How do you monitor and troubleshoot apps with Azure Monitor?

Long Answer

In distributed cloud systems, observability is a first-class concern. On Azure, the trio of Azure Monitor, Application Insights, and Log Analytics forms a comprehensive monitoring stack. My approach is to unify metrics, traces, and logs while enabling actionable troubleshooting.

1) Azure Monitor as the foundation
Azure Monitor aggregates telemetry from Azure resources (VMs, AKS, App Service, Functions) and custom sources. It collects metrics (CPU, memory, request rate), logs, and health data. From there, I define alert rules (threshold, dynamic, anomaly detection) and configure action groups to notify on-call teams via email, Teams, PagerDuty, or ITSM. Workbooks provide dashboards that blend metrics, KQL queries, and visuals for operations and leadership.

2) Application Insights for application-level observability
For web apps and microservices, I enable Application Insights SDKs. This provides:

• Request/response monitoring with latency and throughput.
  
• Dependency tracking (SQL, Storage, external APIs) with success/failure rates.
  
• Distributed tracing across microservices, showing request flows in an Application Map.
  
• Live metrics stream for near real-time insight during incidents.
  
• Smart detection for anomalies (sudden spikes in failures, abnormal response times).
  

This creates an APM layer to connect user experience with backend dependencies.

3) Log Analytics for correlation and root cause
All telemetry (resource logs, custom app logs, security events) flow into Log Analytics workspaces. I use KQL (Kusto Query Language) to slice and correlate data. For example:

• Join application logs with VM diagnostics to confirm if high latency traces align with CPU throttling.
  
• Analyze failed requests by operation name, client region, or dependency.
  
• Build queries that feed alerts (e.g., >5% error rate in 5 mins).
  

Log Analytics makes troubleshooting cross-layer issues possible in a single query interface.

4) Troubleshooting strategy
When issues arise, I:

• Start with Azure Monitor alerts and dashboards to identify anomalies.
  
• Drill into Application Insights traces to see if latency/errors are app- or dependency-driven.
  
• Use Log Analytics queries to correlate events (app error logs + infrastructure signals).
  
• Check distributed transaction maps for bottlenecks between services.
  
• If containerized (AKS), I use Container Insights to inspect node/pod health, network, and logs in the same workspace.
  

This top-down to bottom-up path accelerates MTTR.

5) Governance and compliance
For enterprise use, I set retention policies and diagnostic settings. Logs are exported to Log Analytics, Blob, or Event Hub for SIEM integration. I implement RBAC on workspaces so teams access only their scope. Regulatory workloads often demand immutable storage—then I pair Log Analytics with Azure Storage immutable blob policies.

6) Proactive practices
I build synthetic monitoring with Application Insights Availability Tests (pings, multi-step web tests) to simulate user journeys. I use Azure Monitor Workbooks for SLA/SLI reporting. Continuous export integrates with DevOps pipelines to run smoke tests after deployments. For anomaly detection, I use dynamic thresholds in Azure Monitor to reduce alert fatigue.

7) Integration with DevOps and incident response
Dashboards and alerts are linked to DevOps boards, creating work items automatically. Playbooks in Azure Automation or Logic Apps can remediate common issues (restart service, scale out). Incident runbooks guide teams from detection (alert → Application Insights trace) through RCA (Log Analytics queries).

Summary
By combining Azure Monitor (infrastructure), Application Insights (APM), and Log Analytics (correlation), I deliver full observability: proactive detection, deep troubleshooting, and compliance-ready logging. This layered approach shrinks MTTR, surfaces anomalies early, and makes distributed systems operable at scale.

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Table

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Common Mistakes

• Relying only on Azure Monitor metrics without enabling Application Insights for code-level visibility.
  
• Logging everything without structure—leads to noise and high ingestion cost.
  
• Skipping Log Analytics queries and relying only on dashboards—losing RCA depth.
  
• No synthetic monitoring, so teams discover issues only via user complaints.
  
• Failing to correlate app errors with infrastructure events—blaming the wrong layer.
  
• Not configuring retention/export policies, leading to compliance gaps.
  
• Alert sprawl: too many static thresholds, causing alert fatigue.
  
• Ignoring permissions/RBAC, which risks exposing sensitive logs.

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Sample Answers (Junior / Mid / Senior)

Junior:
“I’d enable Azure Monitor to collect metrics and use Application Insights SDK for requests, dependencies, and errors. Logs go to Log Analytics where I run KQL queries for troubleshooting.”

Mid:
“I set up dashboards with Azure Monitor, use Application Insights for distributed tracing and Application Map, and build alerts on error rates or latency. I query Log Analytics to correlate app failures with infra metrics and export logs for compliance.”

Senior:
“My approach is end-to-end: Azure Monitor handles metrics/alerts with dynamic thresholds; Application Insights provides APM with distributed tracing and synthetic availability tests. Logs flow into Log Analytics where we build KQL-based RCA queries. We integrate with DevOps pipelines, link alerts to ITSM, and enforce RBAC/retention for compliance.”

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Evaluation Criteria

Strong candidates explain how Azure Monitor, Application Insights, and Log Analytics complement each other. They mention metrics, traces, and logs as a unified observability triangle. Strong answers include: Application Map and distributed tracing; KQL queries for correlation; alerts with action groups; availability tests; and RBAC + retention for compliance. The best responses show structured troubleshooting flows (alert → trace → logs). Weak answers are vague (“just use Azure Monitor”), lack mention of App Insights or Log Analytics, or don’t discuss how to reduce noise and speed RCA.

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Preparation Tips

Create a demo: deploy a sample app on App Service with Application Insights. Simulate latency/failures and use Live Metrics to detect spikes. Run KQL queries in Log Analytics to correlate app logs with CPU usage. Build a Workbook dashboard mixing metrics and queries. Configure synthetic Availability Tests for a user flow. Set alerts on error percentage with dynamic thresholds and send to Teams via Action Group. Practice narrating an RCA: “We saw an alert, traced via App Insights, confirmed infra issue via Log Analytics.” This 60-second story shows practical troubleshooting skills.

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Real-world Context

A fintech used Azure Monitor + App Insights to cut MTTR by 40%. Alerts triggered on error spikes, engineers used Application Map to trace failures back to a misconfigured SQL pool. Log Analytics queries confirmed DTU throttling on the database. A SaaS team improved reliability by adding synthetic tests in App Insights—detecting login flow outages before customers did. Another enterprise built compliance dashboards in Log Analytics with RBAC + retention policies for GDPR audits. By unifying metrics, traces, and logs, they turned fragmented monitoring into a proactive incident response system.

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Key Takeaways

• Azure Monitor = metrics/alerts/dashboards.
  
• Application Insights = app-level APM with tracing and dependency map.
  
• Log Analytics = centralized log queries with KQL.
  
• Combine the three for layered observability and RCA.
  
• Add synthetic monitoring and RBAC for compliance.
  

Optimize alerts to reduce fatigue.

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Practice Exercise

Scenario:
You manage a distributed Azure app: App Service frontend, AKS microservices, SQL Database, and Storage. Users report intermittent slowness.

Tasks:

1. Enable Application Insights on frontend + microservices; map dependencies.
   
2. Configure Live Metrics Stream; simulate load to see response time spikes.
   
3. Route diagnostic logs from SQL/Storage into Log Analytics.
   
4. Use KQL to join failed requests from App Insights with SQL throttling logs.
   
5. Build a Workbook dashboard showing latency by service and region.
   
6. Add synthetic Availability Test for login + checkout flow.
   
7. Create alerts: error rate >5% for 5 mins, dynamic latency anomaly detection.
   
8. Define RBAC on Log Analytics workspace and retention policy for 2 years (compliance).
   

Deliverable:
An incident runbook showing: Alert fired → traced via App Insights → RCA with Log Analytics → fix applied. Proves you can monitor, log, and troubleshoot distributed apps end-to-end on Azure.