Hands-on Labs¶

Bicep-based reproduction environments for each troubleshooting scenario. Deploy to your Azure subscription, trigger the symptom, observe signals, then clean up.

graph TD
    A[Hands-on Labs] --> B[Performance]
    A --> C[Startup or Availability]
    A --> D[Outbound or Network]
    B --> E[Memory Pressure]
    B --> F[Intermittent 5xx]
    B --> G[No Space Left on Device]
    B --> H[Slow Start / Cold Start]
    C --> I[Container HTTP Pings]
    C --> J[Deployment Ok Startup Failed]
    C --> K[Failed to Forward Request]
    C --> L[Slot Swap Config Drift]
    D --> M[SNAT Exhaustion]
    D --> N[DNS Resolution VNet]

How Labs Work¶

Each lab includes:

1. main.bicep — Infrastructure template (App Service Plan B1, App Service Python 3.11 Linux, Log Analytics, Diagnostic Settings)
2. app/ — Flask application designed to reproduce a specific symptom
3. trigger.sh — Script to trigger the symptom
4. verify.sh — Script to query Log Analytics and confirm expected signals appeared
5. Documentation page — Step-by-step walkthrough with KQL queries and expected observations

Portal view: Diagnose and solve problems entry point¶

The Diagnose and solve problems blade is the first Portal stop when running these labs. The Common Solutions tab groups seven troubleshooting categories that map to the lab folders below, and the Popular troubleshooting tools row at the bottom links to App Down Workflow, Web App Down, Web App Slow, and Process Full List - the same detectors most labs ask you to open after reproducing the failure mode. The Risk alerts Availability card with 2 Critical alerts is exactly the signal you want to see fire when a lab triggers an outage. Treat this blade as the operational companion to the lab metadata table: it tells you which Portal detector to consult after triggering each scenario.

Available Labs¶

Performance¶

Startup / Availability¶

Outbound / Network¶

Identity & Deployment¶

Prerequisites¶

All labs require:

• Azure subscription with Contributor access
• Azure CLI installed and logged in (az login)
• Bash shell (Linux, macOS, or WSL)

General Workflow¶

# 1. Create resource group
az group create --name rg-lab-<name> --location koreacentral

# 2. Deploy infrastructure
az deployment group create \
  --resource-group rg-lab-<name> \
  --template-file labs/<name>/main.bicep \
  --parameters baseName=lab<short>

# 3. Deploy app code (zip deploy or local git)
# 4. Run trigger script
# 5. Wait 2-5 minutes for logs to appear
# 6. Run verify script or query Log Analytics manually

# 7. Clean up
az group delete --name rg-lab-<name> --yes --no-wait

See Also¶

• Troubleshooting
• First 10 Minutes Checklists
• Playbooks

Sources¶

• Microsoft Learn source 1
• Microsoft Learn source 2