WINA-1171: CPU info rework #37528

guohdd · 2025-05-31T07:59:27Z

What does this PR do?

This PR addresses the issue of inaccurate cpu_cores reported by "gohai". Specifically, The system.cpu.num_cores metric could be wrong on certain Windows platforms. This PR intends to retrieve the correct CPU data from Windows systems across Windows platforms and independent of CPU architecture, providing a generic solution to CPU metric data.
Relevant JIRA cases are:

WINA-1171: Inaccurate cpu_cores reported by "gohai"
AGENT-13192: wrong number of cpu cores in dashboard
AGENT-12417: Inaccurate cpu_cores reported by "gohai"

Motivation

Both external customers and internal users have reported similar issues, the Windows Agent team also reproduced the issue. Inaccurate data could lead to user confusion.

Describe how you validated your changes

The issue can be reproduced relatively easily, which helped develop and validate the solution. The changes were verified by running the test command on the systems which reproduced the problem. Use the following command for local test: go test -tags=test -v ./pkg/gohai/cpu

Possible Drawbacks / Trade-offs

Additional Notes

Based on the investigation, the previous implementation retrieves data from native system calls on Windows, and there were assumptions of certain data sizes and hardcoded data offset values, which was potentially risky and resulted in incorrect data retrieved. This PR removes the above assumptions and made data collection independent of CPU architecture., which means we let the OS do the heavy lifting and use the data provided by the system to figure out the location of each CPU record.

cit-pr-commenter · 2025-05-31T09:00:45Z

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 1f1e5eb2-ca46-4afe-a537-67b468858c8e

Baseline: e1d209a
Comparison: 6668d50
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	quality_gate_logs	% cpu utilization	+4.29	[+1.51, +7.06]	1	Logs bounds checks dashboard
➖	docker_containers_memory	memory utilization	+0.71	[+0.65, +0.76]	1	Logs
➖	docker_containers_cpu	% cpu utilization	+0.61	[-2.41, +3.63]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	+0.43	[+0.35, +0.51]	1	Logs bounds checks dashboard
➖	file_tree	memory utilization	+0.43	[+0.27, +0.58]	1	Logs
➖	otlp_ingest_metrics	memory utilization	+0.36	[+0.19, +0.52]	1	Logs
➖	quality_gate_idle	memory utilization	+0.29	[+0.23, +0.35]	1	Logs bounds checks dashboard
➖	file_to_blackhole_0ms_latency	egress throughput	+0.11	[-0.44, +0.65]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.10	[-0.52, +0.72]	1	Logs
➖	file_to_blackhole_0ms_latency_http2	egress throughput	+0.03	[-0.54, +0.60]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.01	[-0.59, +0.62]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.01	[-0.58, +0.60]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.02, +0.02]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.01	[-0.28, +0.26]	1	Logs
➖	file_to_blackhole_0ms_latency_http1	egress throughput	-0.02	[-0.64, +0.60]	1	Logs
➖	ddot_metrics	memory utilization	-0.04	[-0.16, +0.08]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	-0.07	[-0.31, +0.16]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	-0.08	[-0.66, +0.50]	1	Logs
➖	ddot_logs	memory utilization	-0.10	[-0.24, +0.03]	1	Logs
➖	uds_dogstatsd_20mb_12k_contexts_20_senders	memory utilization	-0.32	[-0.37, -0.27]	1	Logs
➖	otlp_ingest_logs	memory utilization	-0.39	[-0.52, -0.26]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.98	[-1.84, -0.12]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-3.13	[-3.21, -3.05]	1	Logs

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_0ms_latency_http1	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency_http1	memory_usage	10/10
✅	file_to_blackhole_0ms_latency_http2	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency_http2	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	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

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:

Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.
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.
Its configuration does not mark it "erratic".

CI Pass/Fail Decision

✅ Passed. All Quality Gates 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_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_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.

agent-platform-auto-pr · 2025-05-31T09:45:48Z

Static quality checks

✅ Please find below the results from static quality gates
Comparison made with ancestor e1d209a

Successful checks

Info

	Quality gate	Delta	On disk size (MiB)	Delta	On wire size (MiB)
✅	agent_deb_amd64	$${0}$$	$${695.71}$$ < $${752.99}$$	$${+0.02}$$	$${175.94}$$ < $${187.44}$$
✅	agent_deb_amd64_fips	$${0}$$	$${693.93}$$ < $${751.36}$$	$${-0.01}$$	$${175.6}$$ < $${187.06}$$
✅	agent_heroku_amd64	$${0}$$	$${358.2}$$ < $${369.68}$$	$${-0}$$	$${96.44}$$ < $${99.55}$$
✅	agent_msi	$${-0.04}$$	$${957.68}$$ < $${987.01}$$	$${-0.02}$$	$${146.11}$$ < $${150.72}$$
✅	agent_rpm_amd64	$${0}$$	$${695.7}$$ < $${752.98}$$	$${+0.04}$$	$${177.51}$$ < $${190.03}$$
✅	agent_rpm_amd64_fips	$${0}$$	$${693.92}$$ < $${751.35}$$	$${+0.01}$$	$${177.37}$$ < $${189.81}$$
✅	agent_rpm_arm64	$${0}$$	$${685.71}$$ < $${739.42}$$	$${+0.02}$$	$${160.88}$$ < $${171.23}$$
✅	agent_rpm_arm64_fips	$${0}$$	$${684.05}$$ < $${737.91}$$	$${-0.08}$$	$${160.01}$$ < $${170.22}$$
✅	agent_suse_amd64	$${0}$$	$${695.7}$$ < $${752.98}$$	$${+0.04}$$	$${177.51}$$ < $${190.03}$$
✅	agent_suse_amd64_fips	$${0}$$	$${693.92}$$ < $${751.35}$$	$${+0.01}$$	$${177.37}$$ < $${189.81}$$
✅	agent_suse_arm64	$${0}$$	$${685.71}$$ < $${739.42}$$	$${+0.02}$$	$${160.88}$$ < $${171.23}$$
✅	agent_suse_arm64_fips	$${0}$$	$${684.05}$$ < $${737.91}$$	$${-0.08}$$	$${160.01}$$ < $${170.22}$$
✅	docker_agent_amd64	$${-0}$$	$${779.5}$$ < $${849.39}$$	$${-0}$$	$${268.45}$$ < $${288.34}$$
✅	docker_agent_arm64	$${+0}$$	$${792.96}$$ < $${858.97}$$	$${+0}$$	$${255.84}$$ < $${274.36}$$
✅	docker_agent_jmx_amd64	$${+0}$$	$${970.69}$$ < $${1040.59}$$	$${-0}$$	$${337.44}$$ < $${357.3}$$
✅	docker_agent_jmx_arm64	$${-0}$$	$${972.75}$$ < $${1038.77}$$	$${-0}$$	$${320.78}$$ < $${339.32}$$
✅	docker_agent_windows1809	$${-0.04}$$	$${1485.78}$$ < $${1557.19}$$	$${+0.02}$$	$${487.65}$$ < $${507.33}$$
✅	docker_agent_windows1809_core	$${-0.05}$$	$${6215.72}$$ < $${6287.46}$$	$${0}$$	$${2048.0}$$ < $${2067.76}$$
✅	docker_agent_windows1809_core_jmx	$${-0.17}$$	$${6337.31}$$ < $${6409.04}$$	$${0}$$	$${2048.0}$$ < $${2067.76}$$
✅	docker_agent_windows1809_jmx	$${-0.04}$$	$${1607.41}$$ < $${1679.14}$$	$${-0.03}$$	$${529.88}$$ < $${550.27}$$
✅	docker_agent_windows2022	$${-0.2}$$	$${1504.96}$$ < $${1576.85}$$	$${-0.04}$$	$${500.37}$$ < $${520.76}$$
✅	docker_agent_windows2022_core	$${-0.06}$$	$${6188.99}$$ < $${6260.53}$$	$${0}$$	$${2048.0}$$ < $${2067.76}$$
✅	docker_agent_windows2022_core_jmx	$${+0.1}$$	$${6310.8}$$ < $${6382.17}$$	$${0}$$	$${2048.0}$$ < $${2067.76}$$
✅	docker_agent_windows2022_jmx	$${-0.07}$$	$${1626.81}$$ < $${1698.2}$$	$${-0.01}$$	$${542.64}$$ < $${562.99}$$
✅	docker_cluster_agent_amd64	$${+0}$$	$${212.79}$$ < $${259.73}$$	$${-0}$$	$${72.36}$$ < $${103.68}$$
✅	docker_cluster_agent_arm64	$${-0}$$	$${228.68}$$ < $${274.24}$$	$${+0}$$	$${68.64}$$ < $${98.45}$$
✅	docker_cws_instrumentation_amd64	$${0}$$	$${7.08}$$ < $${7.12}$$	$${-0}$$	$${2.95}$$ < $${3.29}$$
✅	docker_cws_instrumentation_arm64	$${0}$$	$${6.69}$$ < $${6.92}$$	$${-0}$$	$${2.7}$$ < $${3.07}$$
✅	docker_dogstatsd_amd64	$${-0}$$	$${39.21}$$ < $${39.57}$$	$${-0}$$	$${15.11}$$ < $${15.76}$$
✅	docker_dogstatsd_arm64	$${0}$$	$${37.88}$$ < $${38.2}$$	$${-0}$$	$${14.53}$$ < $${14.83}$$
✅	dogstatsd_deb_amd64	$${0}$$	$${30.44}$$ < $${31.52}$$	$${+0}$$	$${7.99}$$ < $${8.97}$$
✅	dogstatsd_deb_arm64	$${0}$$	$${29.01}$$ < $${30.08}$$	$${-0}$$	$${6.94}$$ < $${7.92}$$
✅	dogstatsd_rpm_amd64	$${0}$$	$${30.44}$$ < $${31.52}$$	$${+0}$$	$${8.01}$$ < $${8.98}$$
✅	dogstatsd_suse_amd64	$${0}$$	$${30.44}$$ < $${31.52}$$	$${+0}$$	$${8.01}$$ < $${8.98}$$
✅	iot_agent_deb_amd64	$${0}$$	$${50.31}$$ < $${60.17}$$	$${-0}$$	$${12.8}$$ < $${15.82}$$
✅	iot_agent_deb_arm64	$${0}$$	$${47.78}$$ < $${56.94}$$	$${+0}$$	$${11.11}$$ < $${13.86}$$
✅	iot_agent_deb_armhf	$${0}$$	$${47.35}$$ < $${56.41}$$	$${+0}$$	$${11.17}$$ < $${13.86}$$
✅	iot_agent_rpm_amd64	$${0}$$	$${50.31}$$ < $${60.18}$$	$${-0}$$	$${12.82}$$ < $${15.84}$$
✅	iot_agent_rpm_arm64	$${0}$$	$${47.78}$$ < $${56.94}$$	$${-0}$$	$${11.13}$$ < $${13.76}$$
✅	iot_agent_suse_amd64	$${0}$$	$${50.31}$$ < $${60.18}$$	$${-0}$$	$${12.82}$$ < $${15.84}$$

releasenotes/notes/windows-agent-cpu-info-18e54faf1ffbd15a.yaml

pkg/gohai/cpu/cpu_windows.c

pkg/gohai/cpu/cpu_windows.go

pkg/gohai/cpu/cpu_windows.c

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guohdd · 2025-06-12T00:25:20Z

/merge

dd-devflow · 2025-06-12T00:25:23Z

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2025-06-12 01:05:13 UTC ℹ️ MergeQueue: This merge request was merged

WINA-1171: CPU info rework

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WINA-1171: CPU info rework #37528

WINA-1171: CPU info rework #37528

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guohdd commented May 31, 2025 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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What does this PR do?

Motivation

Describe how you validated your changes

Possible Drawbacks / Trade-offs

Additional Notes

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Regression Detector

Regression Detector Results

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

Bounds Checks: ✅ Passed

Explanation

CI Pass/Fail Decision

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Static quality checks

Info

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