Advanced Kusto Query Language Examples | Generated by AI
Kusto Query Language (KQL) is a powerful tool for querying large datasets in Azure services like Azure Data Explorer and Azure Monitor. Below are several complex examples that combine multiple operators, functions, and advanced features such as time-series analysis, anomaly detection, pivoting, and user-defined functions. These are drawn from real-world scenarios like analyzing storm events or sales data. Each includes the query and a brief explanation.
1. Anomaly Detection in Time-Series Data
This query aggregates daily averages from a metrics table and uses series decomposition to identify anomalies, ideal for monitoring unusual patterns in logs or telemetry.
TableName
| make-series Metric = avg(Value) on Timestamp step 1d
| extend Anomalies = series_decompose_anomalies(Metric)
2. User-Defined Function for Parameterized Filtering and Summarization
Here, a reusable function filters sales data by region and threshold, then computes totals—useful for dynamic reporting in Azure Data Explorer dashboards.
let CalculateSales = (region: string, minSales: int) {
SalesData
| where Region == region and Sales > minSales
| summarize TotalSales = sum(Sales)
};
CalculateSales("North America", 1000)
3. Pivoting Aggregated Data for Cross-Tabular Analysis
This aggregates values by category and region, then pivots regions into columns for easier comparison, common in business intelligence queries.
TableName
| summarize Total = sum(Value) by Category, Region
| evaluate pivot(Region, sum(Total))
4. Correlation Analysis Between Time-Series Metrics
Using storm events data, this creates daily series for two metrics and computes their correlation to uncover relationships, such as between damage and fatalities.
StormEvents
| make-series PropertyDamage = avg(DamageProperty), Fatalities = avg(Fatalities) on BeginTime step 1d
| extend Correlation = series_correlation(PropertyDamage, Fatalities)
5. Filtering, Top Selection, and Duration Calculation for Event Analysis
This filters Texas flood events, selects the top 5 by damage, and adds a computed duration column—great for incident response in Azure Sentinel.
StormEvents
| where State == 'TEXAS' and EventType == 'Flood'
| top 5 by DamageProperty
| project StartTime, EndTime, Duration = EndTime - StartTime, DamageProperty
6. Static Value Mapping with Let Statement for Data Transformation
Defines a dictionary to map source types, filters events, and projects friendly names—helpful for normalizing categorical data in logs.
let sourceMapping = dynamic(
{
"Emergency Manager" : "Public",
"Utility Company" : "Private"
});
StormEvents
| where Source == "Emergency Manager" or Source == "Utility Company"
| project EventId, Source, FriendlyName = sourceMapping[Source]
These examples can be run directly in the Azure Data Explorer web UI or Log Analytics. For more, explore the official samples gallery.
Comprehensive Guide to Kusto Query Language (KQL)
Tutorial: Learn common Kusto Query Language operators