startup-perf

Mede o desempenho de inicialização da aplicação Aspire usando dotnet-trace e a ferramenta TraceAnalyzer. Use isto quando for solicitado a medir o impacto de uma alteração de código no Aspire…

npx skills add https://github.com/microsoft/aspire --skill startup-perf

Aspire Startup Profiling with OTEL

Use this skill when measuring, validating, or investigating Aspire startup performance with the CLI self-profile capture flow.

The workflow is the hidden CLI flag --capture-profile. It starts a private standalone dashboard collector, enables profiling-only OTEL instrumentation for the command and child AppHost processes, exports a trace archive, and then exits with the wrapped command's exit code.

Current Profiling Model

Profiling is opt-in and separate from reported telemetry:

  • Enable profiling with ASPIRE_PROFILING_ENABLED=true or 1.
  • CLI profiling spans use the Aspire.Cli.Profiling ActivitySource.
  • Hosting profiling spans use the Aspire.Hosting.Profiling ActivitySource.
  • DCP startup spans use the dcp.startup instrumentation scope when DCP emits startup telemetry.
  • Reported telemetry must not carry profiling session IDs, high-cardinality profiling tags, or profiling spans.

Prerequisites

Use an Aspire CLI build that contains --capture-profile. From a repo checkout:

./restore.sh
./dotnet.sh build src/Aspire.Cli/Aspire.Cli.csproj /p:SkipNativeBuild=true

Repo-local development builds discover the built managed dashboard from artifacts/bin/Aspire.Managed when ASPIRE_REPO_ROOT points at the checkout. Installed or bundled CLIs discover the dashboard from the bundle. Use ASPIRE_DASHBOARD_PATH / ASPIRE_MANAGED_PATH when profiling with a custom dashboard build.

Quick Start

Capture startup for an AppHost and exit automatically after startup:

./dotnet.sh exec artifacts/bin/Aspire.Cli/Debug/net11.0/aspire.dll run \
  --project tests/TestingAppHost1/TestingAppHost1.AppHost/TestingAppHost1.AppHost.csproj \
  --capture-profile \
  --capture-profile-output artifacts/tmp/startup-profile/profile.zip \
  --non-interactive

Capture any other Aspire command:

aspire ls \
  --capture-profile \
  --capture-profile-output artifacts/tmp/startup-profile/ls-profile.zip \
  --non-interactive

If --capture-profile-output is omitted, the CLI writes aspire-profile-<timestamp>-<session>.zip under the current working directory. For long-lived run and start, the CLI exits automatically after startup and waits for profiling data to settle before writing the export.

Self-Profile Options

OptionDescription
--capture-profileHidden recursive root option that enables self-profile capture for any Aspire command.
--capture-profile-output PATHOutput zip path. Relative paths are rooted at the current working directory.
--capture-profile-delay SECONDSOptional warmup delay before stopping long-lived run/start commands. Defaults to 5 seconds so AppHost-side spans have time to flush before shutdown. Increase it when you intentionally want additional post-start resource activity in the capture.

Output Artifacts

The capture writes a dashboard export zip containing:

PathDescription
traces/profile.jsonOTLP JSON trace export from the private dashboard collector.

Inspect the export:

unzip -l artifacts/tmp/startup-profile/profile.zip
tmpdir="$(mktemp -d)"
unzip -q artifacts/tmp/startup-profile/profile.zip -d "$tmpdir"
jq -r '.resourceSpans[]?.scopeSpans[]?.scope.name' "$tmpdir/traces/profile.json" | sort | uniq -c
jq -r '.resourceSpans[]?.scopeSpans[]?.spans[]?.name' "$tmpdir/traces/profile.json" | sort | uniq -c

Expected startup captures include:

  • Aspire.Cli.Profiling spans such as aspire/cli/command, aspire/cli/run, dotnet process spans, backchannel connect spans, and dashboard URL retrieval.
  • Aspire.Hosting.Profiling spans such as DCP model work, resource creation, resource wait, and DCP resource observation.
  • dcp.startup spans when the DCP process emits startup telemetry and the scenario is configured to require them.

Comparing Before/After Changes

Prefer separate worktrees for baseline and feature measurements so branch switching does not disturb a dirty worktree.

# Baseline worktree
aspire run --project path/to/AppHost.csproj \
  --capture-profile \
  --capture-profile-output artifacts/tmp/startup-profile-baseline/profile.zip \
  --non-interactive

# Feature worktree
aspire run --project path/to/AppHost.csproj \
  --capture-profile \
  --capture-profile-output artifacts/tmp/startup-profile-feature/profile.zip \
  --non-interactive

Compare traces/profile.json span names, durations, operation IDs, process IDs, events, and trace correlation. For statistically meaningful wall-clock comparisons, run multiple iterations manually and keep the environment stable. The self-profile capture flow produces artifacts; it is not a statistical benchmark runner by itself.

Parallel captures are supported because each --capture-profile process allocates its own collector ports and profiling session ID. Always use distinct --capture-profile-output paths. If the profiled AppHost launch profile pins dashboard, resource-service, or application ports, those AppHost ports can still conflict across parallel worktrees; use an isolated/randomized profile or adjust the AppHost ports for parallel runs.

Instrumentation Guidance

Keep profiling APIs coarse-grained and profiling-specific:

  • Centralize raw Activity, activity names, tag names, and event names in the profiling telemetry type for the area (Aspire.Cli.Profiling or Aspire.Hosting.Profiling).
  • Do not expose one public/internal method per tag. Prefer operation/result-level methods that accept the data for a phase and set multiple tags/events internally.
  • Good API shape examples: start a dotnet process span with command, project, working directory, and options; record a process start result with started/process ID; record process completion with exit code and output counts; start a Kubernetes API span with operation/resource type; record retry details as one event method.
  • Call sites should describe the operation being profiled, not know tag/event names.
  • Do not add profiling tags/events to Activity.Current unless the current activity is known to be a profiling activity or profiling has explicitly wrapped it.
  • Keep high-cardinality data out of reported telemetry.

Common Issues

SymptomCauseFix
The CLI bundle layout was found, but the dashboard binary (aspire-managed) is missing.The CLI could not find a bundled, repo-local, or override dashboard binary.Build the repo-local CLI, use an installed/bundled CLI, set ASPIRE_REPO_ROOT to the checkout, or set ASPIRE_DASHBOARD_PATH / ASPIRE_MANAGED_PATH to a custom managed dashboard build.
Self-profile export contains CLI spans but not Hosting spansThe AppHost did not run through a profiled startup path, or Hosting telemetry did not reach the collector.Confirm aspire run or aspire start launched the expected AppHost and inspect traces/profile.json for Aspire.Hosting.Profiling.
No exported spans contained aspire.profiling.session_idProfiling was not enabled or telemetry was not exported.Confirm --capture-profile was parsed before -- and inspect traces/profile.json.
No profiling session contained correlated... spansCLI/Hosting/DCP spans did not land in one correlated trace.Inspect traces/profile.json for missing scopes or broken parent/trace IDs.

Mais skills de microsoft

oss-growth
microsoft
Persona de growth hacker OSS
agent-framework-azure-ai-py
microsoft
Crie agentes do Azure AI Foundry usando o SDK Python do Microsoft Agent Framework (agent-framework-azure-ai). Use ao criar agentes persistentes com AzureAIAgentsProvider, usando ferramentas hospedadas (interpretador de código, pesquisa de arquivos, pesquisa na web), integrando servidores MCP, gerenciando threads de conversa ou implementando respostas em streaming. Abrange ferramentas de função, saídas estruturadas e agentes com múltiplas ferramentas.
development
airunway-aks-setup
microsoft
Configure o AI Runway no AKS — do cluster vazio ao modelo em execução. Abrange verificação do cluster, instalação do controlador, avaliação de GPU, configuração do provedor e primeira implantação. QUANDO: "configurar AI Runway", "integrar cluster AKS", "instalar AI Runway", "configuração do airunway", "implantar modelo no AKS", "inferência GPU no AKS", "configuração KAITO no AKS", "executar LLM no AKS", "vLLM no AKS", "configurar serviço de modelo no AKS", "controlador AI Runway".
devops
appinsights-instrumentation
microsoft
Orientação para instrumentar aplicações web com Azure Application Insights. Fornece padrões de telemetria, configuração de SDK e referências de configuração. QUANDO: como instrumentar o app, SDK do App Insights, padrões de telemetria, o que é App Insights, orientação sobre Application Insights, exemplos de instrumentação, melhores práticas de APM.
devops
applicationinsights-web-ts
microsoft
Instrumente aplicativos de navegador/web com o SDK JavaScript do Application Insights (@microsoft/applicationinsights-web). Use para Real User Monitoring (RUM) — visualizações de página, cliques, dependências AJAX/fetch, exceções, eventos personalizados e rastreamentos de agentes GenAI no lado do navegador correlacionados a rastreamentos OpenTelemetry no backend. Abrange o Script de Carregamento do SDK e a configuração via npm, extensões de frameworks (React, React Native, Angular), Click Analytics, inicializadores de telemetria e convenções semânticas GenAI do OTel para spans de agente/ferramenta/modelo emitidos pelo navegador.
devops
azure-ai-anomalydetector-java
microsoft
Crie aplicativos de detecção de anomalias com o SDK do Azure AI Anomaly Detector para Java. Use ao implementar detecção de anomalias univariada/multivariada, análise de séries temporais ou monitoramento com IA.
development
azure-ai-language-conversations-py
microsoft
Implemente o reconhecimento de linguagem conversacional (CLU) usando o SDK Python azure-ai-language-conversations. Use ao trabalhar com ConversationAnalysisClient para analisar intenção e entidades de conversas, criar recursos de NLP ou integrar o reconhecimento de linguagem em aplicativos.
development
azure-ai-ml-py
microsoft
SDK v2 do Azure Machine Learning para Python. Use para workspaces de ML, jobs, modelos, conjuntos de dados, computação e pipelines. Gatilhos: "azure-ai-ml", "MLClient", "workspace", "registro de modelos", "jobs de treinamento", "conjuntos de dados".
development