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[test-infra-definitions][automated] Bump test-infra-definitions to 7e55b9e3279af35d88e4965820114d00e659942b #30026
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[Fast Unit Tests Report] On pipeline 46306540 (CI Visibility). The following jobs did not run any unit tests: Jobs:
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 |
Test changes on VMUse this command from test-infra-definitions to manually test this PR changes on a VM: inv create-vm --pipeline-id=46306540 --os-family=ubuntu Note: This applies to commit d881dbf |
Regression DetectorRegression Detector ResultsRun ID: addd7a3d-36de-44c0-9c7a-12f113f95fe1 Metrics dashboard Target profiles Baseline: ad37c41 Performance changes are noted in the perf column of each table:
No significant changes in experiment optimization goalsConfidence level: 90.00% There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.
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perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
---|---|---|---|---|---|---|
➖ | idle_all_features | memory utilization | +2.62 | [+2.48, +2.75] | 1 | Logs |
➖ | file_tree | memory utilization | +0.74 | [+0.62, +0.87] | 1 | Logs |
➖ | otel_to_otel_logs | ingress throughput | +0.64 | [-0.17, +1.45] | 1 | Logs |
➖ | tcp_syslog_to_blackhole | ingress throughput | +0.58 | [+0.48, +0.67] | 1 | Logs |
➖ | basic_py_check | % cpu utilization | +0.47 | [-2.21, +3.14] | 1 | Logs |
➖ | file_to_blackhole_300ms_latency | egress throughput | +0.13 | [-0.05, +0.30] | 1 | Logs |
➖ | file_to_blackhole_1000ms_latency | egress throughput | +0.09 | [-0.39, +0.57] | 1 | Logs |
➖ | file_to_blackhole_0ms_latency | egress throughput | +0.01 | [-0.32, +0.34] | 1 | Logs |
➖ | uds_dogstatsd_to_api | ingress throughput | +0.00 | [-0.11, +0.11] | 1 | Logs |
➖ | tcp_dd_logs_filter_exclude | ingress throughput | -0.00 | [-0.01, +0.01] | 1 | Logs |
➖ | file_to_blackhole_100ms_latency | egress throughput | -0.00 | [-0.23, +0.22] | 1 | Logs |
➖ | file_to_blackhole_500ms_latency | egress throughput | -0.06 | [-0.31, +0.18] | 1 | Logs |
➖ | idle | memory utilization | -0.26 | [-0.32, -0.21] | 1 | Logs |
➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -0.41 | [-1.13, +0.31] | 1 | Logs |
➖ | pycheck_lots_of_tags | % cpu utilization | -1.09 | [-3.49, +1.32] | 1 | Logs |
Bounds Checks
perf | experiment | bounds_check_name | replicates_passed |
---|---|---|---|
✅ | 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 | memory_usage | 10/10 |
Explanation
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:
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Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.
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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.
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Its configuration does not mark it "erratic".
clusterSerialized, err := json.Marshal(stackOutput.Outputs["dd-Cluster-ecs-cluster"].Value) | ||
suite.Require().NoError(err) | ||
ecsCluster := &ecsComp.ClusterOutput{} | ||
suite.Require().NoError(ecsCluster.Import(clusterSerialized, ecsCluster)) | ||
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suite.ecsClusterName = ecsCluster.ClusterName |
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This looks as if we were JSON-marshaling a structured data line 81 to unmarshal it line 84.
Wasn’t it possible to extract cluster name directly from the original structured data without having to serialize and deserialize it ?
This PR was automatically created by the test-infra-definitions bump task.
This PR bumps the test-infra-definitions submodule to 7e55b9e3279af35d88e4965820114d00e659942b from 08b9d8e048c1.
Here is the full changelog between the two commits: DataDog/test-infra-definitions@08b9d8e...7e55b9e
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