Learning Path review questions: LP08

Learning Path review questions

1. Does Azure Monitor allow you to create alerts from log queries?

Yes, Azure Monitor allows you to create alerts based on log queries. Specifically, you can write Log Analytics queries in Azure Log Analytics and create alerts that trigger when specific conditions or thresholds are met.

Alerts are based on Kusto Query Language (KQL) queries and can be configured to notify you when a certain condition is detected (e.g., a performance issue, error rate increase, or unusual activity in the logs).

You can set up alerts for various types of data such as metrics, logs, or even Application Insights telemetry.

Example:

You can create a log query like:

xxxxxxxxxx
3
1
requests
2
| where timestamp > ago(1h) and success == false
3
| summarize count() by bin(timestamp, 5m)

This query looks for failed requests within the past hour and summarizes their count. You can then set up an alert that triggers when the failure count exceeds a certain threshold.

2. What features are provided by Azure Monitor?

Azure Monitor offers a comprehensive set of features designed to provide full-stack monitoring for applications, infrastructure, and networks.

Some of the key features include:

1. Metrics Collection: Collecting and analyzing metrics from resources like VMs, databases, storage, and networking.

2. Log Analytics: Querying and analyzing log data from various Azure services using Kusto Query Language (KQL). This includes the ability to query logs from Azure resources and applications.

3. Application Insights: Monitoring the performance and usage of your applications, including real-time insights into requests, dependencies, exceptions, and application performance.

4. Alerting: Setting up alerts based on metric thresholds or log query results, allowing you to be notified of potential issues.

5. Autoscaling: Monitoring metrics to trigger automatic scaling for your resources based on demand (e.g., scaling a web app up or down based on CPU utilization).

6. Azure Monitor for Containers: Collecting telemetry from containerized environments (such as Azure Kubernetes Service or Docker containers) to monitor health and performance.

7. Azure Monitor for VMs: Provides insights into the health and performance of virtual machines, including guest-level performance metrics.

8. Network Monitoring: Monitoring and analyzing network traffic, including network security groups (NSGs), firewalls, and Azure load balancers.

9. Diagnostic and Health Data: Collecting diagnostic data from Azure resources to help with troubleshooting and root cause analysis.

10. Workbooks: Visualizing and analyzing metrics and logs through customizable dashboards called Workbooks.

11. Azure Monitor Insights: Out-of-the-box solutions for monitoring specific workloads, like VM Insights, SQL Insights, or Kubernetes Insights.

12. Smart Detection: Automatically detecting anomalies or deviations from normal patterns in telemetry data, especially useful in Application Insights.

3. What query language can you use to query Azure Log Analytics?

You can use Kusto Query Language (KQL) to query Azure Log Analytics. KQL is a powerful and flexible query language used to interact with data in Azure Monitor and Azure Log Analytics.

KQL allows you to write complex queries to filter, aggregate, and analyze log data.

Common operators in KQL include where, summarize, project, extend, join, and parse.

It is designed to work with large datasets, providing fast and efficient query execution across large volumes of telemetry data.

Example of a KQL query:

xxxxxxxxxx
4
1
Heartbeat
2
| where TimeGenerated > ago(1d)
3
| summarize Count = count() by Computer
4
| order by Count desc

This query retrieves the number of heartbeat records from different computers over the last day.

4. What platform integrations does Azure Monitor provide to visualize your logs in real time?

Azure Monitor provides several platform integrations to visualize your logs and metrics in real-time:

Azure Dashboards: You can create custom dashboards within the Azure portal to display metrics, logs, and insights from Azure Monitor, providing a consolidated view of your environment.

Workbooks: Workbooks are highly customizable dashboards that allow you to visualize log data from Azure Monitor and other Azure services. Workbooks support real-time data visualization and can display metrics, logs, and custom queries.

Azure Monitor Logs (Log Analytics): Through the Log Analytics workspace, you can run queries in real time and visualize results using charts, graphs, and tables. This is an integral part of the Azure Monitor interface.

Power BI: You can integrate Power BI with Azure Monitor to create rich, interactive visualizations of your log and metric data. Power BI can be used to pull real-time telemetry from Azure Monitor and display it in a highly customizable way.

Grafana: Azure Monitor integrates with Grafana, a popular open-source platform for monitoring and observability. Grafana can be configured to pull metrics and log data from Azure Monitor and display real-time visualizations on Grafana dashboards.

Application Insights: For application monitoring, Application Insights provides real-time telemetry and performance metrics, which can be visualized through the Application Insights resource in the Azure portal.

Azure Monitor for Containers: Real-time monitoring of containers, including AKS (Azure Kubernetes Service), is available through Azure Monitor for Containers. Logs and metrics from containerized workloads are presented in real time.

5. True or False: Application Insights analyses the traffic from your website against historic trends and sends you smart detection notifications on degradation.

True.

Application Insights uses Smart Detection to automatically analyze your application’s telemetry data (e.g., requests, dependencies, exceptions) and compare it to historical trends. It can then send notifications if it detects any anomalies or degradation in performance.

Smart Detection is designed to identify patterns that deviate from normal behavior (e.g., a sudden spike in error rates or a slowdown in response times) without requiring explicit configuration from the user.

It can notify you about potential issues such as performance degradation, increased failure rates, or unusual traffic patterns, helping you to proactively manage the health of your application.

This feature is useful for catching performance issues early, even before end-users report them, and is based on machine learning models that learn from historical data.