datadog-agent
datadog-agent copied to clipboard
AML-1804 Asynchronous dogstatsd flush
What does this PR do?
Add an option (off by default) to perform the bulk of the dogstatsd flushing without blocking the rest of the pipeline.
Motivation
Dogstatsd server has a queue that buffers incoming packets if they can not be processed immediately. Flushing large number of metrics can block the pipeline for a long time (around 1-2 seconds) leading to queue to buffer a lot of packets.
Additional Notes
Processing new metrics needs to write to the context map when it encounters a new metric context. At the same time, flush process reads the same map to extract context information for metrics being flushed. To avoid lock overhead, asynchronous flush instead copies the context map, which is fast (~100ms) and needs less space than the buffer queue. Then we extract and remove buckets that need to be flushed from the working set. After this, metrics processing can resume, while the flush process works in background.
Possible Drawbacks / Trade-offs
Describe how to test/QA your changes
Serverless Benchmark Results
BenchmarkStartEndInvocation comparison between 0e3a2a61f13e4a208b0b0fa40cd3d2626b938054 and 61ce86493b56372d3cc670f401c847817ba463bb.
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 86.56µ ± 4% 87.65µ ± 2% ~ (p=0.247 n=10)
api-gateway-kong-appsec.json 68.09µ ± 2% 68.09µ ± 1% ~ (p=0.670 n=10)
api-gateway-kong.json 67.47µ ± 2% 67.22µ ± 2% ~ (p=0.436 n=10)
api-gateway-non-proxy-async.json 105.7µ ± 1% 106.2µ ± 1% ~ (p=0.481 n=10)
api-gateway-non-proxy.json 107.1µ ± 1% 106.6µ ± 1% ~ (p=0.280 n=10)
api-gateway-websocket-connect.json 70.63µ ± 1% 70.09µ ± 1% ~ (p=0.101 n=10)
api-gateway-websocket-default.json 63.51µ ± 1% 62.98µ ± 2% ~ (p=0.149 n=10)
api-gateway-websocket-disconnect.json 63.97µ ± 2% 63.31µ ± 1% -1.02% (p=0.029 n=10)
api-gateway.json 117.3µ ± 3% 115.3µ ± 2% ~ (p=0.063 n=10)
application-load-balancer.json 64.57µ ± 1% 64.07µ ± 1% ~ (p=0.165 n=10)
cloudfront.json 48.35µ ± 2% 47.27µ ± 1% -2.23% (p=0.000 n=10)
cloudwatch-events.json 39.32µ ± 2% 37.36µ ± 3% -4.99% (p=0.000 n=10)
cloudwatch-logs.json 67.94µ ± 2% 66.42µ ± 1% -2.24% (p=0.000 n=10)
custom.json 30.92µ ± 3% 31.36µ ± 2% ~ (p=0.353 n=10)
dynamodb.json 95.16µ ± 2% 95.00µ ± 1% ~ (p=0.529 n=10)
empty.json 29.59µ ± 2% 29.43µ ± 2% ~ (p=0.247 n=10)
eventbridge-custom.json 42.12µ ± 2% 42.14µ ± 2% ~ (p=0.927 n=10)
http-api.json 72.47µ ± 1% 72.56µ ± 2% ~ (p=0.631 n=10)
kinesis-batch.json 70.53µ ± 1% 70.53µ ± 1% ~ (p=0.971 n=10)
kinesis.json 54.48µ ± 2% 53.46µ ± 2% -1.86% (p=0.011 n=10)
s3.json 60.88µ ± 2% 59.74µ ± 1% ~ (p=0.143 n=10)
sns-batch.json 89.53µ ± 1% 89.81µ ± 1% ~ (p=0.481 n=10)
sns.json 64.41µ ± 1% 65.26µ ± 1% +1.32% (p=0.012 n=10)
snssqs.json 111.5µ ± 3% 114.0µ ± 2% +2.23% (p=0.023 n=10)
snssqs_no_dd_context.json 97.15µ ± 2% 99.26µ ± 2% +2.17% (p=0.009 n=10)
sqs-aws-header.json 54.59µ ± 3% 56.15µ ± 3% +2.86% (p=0.015 n=10)
sqs-batch.json 92.47µ ± 2% 94.96µ ± 1% +2.70% (p=0.001 n=10)
sqs.json 67.98µ ± 2% 70.38µ ± 2% +3.53% (p=0.000 n=10)
sqs_no_dd_context.json 62.02µ ± 5% 63.86µ ± 1% +2.96% (p=0.015 n=10)
geomean 67.33µ 67.36µ +0.04%
│ baseline/benchmark.log │ current/benchmark.log │
│ B/op │ B/op vs base │
api-gateway-appsec.json 37.26Ki ± 0% 37.32Ki ± 0% +0.18% (p=0.000 n=10)
api-gateway-kong-appsec.json 26.91Ki ± 0% 26.92Ki ± 0% ~ (p=0.591 n=10)
api-gateway-kong.json 24.41Ki ± 0% 24.42Ki ± 0% ~ (p=0.223 n=10)
api-gateway-non-proxy-async.json 48.01Ki ± 0% 48.07Ki ± 0% +0.13% (p=0.000 n=10)
api-gateway-non-proxy.json 47.24Ki ± 0% 47.30Ki ± 0% +0.12% (p=0.000 n=10)
api-gateway-websocket-connect.json 25.44Ki ± 0% 25.47Ki ± 0% +0.11% (p=0.000 n=10)
api-gateway-websocket-default.json 21.35Ki ± 0% 21.39Ki ± 0% +0.17% (p=0.000 n=10)
api-gateway-websocket-disconnect.json 21.14Ki ± 0% 21.17Ki ± 0% +0.17% (p=0.000 n=10)
api-gateway.json 49.53Ki ± 0% 49.54Ki ± 0% ~ (p=0.138 n=10)
application-load-balancer.json 22.32Ki ± 0% 23.26Ki ± 0% +4.21% (p=0.000 n=10)
cloudfront.json 17.64Ki ± 0% 17.66Ki ± 0% +0.09% (p=0.049 n=10)
cloudwatch-events.json 11.69Ki ± 0% 11.71Ki ± 0% +0.19% (p=0.000 n=10)
cloudwatch-logs.json 53.38Ki ± 0% 53.37Ki ± 0% ~ (p=0.516 n=10)
custom.json 9.718Ki ± 0% 9.730Ki ± 0% +0.13% (p=0.009 n=10)
dynamodb.json 40.69Ki ± 0% 40.69Ki ± 0% ~ (p=0.896 n=10)
empty.json 9.277Ki ± 0% 9.296Ki ± 0% ~ (p=0.063 n=10)
eventbridge-custom.json 13.41Ki ± 0% 13.44Ki ± 0% +0.24% (p=0.014 n=10)
http-api.json 23.70Ki ± 0% 23.80Ki ± 0% +0.41% (p=0.000 n=10)
kinesis-batch.json 27.00Ki ± 0% 27.04Ki ± 0% +0.12% (p=0.009 n=10)
kinesis.json 17.80Ki ± 0% 17.83Ki ± 0% +0.15% (p=0.011 n=10)
s3.json 20.33Ki ± 0% 20.35Ki ± 0% ~ (p=0.239 n=10)
sns-batch.json 38.65Ki ± 0% 38.63Ki ± 0% ~ (p=0.353 n=10)
sns.json 23.95Ki ± 0% 23.99Ki ± 0% +0.17% (p=0.035 n=10)
snssqs.json 50.75Ki ± 0% 50.81Ki ± 0% +0.11% (p=0.007 n=10)
snssqs_no_dd_context.json 44.78Ki ± 0% 44.89Ki ± 0% +0.24% (p=0.000 n=10)
sqs-aws-header.json 18.79Ki ± 0% 18.83Ki ± 0% ~ (p=0.143 n=10)
sqs-batch.json 41.64Ki ± 0% 41.66Ki ± 0% ~ (p=0.239 n=10)
sqs.json 25.54Ki ± 0% 25.59Ki ± 0% ~ (p=0.755 n=10)
sqs_no_dd_context.json 20.69Ki ± 1% 20.68Ki ± 0% ~ (p=0.839 n=10)
geomean 25.70Ki 25.76Ki +0.26%
│ baseline/benchmark.log │ current/benchmark.log │
│ allocs/op │ allocs/op vs base │
api-gateway-appsec.json 629.5 ± 0% 630.0 ± 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)
api-gateway-non-proxy-async.json 725.0 ± 0% 726.0 ± 0% ~ (p=0.370 n=10)
api-gateway-non-proxy.json 716.0 ± 0% 716.0 ± 0% ~ (p=1.000 n=10)
api-gateway-websocket-connect.json 453.0 ± 0% 453.0 ± 0% ~ (p=1.000 n=10)
api-gateway-websocket-default.json 379.0 ± 0% 379.0 ± 0% ~ (p=0.303 n=10)
api-gateway-websocket-disconnect.json 370.0 ± 0% 370.0 ± 0% ~ (p=0.582 n=10)
api-gateway.json 791.0 ± 0% 791.0 ± 0% ~ (p=0.582 n=10)
application-load-balancer.json 352.0 ± 0% 353.0 ± 0% +0.28% (p=0.000 n=10)
cloudfront.json 284.0 ± 0% 284.0 ± 0% ~ (p=0.211 n=10)
cloudwatch-events.json 220.0 ± 0% 220.0 ± 0% ~ (p=1.000 n=10)
cloudwatch-logs.json 216.0 ± 0% 216.0 ± 0% ~ (p=1.000 n=10)
custom.json 168.0 ± 0% 168.0 ± 0% ~ (p=1.000 n=10)
dynamodb.json 589.0 ± 0% 589.0 ± 0% ~ (p=0.210 n=10)
empty.json 160.0 ± 1% 160.0 ± 1% ~ (p=1.000 n=10)
eventbridge-custom.json 254.0 ± 0% 254.0 ± 0% ~ (p=1.000 n=10)
http-api.json 432.5 ± 0% 433.0 ± 0% ~ (p=0.470 n=10)
kinesis-batch.json 390.0 ± 0% 390.0 ± 0% ~ (p=1.000 n=10)
kinesis.json 285.0 ± 0% 285.0 ± 0% ~ (p=0.474 n=10)
s3.json 358.0 ± 0% 358.0 ± 1% ~ (p=1.000 n=10)
sns-batch.json 455.0 ± 0% 455.0 ± 0% ~ (p=0.520 n=10)
sns.json 322.5 ± 0% 323.0 ± 0% ~ (p=0.081 n=10)
snssqs.json 450.5 ± 0% 450.5 ± 0% ~ (p=0.977 n=10)
snssqs_no_dd_context.json 399.0 ± 1% 400.0 ± 0% +0.25% (p=0.008 n=10)
sqs-aws-header.json 273.5 ± 0% 274.0 ± 0% ~ (p=0.206 n=10)
sqs-batch.json 503.5 ± 0% 504.0 ± 0% ~ (p=0.331 n=10)
sqs.json 351.0 ± 0% 351.0 ± 1% ~ (p=0.952 n=10)
sqs_no_dd_context.json 324.5 ± 0% 324.0 ± 1% ~ (p=0.490 n=10)
geomean 376.8 376.9 +0.04%
¹ all samples are equal
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=38165569 --os-family=ubuntu
Note: This applies to commit 77646f64
Regression Detector
Regression Detector Results
Run ID: 82e10bdc-dbf6-46d0-9ca5-49a277e3b470 Metrics dashboard Target profiles
Baseline: b3e5cab1c83d5156e6619e2d470431afc0306879 Comparison: 77646f648fd98fd163cc61f683da6697340d93bd
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.
Fine details of change detection per experiment
| perf | experiment | goal | Δ mean % | Δ mean % CI | links |
|---|---|---|---|---|---|
| ➖ | tcp_syslog_to_blackhole | ingress throughput | +8.95 | [-4.60, +22.50] | Logs |
| ➖ | pycheck_1000_100byte_tags | % cpu utilization | +0.82 | [-3.97, +5.61] | Logs |
| ➖ | uds_dogstatsd_to_api | ingress throughput | +0.00 | [-0.00, +0.00] | Logs |
| ➖ | tcp_dd_logs_filter_exclude | ingress throughput | -0.00 | [-0.01, +0.01] | Logs |
| ➖ | idle | memory utilization | -0.14 | [-0.18, -0.10] | Logs |
| ➖ | basic_py_check | % cpu utilization | -0.48 | [-3.09, +2.13] | Logs |
| ➖ | otel_to_otel_logs | ingress throughput | -0.63 | [-1.44, +0.18] | Logs |
| ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -0.65 | [-1.54, +0.24] | Logs |
| ➖ | file_tree | memory utilization | -0.81 | [-0.90, -0.72] | Logs |
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".