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Update python version used in workflows to the one of the repo #32155

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What does this PR do?

Set the python version used in workflows to the one of the repo.

Motivation

It's safer to use the same version as the one we build the agent with, and have every workflow use the same version.
I initially wanted to update the version for some workflows owned by my team and thought I could update them all.

Describe how you validated your changes

Possible Drawbacks / Trade-offs

Additional Notes

If you don't want one of these changes let me know and I'll remove it.

@pgimalac pgimalac added changelog/no-changelog qa/no-code-change No code change in Agent code requiring validation labels Dec 13, 2024
@github-actions github-actions bot added the long review PR is complex, plan time to review it label Dec 13, 2024
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[Fast Unit Tests Report]

On pipeline 51077066 (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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Package size comparison

Comparison with ancestor bbbad328323faa7ff69ee252e4a07e4723f052dc

Diff per package
package diff status size ancestor threshold
datadog-agent-amd64-deb 0.00MB 1268.87MB 1268.87MB 140.00MB
datadog-iot-agent-amd64-deb 0.00MB 113.29MB 113.29MB 10.00MB
datadog-dogstatsd-amd64-deb 0.00MB 78.52MB 78.52MB 10.00MB
datadog-heroku-agent-amd64-deb 0.00MB 505.43MB 505.43MB 70.00MB
datadog-agent-x86_64-rpm 0.00MB 1278.10MB 1278.10MB 140.00MB
datadog-agent-x86_64-suse 0.00MB 1278.10MB 1278.10MB 140.00MB
datadog-iot-agent-x86_64-rpm 0.00MB 113.35MB 113.35MB 10.00MB
datadog-iot-agent-x86_64-suse 0.00MB 113.35MB 113.35MB 10.00MB
datadog-dogstatsd-x86_64-rpm 0.00MB 78.59MB 78.59MB 10.00MB
datadog-dogstatsd-x86_64-suse 0.00MB 78.59MB 78.59MB 10.00MB
datadog-agent-arm64-deb 0.00MB 1003.94MB 1003.94MB 140.00MB
datadog-iot-agent-arm64-deb 0.00MB 108.76MB 108.76MB 10.00MB
datadog-dogstatsd-arm64-deb 0.00MB 55.74MB 55.74MB 10.00MB
datadog-agent-aarch64-rpm 0.00MB 1013.15MB 1013.15MB 140.00MB
datadog-iot-agent-aarch64-rpm 0.00MB 108.83MB 108.83MB 10.00MB

Decision

✅ Passed

@pgimalac pgimalac marked this pull request as ready for review December 13, 2024 15:17
@pgimalac pgimalac requested review from a team as code owners December 13, 2024 15:17
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Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: ef88b20f-9341-4e5a-86d0-d78280088789

Baseline: bbbad32
Comparison: 779c68a
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf experiment goal Δ mean % Δ mean % CI trials links
otel_to_otel_logs ingress throughput +1.02 [+0.31, +1.72] 1 Logs
file_tree memory utilization +0.84 [+0.72, +0.97] 1 Logs
file_to_blackhole_1000ms_latency_linear_load egress throughput +0.29 [-0.18, +0.76] 1 Logs
file_to_blackhole_500ms_latency egress throughput +0.07 [-0.71, +0.85] 1 Logs
uds_dogstatsd_to_api ingress throughput +0.01 [-0.09, +0.11] 1 Logs
tcp_dd_logs_filter_exclude ingress throughput -0.00 [-0.01, +0.01] 1 Logs
file_to_blackhole_0ms_latency_http2 egress throughput -0.00 [-0.85, +0.84] 1 Logs
file_to_blackhole_300ms_latency egress throughput -0.02 [-0.65, +0.61] 1 Logs
file_to_blackhole_0ms_latency egress throughput -0.03 [-0.90, +0.85] 1 Logs
file_to_blackhole_0ms_latency_http1 egress throughput -0.04 [-0.87, +0.79] 1 Logs
file_to_blackhole_100ms_latency egress throughput -0.04 [-0.74, +0.66] 1 Logs
file_to_blackhole_1000ms_latency egress throughput -0.20 [-0.97, +0.57] 1 Logs
tcp_syslog_to_blackhole ingress throughput -0.30 [-0.36, -0.24] 1 Logs
quality_gate_idle memory utilization -0.55 [-0.60, -0.51] 1 Logs bounds checks dashboard
quality_gate_logs % cpu utilization -0.59 [-3.54, +2.36] 1 Logs
uds_dogstatsd_to_api_cpu % cpu utilization -0.75 [-1.48, -0.03] 1 Logs
quality_gate_idle_all_features memory utilization -2.22 [-2.33, -2.12] 1 Logs bounds checks dashboard

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 lost_bytes: 10/10 replicas passed. Gate passed.
  • quality_gate_logs, 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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