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(fleet) small improvements to the mirroring logic #31504

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@arbll arbll commented Nov 27, 2024

This PR adds a few improvements to the mirroring logic:

  • Don't try to parse manifest JSON on non-200 to avoid hard to understand logs with JSON parsing
  • Set the request host header properly, fixing tracing and potentially mirrors behind a LB

Describe how to test/QA your changes

TODO

This PR adds a few improvements to the mirroring logic:
- Don't try to parse manifest JSON on non-200 to avoid hard to understand logs with JSON parsing
@github-actions github-actions bot added team/windows-agent short review PR is simple enough to be reviewed quickly labels Nov 27, 2024
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cit-pr-commenter bot commented Nov 27, 2024

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: b83f2a77-225a-4f31-aedf-ca6f43f3315b

Baseline: a768373
Comparison: 3b16e19
Diff

Optimization Goals: ❌ Significant changes detected

perf experiment goal Δ mean % Δ mean % CI trials links
pycheck_lots_of_tags % cpu utilization -5.15 [-8.51, -1.78] 1 Logs

Fine details of change detection per experiment

perf experiment goal Δ mean % Δ mean % CI trials links
quality_gate_idle_all_features memory utilization +2.49 [+2.38, +2.60] 1 Logs bounds checks dashboard
uds_dogstatsd_to_api_cpu % cpu utilization +1.27 [+0.55, +2.00] 1 Logs
file_to_blackhole_500ms_latency egress throughput +0.51 [-0.25, +1.27] 1 Logs
file_to_blackhole_1000ms_latency_linear_load egress throughput +0.46 [+0.01, +0.92] 1 Logs
tcp_syslog_to_blackhole ingress throughput +0.21 [+0.15, +0.27] 1 Logs
file_to_blackhole_0ms_latency egress throughput +0.05 [-0.80, +0.90] 1 Logs
uds_dogstatsd_to_api ingress throughput +0.03 [-0.07, +0.13] 1 Logs
file_to_blackhole_100ms_latency egress throughput +0.01 [-0.67, +0.70] 1 Logs
tcp_dd_logs_filter_exclude ingress throughput +0.00 [-0.01, +0.01] 1 Logs
file_to_blackhole_1000ms_latency egress throughput -0.09 [-0.88, +0.70] 1 Logs
otel_to_otel_logs ingress throughput -0.09 [-0.77, +0.58] 1 Logs
file_tree memory utilization -0.15 [-0.28, -0.01] 1 Logs
file_to_blackhole_300ms_latency egress throughput -0.16 [-0.80, +0.47] 1 Logs
quality_gate_idle memory utilization -0.21 [-0.25, -0.17] 1 Logs bounds checks dashboard
basic_py_check % cpu utilization -4.68 [-8.36, -1.00] 1 Logs
pycheck_lots_of_tags % cpu utilization -5.15 [-8.51, -1.78] 1 Logs

Bounds Checks: ❌ Failed

perf experiment bounds_check_name replicates_passed links
file_to_blackhole_500ms_latency lost_bytes 8/10
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 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 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, 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.

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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=50055457 --os-family=ubuntu

Note: This applies to commit 3b16e19

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