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Main repository for Datadog Agent
https://docs.datadoghq.com/
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Revert "[ASCII-2591] Migrate Agent IPC clients to check IPC cert (#32369)" #34067

Closed sgnn7 closed 1 month ago

sgnn7 commented 1 month ago

This reverts commit 6915a52da4c84e0dcd094166c9a5ae055350718d.

Change applied causes failures in cluster-agent E2E tests

What does this PR do?

Reverts commit 6915a52da4c84e0dcd094166c9a5ae055350718d (https://github.com/DataDog/datadog-agent/pull/32369)

Motivation

Cluster-agent test failures

Describe how you validated your changes

Unit tests pass and git log shows no overlap of files in the interim period. E2E test should confirm.

Possible Drawbacks / Trade-offs

N/A

Additional Notes

N/A

cit-pr-commenter[bot] commented 1 month ago

Go Package Import Differences

Baseline: c760d79f7012f6dc29b27acd5ee13774d0443885 Comparison: e160cf1cd7049267952246fe4ec8a7d17337dcc2

binaryosarchchange
serverlesslinuxamd64
+0, -2 
-github.com/DataDog/datadog-agent/comp/api/authtoken
-github.com/DataDog/datadog-agent/comp/api/authtoken/noneimpl
serverlesslinuxarm64
+0, -2 
-github.com/DataDog/datadog-agent/comp/api/authtoken
-github.com/DataDog/datadog-agent/comp/api/authtoken/noneimpl
system-probelinuxamd64
+0, -2 
-github.com/DataDog/datadog-agent/comp/api/authtoken
-github.com/DataDog/datadog-agent/comp/api/authtoken/fetchonlyimpl
system-probelinuxarm64
+0, -2 
-github.com/DataDog/datadog-agent/comp/api/authtoken
-github.com/DataDog/datadog-agent/comp/api/authtoken/fetchonlyimpl
system-probewindowsamd64
+0, -2 
-github.com/DataDog/datadog-agent/comp/api/authtoken
-github.com/DataDog/datadog-agent/comp/api/authtoken/fetchonlyimpl
trace-agentlinuxamd64
+0, -2 
-github.com/DataDog/datadog-agent/comp/core/log/fx
-github.com/DataDog/datadog-agent/comp/core/log/impl
trace-agentlinuxarm64
+0, -2 
-github.com/DataDog/datadog-agent/comp/core/log/fx
-github.com/DataDog/datadog-agent/comp/core/log/impl
trace-agentwindowsamd64
+0, -2 
-github.com/DataDog/datadog-agent/comp/core/log/fx
-github.com/DataDog/datadog-agent/comp/core/log/impl
trace-agentdarwinamd64
+0, -2 
-github.com/DataDog/datadog-agent/comp/core/log/fx
-github.com/DataDog/datadog-agent/comp/core/log/impl
trace-agentdarwinarm64
+0, -2 
-github.com/DataDog/datadog-agent/comp/core/log/fx
-github.com/DataDog/datadog-agent/comp/core/log/impl
heroku-trace-agentlinuxamd64
+0, -2 
-github.com/DataDog/datadog-agent/comp/core/log/fx
-github.com/DataDog/datadog-agent/comp/core/log/impl
github-actions[bot] commented 1 month ago

Serverless Benchmark Results

BenchmarkStartEndInvocation comparison between c760d79f7012f6dc29b27acd5ee13774d0443885 and 1c594b9a409d01729db470d9de1b0553d34513de.

tl;dr Use these benchmarks as an insight tool during development. 1. Skim down the `vs base` column in each chart. If there is a `~`, then there was no statistically significant change to the benchmark. Otherwise, ensure the estimated percent change is either negative or very small. 2. The last row of each chart is the `geomean`. Ensure this percentage is either negative or very small.
What is this benchmarking? The [`BenchmarkStartEndInvocation`](https://github.com/DataDog/datadog-agent/blob/main/pkg/serverless/daemon/routes_test.go) compares the amount of time it takes to call the `start-invocation` and `end-invocation` endpoints. For universal instrumentation languages (Dotnet, Golang, Java, Ruby), this represents the majority of the duration overhead added by our tracing layer. The benchmark is run using a large variety of lambda request payloads. In the charts below, there is one row for each event payload type.
How do I interpret these charts? The charts below comes from [`benchstat`](https://pkg.go.dev/golang.org/x/perf/cmd/benchstat). They represent the statistical change in _duration (sec/op)_, _memory overhead (B/op)_, and _allocations (allocs/op)_. The benchstat docs explain how to interpret these charts. > Before the comparison table, we see common file-level configuration. If there are benchmarks with different configuration (for example, from different packages), benchstat will print separate tables for each configuration. > > The table then compares the two input files for each benchmark. It shows the median and 95% confidence interval summaries for each benchmark before and after the change, and an A/B comparison under "vs base". ... The p-value measures how likely it is that any differences were due to random chance (i.e., noise). The "~" means benchstat did not detect a statistically significant difference between the two inputs. ... > > Note that "statistically significant" is not the same as "large": with enough low-noise data, even very small changes can be distinguished from noise and considered statistically significant. It is, of course, generally easier to distinguish large changes from noise. > > Finally, the last row of the table shows the geometric mean of each column, giving an overall picture of how the benchmarks changed. Proportional changes in the geomean reflect proportional changes in the benchmarks. For example, given n benchmarks, if sec/op for one of them increases by a factor of 2, then the sec/op geomean will increase by a factor of ⁿ√2.
I need more help First off, do not worry if the benchmarks are failing. They are not tests. The intention is for them to be a tool for you to use during development. If you would like a hand interpreting the results come chat with us in `#serverless-agent` in the internal DataDog slack or in `#serverless` in the [public DataDog slack](https://chat.datadoghq.com/). We're happy to help!
Benchmark stats ``` goos: linux goarch: amd64 pkg: github.com/DataDog/datadog-agent/pkg/serverless/daemon cpu: AMD EPYC 7763 64-Core Processor │ baseline/benchmark.log │ current/benchmark.log │ │ sec/op │ sec/op vs base │ api-gateway-appsec.json 85.11µ ± 2% 87.46µ ± 1% +2.75% (p=0.000 n=10) api-gateway-kong-appsec.json 65.35µ ± 3% 69.06µ ± 5% +5.69% (p=0.002 n=10) api-gateway-kong.json 62.72µ ± 1% 66.24µ ± 1% +5.61% (p=0.000 n=10) api-gateway-non-proxy-async.json 100.4µ ± 2% 104.0µ ± 2% +3.65% (p=0.002 n=10) api-gateway-non-proxy.json 98.81µ ± 1% 104.15µ ± 1% +5.41% (p=0.000 n=10) api-gateway-websocket-connect.json 64.00µ ± 1% 67.71µ ± 1% +5.80% (p=0.000 n=10) api-gateway-websocket-default.json 56.64µ ± 1% 59.16µ ± 1% +4.45% (p=0.000 n=10) api-gateway-websocket-disconnect.json 57.26µ ± 1% 59.65µ ± 1% +4.16% (p=0.000 n=10) api-gateway.json 104.4µ ± 1% 110.6µ ± 1% +5.93% (p=0.000 n=10) application-load-balancer.json 57.29µ ± 3% 59.88µ ± 1% +4.52% (p=0.000 n=10) cloudfront.json 43.75µ ± 1% 45.70µ ± 1% +4.46% (p=0.000 n=10) cloudwatch-events.json 36.05µ ± 1% 37.80µ ± 1% +4.85% (p=0.000 n=10) cloudwatch-logs.json 58.83µ ± 3% 62.33µ ± 2% +5.95% (p=0.000 n=10) custom.json 30.43µ ± 1% 31.84µ ± 1% +4.64% (p=0.000 n=10) dynamodb.json 84.34µ ± 3% 86.67µ ± 1% +2.77% (p=0.019 n=10) empty.json 28.52µ ± 1% 29.88µ ± 1% +4.78% (p=0.000 n=10) eventbridge-custom.json 44.79µ ± 1% 46.89µ ± 1% +4.69% (p=0.000 n=10) eventbridge-no-bus.json 43.71µ ± 2% 45.21µ ± 1% +3.44% (p=0.000 n=10) eventbridge-no-timestamp.json 43.81µ ± 1% 46.07µ ± 2% +5.16% (p=0.000 n=10) eventbridgesns.json 58.40µ ± 1% 60.78µ ± 2% +4.08% (p=0.000 n=10) eventbridgesqs.json 64.28µ ± 1% 67.96µ ± 1% +5.73% (p=0.000 n=10) http-api.json 63.80µ ± 0% 66.05µ ± 1% +3.54% (p=0.000 n=10) kinesis-batch.json 64.08µ ± 0% 66.93µ ± 1% +4.45% (p=0.000 n=10) kinesis.json 50.91µ ± 1% 53.45µ ± 1% +4.98% (p=0.000 n=10) s3.json 54.76µ ± 1% 56.33µ ± 1% +2.85% (p=0.000 n=10) sns-batch.json 82.47µ ± 1% 84.82µ ± 1% +2.84% (p=0.000 n=10) sns.json 62.14µ ± 1% 63.37µ ± 1% +1.98% (p=0.000 n=10) snssqs.json 104.6µ ± 1% 107.1µ ± 1% +2.41% (p=0.000 n=10) snssqs_no_dd_context.json 95.22µ ± 1% 97.17µ ± 1% +2.05% (p=0.000 n=10) sqs-aws-header.json 52.29µ ± 2% 53.33µ ± 1% +2.00% (p=0.011 n=10) sqs-batch.json 85.64µ ± 1% 87.04µ ± 1% +1.63% (p=0.001 n=10) sqs.json 62.72µ ± 1% 64.38µ ± 1% +2.65% (p=0.000 n=10) sqs_no_dd_context.json 57.17µ ± 1% 58.87µ ± 1% +2.98% (p=0.000 n=10) stepfunction.json 40.41µ ± 2% 41.64µ ± 1% +3.03% (p=0.002 n=10) geomean 60.38µ 62.78µ +3.99% │ baseline/benchmark.log │ current/benchmark.log │ │ B/op │ B/op vs base │ api-gateway-appsec.json 37.40Ki ± 0% 37.40Ki ± 0% ~ (p=0.494 n=10) api-gateway-kong-appsec.json 27.13Ki ± 0% 27.13Ki ± 0% ~ (p=0.778 n=10) api-gateway-kong.json 24.61Ki ± 0% 24.62Ki ± 0% ~ (p=0.340 n=10) api-gateway-non-proxy-async.json 47.98Ki ± 0% 48.00Ki ± 0% ~ (p=0.079 n=10) api-gateway-non-proxy.json 47.19Ki ± 0% 47.19Ki ± 0% ~ (p=0.643 n=10) api-gateway-websocket-connect.json 25.37Ki ± 0% 25.37Ki ± 0% +0.01% (p=0.044 n=10) api-gateway-websocket-default.json 20.09Ki ± 0% 20.08Ki ± 0% ~ (p=0.113 n=10) api-gateway-websocket-disconnect.json 21.00Ki ± 0% 21.00Ki ± 0% ~ (p=0.816 n=10) api-gateway.json 49.18Ki ± 0% 49.18Ki ± 0% ~ (p=0.380 n=10) application-load-balancer.json 23.40Ki ± 0% 23.40Ki ± 0% ~ (p=0.188 n=10) cloudfront.json 17.48Ki ± 0% 17.48Ki ± 0% ~ (p=0.087 n=10) cloudwatch-events.json 11.55Ki ± 0% 11.55Ki ± 0% ~ (p=0.426 n=10) cloudwatch-logs.json 53.07Ki ± 0% 53.07Ki ± 0% ~ (p=0.463 n=10) custom.json 9.572Ki ± 0% 9.572Ki ± 0% ~ (p=0.744 n=10) dynamodb.json 40.37Ki ± 0% 40.37Ki ± 0% ~ (p=0.745 n=10) empty.json 9.110Ki ± 0% 9.111Ki ± 0% ~ (p=0.747 n=10) eventbridge-custom.json 14.73Ki ± 0% 14.73Ki ± 0% ~ (p=0.139 n=10) eventbridge-no-bus.json 13.70Ki ± 0% 13.70Ki ± 0% ~ (p=0.909 n=10) eventbridge-no-timestamp.json 13.70Ki ± 0% 13.70Ki ± 0% ~ (p=0.300 n=10) eventbridgesns.json 20.50Ki ± 0% 20.50Ki ± 0% -0.01% (p=0.017 n=10) eventbridgesqs.json 24.63Ki ± 0% 24.63Ki ± 0% ~ (p=0.507 n=10) http-api.json 23.46Ki ± 0% 23.46Ki ± 0% -0.01% (p=0.009 n=10) kinesis-batch.json 26.57Ki ± 0% 26.57Ki ± 0% ~ (p=0.726 n=10) kinesis.json 17.45Ki ± 0% 17.45Ki ± 0% ~ (p=0.080 n=10) s3.json 19.96Ki ± 0% 19.96Ki ± 0% ~ (p=0.744 n=10) sns-batch.json 39.13Ki ± 0% 39.13Ki ± 0% ~ (p=0.563 n=10) sns.json 24.57Ki ± 0% 24.56Ki ± 0% -0.02% (p=0.036 n=10) snssqs.json 52.81Ki ± 0% 52.81Ki ± 0% ~ (p=0.422 n=10) snssqs_no_dd_context.json 46.56Ki ± 0% 46.56Ki ± 0% ~ (p=0.780 n=10) sqs-aws-header.json 18.80Ki ± 0% 18.80Ki ± 0% ~ (p=0.324 n=10) sqs-batch.json 41.34Ki ± 0% 41.34Ki ± 0% ~ (p=0.076 n=10) sqs.json 25.44Ki ± 0% 25.44Ki ± 0% ~ (p=0.120 n=10) sqs_no_dd_context.json 21.09Ki ± 0% 21.09Ki ± 0% ~ (p=0.114 n=10) stepfunction.json 13.53Ki ± 0% 13.53Ki ± 0% ~ (p=0.468 n=10) geomean 24.16Ki 24.16Ki +0.00% │ baseline/benchmark.log │ current/benchmark.log │ │ allocs/op │ allocs/op vs base │ api-gateway-appsec.json 637.0 ± 0% 637.0 ± 0% ~ (p=1.000 n=10) ¹ api-gateway-kong-appsec.json 495.0 ± 0% 495.0 ± 0% ~ (p=1.000 n=10) ¹ api-gateway-kong.json 473.0 ± 0% 473.0 ± 0% ~ (p=1.000 n=10) ¹ api-gateway-non-proxy-async.json 738.0 ± 0% 738.0 ± 0% ~ (p=1.000 n=10) ¹ api-gateway-non-proxy.json 728.0 ± 0% 728.0 ± 0% ~ (p=1.000 n=10) ¹ api-gateway-websocket-connect.json 455.0 ± 0% 455.0 ± 0% ~ (p=1.000 n=10) ¹ api-gateway-websocket-default.json 376.0 ± 0% 376.0 ± 0% ~ (p=1.000 n=10) ¹ api-gateway-websocket-disconnect.json 368.0 ± 0% 368.0 ± 0% ~ (p=1.000 n=10) ¹ api-gateway.json 799.0 ± 0% 799.0 ± 0% ~ (p=1.000 n=10) ¹ application-load-balancer.json 353.0 ± 0% 353.0 ± 0% ~ (p=1.000 n=10) ¹ cloudfront.json 278.0 ± 0% 278.0 ± 0% ~ (p=1.000 n=10) ¹ cloudwatch-events.json 215.0 ± 0% 215.0 ± 0% ~ (p=1.000 n=10) ¹ cloudwatch-logs.json 208.0 ± 0% 208.0 ± 0% ~ (p=1.000 n=10) ¹ custom.json 163.0 ± 0% 163.0 ± 0% ~ (p=1.000 n=10) ¹ dynamodb.json 581.0 ± 0% 581.0 ± 0% ~ (p=1.000 n=10) ¹ empty.json 154.0 ± 0% 154.0 ± 0% ~ (p=1.000 n=10) ¹ eventbridge-custom.json 260.0 ± 0% 260.0 ± 0% ~ (p=1.000 n=10) ¹ eventbridge-no-bus.json 251.0 ± 0% 251.0 ± 0% ~ (p=1.000 n=10) ¹ eventbridge-no-timestamp.json 251.0 ± 0% 251.0 ± 0% ~ (p=1.000 n=10) ¹ eventbridgesns.json 315.0 ± 0% 315.0 ± 0% ~ (p=1.000 n=10) ¹ eventbridgesqs.json 355.0 ± 0% 355.0 ± 0% ~ (p=1.000 n=10) ¹ http-api.json 431.0 ± 0% 431.0 ± 0% ~ (p=1.000 n=10) ¹ kinesis-batch.json 382.0 ± 0% 382.0 ± 0% ~ (p=1.000 n=10) ¹ kinesis.json 278.0 ± 0% 278.0 ± 0% ~ (p=1.000 n=10) ¹ s3.json 350.0 ± 0% 350.0 ± 0% ~ (p=1.000 n=10) ¹ sns-batch.json 466.0 ± 0% 466.0 ± 0% ~ (p=1.000 n=10) ¹ sns.json 337.0 ± 0% 337.0 ± 0% ~ (p=1.000 n=10) ¹ snssqs.json 459.0 ± 0% 459.0 ± 0% ~ (p=1.000 n=10) ¹ snssqs_no_dd_context.json 419.0 ± 0% 419.0 ± 0% ~ (p=1.000 n=10) ¹ sqs-aws-header.json 275.0 ± 0% 275.0 ± 0% ~ (p=1.000 n=10) ¹ sqs-batch.json 501.0 ± 0% 501.0 ± 0% ~ (p=1.000 n=10) ¹ sqs.json 350.0 ± 0% 350.0 ± 0% ~ (p=1.000 n=10) ¹ sqs_no_dd_context.json 336.0 ± 0% 336.0 ± 0% ~ (p=1.000 n=10) ¹ stepfunction.json 223.0 ± 0% 223.0 ± 0% ~ (p=1.000 n=10) ¹ geomean 360.1 360.1 +0.00% ¹ all samples are equal ```
agent-platform-auto-pr[bot] commented 1 month ago

Uncompressed package size comparison

Comparison with ancestor c760d79f7012f6dc29b27acd5ee13774d0443885

Diff per package |package|diff|status|size|ancestor|threshold| |--|--|--|--|--|--| |datadog-iot-agent-x86_64-rpm|0.00MB|✅|61.84MB|61.84MB|0.50MB| |datadog-iot-agent-x86_64-suse|0.00MB|✅|61.84MB|61.84MB|0.50MB| |datadog-dogstatsd-arm64-deb|0.00MB|✅|39.61MB|39.61MB|0.50MB| |datadog-iot-agent-amd64-deb|0.00MB|✅|61.77MB|61.77MB|0.50MB| |datadog-iot-agent-arm64-deb|0.00MB|✅|59.03MB|59.03MB|0.50MB| |datadog-iot-agent-aarch64-rpm|0.00MB|✅|59.10MB|59.10MB|0.50MB| |datadog-dogstatsd-amd64-deb|-0.00MB|✅|41.34MB|41.35MB|0.50MB| |datadog-dogstatsd-x86_64-rpm|-0.00MB|✅|41.42MB|41.43MB|0.50MB| |datadog-dogstatsd-x86_64-suse|-0.00MB|✅|41.42MB|41.43MB|0.50MB| |datadog-heroku-agent-amd64-deb|-0.01MB|✅|442.02MB|442.03MB|0.50MB| |datadog-agent-aarch64-rpm|-0.04MB|✅|865.15MB|865.20MB|0.50MB| |datadog-agent-arm64-deb|-0.04MB|✅|855.41MB|855.45MB|0.50MB| |datadog-agent-x86_64-rpm|-0.05MB|✅|876.70MB|876.75MB|0.50MB| |datadog-agent-x86_64-suse|-0.05MB|✅|876.70MB|876.75MB|0.50MB| |datadog-agent-amd64-deb|-0.05MB|✅|866.93MB|866.98MB|0.50MB|

Decision

✅ Passed

agent-platform-auto-pr[bot] commented 1 month ago

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv aws.create-vm --pipeline-id=55998313 --os-family=ubuntu

Note: This applies to commit e160cf1c

agent-platform-auto-pr[bot] commented 1 month ago

Static quality checks ✅

Please find below the results from static quality gates

Successful checks ### Info |Result|Quality gate|On disk size|On disk size limit|On wire size|On wire size limit| |----|----|----|----|----|----| |✅|static_quality_gate_agent_deb_amd64|838.73MiB|847.49MiB|202.73MiB|212.83MiB| |✅|static_quality_gate_agent_deb_arm64|827.68MiB|836.66MiB|182.31MiB|192.17MiB| |✅|static_quality_gate_agent_rpm_amd64|838.72MiB|858.45MiB|206.21MiB|214.3MiB| |✅|static_quality_gate_agent_rpm_arm64|827.67MiB|836.66MiB|184.5MiB|194.46MiB| |✅|static_quality_gate_agent_suse_amd64|838.7MiB|858.45MiB|206.21MiB|214.3MiB| |✅|static_quality_gate_agent_suse_arm64|827.68MiB|836.66MiB|184.5MiB|194.46MiB| |✅|static_quality_gate_dogstatsd_deb_amd64|39.5MiB|49.7MiB|10.54MiB|20.6MiB| |✅|static_quality_gate_dogstatsd_deb_arm64|37.85MiB|48.1MiB|9.11MiB|19.1MiB| |✅|static_quality_gate_dogstatsd_rpm_amd64|39.5MiB|49.7MiB|10.55MiB|20.6MiB| |✅|static_quality_gate_dogstatsd_suse_amd64|39.5MiB|49.7MiB|10.55MiB|20.6MiB| |✅|static_quality_gate_iot_agent_deb_amd64|58.99MiB|69.0MiB|14.82MiB|24.8MiB| |✅|static_quality_gate_iot_agent_deb_arm64|56.37MiB|66.4MiB|12.79MiB|22.8MiB| |✅|static_quality_gate_iot_agent_rpm_amd64|58.99MiB|69.0MiB|14.85MiB|24.8MiB| |✅|static_quality_gate_iot_agent_rpm_arm64|56.37MiB|66.4MiB|12.8MiB|22.8MiB| |✅|static_quality_gate_iot_agent_suse_amd64|58.99MiB|69.0MiB|14.85MiB|24.8MiB| |✅|static_quality_gate_docker_agent_amd64|922.93MiB|931.7MiB|308.91MiB|318.67MiB| |✅|static_quality_gate_docker_agent_arm64|935.26MiB|944.08MiB|292.91MiB|303.0MiB| |✅|static_quality_gate_docker_agent_jmx_amd64|1.09GiB|1.1GiB|384.04MiB|393.75MiB| |✅|static_quality_gate_docker_agent_jmx_arm64|1.1GiB|1.1GiB|363.98MiB|373.71MiB| |✅|static_quality_gate_docker_dogstatsd_amd64|47.65MiB|57.88MiB|18.24MiB|28.29MiB| |✅|static_quality_gate_docker_dogstatsd_arm64|46.04MiB|56.27MiB|17.0MiB|27.06MiB| |✅|static_quality_gate_docker_cluster_agent_amd64|267.78MiB|277.7MiB|107.28MiB|117.28MiB| |✅|static_quality_gate_docker_cluster_agent_arm64|283.82MiB|293.73MiB|102.15MiB|112.12MiB|
cit-pr-commenter[bot] commented 1 month ago

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: ab1d9fac-b9f6-4a04-8062-94004c414a55

Baseline: c760d79f7012f6dc29b27acd5ee13774d0443885 Comparison: e160cf1cd7049267952246fe4ec8a7d17337dcc2 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 | +2.14 | [-0.92, +5.19] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aquality_gate_logs%20run_id%3Aab1d9fac-b9f6-4a04-8062-94004c414a55&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1739567904000&to_ts=1739579304000&live=false) | | ➖ | quality_gate_idle_all_features | memory utilization | +0.61 | [+0.56, +0.67] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aquality_gate_idle_all_features%20run_id%3Aab1d9fac-b9f6-4a04-8062-94004c414a55&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1739567904000&to_ts=1739579304000&live=false) [bounds checks dashboard](https://app.datadoghq.com/dashboard/vz3-jd5-bdi?fromUser=true&refresh_mode=paused&tpl_var_experiment%5B0%5D=quality_gate_idle_all_features&tpl_var_job_id%5B0%5D=ab1d9fac-b9f6-4a04-8062-94004c414a55&tpl_var_run-id%5B0%5D=ab1d9fac-b9f6-4a04-8062-94004c414a55&view=spans&from_ts=1739575104000&to_ts=1739575704000&live=false) | | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +0.52 | [-0.38, +1.41] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3Aab1d9fac-b9f6-4a04-8062-94004c414a55&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1739567904000&to_ts=1739579304000&live=false) | | ➖ | file_tree | memory utilization | +0.38 | [+0.32, +0.43] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3Aab1d9fac-b9f6-4a04-8062-94004c414a55&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1739567904000&to_ts=1739579304000&live=false) | | ➖ | quality_gate_idle | memory utilization | +0.33 | [+0.29, +0.37] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aquality_gate_idle%20run_id%3Aab1d9fac-b9f6-4a04-8062-94004c414a55&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1739567904000&to_ts=1739579304000&live=false) [bounds checks dashboard](https://app.datadoghq.com/dashboard/vz3-jd5-bdi?fromUser=true&refresh_mode=paused&tpl_var_experiment%5B0%5D=quality_gate_idle&tpl_var_job_id%5B0%5D=ab1d9fac-b9f6-4a04-8062-94004c414a55&tpl_var_run-id%5B0%5D=ab1d9fac-b9f6-4a04-8062-94004c414a55&view=spans&from_ts=1739575104000&to_ts=1739575704000&live=false) | | ➖ | file_to_blackhole_500ms_latency | egress throughput | +0.11 | [-0.67, +0.88] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_500ms_latency%20run_id%3Aab1d9fac-b9f6-4a04-8062-94004c414a55&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1739567904000&to_ts=1739579304000&live=false) | | ➖ | file_to_blackhole_0ms_latency_http2 | egress throughput | +0.02 | [-0.87, +0.90] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_0ms_latency_http2%20run_id%3Aab1d9fac-b9f6-4a04-8062-94004c414a55&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1739567904000&to_ts=1739579304000&live=false) | | ➖ | tcp_dd_logs_filter_exclude | ingress throughput | -0.00 | [-0.02, +0.02] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_dd_logs_filter_exclude%20run_id%3Aab1d9fac-b9f6-4a04-8062-94004c414a55&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1739567904000&to_ts=1739579304000&live=false) | | ➖ | file_to_blackhole_0ms_latency | egress throughput | -0.01 | [-0.87, +0.85] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_0ms_latency%20run_id%3Aab1d9fac-b9f6-4a04-8062-94004c414a55&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1739567904000&to_ts=1739579304000&live=false) | | ➖ | uds_dogstatsd_to_api | ingress throughput | -0.02 | [-0.30, +0.26] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api%20run_id%3Aab1d9fac-b9f6-4a04-8062-94004c414a55&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1739567904000&to_ts=1739579304000&live=false) | | ➖ | file_to_blackhole_100ms_latency | egress throughput | -0.03 | [-0.72, +0.67] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_100ms_latency%20run_id%3Aab1d9fac-b9f6-4a04-8062-94004c414a55&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1739567904000&to_ts=1739579304000&live=false) | | ➖ | file_to_blackhole_300ms_latency | egress throughput | -0.03 | [-0.66, +0.61] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_300ms_latency%20run_id%3Aab1d9fac-b9f6-4a04-8062-94004c414a55&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1739567904000&to_ts=1739579304000&live=false) | | ➖ | file_to_blackhole_0ms_latency_http1 | egress throughput | -0.09 | [-0.94, +0.76] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_0ms_latency_http1%20run_id%3Aab1d9fac-b9f6-4a04-8062-94004c414a55&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1739567904000&to_ts=1739579304000&live=false) | | ➖ | file_to_blackhole_1000ms_latency | egress throughput | -0.19 | [-0.97, +0.58] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_1000ms_latency%20run_id%3Aab1d9fac-b9f6-4a04-8062-94004c414a55&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1739567904000&to_ts=1739579304000&live=false) | | ➖ | file_to_blackhole_1000ms_latency_linear_load | egress throughput | -0.26 | [-0.72, +0.20] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_1000ms_latency_linear_load%20run_id%3Aab1d9fac-b9f6-4a04-8062-94004c414a55&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1739567904000&to_ts=1739579304000&live=false) | | ➖ | tcp_syslog_to_blackhole | ingress throughput | -1.82 | [-1.89, -1.75] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3Aab1d9fac-b9f6-4a04-8062-94004c414a55&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1739567904000&to_ts=1739579304000&live=false) |

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 | intake_connections | 10/10 | [bounds checks dashboard](https://app.datadoghq.com/dashboard/vz3-jd5-bdi?fromUser=true&refresh_mode=paused&tpl_var_experiment%5B0%5D=quality_gate_idle&tpl_var_job_id%5B0%5D=ab1d9fac-b9f6-4a04-8062-94004c414a55&tpl_var_run-id%5B0%5D=ab1d9fac-b9f6-4a04-8062-94004c414a55&view=spans&from_ts=1739575104000&to_ts=1739575704000&live=false) | | ✅ | quality_gate_idle | memory_usage | 10/10 | [bounds checks dashboard](https://app.datadoghq.com/dashboard/vz3-jd5-bdi?fromUser=true&refresh_mode=paused&tpl_var_experiment%5B0%5D=quality_gate_idle&tpl_var_job_id%5B0%5D=ab1d9fac-b9f6-4a04-8062-94004c414a55&tpl_var_run-id%5B0%5D=ab1d9fac-b9f6-4a04-8062-94004c414a55&view=spans&from_ts=1739575104000&to_ts=1739575704000&live=false) | | ✅ | quality_gate_idle_all_features | intake_connections | 10/10 | [bounds checks dashboard](https://app.datadoghq.com/dashboard/vz3-jd5-bdi?fromUser=true&refresh_mode=paused&tpl_var_experiment%5B0%5D=quality_gate_idle_all_features&tpl_var_job_id%5B0%5D=ab1d9fac-b9f6-4a04-8062-94004c414a55&tpl_var_run-id%5B0%5D=ab1d9fac-b9f6-4a04-8062-94004c414a55&view=spans&from_ts=1739575104000&to_ts=1739575704000&live=false) | | ✅ | quality_gate_idle_all_features | memory_usage | 10/10 | [bounds checks dashboard](https://app.datadoghq.com/dashboard/vz3-jd5-bdi?fromUser=true&refresh_mode=paused&tpl_var_experiment%5B0%5D=quality_gate_idle_all_features&tpl_var_job_id%5B0%5D=ab1d9fac-b9f6-4a04-8062-94004c414a55&tpl_var_run-id%5B0%5D=ab1d9fac-b9f6-4a04-8062-94004c414a55&view=spans&from_ts=1739575104000&to_ts=1739575704000&live=false) | | ✅ | quality_gate_logs | intake_connections | 10/10 | | | ✅ | 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.