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Add an option to skip ignore if labels or milestones already exist #32532

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@FlorentClarret FlorentClarret commented Dec 27, 2024

What does this PR do?

Add an option to skip ignore if labels or milestones already exist

Motivation

Make this step idempotent

Describe how you validated your changes

Possible Drawbacks / Trade-offs

Additional Notes

@FlorentClarret FlorentClarret requested review from a team as code owners December 27, 2024 09:23
@FlorentClarret FlorentClarret added changelog/no-changelog qa/no-code-change No code change in Agent code requiring validation labels Dec 27, 2024
@github-actions github-actions bot added the short review PR is simple enough to be reviewed quickly label Dec 27, 2024
@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 Dec 27, 2024
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agent-platform-auto-pr bot commented Dec 27, 2024

[Fast Unit Tests Report]

On pipeline 51839266 (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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@alopezz alopezz left a comment

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I left a few comments; seeing that they mostly follow existing patterns I would call them optional.

tasks/libs/ciproviders/github_api.py Outdated Show resolved Hide resolved
"""
Creates a label in the given GitHub repository.
"""
return self._repository.create_label(name, color, description)
import github
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We already import GithubException above so this should not be necessary.

from github import Auth, Github, GithubException, GithubIntegration, GithubObject, PullRequest

tasks/libs/ciproviders/github_api.py Outdated Show resolved Hide resolved
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agent-platform-auto-pr bot commented Dec 27, 2024

Uncompressed package size comparison

Comparison with ancestor 237b1f2a08434387e1a22e5e42cecabead23c543

Diff per package
package diff status size ancestor threshold
datadog-agent-aarch64-rpm 0.01MB ⚠️ 944.70MB 944.69MB 140.00MB
datadog-agent-arm64-deb 0.01MB ⚠️ 935.40MB 935.39MB 140.00MB
datadog-agent-amd64-deb 0.01MB ⚠️ 1191.11MB 1191.10MB 140.00MB
datadog-agent-x86_64-rpm 0.01MB ⚠️ 1200.42MB 1200.42MB 140.00MB
datadog-agent-x86_64-suse 0.01MB ⚠️ 1200.42MB 1200.42MB 140.00MB
datadog-dogstatsd-amd64-deb 0.00MB 78.57MB 78.57MB 10.00MB
datadog-dogstatsd-arm64-deb 0.00MB 55.77MB 55.77MB 10.00MB
datadog-heroku-agent-amd64-deb 0.00MB 505.27MB 505.27MB 70.00MB
datadog-iot-agent-amd64-deb 0.00MB 113.35MB 113.35MB 10.00MB
datadog-iot-agent-arm64-deb 0.00MB 108.81MB 108.81MB 10.00MB
datadog-dogstatsd-x86_64-rpm -0.00MB 78.65MB 78.65MB 10.00MB
datadog-dogstatsd-x86_64-suse -0.00MB 78.65MB 78.65MB 10.00MB
datadog-iot-agent-x86_64-rpm -0.00MB 113.42MB 113.42MB 10.00MB
datadog-iot-agent-x86_64-suse -0.00MB 113.42MB 113.42MB 10.00MB
datadog-iot-agent-aarch64-rpm -0.00MB 108.88MB 108.88MB 10.00MB

Decision

⚠️ Warning

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Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: f4440eca-8b15-4ea0-b29a-73ad4cde6745

Baseline: 237b1f2
Comparison: e05ac83
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 +3.11 [-0.20, +6.41] 1 Logs
file_tree memory utilization +0.60 [+0.47, +0.72] 1 Logs
file_to_blackhole_500ms_latency egress throughput +0.13 [-0.65, +0.90] 1 Logs
quality_gate_idle_all_features memory utilization +0.13 [+0.05, +0.21] 1 Logs bounds checks dashboard
file_to_blackhole_0ms_latency egress throughput +0.10 [-0.71, +0.91] 1 Logs
file_to_blackhole_100ms_latency egress throughput +0.04 [-0.69, +0.78] 1 Logs
file_to_blackhole_0ms_latency_http1 egress throughput +0.02 [-0.87, +0.91] 1 Logs
tcp_dd_logs_filter_exclude ingress throughput +0.00 [-0.01, +0.01] 1 Logs
uds_dogstatsd_to_api ingress throughput -0.01 [-0.13, +0.11] 1 Logs
file_to_blackhole_0ms_latency_http2 egress throughput -0.04 [-0.90, +0.82] 1 Logs
file_to_blackhole_300ms_latency egress throughput -0.04 [-0.69, +0.60] 1 Logs
file_to_blackhole_1000ms_latency egress throughput -0.06 [-0.83, +0.72] 1 Logs
file_to_blackhole_1000ms_latency_linear_load egress throughput -0.34 [-0.80, +0.12] 1 Logs
quality_gate_idle memory utilization -0.49 [-0.52, -0.46] 1 Logs bounds checks dashboard
tcp_syslog_to_blackhole ingress throughput -0.62 [-0.68, -0.55] 1 Logs
uds_dogstatsd_to_api_cpu % cpu utilization -1.85 [-2.52, -1.17] 1 Logs

Bounds Checks: ✅ Passed

perf experiment bounds_check_name replicates_passed links
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 memory_usage 10/10 bounds checks dashboard
quality_gate_idle_all_features memory_usage 10/10 bounds checks dashboard
quality_gate_logs lost_bytes 10/10
quality_gate_logs memory_usage 10/10

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_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
  • quality_gate_logs, bounds check lost_bytes: 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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