Monitoring and analyzing metrics, logs, and traces in Azure is essential for maintaining healthy applications and quickly identifying issues. Azure provides comprehensive tools through Azure Monitor, which serves as the central platform for observability.
**Metrics** are numerical values collected…Monitoring and analyzing metrics, logs, and traces in Azure is essential for maintaining healthy applications and quickly identifying issues. Azure provides comprehensive tools through Azure Monitor, which serves as the central platform for observability.
**Metrics** are numerical values collected at regular intervals that describe system performance. Azure Monitor Metrics stores time-series data in a specialized database optimized for fast retrieval. You can view metrics through the Azure portal, query them using Kusto Query Language (KQL), and create alerts based on thresholds. Common metrics include CPU usage, memory consumption, request counts, and response times. Application Insights provides application-specific metrics like dependency call durations and failure rates.
**Logs** contain detailed records of events and activities within your applications and infrastructure. Azure Monitor Logs uses Log Analytics workspaces to store and analyze log data. You write KQL queries to search, filter, and aggregate log information. Diagnostic settings enable you to route platform logs from Azure resources to Log Analytics, storage accounts, or Event Hubs. Application Insights automatically collects request logs, exceptions, and custom events from your applications.
**Traces** provide distributed tracing capabilities that track requests as they flow through multiple services. Application Insights uses correlation IDs to link related telemetry across service boundaries. The Application Map visualizes dependencies between components, while the Transaction Search allows you to investigate specific request flows. End-to-end transaction details show timing breakdowns for each step in a request.
**Practical Implementation** involves instrumenting applications with the Application Insights SDK, configuring diagnostic settings for Azure resources, creating dashboards for visualization, and setting up alert rules for proactive notification. You can also export data to external systems using continuous export or Azure Data Explorer for advanced analysis. Understanding these monitoring capabilities enables developers to troubleshoot performance bottlenecks, detect anomalies, and ensure application reliability in production environments.
Monitor and Analyze Metrics, Logs, and Traces - Complete Guide for AZ-204
Why is Monitor and Analyze Metrics, Logs, and Traces Important?
Monitoring is essential for maintaining healthy, performant, and reliable Azure applications. As an Azure developer, understanding how to collect, analyze, and act on telemetry data enables you to:
• Identify and resolve issues before they impact users • Optimize application performance • Understand user behavior and application usage patterns • Meet compliance and auditing requirements • Make data-driven decisions for scaling and improvements
What is Monitor and Analyze Metrics, Logs, and Traces?
Metrics are numerical values that describe some aspect of a system at a particular point in time. They are lightweight and capable of supporting near real-time scenarios. Examples include CPU usage, memory consumption, and request counts.
Logs are records of events that occurred within the system. They contain detailed information about operations, errors, and activities. Azure Monitor Logs uses Azure Data Explorer to store and query log data using Kusto Query Language (KQL).
Traces are distributed traces that follow the path of a request through various components of a distributed application. They help you understand the flow and identify bottlenecks or failures.
Key Azure Services for Monitoring:
• Azure Monitor - The unified monitoring platform for all Azure resources • Application Insights - APM service for web applications • Log Analytics - Service for querying and analyzing log data • Azure Monitor Metrics - Time-series database for metrics data
How Does It Work?
1. Data Collection: • Application Insights SDK collects telemetry from your application • Azure resources emit platform metrics and logs • Diagnostic settings route data to appropriate destinations • Custom metrics and logs can be sent via SDK or REST API
2. Data Storage: • Metrics are stored in Azure Monitor Metrics (time-series database) • Logs are stored in Log Analytics workspaces • Data retention can be configured based on requirements
3. Data Analysis: • Use KQL (Kusto Query Language) to query logs • Metrics Explorer for visualizing metric data • Workbooks for creating interactive reports • Smart detection for automatic anomaly detection
4. Alerting and Action: • Configure alert rules based on metrics or log queries • Action groups define notification and automation responses • Autoscale based on metric thresholds
• requests | where resultCode >= 500 - Find failed requests • dependencies | where duration > 1000 - Find slow dependencies • exceptions | summarize count() by type - Count exceptions by type • traces | where severityLevel >= 3 - Find warning and error traces
Exam Tips: Answering Questions on Monitor and Analyze Metrics, Logs, and Traces
1. Know the Telemetry Types: Understand the difference between requests, dependencies, exceptions, traces, and custom events. Questions often ask which telemetry type captures specific data.
2. Understand Sampling: Know the three types of sampling - Adaptive (default), Fixed-rate, and Ingestion. Adaptive sampling automatically adjusts volume, while fixed-rate gives you control.
3. Connection String vs Instrumentation Key: Connection strings are the modern approach and support regional endpoints. Be prepared for questions about configuration.
4. KQL Basics: Familiarize yourself with basic KQL operators: where, summarize, project, extend, join, and render. You may need to identify or complete queries.
5. Alert Configuration: Understand metric alerts vs log alerts. Metric alerts evaluate metrics at regular intervals, while log alerts run KQL queries against Log Analytics.
6. Diagnostic Settings: Know that diagnostic settings are used to route platform logs and metrics to Log Analytics, Storage Accounts, or Event Hubs.
7. Application Map: Remember that Application Map visualizes dependencies and helps identify failing or slow components in distributed applications.
8. Availability Tests: Know the types: URL ping test (basic), Standard test (advanced), and Custom TrackAvailability test. Standard tests support multiple HTTP requests.
9. Live Metrics Stream: This provides real-time monitoring with approximately 1-second latency. Useful for monitoring deployments or investigating live issues.
10. Code-based vs Codeless Monitoring: Application Insights supports both approaches. Codeless (auto-instrumentation) works for certain platforms like App Service, while code-based requires SDK integration.
Common Exam Scenarios:
• Configuring Application Insights for a web application • Writing KQL queries to find specific telemetry • Setting up alerts based on application performance • Troubleshooting slow requests using dependency tracking • Implementing custom telemetry for business metrics • Configuring sampling to manage telemetry volume