Understand the essential information about the KQL Language in Azure

Kusto Query Language (KQL) is a powerful query language used in Azure for querying large volumes of data across different services, particularly in Azure Monitor, Log Analytics, Azure Sentinel, and Application Insights. KQL is designed to be efficient, flexible, and optimized for fast data exploration, monitoring, and analysis.

Key Features and Concepts of KQL

1. Case Sensitivity: KQL is case-insensitive by default for most identifiers (like table names and column names). However, string comparisons and certain operators (e.g., matches regex) are case-sensitive.

2. Tables: Data in Azure is stored in tables, such as AzureDiagnostics, Perf, SecurityEvent, Heartbeat, and CustomLogs_CL. You interact with these tables in your queries to filter, transform, and aggregate data.

3. Columns: Data retrieved from KQL queries is presented in columns. Each column represents an attribute of the log data (e.g., TimeGenerated, Resource, Message).

4. Pipes (|): The pipe (|) operator is used to chain multiple operators in a query. This allows you to perform sequential operations on your data. The general syntax follows a pipeline structure:

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TableName
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| Operator1
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| Operator2
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| Operator3

5. Basic Syntax Components in KQL:

   • Tables: Each query begins with the table name from which data is retrieved.

   • Operators: These are applied to the data in a sequential manner. Examples include where, project, summarize, extend, etc.

   • Functions: KQL allows you to define and use functions to reuse query logic. Functions can be either built-in or custom.

Common Operators and Functions in KQL

1. where: Filters rows based on conditions.

   Example:

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AzureDiagnostics
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| where TimeGenerated > ago(1d)

This filters rows where the TimeGenerated column is within the last 24 hours.

2. project: Selects specific columns to return.

   Example:

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AzureDiagnostics
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| project TimeGenerated, Resource, Message

3. extend: Adds new columns based on expressions.

   Example:

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AzureDiagnostics
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| extend NewColumn = Column1 + Column2

4. summarize: Aggregates data (e.g., count, average, sum, etc.).

   Example (counting events per hour):

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AzureDiagnostics
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| summarize EventCount = count() by bin(TimeGenerated, 1h)

5. order by: Sorts the results by specified columns.

   Example:

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AzureDiagnostics
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| order by TimeGenerated desc

6. join: Combines data from two tables.

   Example:

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Table1
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| join kind=inner (Table2) on ColumnName

7. project-away: Excludes specified columns from the result.

   Example:

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AzureDiagnostics
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| project-away ColumnToRemove

8. top: Retrieves the top N rows based on a column’s values.

   Example:

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AzureDiagnostics
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| top 10 by TimeGenerated desc

9. count(): Returns the number of records in a group.

   Example:

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AzureDiagnostics
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| summarize EventCount = count() by Resource

10. bin(): Groups data into bins (typically used for time-based aggregation).

Example:

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AzureDiagnostics
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| summarize Count = count() by bin(TimeGenerated, 1h)

11. let: Used to define variables or temporary tables within a query.

Example:

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let myData = AzureDiagnostics | where Resource == "VM";
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myData | summarize Count = count() by bin(TimeGenerated, 1h)

12. mv-expand: Expands multivalue fields (arrays or lists) into separate rows.

Example:

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MyTable
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| mv-expand ColumnName

Time Series and Date Functions

1. ago(): Returns a relative time from the current timestamp.

   Example:

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AzureDiagnostics
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| where TimeGenerated > ago(1d)

2. now(): Returns the current timestamp.

   Example:

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AzureDiagnostics
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| where TimeGenerated > now() - 1d

3. bin(): Used to round time to a specific interval (e.g., hourly, daily).

   Example:

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AzureDiagnostics
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| summarize Count = count() by bin(TimeGenerated, 1h)

4. datetime: Allows explicit date/time values in queries.

   Example:

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AzureDiagnostics
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| where TimeGenerated between (datetime(2024-01-01) .. datetime(2024-01-02))

Aggregations and Windowing

1. summarize: Performs aggregation on data, like count(), avg(), sum(), min(), max(), etc.

   Example (calculate the average CPU usage):

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Perf
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| where ObjectName == "Processor" and CounterName == "% Processor Time"
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| summarize avg(CounterValue) by Computer, bin(TimeGenerated, 1h)

2. partition: Used for more advanced aggregations, such as windowing functions (e.g., moving averages).

   Example:

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Perf
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| partition by Computer 
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| summarize avg(CounterValue) by bin(TimeGenerated, 1h)

String Functions

1. strcat(): Concatenates strings.

   Example:

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AzureDiagnostics
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| extend FullMessage = strcat("Alert: ", Message)

2. contains: Checks if a substring exists within a string.

   Example:

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AzureDiagnostics
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| where Message contains "error"

3. replace(): Replaces occurrences of a substring with another string.

   Example:

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AzureDiagnostics
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| extend CleanMessage = replace("error", "issue", Message)

4. matches regex: Performs regex-based matching within strings.

   Example:

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AzureDiagnostics
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| where Message matches regex @"\d+"

Joins and Subqueries

1. join: Combines rows from two tables based on a matching column.

   Example:

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Table1
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| join kind=inner (Table2) on ColumnName

2. Subqueries: You can use subqueries to reference temporary results in the main query.

   Example:

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let Subquery = AzureDiagnostics | where Resource == "VM";
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Subquery | summarize Count = count() by Resource

Working with Functions

1. User-defined Functions: You can create your own functions to reuse logic across multiple queries.

   Example:

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create function getErrorLogs() {
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   AzureDiagnostics
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   | where Level == "Error"
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};
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getErrorLogs() | summarize Count = count()

2. Built-in Functions: KQL provides many built-in functions for performing calculations, aggregations, and manipulations (e.g., avg(), sum(), count(), min(), max()).

Optimization Tips

1. Avoid unnecessary joins: Joins can be expensive in terms of query performance, so avoid them unless necessary.

2. Use project early: Use project early in your queries to limit the number of columns processed, improving performance.

3. Limit time range: Narrowing down the time range of the query reduces the data volume, improving performance.

Example Query

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1
AzureDiagnostics
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| where TimeGenerated > ago(1d)
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| where Resource == "myVM"
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| summarize count() by bin(TimeGenerated, 1h)
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| order by TimeGenerated desc

This query filters logs from the past 24 hours for a specific VM, aggregates the data into hourly bins, and returns the result ordered by time.

Summary

KQL is highly powerful for analyzing, transforming, and visualizing large datasets in Azure. By understanding its syntax and operators, you can efficiently query, monitor, and troubleshoot resources in Azure environments.