An extensive guide to Azure Log Analytics

Azure Log Analytics is a central part of Azure Monitor, providing a robust and scalable solution for collecting, analyzing, and visualizing log and telemetry data from a variety of sources. It enables organizations to monitor the health and performance of their Azure resources, on-premises systems, and applications in real time. By leveraging Kusto Query Language (KQL), Azure Log Analytics enables detailed querying and analysis of vast amounts of log data to troubleshoot issues, gain insights, and ensure compliance.

Let's explore key concepts of Azure Log Analytics.

Log Analytics Workspaces

Workspaces are the foundation of Azure Log Analytics. They are logical containers for storing and managing logs from various data sources, including Azure resources, on-premises systems, and custom applications. Each workspace is associated with specific data retention policies, access control, and resource settings.

Workspaces allow organizations to organize logs by use case, department, geography, or project. For example, a company might create separate workspaces for different regions (e.g., US, Europe) or business units (e.g., Finance, Engineering).

Data Retention:

Data retention in Log Analytics can be customized, with a default retention period of 31 days. However, users can extend this to retain logs for up to two years or more, depending on business needs. Longer retention periods may result in additional costs.

Data Collection

Azure Log Analytics collects data from a wide range of sources. These sources can be divided into two main categories: Azure Resources and External Sources.

1. Azure Resources: Logs from various Azure services, such as virtual machines, storage accounts, databases, web apps, networking, and more. These logs include diagnostic logs, activity logs, and resource-specific data, such as performance counters and errors.

2. External Sources: Log Analytics can also ingest data from on-premises resources, including virtual machines, physical servers, network appliances, and other external systems. These resources must be configured to send logs to Log Analytics using the Log Analytics agent (also called Microsoft Monitoring Agent).

3. Custom Applications: Applications can be configured to send custom log data, such as error logs, transaction logs, or system performance metrics, to Log Analytics. This allows organizations to monitor and analyze application-level health in addition to infrastructure performance.

Data Types Collected

1. Activity Logs: Provides details about who performed an operation on a resource and what was done, such as resource creation, deletion, or configuration changes.

2. Diagnostic Logs: Logs that provide deep operational details about how resources are performing, such as virtual machine health, application performance, and Azure service logs.

3. Custom Logs: These can be generated by custom scripts, applications, or services running in your environment. Examples include application logs or specific error codes.

4. Performance Metrics: Performance-related data from Azure resources, such as CPU usage, memory utilization, disk I/O, network traffic, and more.

5. Audit Logs: Logs that track changes in access control, authentication attempts, and role assignments.

Log Analytics Agent (Microsoft Monitoring Agent)

The Log Analytics Agent is responsible for collecting data from both Azure and non-Azure resources and forwarding it to a Log Analytics workspace. The agent collects a range of data types, including performance counters, system logs (e.g., Windows Event Logs, Linux syslogs), and custom logs.

1. Agent Configuration: The agent can be installed manually or automatically through Azure’s management interfaces (e.g., Azure portal, Azure CLI, PowerShell). Once installed, the agent can be configured to send data from your servers (on-premises or in Azure) to Log Analytics.

2. Cross-Platform Support: The Log Analytics agent supports both Windows and Linux systems, enabling hybrid cloud scenarios where both on-premises and cloud-based systems can be monitored from the same workspace.

Kusto Query Language (KQL)

KQL (Kusto Query Language) is the language used to query data in Azure Log Analytics. It is designed to work with large-scale datasets efficiently, allowing users to perform complex queries, aggregations, filtering, and analysis on log data.

Core KQL Concepts:

1. Where: Filters data based on specific conditions. Example: | where EventLevelName == "Error".

2. Summarize: Aggregates data based on a grouping criterion. Example: | summarize Count = count() by Resource, bin(TimeGenerated, 1h).

3. Project: Selects specific columns to display in the query results. Example: | project TimeGenerated, Resource, EventLevelName.

4. Extend: Adds new calculated columns to the query results. Example: | extend ErrorType = iif(EventLevelName == "Error", "Critical", "Normal").

5. Join: Combines two datasets based on a common field. Example: | join kind=inner (other_table) on common_field.

6. Top: Returns the top N rows based on specific sorting criteria. Example: | top 10 by Count desc.

7. Order by: Sorts results based on one or more columns. Example: | order by TimeGenerated desc. Example KQL query to find errors over the last 24 hours:

xxxxxxxxxx
4
1
AzureDiagnostics
2
| where TimeGenerated > ago(1d)
3
| where Severity == "Error"
4
| summarize Count = count() by Resource, bin(TimeGenerated, 1h)

Log Analytics Tables

Log data in Azure Log Analytics is stored in tables, and each table represents a specific type of log data. Common tables include:

1. AzureDiagnostics: Contains diagnostic logs for various Azure services such as virtual machines, databases, and web apps.

2. Heartbeat: Contains status and health information about monitored machines, helping to identify when a system is down or unreachable.

3. Perf: Contains performance data collected from monitored resources, such as CPU usage, memory, and disk I/O.

4. SecurityEvent: Stores security-related event data, including logins, access control changes, and potential security threats.

5. Syslog: Contains logs from Linux-based systems, including application logs and system-level events.

6. AppTraces: Contains application trace data, typically from Application Insights, helping to monitor and debug applications.

7. Custom Tables: Custom logs can be stored in custom tables based on the application or environment's needs.

These tables are queried using KQL, which allows you to filter, aggregate, and manipulate the data to derive insights.

Azure Monitor Workbooks

Workbooks are a feature in Azure Monitor that enables users to create customized, interactive dashboards using data from Log Analytics. Workbooks allow you to visualize log and performance data in rich formats such as graphs, charts, tables, and maps.

1. Interactivity: Workbooks are highly interactive, allowing users to drill down into data, filter results, and customize visualizations dynamically.

2. Multiple Data Sources: Workbooks can aggregate data from multiple Azure services, including Azure Monitor, Application Insights, and Log Analytics, providing a unified view of resource health and performance.

3. Dashboards for Teams: Workbooks can be shared across teams or departments, providing a tailored view of system performance, application health, and other critical metrics.

Azure Monitor Logs (Log Analytics) Alerts

Log Analytics integrates seamlessly with Azure Monitor Alerts to allow organizations to proactively respond to issues. You can create Log Alerts based on KQL queries, which will trigger notifications or automated actions when certain conditions are met.

Alerting Use Cases:

1. Threshold Breaches: For example, if CPU usage exceeds 80% for more than 5 minutes, an alert can be triggered.

2. Error Detection: Create alerts when specific error codes or patterns are detected in application logs.

3. Security Events: Set alerts for abnormal login attempts or unauthorized access to resources.

4. Alert Actions: When an alert is triggered, it can perform various actions such as sending an email, triggering a webhook, invoking an Azure Logic App, or running an Azure Automation Runbook.

Data Retention and Pricing

Data retention in Azure Log Analytics is highly configurable. By default, logs are retained for 31 days, but organizations can extend this retention period to up to two years or longer, depending on their needs. Extended retention may incur additional storage costs.

1. Pricing Model: Azure Log Analytics uses a pay-as-you-go pricing model, based on:

2. Data Ingestion: The amount of log data ingested into the workspace. This can include performance metrics, logs from Azure services, and custom logs.

3. Data Retention: Longer retention periods beyond the default (31 days) come with additional costs.

4. Query Execution: Azure charges for the execution of queries, especially complex queries or queries that run frequently.

Integration with Other Azure Services

Azure Log Analytics integrates with many other Azure services to provide a holistic view of an organization's infrastructure, security, and application performance:

1. Azure Security Center: Security events and threat alerts from Azure Security Center can be routed to Log Analytics for further analysis and monitoring.

2. Azure Sentinel: Azure Sentinel is a SIEM (Security Information and Event Management) tool that leverages Log Analytics to aggregate, analyze, and detect threats across your entire environment.

3. Azure Automation: Automate workflows based on alerts or log data, such as scaling resources or remediation actions in response to system performance issues.

4. Azure Application Insights: Combine Log Analytics with Application Insights to monitor application performance, detect exceptions, and troubleshoot application errors.

Best Practices for Using Azure Log Analytics

1. Organize Data into Workspaces: Use workspaces to logically group and organize log data based on teams, projects, or environments.

2. Optimize Query Performance: Use KQL efficiently by optimizing queries to minimize resource consumption. Avoid unnecessarily broad queries and leverage aggregation to reduce the amount of data being processed.

3. Set Up Alerts: Proactively monitor your resources by configuring alerts on key performance indicators, error logs, or security events.

4. Review Retention Policies: Regularly review and update data retention policies to ensure that you are keeping data for an appropriate amount of time while managing costs effectively.

5. Use Workbooks for Visualization: Create custom dashboards and workbooks for key metrics and insights, allowing teams to stay informed and take action based on real-time data.

Summary

Azure Log Analytics is an essential tool for organizations leveraging Azure to monitor, troubleshoot, and analyze their cloud and hybrid environments. By centralizing log and telemetry data, providing deep querying capabilities via KQL, and integrating with other Azure services, Log Analytics empowers teams to gain valuable insights, ensure system health, and take proactive actions to prevent downtime and security incidents. With its flexible architecture and powerful analytics, Azure Log Analytics is a cornerstone of any modern IT operations strategy in the cloud.