auto-pr]

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=50485305 --os-family=ubuntu

Note: This applies to commit 196ffb5

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 00f2c37-e196-48d3-878b-f658aaa6804b

Baseline: ed82389
Comparison: 196ffb5
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	basic_py_check	% cpu utilization	+2.82	[-1.04, +6.67]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+2.09	[+1.36, +2.81]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	+0.68	[-2.79, +4.15]	1	Logs
➖	file_tree	memory utilization	+0.59	[+0.44, +0.73]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+0.55	[+0.49, +0.60]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	+0.54	[+0.45, +0.63]	1	Logs bounds checks dashboard
➖	file_to_blackhole_0ms_latency	egress throughput	+0.08	[-0.81, +0.98]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.08	[-0.67, +0.84]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	+0.08	[-0.38, +0.54]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.00	[-0.77, +0.77]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.06, +0.06]	1	Logs
➖	quality_gate_idle	memory utilization	-0.12	[-0.17, -0.07]	1	Logs bounds checks dashboard
➖	file_to_blackhole_300ms_latency	egress throughput	-0.20	[-0.84, +0.43]	1	Logs
➖	quality_gate_logs	% cpu utilization	-0.26	[-3.17, +2.66]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.26	[-1.03, +0.51]	1	Logs
➖	otel_to_otel_logs	ingress throughput	-0.57	[-1.26, +0.13]	1	Logs

Bounds Checks: ❌ Failed

perf	experiment	bounds_check_name	replicates_passed	links
❌	file_to_blackhole_0ms_latency	lost_bytes	9/10
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency_linear_load	memory_usage	10/10
✅	file_to_blackhole_100ms_latency	lost_bytes	10/10
✅	file_to_blackhole_100ms_latency	memory_usage	10/10
✅	file_to_blackhole_300ms_latency	lost_bytes	10/10
✅	file_to_blackhole_300ms_latency	memory_usage	10/10
✅	file_to_blackhole_500ms_latency	lost_bytes	10/10
✅	file_to_blackhole_500ms_latency	memory_usage	10/10
✅	quality_gate_idle	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_logs	lost_bytes	10/10
✅	quality_gate_logs	memory_usage	10/10

Explanation

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

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

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".

CI Pass/Fail Decision

✅ Passed. All Quality Gates passed.

• quality_gate_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.