6.1.3.2. Perform Log Queries by Using Kusto Query Language (KQL)
š” First Principle: Kusto Query Language (KQL) is the fundamental tool for transforming raw log data into actionable insights, enabling powerful, flexible, and efficient analysis of telemetry in Azure Monitor.
Scenario: Your application is experiencing intermittent errors, and the logs are centralized in a Log Analytics workspace. You need to quickly find all "Error" messages, count them by the Virtual Machine they originated from, and sort them to see which VMs are reporting the most errors.
What It Is: KQL is a powerful, read-only query language used to query your data in Azure Monitor Logs.
Basic KQL Query Structure:
A KQL query is composed of a series of statements, each starting with a table name and followed by one or more operators, separated by a pipe (|).
Common KQL Operators:
| Operator | Purpose | Example Usage |
|---|---|---|
where | Filters rows by a specified condition. | ` |
project | Selects specific columns to display. | ` |
summarize | Aggregates data by groups (count, avg, sum, etc.). | ` |
extend | Adds calculated columns to the result set. | ` |
sort by | Sorts the results by one or more columns. | ` |
top | Returns the top N rows based on a column. | ` |
join | Combines rows from two tables based on a common value. | ` |
union | Combines rows from multiple tables. | union Heartbeat, Perf |
Practical Implementation: KQL Query for the Scenario
// Find all error events and count them by computer
Syslog
| where SeverityLevel == "error"
| summarize ErrorCount = count() by Computer
| sort by ErrorCount desc
Visual: KQL Query Flow for Log Analysis
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ā ļø Common Pitfall: Writing inefficient KQL queries, such as filtering data late in the query pipeline. Always apply where clauses as early as possible to reduce the amount of data processed by subsequent operators.
Key Trade-Offs:
- Query Complexity vs. Performance: More complex queries with joins and multiple summarizations can provide deeper insights but may take longer to run and consume more resources.
Reflection Question: How does using KQL with operators like where, summarize, and sort by fundamentally enable you to extract, filter, and aggregate large volumes of log data efficiently, transforming complex datasets into clear, actionable operational answers for troubleshooting?