2.1.4.1. Design for Azure Monitor

💡 First Principle: A unified observability platform that centralizes telemetry data (metrics and logs) from all sources is essential for achieving end-to-end visibility and enabling proactive operational management.

Scenario: You are designing the monitoring solution for a new enterprise application. This involves collecting performance data from Virtual Machines and Azure SQL Databases, aggregating all application logs into a central repository, and setting up alerts for critical events like high CPU usage or application errors.

Azure Monitor is a unified monitoring solution that helps you understand how your applications and other resources are performing and proactively identifies problems.

Azure Monitor Architecture:

• Data Sources: Telemetry originates from Azure resources (VMs, databases, containers), applications (via SDKs), operating systems, and custom sources.
• Data Platform: Ingests and stores metrics (numerical, near real-time) and logs (structured/unstructured, detailed events). Metrics are optimized for fast querying; logs are stored in Log Analytics for deep analysis with KQL.
• Insights: Pre-built solutions like VM Insights and Container Insights deliver tailored monitoring and recommendations for specific workloads.
• Visualization: Data is visualized through Azure dashboards, Workbooks (customizable reports), and Power BI integration.
• Respond: Automated responses are enabled via alerts, autoscale, and integrations with ITSM tools or webhooks.

Designing Data Collection Strategies:

• Metrics: Capture real-time performance and health (e.g., CPU, memory). Ideal for dashboards and threshold-based alerts.
• Logs: Store detailed diagnostics, security, and audit data for root cause analysis and compliance.
• Diagnostic Settings: Configure resources to send logs/metrics to Log Analytics (for querying/alerting), Storage Accounts (for retention), or Event Hubs (for streaming).

⚠️ Common Pitfall: Relying only on metrics. Metrics tell you that something is wrong (e.g., high CPU), but logs tell you why it's wrong (e.g., a specific process is consuming all the CPU). A complete solution requires both.

Key Trade-Offs:

• Metrics vs. Logs: Metrics are lightweight, fast, and good for alerting on known conditions. Logs are more verbose, providing rich context for deep troubleshooting, but are more expensive to ingest and query.

Practical Implementation: KQL Query in Log Analytics

// This KQL query finds all performance records for a specific computer
// where the CPU utilization was over 90% in the last hour.
Perf
| where Computer == "MyWebAppVM1"
| where CounterName == "% Processor Time" and CounterValue > 90
| where TimeGenerated > ago(1h)

Reflection Question: How does designing for Azure Monitor, integrating its data collection strategies (metrics, logs via diagnostic settings) with alerting and visualization capabilities, fundamentally ensure end-to-end visibility and proactive operational management for your Azure applications and infrastructure?