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Downgrade log level of SBOM logs #31528

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What does this PR do?

Errors already reported in WLM, functional errors should not be logged.

Motivation

Log volume.

Describe how to test/QA your changes

Logs not visible anymore without DEBUG level.

Possible Drawbacks / Trade-offs

Additional Notes

@vboulineau vboulineau added team/containers changelog/no-changelog qa/no-code-change No code change in Agent code requiring validation labels Nov 27, 2024
@vboulineau vboulineau added this to the 7.61.0 milestone Nov 27, 2024
@vboulineau vboulineau requested a review from a team as a code owner November 27, 2024 14:05
@github-actions github-actions bot added the short review PR is simple enough to be reviewed quickly label Nov 27, 2024
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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=50059999 --os-family=ubuntu

Note: This applies to commit f809e6b

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Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: a891bcc4-a1d1-4c1c-b0f8-41b07eb9d617

Baseline: c5298c3
Comparison: f809e6b
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf experiment goal Δ mean % Δ mean % CI trials links
pycheck_lots_of_tags % cpu utilization +4.49 [+0.82, +8.17] 1 Logs
file_to_blackhole_500ms_latency egress throughput +0.53 [-0.23, +1.29] 1 Logs
otel_to_otel_logs ingress throughput +0.27 [-0.36, +0.90] 1 Logs
file_to_blackhole_100ms_latency egress throughput +0.16 [-0.55, +0.87] 1 Logs
file_to_blackhole_300ms_latency egress throughput +0.12 [-0.51, +0.75] 1 Logs
file_to_blackhole_1000ms_latency_linear_load egress throughput +0.10 [-0.35, +0.56] 1 Logs
uds_dogstatsd_to_api ingress throughput +0.00 [-0.10, +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 -0.05 [-0.90, +0.80] 1 Logs
quality_gate_idle memory utilization -0.21 [-0.25, -0.16] 1 Logs bounds checks dashboard
file_tree memory utilization -0.37 [-0.52, -0.22] 1 Logs
file_to_blackhole_1000ms_latency egress throughput -0.85 [-1.65, -0.06] 1 Logs
tcp_syslog_to_blackhole ingress throughput -0.87 [-0.94, -0.80] 1 Logs
uds_dogstatsd_to_api_cpu % cpu utilization -1.69 [-2.41, -0.98] 1 Logs
quality_gate_idle_all_features memory utilization -2.07 [-2.17, -1.97] 1 Logs bounds checks dashboard
basic_py_check % cpu utilization -2.24 [-6.09, +1.62] 1 Logs

Bounds Checks: ❌ Failed

perf experiment bounds_check_name replicates_passed links
file_to_blackhole_300ms_latency lost_bytes 7/10
file_to_blackhole_100ms_latency lost_bytes 8/10
file_to_blackhole_500ms_latency lost_bytes 9/10
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_1000ms_latency_linear_load memory_usage 10/10
file_to_blackhole_100ms_latency memory_usage 10/10
file_to_blackhole_300ms_latency memory_usage 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

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_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.

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