datadog-agent
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[SVLS] Update list of known env vars for AWS lambda extension and AAS windows extensions
What does this PR do?
| Environment Variable | AWS Lambda | AAS Windows .NET | AAS Windows Java | AAS Windows Node |
|---|---|---|---|---|
| DD_AAS_DOTNET_EXTENSION_VERSION | ✅ | |||
| DD_AAS_EXTENSION_VERSION | ✅ | |||
| DD_AAS_JAVA_EXTENSION_VERSION | ✅ | |||
| DD_AGENT_PIPE_NAME | ✅ | ✅ | ✅ | |
| DD_APPSEC_ENABLED | ✅ | ✅ | ✅ | |
| DD_AZURE_APP_SERVICES | ✅ | ✅ | ✅ | |
| DD_DOGSTATSD_ARGS | ✅ | ✅ | ✅ | |
| DD_DOGSTATSD_PATH | ✅ | ✅ | ✅ | |
| DD_DOGSTATSD_WINDOWS_PIPE_NAME | ✅ | ✅ | ✅ | |
| DD_EXTENSION_PATH | ✅ | |||
| DD_INTEGRATIONS | ✅ | |||
| DD_INTERNAL_NATIVE_LOADER_PATH | ✅ | |||
| DD_INTERNAL_PROFILING_NATIVE_ENGINE_PATH | ✅ | |||
| DD_PROFILER_EXCLUDE_PROCESSES | ✅ | |||
| DD_PROFILING_LOG_DIR | ✅ | |||
| DD_RUNTIME_METRICS_ENABLED | ✅ | ✅ | ✅ | |
| DD_SERVERLESS_FLUSH_STRATEGY | ✅ | |||
| DD_TRACE_AGENT_ARGS | ✅ | ✅ | ✅ | |
| DD_TRACE_AGENT_PATH | ✅ | ✅ | ✅ | |
| DD_TRACE_AGENT_URL | ✅ | |||
| DD_TRACE_LOG_DIRECTORY | ✅ | |||
| DD_TRACE_LOG_PATH | ✅ | |||
| DD_TRACE_METRICS_ENABLED | ✅ | |||
| DD_TRACE_PIPE_NAME | ✅ | ✅ | ✅ | |
| DD_TRACE_TRANSPORT | ✅ | ✅ |
Motivation
Additional Notes
Possible Drawbacks / Trade-offs
Describe how to test/QA your changes
Bloop Bleep... Dogbot Here
Regression Detector Results
Run ID: e40d7c59-1330-4cf3-b762-f1c2e4baa6bb Baseline: acd202029ca9173e256e69eb2ae2b8a2dd88a556 Comparison: 632ccb8497102a46a1a8896ede5beb6041755781
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
Experiments with missing or malformed data
- basic_py_check
Usually, this warning means that there is no usable optimization goal data for that experiment, which could be a result of misconfiguration.
No significant changes in experiment optimization goals
Confidence level: 90.00% Effect size tolerance: |Δ mean %| ≥ 5.00%
There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.
Experiments ignored for regressions
Regressions in experiments with settings containing erratic: true are ignored.
| perf | experiment | goal | Δ mean % | Δ mean % CI |
|---|---|---|---|---|
| ➖ | file_to_blackhole | % cpu utilization | +1.27 | [-5.32, +7.86] |
Fine details of change detection per experiment
| perf | experiment | goal | Δ mean % | Δ mean % CI |
|---|---|---|---|---|
| ➖ | file_to_blackhole | % cpu utilization | +1.27 | [-5.32, +7.86] |
| ➖ | process_agent_standard_check | memory utilization | +0.64 | [+0.60, +0.67] |
| ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +0.56 | [-0.89, +2.02] |
| ➖ | process_agent_standard_check_with_stats | memory utilization | +0.19 | [+0.16, +0.23] |
| ➖ | trace_agent_json | ingress throughput | +0.01 | [-0.03, +0.06] |
| ➖ | uds_dogstatsd_to_api | ingress throughput | -0.00 | [-0.00, +0.00] |
| ➖ | tcp_dd_logs_filter_exclude | ingress throughput | -0.00 | [-0.00, +0.00] |
| ➖ | trace_agent_msgpack | ingress throughput | -0.01 | [-0.02, +0.01] |
| ➖ | file_tree | memory utilization | -0.15 | [-0.25, -0.04] |
| ➖ | idle | memory utilization | -0.21 | [-0.26, -0.17] |
| ➖ | tcp_syslog_to_blackhole | ingress throughput | -0.26 | [-0.32, -0.21] |
| ➖ | process_agent_real_time_mode | memory utilization | -0.77 | [-0.81, -0.73] |
| ➖ | otel_to_otel_logs | ingress throughput | -0.83 | [-1.48, -0.18] |
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:
-
Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.
-
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.
-
Its configuration does not mark it "erratic".
@purple4reina I actually don't think there is a specific "service" config in the agent
@purple4reina I actually don't think there is a specific "service" config in the agent
@pgimalac Yes, you are correct. I met with @duncanpharvey and we worked through this together. Thanks for confirming that for us.
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