Welcome to the KQL Hit Doc – your all-in-one, charming, exhaustive, and battle-ready knowledge base on Kusto Query Language (KQL). This document is crafted as both a learning journal and an advanced cheat sheet to empower you with everything from beginner syntax to advanced analytics. Ideal for Azure Monitor, Application Insights, Log Analytics, and more.

1. Introduction to KQL
2. Core Concepts
3. Basic Syntax
4. Query Operators
5. Data Types
6. Data Ingestion and Schema
7. Commonly Used Tables (Azure)
8. Filtering, Sorting, Projection
9. Aggregation & Summarization
10. Joins and Lookups
11. Subqueries and Let Statements
12. Time Series Analysis
13. Rendering & Visualization
14. Advanced Functions & Plugins
15. Security and Access Control
16. Performance Tuning
17. Tips, Tricks, and Patterns
18. Real-life Scenarios and Examples
19. Further Reading & References

Kusto Query Language (KQL) is a read-only query language designed for big data analytics. Used primarily in Azure Monitor, Log Analytics, and Application Insights, it is optimized for interactive ad hoc queries.

• Developed by Microsoft for Azure Data Explorer.
• Declarative and SQL-like but tailored for telemetry data.
• Use-cases: performance monitoring, diagnostics, security analysis.

• Everything is table-based.
• Queries are pipeline-based, flowing through | operators.
• Supports structured, semi-structured, and time-series data.
• Read-only: No data updates via KQL.

TableName
| where Condition
| summarize Count = count() by Column
| order by Count desc

• Case-sensitive.
• Whitespace and indentation improve readability but are not required.

• project, extend, summarize, where, order by, take, join
• Control flow: if, case
• Type casting: tostring(), toint(), etc.

• Primitive: string, int, long, datetime, bool, real, guid
• Complex: dynamic (JSON-like), timespan

• In Azure, data is pushed into tables like Perf, Heartbeat, SecurityEvent, etc.
• Schema = columns + data types + metadata

Use .show and .ingest commands (in ADX context).

• AzureActivity, SigninLogs, SecurityEvent
• Perf, Syslog, Heartbeat, AppRequests
• Usage, InsightsMetrics

SecurityEvent
| where EventID == 4625
| project TimeGenerated, Computer, Account
| order by TimeGenerated desc

SigninLogs
| summarize Failures = count() by bin(TimeGenerated, 1h), ResultType

• count(), avg(), max(), sum(), percentiles(), make_list()

Table1
| join kind=inner (Table2) on CommonColumn

• inner, leftouter, rightouter, fullouter, anti, semi

let FailedLogins = SigninLogs | where ResultType != 0;
FailedLogins | summarize count() by UserPrincipalName

• Use let for modular, reusable logic.

Perf
| where ObjectName == "Processor" and CounterName == "% Processor Time"
| summarize avg(CounterValue) by bin(TimeGenerated, 5m)

• Use make-series, range, bin(), and serialize

Perf
| summarize avg(CounterValue) by bin(TimeGenerated, 5m)
| render timechart

• Types: timechart, barchart, piechart, table

• Math/Stats: percentile(), variance(), stdev()
• String: split(), substring(), replace_string()
• Datetime: ago(), now(), startofhour()
• Dynamic: parse_json(), extractjson()
• Plugins: predict, anomaly_detection, cluster()

• RBAC at Azure Resource and Log Analytics Workspace level.
• Table-level permissions.
• KQL itself doesn’t write data, ensuring read-only integrity.

• Use project early to limit columns.
• Filter early (where) to reduce row count.
• Avoid wildcard searches (contains → has / startswith)
• Index-aware queries improve performance.

• Prefer has over contains for word-matching.
• Use toscalar() to assign values from a query.
• Use distinct for unique rows.
• Combine logs with union.
• Debug using print.

• Find top 5 IPs with failed login attempts:

SigninLogs
| where ResultType != 0
| summarize Count = count() by IPAddress
| top 5 by Count desc

• Analyze CPU over time:

Perf
| where CounterName == "% Processor Time"
| summarize avg(CounterValue) by bin(TimeGenerated, 10m), Computer
| render timechart

• KQL Official Docs
• Azure Monitor
• KQL Tutorials
• KQL Quick Reference

"Querying logs should be poetic."

KQL is not just a query language; it's an analyst's wand, a developer's magnifier, and a security guardian's shield. Use it to slice time, stitch patterns, sniff anomalies, and trace problems — all with readable, powerful syntax.

Play Ground For Challenges

My Achievements:

Happy querying! ⚡

📁 Pro Tip: Use this README.md as your reference card, knowledge base, and personal guide while mastering KQL.