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Add Metadata to Profiles from Azure Container Apps#30428

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dd-mergequeue[bot] merged 4 commits into
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duncan-harvey/azure-container-app-profile-tags
Oct 24, 2024
Merged

Add Metadata to Profiles from Azure Container Apps#30428
dd-mergequeue[bot] merged 4 commits into
mainfrom
duncan-harvey/azure-container-app-profile-tags

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@duncanpharvey

@duncanpharvey duncanpharvey commented Oct 23, 2024

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What does this PR do?

Adds subscription_id, resource_group, and resource_id tags to profiles from Azure Container Apps.

Motivation

Provide profiling backend with Azure Container App metadata to enable proper billing.

https://datadoghq.atlassian.net/browse/SVLS-5848

Describe how to test/QA your changes

Build serverless-init test image from https://github.com/DataDog/datadog-lambda-extension/tree/main/scripts.

VERSION=1.0.0 ./build_serverless_init.sh
cp -r .layers/. ./scripts/bin/
docker build --build-arg="TARGET_ARCH=amd64" --platform linux/amd64 -f Dockerfile.serverless-init.build -t duncanacrtesting.azurecr.io/serverless-init-testing:1.0.0 .
docker push duncanacrtesting.azurecr.io/serverless-init-testing:1.0.0

Deploy to Azure Container Apps

az containerapp up \
  --name node-container-profiling \
  --resource-group duncan-harvey-rg \
  --ingress external \
  --target-port 8080 \
  --env-vars "DD_API_KEY=<api key>" \
             "DD_SITE=datadoghq.com" \
             "DD_TRACE_ENABLED=true" \
             "DD_LOGS_ENABLED=true" \
             "DD_PROFILING_ENABLED=true" \
             "DD_AZURE_SUBSCRIPTION_ID=<subscription_id>" \
             "DD_AZURE_RESOURCE_GROUP=<resource group>" \
             "DD_SERVICE=node-container-profiling" \
             "DD_ENV=dev" \
             "DD_VERSION=1.0.0" \
             "DD_LOG_LEVEL=debug" \
  --image duncanacrtesting.azurecr.io/azure-container-app-hello-world-node:1.0.0

Application Code

var express = require('express');
var app = express();

app.get('/', async function (req, res) {
  console.log('node testing');
  res.send('Hello Datadog!');
});

app.listen(8080, function () {
  console.log('Example app listening on port 8080!');
});

Dockerfile

FROM node:22
WORKDIR /app
COPY package*.json index.js ./
RUN npm install
EXPOSE 8080

COPY --from=duncanacrtesting.azurecr.io/serverless-init-testing:1.0.0 /datadog-init /app/datadog-init

RUN npm install --prefix /dd_tracer/node dd-trace --save
ENV NODE_PATH="/dd_tracer/node/node_modules"
ENV NODE_OPTIONS="--require dd-trace/init"
ENTRYPOINT ["/app/datadog-init"]

CMD ["node", "index.js"]

Possible Drawbacks / Trade-offs

Additional Notes

These tags are already available on spans. With this change they are available on profiles as well.

@cit-pr-commenter

cit-pr-commenter Bot commented Oct 23, 2024

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Regression Detector

Regression Detector Results

Run ID: 276d066e-6206-432e-9248-6798289c0a0c Metrics dashboard Target profiles

Baseline: b718ef8
Comparison: f17d507

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 trials links
basic_py_check % cpu utilization +1.90 [-0.78, +4.57] 1 Logs
quality_gate_idle_all_features memory utilization +0.84 [+0.75, +0.94] 1 Logs bounds checks dashboard
uds_dogstatsd_to_api_cpu % cpu utilization +0.75 [+0.03, +1.47] 1 Logs
idle memory utilization +0.39 [+0.34, +0.43] 1 Logs bounds checks dashboard
quality_gate_idle memory utilization +0.21 [+0.16, +0.26] 1 Logs bounds checks dashboard
file_to_blackhole_300ms_latency egress throughput +0.02 [-0.16, +0.20] 1 Logs
tcp_dd_logs_filter_exclude ingress throughput +0.00 [-0.01, +0.01] 1 Logs
uds_dogstatsd_to_api ingress throughput -0.01 [-0.10, +0.07] 1 Logs
file_to_blackhole_100ms_latency egress throughput -0.01 [-0.23, +0.21] 1 Logs
file_to_blackhole_0ms_latency egress throughput -0.03 [-0.38, +0.31] 1 Logs
file_to_blackhole_500ms_latency egress throughput -0.04 [-0.29, +0.20] 1 Logs
idle_all_features memory utilization -0.05 [-0.15, +0.05] 1 Logs bounds checks dashboard
file_to_blackhole_1000ms_latency egress throughput -0.07 [-0.56, +0.43] 1 Logs
file_tree memory utilization -0.25 [-0.37, -0.12] 1 Logs
otel_to_otel_logs ingress throughput -0.36 [-1.16, +0.45] 1 Logs
tcp_syslog_to_blackhole ingress throughput -0.92 [-0.98, -0.87] 1 Logs
pycheck_lots_of_tags % cpu utilization -1.01 [-3.52, +1.50] 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
idle_all_features memory_usage 10/10
quality_gate_idle memory_usage 10/10
quality_gate_idle_all_features 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:

  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".

@duncanpharvey
duncanpharvey marked this pull request as ready for review October 23, 2024 17:19
@duncanpharvey
duncanpharvey requested a review from a team as a code owner October 23, 2024 17:19
@duncanpharvey duncanpharvey added the qa/done QA done before merge and regressions are covered by tests label Oct 23, 2024
@agent-platform-auto-pr

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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=47266622 --os-family=ubuntu

Note: This applies to commit f17d507

@duncanpharvey

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/merge

@dd-devflow

dd-devflow Bot commented Oct 24, 2024

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🚂 MergeQueue: waiting for PR to be ready

This merge request is not mergeable yet, because of pending checks/missing approvals. It will be added to the queue as soon as checks pass and/or get approvals.
Note: if you pushed new commits since the last approval, you may need additional approval.
You can remove it from the waiting list with /remove command.

Use /merge -c to cancel this operation!

@duncanpharvey

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/remove

@dd-devflow

dd-devflow Bot commented Oct 24, 2024

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🚂 Devflow: /remove

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dd-devflow Bot commented Oct 24, 2024

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⚠️ MergeQueue: This merge request was unqueued

This merge request was unqueued

If you need support, contact us on Slack #devflow!

@duncanpharvey

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/merge

@dd-devflow

dd-devflow Bot commented Oct 24, 2024

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🚂 MergeQueue: pull request added to the queue

The median merge time in main is 22m.

Use /merge -c to cancel this operation!

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3 participants