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Add new-e2e example test for NewDDAWithOperator #31136

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Add new-e2e example test for NewDDAWithOperator #31136

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@fanny-jiang fanny-jiang commented Nov 15, 2024 •
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What does this PR do?

  • Add deployOperator option to the AWS kind provisioner
  • Add 2 examples of e2e tests for deploying the agent using the operator for local kind and AWS kind

Motivation

  • Drive increased E2E for operator-deployed agents

Describe how to test/QA your changes

Possible Drawbacks / Trade-offs

Additional Notes

@github-actions github-actions bot added short review PR is simple enough to be reviewed quickly team/agent-devx-loops labels Nov 15, 2024
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agent-platform-auto-pr bot commented Nov 15, 2024 •
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[Fast Unit Tests Report]

On pipeline 49332443 (CI Visibility). The following jobs did not run any unit tests:

Jobs:
  • tests_deb-arm64-py3
  • tests_deb-x64-py3
  • tests_flavor_dogstatsd_deb-x64
  • tests_flavor_heroku_deb-x64
  • tests_flavor_iot_deb-x64
  • tests_rpm-arm64-py3
  • tests_rpm-x64-py3
  • tests_windows-x64

If you modified Go files and expected unit tests to run in these jobs, please double check the job logs. If you think tests should have been executed reach out to #agent-devx-help

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cit-pr-commenter bot commented Nov 15, 2024 •
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Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: e5454e1a-82a2-4bd8-a005-c63ea6037873

Baseline: 57d7604
Comparison: 24c789a
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 +1.67 [-1.77, +5.11] 1 Logs
➖ otel_to_otel_logs ingress throughput +0.77 [+0.11, +1.42] 1 Logs
➖ quality_gate_idle_all_features memory utilization +0.48 [+0.37, +0.59] 1 Logs bounds checks dashboard
➖ quality_gate_idle memory utilization +0.31 [+0.26, +0.36] 1 Logs bounds checks dashboard
➖ file_to_blackhole_1000ms_latency egress throughput +0.26 [-0.53, +1.05] 1 Logs
➖ file_to_blackhole_500ms_latency egress throughput +0.25 [-0.52, +1.02] 1 Logs
➖ basic_py_check % cpu utilization +0.13 [-3.71, +3.98] 1 Logs
➖ file_to_blackhole_1000ms_latency_linear_load egress throughput +0.13 [-0.33, +0.59] 1 Logs
➖ file_to_blackhole_0ms_latency egress throughput +0.10 [-0.77, +0.96] 1 Logs
➖ file_to_blackhole_100ms_latency egress throughput +0.09 [-0.64, +0.82] 1 Logs
➖ file_tree memory utilization +0.08 [-0.06, +0.22] 1 Logs
➖ uds_dogstatsd_to_api ingress throughput +0.03 [-0.06, +0.11] 1 Logs
➖ tcp_dd_logs_filter_exclude ingress throughput +0.00 [-0.01, +0.01] 1 Logs
➖ file_to_blackhole_300ms_latency egress throughput -0.28 [-0.91, +0.35] 1 Logs
➖ uds_dogstatsd_to_api_cpu % cpu utilization -0.31 [-1.03, +0.42] 1 Logs
➖ tcp_syslog_to_blackhole ingress throughput -0.37 [-0.45, -0.29] 1 Logs

Bounds Checks: ❌ Failed

perf experiment bounds_check_name replicates_passed links
❌ file_to_blackhole_1000ms_latency lost_bytes 0/10
❌ 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.

@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 18, 2024
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