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[CI] Right-size k8s agent integration tests job #30847

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amenasria
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What does this PR do?

This PR adjusts the k8s settings for the agent_integration_tests job to perform optimally.
Thanks a lot to @chagui for this !

Motivation

The job is very flakey with a lot of different failures making us think this could be an infra problem.

Describe how to test/QA your changes

Possible Drawbacks / Trade-offs

Additional Notes

@amenasria amenasria added changelog/no-changelog qa/no-code-change No code change in Agent code requiring validation team/agent-devx-infra labels Nov 7, 2024
@amenasria amenasria requested a review from a team as a code owner November 7, 2024 09:43
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bits-bot commented Nov 7, 2024

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@github-actions github-actions bot added the short review PR is simple enough to be reviewed quickly label Nov 7, 2024
KUBERNETES_SERVICE_CPU_LIMIT: "8"
KUBERNETES_SERVICE_MEMORY_REQUEST: "32Gi"
KUBERNETES_SERVICE_MEMORY_LIMIT: "32Gi"
KUBERNETES_HELPER_CPU_LIMIT: "999m"
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Why not "1" ?

@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 Nov 7, 2024
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Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 300865b5-e8f3-4f42-b0d6-fc2ae7ffc717

Baseline: 9292430
Comparison: 7fb697e
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 +2.55 [-1.44, +6.55] 1 Logs
uds_dogstatsd_to_api_cpu % cpu utilization +1.46 [+0.73, +2.19] 1 Logs
idle memory utilization +0.54 [+0.48, +0.59] 1 Logs bounds checks dashboard
idle_all_features memory utilization +0.24 [+0.13, +0.34] 1 Logs bounds checks dashboard
quality_gate_idle memory utilization +0.21 [+0.17, +0.26] 1 Logs bounds checks dashboard
tcp_syslog_to_blackhole ingress throughput +0.17 [+0.13, +0.22] 1 Logs
quality_gate_idle_all_features memory utilization +0.15 [+0.06, +0.24] 1 Logs bounds checks dashboard
uds_dogstatsd_to_api ingress throughput +0.01 [-0.09, +0.12] 1 Logs
file_to_blackhole_300ms_latency egress throughput +0.00 [-0.18, +0.18] 1 Logs
tcp_dd_logs_filter_exclude ingress throughput -0.00 [-0.01, +0.01] 1 Logs
file_to_blackhole_100ms_latency egress throughput -0.01 [-0.24, +0.21] 1 Logs
file_to_blackhole_0ms_latency egress throughput -0.02 [-0.35, +0.32] 1 Logs
file_to_blackhole_1000ms_latency egress throughput -0.13 [-0.60, +0.35] 1 Logs
file_to_blackhole_500ms_latency egress throughput -0.22 [-0.47, +0.03] 1 Logs
file_tree memory utilization -0.70 [-0.83, -0.57] 1 Logs
pycheck_lots_of_tags % cpu utilization -2.23 [-5.69, +1.23] 1 Logs

Bounds Checks: ❌ Failed

perf experiment bounds_check_name replicates_passed links
idle memory_usage 9/10 bounds checks dashboard
file_to_blackhole_0ms_latency memory_usage 10/10
file_to_blackhole_1000ms_latency 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
idle_all_features memory_usage 10/10 bounds checks dashboard
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".

@amenasria
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Change of plan, we want to do a bit more tests before merging this

@amenasria amenasria closed this Nov 7, 2024
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