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Fix otel config with nodetreemodel implementation - #42031

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maixme/fix-otel-config-init
Oct 21, 2025
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Fix otel config with nodetreemodel implementation#42031
dd-mergequeue[bot] merged 1 commit into
mainfrom
maixme/fix-otel-config-init

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@hush-hush

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What does this PR do?

Not using the default config means doing the init steps manually

Describe how you validated your changes

opentelemetry-agent need to validate the change with manual QA.

Not using the default config means doing the init steps manually
@hush-hush
hush-hush requested a review from a team as a code owner October 16, 2025 10:30
@hush-hush
hush-hush requested a review from liustanley October 16, 2025 10:30
@hush-hush hush-hush added changelog/no-changelog No changelog entry needed qa/rc-required Only for a PR that requires validation on the Release Candidate team/opentelemetry-agent labels Oct 16, 2025
@github-actions github-actions Bot added the short review PR is simple enough to be reviewed quickly label Oct 16, 2025
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Static quality checks

✅ Please find below the results from static quality gates
Comparison made with ancestor bf4f327

Successful checks

Info

Quality gate Delta On disk size (MiB) Delta On wire size (MiB)
agent_deb_amd64 $${+0}$$ $${674.78}$$ < $${702.15}$$ $${+0.01}$$ $${164.65}$$ < $${177.79}$$
agent_deb_amd64_fips $${+0}$$ $${669.28}$$ < $${696.65}$$ $${+0.02}$$ $${163.85}$$ < $${176.53}$$
agent_heroku_amd64 $${+0}$$ $${336.54}$$ < $${340.18}$$ $${+0}$$ $${89.83}$$ < $${91.08}$$
agent_msi $${+0}$$ $${1013.54}$$ < $${1015.38}$$ $${0}$$ $${148.41}$$ < $${150.78}$$
agent_rpm_amd64 $${+0}$$ $${674.77}$$ < $${702.14}$$ $${-0.01}$$ $${166.98}$$ < $${180.53}$$
agent_rpm_amd64_fips $${+0}$$ $${669.27}$$ < $${696.64}$$ $${+0.01}$$ $${165.52}$$ < $${178.79}$$
agent_rpm_arm64 $${+0}$$ $${664.29}$$ < $${686.31}$$ $${-0.03}$$ $${153.19}$$ < $${161.22}$$
agent_rpm_arm64_fips $${+0}$$ $${659.8}$$ < $${681.84}$$ $${+0.05}$$ $${152.71}$$ < $${160.18}$$
agent_suse_amd64 $${+0}$$ $${674.77}$$ < $${702.14}$$ $${-0.01}$$ $${166.98}$$ < $${180.53}$$
agent_suse_amd64_fips $${+0}$$ $${669.27}$$ < $${696.64}$$ $${+0.01}$$ $${165.52}$$ < $${178.79}$$
agent_suse_arm64 $${+0}$$ $${664.29}$$ < $${686.31}$$ $${-0.03}$$ $${153.19}$$ < $${161.22}$$
agent_suse_arm64_fips $${+0}$$ $${659.8}$$ < $${681.84}$$ $${+0.05}$$ $${152.71}$$ < $${160.18}$$
docker_agent_amd64 $${+0}$$ $${746.2}$$ < $${773.59}$$ $${+0}$$ $${251.8}$$ < $${266.06}$$
docker_agent_arm64 $${+0}$$ $${759.68}$$ < $${781.7}$$ $${+0}$$ $${242.4}$$ < $${251.85}$$
docker_agent_jmx_amd64 $${+0}$$ $${937.07}$$ < $${964.45}$$ $${+0}$$ $${320.42}$$ < $${334.68}$$
docker_agent_jmx_arm64 $${+0}$$ $${939.14}$$ < $${961.17}$$ $${-0}$$ $${307.01}$$ < $${316.44}$$
docker_cluster_agent_amd64 $${0}$$ $${213.01}$$ < $${213.74}$$ $${-0}$$ $${72.26}$$ < $${73.14}$$
docker_cluster_agent_arm64 $${+0}$$ $${228.92}$$ < $${229.68}$$ $${+0}$$ $${68.53}$$ < $${69.41}$$
docker_cws_instrumentation_amd64 $${0}$$ $${7.07}$$ < $${7.12}$$ $${-0}$$ $${2.95}$$ < $${3.29}$$
docker_cws_instrumentation_arm64 $${0}$$ $${6.69}$$ < $${6.92}$$ $${-0}$$ $${2.7}$$ < $${3.07}$$
docker_dogstatsd_amd64 $${0}$$ $${38.44}$$ < $${39.3}$$ $${+0}$$ $${14.84}$$ < $${15.76}$$
docker_dogstatsd_arm64 $${0}$$ $${37.13}$$ < $${37.94}$$ $${+0}$$ $${14.29}$$ < $${14.83}$$
dogstatsd_deb_amd64 $${0}$$ $${29.66}$$ < $${30.53}$$ $${-0}$$ $${7.82}$$ < $${8.75}$$
dogstatsd_deb_arm64 $${0}$$ $${28.24}$$ < $${29.11}$$ $${+0}$$ $${6.77}$$ < $${7.71}$$
dogstatsd_rpm_amd64 $${0}$$ $${29.66}$$ < $${30.53}$$ $${+0}$$ $${7.83}$$ < $${8.76}$$
dogstatsd_suse_amd64 $${0}$$ $${29.66}$$ < $${30.53}$$ $${+0}$$ $${7.83}$$ < $${8.76}$$
iot_agent_deb_amd64 $${0}$$ $${41.88}$$ < $${54.97}$$ $${-0}$$ $${10.9}$$ < $${14.45}$$
iot_agent_deb_arm64 $${0}$$ $${39.7}$$ < $${51.9}$$ $${+0}$$ $${9.41}$$ < $${12.63}$$
iot_agent_deb_armhf $${0}$$ $${39.57}$$ < $${51.84}$$ $${+0}$$ $${9.49}$$ < $${12.74}$$
iot_agent_rpm_amd64 $${0}$$ $${41.88}$$ < $${54.97}$$ $${+0}$$ $${10.91}$$ < $${14.47}$$
iot_agent_suse_amd64 $${0}$$ $${41.88}$$ < $${54.97}$$ $${+0}$$ $${10.91}$$ < $${14.47}$$

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Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 789a2c05-bca7-46e8-bd82-cfe7f18fbe69

Baseline: bf4f327
Comparison: f6ccc98
Diff

Optimization Goals: ✅ No significant changes detected

Experiments ignored for regressions

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

perf experiment goal Δ mean % Δ mean % CI trials links
docker_containers_cpu % cpu utilization +40.71 [+39.36, +42.06] 1 Logs

Fine details of change detection per experiment

perf experiment goal Δ mean % Δ mean % CI trials links
docker_containers_cpu % cpu utilization +40.71 [+39.36, +42.06] 1 Logs
docker_containers_memory memory utilization +1.38 [+0.93, +1.82] 1 Logs
tcp_syslog_to_blackhole ingress throughput +0.84 [+0.77, +0.91] 1 Logs
quality_gate_metrics_logs memory utilization +0.74 [+0.53, +0.95] 1 Logs bounds checks dashboard
quality_gate_idle_all_features memory utilization +0.32 [+0.28, +0.36] 1 Logs bounds checks dashboard
ddot_logs memory utilization +0.27 [+0.22, +0.33] 1 Logs
file_to_blackhole_100ms_latency egress throughput +0.11 [-0.50, +0.71] 1 Logs
file_to_blackhole_500ms_latency egress throughput +0.07 [-0.54, +0.68] 1 Logs
quality_gate_idle memory utilization +0.07 [+0.03, +0.11] 1 Logs bounds checks dashboard
uds_dogstatsd_20mb_12k_contexts_20_senders memory utilization +0.06 [+0.02, +0.10] 1 Logs
tcp_dd_logs_filter_exclude ingress throughput -0.00 [-0.01, +0.01] 1 Logs
file_to_blackhole_0ms_latency egress throughput -0.00 [-0.61, +0.60] 1 Logs
otlp_ingest_logs memory utilization -0.01 [-0.14, +0.12] 1 Logs
uds_dogstatsd_to_api ingress throughput -0.02 [-0.26, +0.22] 1 Logs
ddot_metrics_sum_delta memory utilization -0.03 [-0.18, +0.12] 1 Logs
ddot_metrics_sum_cumulative memory utilization -0.04 [-0.16, +0.08] 1 Logs
file_to_blackhole_1000ms_latency egress throughput -0.05 [-0.66, +0.56] 1 Logs
otlp_ingest_metrics memory utilization -0.07 [-0.19, +0.05] 1 Logs
file_tree memory utilization -0.11 [-0.15, -0.07] 1 Logs
ddot_metrics_sum_cumulativetodelta_exporter memory utilization -0.24 [-0.44, -0.03] 1 Logs
ddot_metrics memory utilization -0.49 [-0.67, -0.32] 1 Logs
quality_gate_logs % cpu utilization -1.90 [-4.63, +0.82] 1 Logs bounds checks dashboard

Bounds Checks: ✅ Passed

perf experiment bounds_check_name replicates_passed links
docker_containers_cpu simple_check_run 10/10
docker_containers_memory memory_usage 10/10
docker_containers_memory simple_check_run 10/10
file_to_blackhole_0ms_latency lost_bytes 10/10
file_to_blackhole_0ms_latency memory_usage 10/10
file_to_blackhole_1000ms_latency 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_500ms_latency lost_bytes 10/10
file_to_blackhole_500ms_latency memory_usage 10/10
quality_gate_idle intake_connections 10/10 bounds checks dashboard
quality_gate_idle memory_usage 10/10 bounds checks dashboard
quality_gate_idle_all_features intake_connections 10/10 bounds checks dashboard
quality_gate_idle_all_features memory_usage 10/10 bounds checks dashboard
quality_gate_logs intake_connections 10/10 bounds checks dashboard
quality_gate_logs lost_bytes 10/10 bounds checks dashboard
quality_gate_logs memory_usage 10/10 bounds checks dashboard
quality_gate_metrics_logs cpu_usage 10/10 bounds checks dashboard
quality_gate_metrics_logs intake_connections 10/10 bounds checks dashboard
quality_gate_metrics_logs lost_bytes 10/10 bounds checks dashboard
quality_gate_metrics_logs memory_usage 10/10 bounds checks dashboard

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

Comment on lines +125 to +126
pkgconfigsetup.InitConfig(pkgconfig)
pkgconfig.BuildSchema()

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Is this safe to be called multiple times and/or when there is no actual config?

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BuildSchema don't load the config but build internal schema structure used later for validation. It's based on all the BindEnvAndSetDefault calls that register all our settings.

And yes it's safe to call multiple times.

@hush-hush
hush-hush requested review from dustmop and songy23 October 16, 2025 14:43
@github-actions github-actions Bot added medium review PR review might take time and removed short review PR is simple enough to be reviewed quickly labels Oct 20, 2025
@songy23 songy23 added qa/done QA done before merge and regressions are covered by tests and removed qa/rc-required Only for a PR that requires validation on the Release Candidate labels Oct 20, 2025
@songy23

songy23 commented Oct 20, 2025

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removed qa label because this function is never meant to be used in Agent (only in otel collector) so we cannot qa in Agent

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/merge

@dd-mergequeue
dd-mergequeue Bot merged commit 6834c57 into main Oct 21, 2025
326 checks passed
@dd-mergequeue
dd-mergequeue Bot deleted the maixme/fix-otel-config-init branch October 21, 2025 16:28
@github-actions github-actions Bot added this to the 7.73.0 milestone Oct 21, 2025
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dd-devflow-routing-codex Bot commented Oct 21, 2025

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View all feedbacks in Devflow UI.

2025-10-21 15:55:50 UTC ℹ️ Start processing command /merge


2025-10-21 16:06:07 UTC ℹ️ MergeQueue: pull request added to the queue

The expected merge time in main is approximately 46m (p90).


2025-10-21 16:28:58 UTC ℹ️ MergeQueue: This merge request was merged

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changelog/no-changelog No changelog entry needed medium review PR review might take time qa/done QA done before merge and regressions are covered by tests team/opentelemetry-agent

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