DataDog
Remove pkg/util/containers/metrics dependency from serverless build
What does this PR do?
Remove pkg/util/containers/metrics packages from the serverless build.
Motivation
Remove unused dependencies from serverless agent, in order to reduce init time duration and reduce binary size.
Additional Notes
There should be no functional change.
Possible Drawbacks / Trade-offs
Describe how to test/QA your changes
Go Package Import Differences
Baseline: a939f454e4893fab18fe2397eb52c3f1271cbff8 Comparison: 88137416669f128abb16e3d504fff02b16759b41
| binary | os | arch | change |
|---|---|---|---|
| serverless | linux | amd64 | +0, -7
-github.com/DataDog/datadog-agent/pkg/util/containers/metrics
-github.com/DataDog/datadog-agent/pkg/util/containers/metrics/containerd
-github.com/DataDog/datadog-agent/pkg/util/containers/metrics/cri
-github.com/DataDog/datadog-agent/pkg/util/containers/metrics/docker
-github.com/DataDog/datadog-agent/pkg/util/containers/metrics/ecsfargate
-github.com/DataDog/datadog-agent/pkg/util/containers/metrics/kubelet
-github.com/DataDog/datadog-agent/pkg/util/containers/metrics/system
|
| serverless | linux | arm64 | +0, -7
-github.com/DataDog/datadog-agent/pkg/util/containers/metrics
-github.com/DataDog/datadog-agent/pkg/util/containers/metrics/containerd
-github.com/DataDog/datadog-agent/pkg/util/containers/metrics/cri
-github.com/DataDog/datadog-agent/pkg/util/containers/metrics/docker
-github.com/DataDog/datadog-agent/pkg/util/containers/metrics/ecsfargate
-github.com/DataDog/datadog-agent/pkg/util/containers/metrics/kubelet
-github.com/DataDog/datadog-agent/pkg/util/containers/metrics/system
|
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Regression Detector Results
Run ID: 724549c5-23aa-43bc-95d0-42e5c41d91f7 Baseline: 08ffe66297a50132d1d00738b8bb241444ca00de Comparison: b1ee8d5773407d2d1f390e111b3d244ef6fa5be8 Total CPUs: 7
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 | +1.28 | [-5.36, +7.91] |
Fine details of change detection per experiment
| perf | experiment | goal | Δ mean % | Δ mean % CI |
|---|---|---|---|---|
| ➖ | file_to_blackhole | % cpu utilization | +1.28 | [-5.36, +7.91] |
| ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +0.73 | [-0.71, +2.16] |
| ➖ | process_agent_standard_check | memory utilization | +0.12 | [+0.06, +0.17] |
| ➖ | process_agent_standard_check_with_stats | memory utilization | +0.09 | [+0.05, +0.14] |
| ➖ | idle | memory utilization | +0.04 | [-0.02, +0.09] |
| ➖ | trace_agent_msgpack | ingress throughput | +0.02 | [+0.01, +0.03] |
| ➖ | tcp_dd_logs_filter_exclude | ingress throughput | +0.00 | [-0.00, +0.00] |
| ➖ | uds_dogstatsd_to_api | ingress throughput | +0.00 | [-0.00, +0.00] |
| ➖ | trace_agent_json | ingress throughput | -0.01 | [-0.04, +0.01] |
| ➖ | otel_to_otel_logs | ingress throughput | -0.05 | [-0.69, +0.59] |
| ➖ | tcp_syslog_to_blackhole | ingress throughput | -0.19 | [-0.25, -0.14] |
| ➖ | file_tree | memory utilization | -0.30 | [-0.40, -0.21] |
| ➖ | process_agent_real_time_mode | memory utilization | -0.52 | [-0.57, -0.47] |
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".
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Serverless Benchmark Results
BenchmarkStartEndInvocation comparison between a939f454e4893fab18fe2397eb52c3f1271cbff8 and 2856c8694c13833e27da59fd90fdaacaa9de46ae.
tl;dr
Use these benchmarks as an insight tool during development.
-
Skim down the
vs basecolumn 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. -
The last row of each chart is the
geomean. Ensure this percentage is either negative or very small.
What is this benchmarking?
The BenchmarkStartEndInvocation 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. 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. 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 83.41µ ± 3% 88.56µ ± 4% +6.17% (p=0.000 n=10)
api-gateway-kong-appsec.json 65.03µ ± 2% 67.21µ ± 1% +3.36% (p=0.002 n=10)
api-gateway-kong.json 62.82µ ± 2% 65.06µ ± 1% +3.57% (p=0.000 n=10)
api-gateway-non-proxy-async.json 100.8µ ± 1% 104.8µ ± 2% +4.02% (p=0.000 n=10)
api-gateway-non-proxy.json 100.2µ ± 1% 104.7µ ± 1% +4.55% (p=0.000 n=10)
api-gateway-websocket-connect.json 66.69µ ± 1% 69.54µ ± 1% +4.28% (p=0.000 n=10)
api-gateway-websocket-default.json 59.39µ ± 1% 62.32µ ± 1% +4.94% (p=0.000 n=10)
api-gateway-websocket-disconnect.json 59.37µ ± 3% 62.39µ ± 2% +5.09% (p=0.000 n=10)
api-gateway.json 110.7µ ± 1% 115.2µ ± 2% +4.01% (p=0.000 n=10)
application-load-balancer.json 60.24µ ± 1% 62.99µ ± 2% +4.56% (p=0.000 n=10)
cloudfront.json 45.74µ ± 3% 47.54µ ± 1% +3.94% (p=0.001 n=10)
cloudwatch-events.json 37.36µ ± 4% 38.10µ ± 2% ~ (p=0.218 n=10)
cloudwatch-logs.json 65.12µ ± 2% 65.68µ ± 1% ~ (p=0.105 n=10)
custom.json 29.45µ ± 2% 29.86µ ± 1% ~ (p=0.165 n=10)
dynamodb.json 91.94µ ± 1% 95.23µ ± 1% +3.58% (p=0.000 n=10)
empty.json 27.74µ ± 2% 28.89µ ± 2% +4.14% (p=0.001 n=10)
eventbridge-custom.json 40.59µ ± 2% 41.69µ ± 1% +2.71% (p=0.000 n=10)
http-api.json 71.88µ ± 1% 74.51µ ± 2% +3.66% (p=0.000 n=10)
kinesis-batch.json 70.07µ ± 1% 72.64µ ± 2% +3.68% (p=0.000 n=10)
kinesis.json 52.81µ ± 2% 53.81µ ± 2% ~ (p=0.052 n=10)
s3.json 57.80µ ± 1% 59.73µ ± 2% +3.35% (p=0.000 n=10)
sns-batch.json 87.96µ ± 1% 92.62µ ± 2% +5.29% (p=0.000 n=10)
sns.json 62.99µ ± 1% 64.89µ ± 2% +3.01% (p=0.000 n=10)
snssqs.json 109.5µ ± 2% 114.2µ ± 1% +4.26% (p=0.000 n=10)
snssqs_no_dd_context.json 97.85µ ± 2% 100.82µ ± 1% +3.04% (p=0.000 n=10)
sqs-aws-header.json 54.37µ ± 1% 55.17µ ± 2% ~ (p=0.105 n=10)
sqs-batch.json 92.48µ ± 1% 97.08µ ± 2% +4.98% (p=0.000 n=10)
sqs.json 67.03µ ± 2% 69.48µ ± 2% +3.65% (p=0.000 n=10)
sqs_no_dd_context.json 60.79µ ± 3% 61.10µ ± 2% ~ (p=0.796 n=10)
geomean 64.79µ 67.07µ +3.51%
│ baseline/benchmark.log │ current/benchmark.log │
│ B/op │ B/op vs base │
api-gateway-appsec.json 37.19Ki ± 0% 37.20Ki ± 0% ~ (p=0.446 n=10)
api-gateway-kong-appsec.json 26.79Ki ± 0% 26.80Ki ± 0% ~ (p=0.287 n=10)
api-gateway-kong.json 24.28Ki ± 0% 24.29Ki ± 0% ~ (p=0.446 n=10)
api-gateway-non-proxy-async.json 47.99Ki ± 0% 48.00Ki ± 0% ~ (p=0.101 n=10)
api-gateway-non-proxy.json 47.19Ki ± 0% 47.20Ki ± 0% ~ (p=0.469 n=10)
api-gateway-websocket-connect.json 25.40Ki ± 0% 25.40Ki ± 0% ~ (p=0.254 n=10)
api-gateway-websocket-default.json 21.29Ki ± 0% 21.30Ki ± 0% ~ (p=0.255 n=10)
api-gateway-websocket-disconnect.json 21.08Ki ± 0% 21.09Ki ± 0% +0.04% (p=0.024 n=10)
api-gateway.json 49.44Ki ± 0% 49.46Ki ± 0% ~ (p=0.224 n=10)
application-load-balancer.json 23.16Ki ± 0% 23.17Ki ± 0% +0.03% (p=0.012 n=10)
cloudfront.json 17.56Ki ± 0% 17.58Ki ± 0% +0.09% (p=0.005 n=10)
cloudwatch-events.json 11.65Ki ± 0% 11.65Ki ± 0% ~ (p=0.643 n=10)
cloudwatch-logs.json 53.28Ki ± 0% 53.27Ki ± 0% ~ (p=0.898 n=10)
custom.json 9.653Ki ± 0% 9.675Ki ± 0% ~ (p=0.055 n=10)
dynamodb.json 40.61Ki ± 0% 40.61Ki ± 0% ~ (p=1.000 n=10)
empty.json 9.208Ki ± 0% 9.215Ki ± 0% ~ (p=0.271 n=10)
eventbridge-custom.json 13.34Ki ± 0% 13.38Ki ± 0% +0.29% (p=0.003 n=10)
http-api.json 23.69Ki ± 0% 23.70Ki ± 0% ~ (p=0.325 n=10)
kinesis-batch.json 26.95Ki ± 0% 26.96Ki ± 0% ~ (p=0.254 n=10)
kinesis.json 17.73Ki ± 0% 17.77Ki ± 0% +0.17% (p=0.006 n=10)
s3.json 20.26Ki ± 0% 20.30Ki ± 0% ~ (p=0.218 n=10)
sns-batch.json 38.53Ki ± 0% 38.58Ki ± 0% +0.13% (p=0.001 n=10)
sns.json 23.89Ki ± 0% 23.89Ki ± 0% ~ (p=0.436 n=10)
snssqs.json 50.56Ki ± 0% 50.62Ki ± 0% ~ (p=0.105 n=10)
snssqs_no_dd_context.json 44.76Ki ± 0% 44.79Ki ± 0% ~ (p=0.123 n=10)
sqs-aws-header.json 18.78Ki ± 0% 18.80Ki ± 0% ~ (p=0.838 n=10)
sqs-batch.json 41.53Ki ± 0% 41.62Ki ± 0% +0.21% (p=0.043 n=10)
sqs.json 25.43Ki ± 0% 25.50Ki ± 0% +0.31% (p=0.019 n=10)
sqs_no_dd_context.json 20.64Ki ± 1% 20.64Ki ± 1% ~ (p=0.382 n=10)
geomean 25.66Ki 25.68Ki +0.08%
│ baseline/benchmark.log │ current/benchmark.log │
│ allocs/op │ allocs/op vs base │
api-gateway-appsec.json 629.5 ± 0% 629.5 ± 0% ~ (p=1.000 n=10)
api-gateway-kong-appsec.json 488.0 ± 0% 488.0 ± 0% ~ (p=1.000 n=10) ¹
api-gateway-kong.json 466.0 ± 0% 466.0 ± 0% ~ (p=1.000 n=10) ¹
api-gateway-non-proxy-async.json 725.0 ± 0% 725.0 ± 0% ~ (p=1.000 n=10)
api-gateway-non-proxy.json 715.5 ± 0% 716.0 ± 0% ~ (p=0.650 n=10)
api-gateway-websocket-connect.json 453.0 ± 0% 453.0 ± 0% ~ (p=1.000 n=10) ¹
api-gateway-websocket-default.json 379.0 ± 0% 379.0 ± 0% ~ (p=1.000 n=10) ¹
api-gateway-websocket-disconnect.json 369.0 ± 0% 369.0 ± 0% ~ (p=1.000 n=10)
api-gateway.json 790.0 ± 0% 790.0 ± 0% ~ (p=1.000 n=10)
application-load-balancer.json 352.0 ± 0% 352.0 ± 0% ~ (p=0.474 n=10)
cloudfront.json 283.0 ± 0% 283.0 ± 0% ~ (p=0.087 n=10)
cloudwatch-events.json 220.0 ± 0% 220.0 ± 0% ~ (p=1.000 n=10)
cloudwatch-logs.json 215.0 ± 0% 215.0 ± 0% ~ (p=1.000 n=10)
custom.json 168.0 ± 0% 168.0 ± 0% ~ (p=1.000 n=10)
dynamodb.json 588.5 ± 0% 588.5 ± 0% ~ (p=1.000 n=10)
empty.json 159.0 ± 0% 159.0 ± 1% ~ (p=0.303 n=10)
eventbridge-custom.json 253.0 ± 0% 254.0 ± 0% +0.40% (p=0.035 n=10)
http-api.json 432.0 ± 0% 432.0 ± 0% ~ (p=0.768 n=10)
kinesis-batch.json 390.0 ± 0% 390.0 ± 0% ~ (p=0.628 n=10)
kinesis.json 284.5 ± 0% 285.0 ± 0% +0.18% (p=0.033 n=10)
s3.json 357.0 ± 0% 358.0 ± 0% ~ (p=0.561 n=10)
sns-batch.json 454.0 ± 0% 455.0 ± 0% +0.22% (p=0.003 n=10)
sns.json 322.5 ± 0% 323.0 ± 0% ~ (p=0.618 n=10)
snssqs.json 445.0 ± 0% 446.0 ± 0% ~ (p=0.053 n=10)
snssqs_no_dd_context.json 399.0 ± 0% 399.0 ± 0% ~ (p=0.406 n=10)
sqs-aws-header.json 273.0 ± 0% 274.0 ± 0% ~ (p=0.465 n=10)
sqs-batch.json 503.0 ± 0% 504.0 ± 0% ~ (p=0.098 n=10)
sqs.json 350.0 ± 0% 351.0 ± 0% +0.29% (p=0.040 n=10)
sqs_no_dd_context.json 324.5 ± 1% 324.5 ± 1% ~ (p=0.463 n=10)
geomean 376.1 376.4 +0.08%
¹ all samples are equal
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Test changes on VM
Use this command from test-infra-definitions to manually test this PR changes on a VM:
inv create-vm --pipeline-id=33427319 --os-family=ubuntu
Regression Detector
Regression Detector Results
Run ID: d7281e52-50a5-4597-9b2f-04a110afcb6a Baseline: a939f454e4893fab18fe2397eb52c3f1271cbff8 Comparison: 88137416669f128abb16e3d504fff02b16759b41
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
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 | -34.86 | [-40.32, -29.39] |
Fine details of change detection per experiment
| perf | experiment | goal | Δ mean % | Δ mean % CI |
|---|---|---|---|---|
| ➖ | tcp_syslog_to_blackhole | ingress throughput | +4.01 | [-18.08, +26.09] |
| ➖ | basic_py_check | % cpu utilization | +1.69 | [-0.83, +4.21] |
| ➖ | pycheck_1000_100byte_tags | % cpu utilization | +1.21 | [-3.66, +6.08] |
| ➖ | process_agent_real_time_mode | memory utilization | +0.29 | [+0.23, +0.34] |
| ➖ | process_agent_standard_check | memory utilization | +0.20 | [+0.14, +0.25] |
| ➖ | otel_to_otel_logs | ingress throughput | +0.03 | [-0.33, +0.38] |
| ➖ | uds_dogstatsd_to_api | ingress throughput | +0.00 | [-0.20, +0.20] |
| ➖ | trace_agent_json | ingress throughput | -0.00 | [-0.01, +0.01] |
| ➖ | trace_agent_msgpack | ingress throughput | -0.00 | [-0.01, +0.00] |
| ➖ | tcp_dd_logs_filter_exclude | ingress throughput | -0.04 | [-0.08, +0.00] |
| ➖ | process_agent_standard_check_with_stats | memory utilization | -0.13 | [-0.19, -0.07] |
| ➖ | idle | memory utilization | -0.35 | [-0.39, -0.31] |
| ➖ | file_tree | memory utilization | -0.81 | [-0.91, -0.71] |
| ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -1.13 | [-3.99, +1.73] |
| ✅ | file_to_blackhole | % cpu utilization | -34.86 | [-40.32, -29.39] |
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".
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