What are the most commonly used KQL operators for log analysis?

The essential operators are: where (filter rows), summarize (aggregate with count, avg, sum, percentile), extend (add calculated columns), project (select and rename columns), order by (sort results), render (visualize as chart), join (combine tables), and parse (extract fields from unstructured text). For time-series analysis, make-series and render timechart are invaluable. Most queries follow a pattern of: table | where TimeGenerated > ago(1h) | where condition | summarize metric by dimension | order by metric desc.

How do log alert rules work?

A log alert rule runs a KQL query at a specified frequency (e.g., every 5 minutes) against a time window (e.g., last 15 minutes). If the query returns results matching the condition (number of results > threshold, or a metric value crosses a threshold), the alert fires and triggers an action group (email, SMS, webhook, Logic App, etc.). You can configure alert severity, auto-resolution, and suppression windows. For cost control, keep queries efficient and use appropriate evaluation frequencies — running complex queries every minute across months of data generates significant ingestion and query charges.

Log Analytics Query Builder

MonitoringAzure

Build KQL queries for Azure Log Analytics with saved searches and alert rules.

Log Analytics KQL Query Builder

Table

Time Range

Column

Operator

Value

Summarize (e.g. count(), avg(Value))

Summarize By

Project Columns (comma-separated)

Sort By

Sort Order

Limit (take N)

Generated KQL Query

Output will appear here...

About This Tool

Azure Log Analytics workspaces are the central repository for log data in Azure Monitor, storing data from Azure resources, applications, agents, and custom sources. Querying this data uses Kusto Query Language (KQL), a powerful language for filtering, aggregating, joining, and visualizing log data. Building effective KQL queries requires understanding table schemas, time-range filtering, summarize operators, and join patterns. The Log Analytics Query Builder helps you construct KQL queries with proper syntax, common table references, and alert rule configurations for operational monitoring scenarios.

When to Use This Tool

•Building KQL queries that aggregate error rates by service and time bucket for operational dashboards
•Creating saved searches for common investigation patterns like failed sign-ins or resource modification events
•Defining log alert rules with proper time windows, thresholds, and action group notifications

Frequently Asked Questions

What are the most commonly used KQL operators for log analysis?: The essential operators are: where (filter rows), summarize (aggregate with count, avg, sum, percentile), extend (add calculated columns), project (select and rename columns), order by (sort results), render (visualize as chart), join (combine tables), and parse (extract fields from unstructured text). For time-series analysis, make-series and render timechart are invaluable. Most queries follow a pattern of: table | where TimeGenerated > ago(1h) | where condition | summarize metric by dimension | order by metric desc.
How do log alert rules work?: A log alert rule runs a KQL query at a specified frequency (e.g., every 5 minutes) against a time window (e.g., last 15 minutes). If the query returns results matching the condition (number of results > threshold, or a metric value crosses a threshold), the alert fires and triggers an action group (email, SMS, webhook, Logic App, etc.). You can configure alert severity, auto-resolution, and suppression windows. For cost control, keep queries efficient and use appropriate evaluation frequencies — running complex queries every minute across months of data generates significant ingestion and query charges.

Related Learning Guides

Azure Monitor & Application Insights26 min read

Disclaimer: This tool runs entirely in your browser. No data is sent to our servers. Always verify outputs before using them in production. AWS, Azure, and GCP are trademarks of their respective owners.