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Monitoring and Observability Query language

KQL function

KQL function controls how teams reuse common filters, joins, calculations, detections, and normalization logic across monitoring, security, and analytics workloads. Teams see it in azure data explorer databases, microsoft sentinel hunting queries. It is not an Azure Function app, SQL stored procedure, Logic Apps workflow, user-defined scalar operator, or ARM template function; confusing them can create stale detection logic, broken dashboards. Use the term when reviewing access, monitoring, cost, recovery, or performance. It keeps architects, operators, security reviewers, and support teams focused on the same setting, resource, or behavior.

Source: Microsoft Learn - User-defined functions in Kusto Query Language Reviewed 2026-05-15

Exam trap
Treating KQL function as a harmless label instead of checking the live resource, scope, owner, and dependencies.
Production check
Verify the intended scope, owner tags, diagnostics, and dependent resources before changing KQL function.
Practice this topic Microsoft Learn
Article details and learning context
Aliases
Kusto function, user-defined KQL function, stored Kusto function, query-defined function
Difficulty
Intermediate
CLI mappings
5
Last verified
2026-05-15
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Understand the concept

Why it matters

KQL function matters because it turns an architecture choice into day-to-day workload behavior. If the team misunderstands it, the failure usually appears as stale detection logic, broken dashboards, hidden expensive joins before anyone notices the documentation gap. The term also affects security, reliability, operations, cost, and performance because one setting can influence access, recovery, automation, user experience, and budget. Naming it precisely helps engineers compare portal settings, CLI output, infrastructure-as-code, monitoring data, and incident notes without guessing. It also gives reviewers a practical checklist: where is it configured, who owns it, what depends on it, what evidence proves it works, and how rollback happens.

Official wording and source

KQL function controls how teams reuse common filters, joins, calculations, detections, and normalization logic across monitoring, security, and analytics workloads. Microsoft Learn places it in User-defined functions in Kusto Query Language; operators confirm scope, configuration, dependencies, and production impact. Use the linked source for exact Azure behavior.

Open Microsoft Learn

Technical context

Technically, KQL function sits in Azure Data Explorer databases, Microsoft Sentinel hunting queries, Log Analytics saved content, function definitions. Key fields include function name, parameters, folder, docstring. Operators verify it with stored function metadata, query text, invocation results, execution errors. In production reviews, connect the term to resource scope, identity, network path, diagnostics, cost ownership, and rollback. Confirm subscription, resource group, service tier, dependent workload, and current Azure evidence before changing it. Capture the current resource ID, region, and dependency path before approving changes.

Exam context

Compare with

Function Expression language

Where it is used

Where you see it

  1. In the Azure portal, KQL function appears near azure data explorer databases, microsoft sentinel hunting queries, where owners review configuration, health, access, and dependent workload impact before safe production changes.
  2. In CLI or REST output, KQL function shows up through stored function metadata, query text and related fields that confirm live Azure state during audits, releases, and incidents.
  3. In incident reviews, KQL function is discussed when users report stale detection logic, and engineers compare logs, metrics, ownership, dependencies, recent changes, support impact, and deployment evidence together.

Common situations

  • Design and review KQL function as part of a production Azure workload.
  • Troubleshoot incidents where KQL function affects user-visible behavior or operator evidence.
  • Document ownership, rollback, monitoring, and cost impact for KQL function during governance reviews.

Illustrative Azure scenarios

These examples show how the concept can affect design and operations. They are illustrative scenarios, not customer claims.

Scenario 01 KQL function in action for shared error definition Scenario, objectives, solution, measured impact, and takeaway.
Scenario

Fabrikam Claims, a insurance organization, needed to make every incident dashboard use the same definition of high-priority application errors. The team had to improve the design without disrupting existing users or weakening governance.

Goals
  • Use KQL function to solve the immediate workload problem
  • Keep security and compliance evidence available for review
  • Reduce manual support effort during operations
  • Measure results with production telemetry and owner signoff
Approach using KQL function

Architects treated KQL function as a production control point rather than a background detail. They reviewed the current Azure resources, confirmed owners, and documented how the term connected to identity, networking, monitoring, cost, and rollback. Engineers implemented KQL functions, standardized filters, workbook references, scheduled-query alert rules, and query tests against recent incidents, then validated the change with read-only CLI checks and portal evidence. The rollout used a pilot scope first, with diagnostic logging enabled before wider release. Support teams received a runbook explaining expected output, common failure modes, and the safest rollback path. Security reviewers checked access boundaries and data-handling assumptions before the change moved to production.

Potential outcomes
  • reduced conflicting incident reports by 73 percent
  • cut dashboard maintenance by 35 percent
  • kept alert thresholds tied to one reviewed function
  • improved post-incident evidence quality
What to learn

KQL function is valuable when teams connect the Azure setting to measurable security, reliability, operational, cost, and performance outcomes.

Scenario 02 KQL function in action for sentinel detection reuse Scenario, objectives, solution, measured impact, and takeaway.
Scenario

Contoso Bank, a financial services organization, needed to reuse identity-risk normalization across multiple Microsoft Sentinel analytics rules. The team had to improve the design without disrupting existing users or weakening governance.

Goals
  • Use KQL function to solve the immediate workload problem
  • Keep security and compliance evidence available for review
  • Reduce manual support effort during operations
  • Measure results with production telemetry and owner signoff
Approach using KQL function

Architects treated KQL function as a production control point rather than a background detail. They reviewed the current Azure resources, confirmed owners, and documented how the term connected to identity, networking, monitoring, cost, and rollback. Engineers implemented stored KQL functions, watchlist joins, parameterized time windows, change review, and scheduled validation queries, then validated the change with read-only CLI checks and portal evidence. The rollout used a pilot scope first, with diagnostic logging enabled before wider release. Support teams received a runbook explaining expected output, common failure modes, and the safest rollback path. Security reviewers checked access boundaries and data-handling assumptions before the change moved to production.

Potential outcomes
  • lowered duplicate detection code by 48 percent
  • improved rule deployment speed by 29 percent
  • kept sensitive table access reviewed
  • reduced false positives through consistent filters
What to learn

KQL function is valuable when teams connect the Azure setting to measurable security, reliability, operational, cost, and performance outcomes.

Scenario 03 KQL function in action for plant quality analytics Scenario, objectives, solution, measured impact, and takeaway.
Scenario

Alpine Manufacturing, a manufacturing organization, needed to standardize calculation of defective-part rates across production dashboards. The team had to improve the design without disrupting existing users or weakening governance.

Goals
  • Use KQL function to solve the immediate workload problem
  • Keep security and compliance evidence available for review
  • Reduce manual support effort during operations
  • Measure results with production telemetry and owner signoff
Approach using KQL function

Architects treated KQL function as a production control point rather than a background detail. They reviewed the current Azure resources, confirmed owners, and documented how the term connected to identity, networking, monitoring, cost, and rollback. Engineers implemented ADX stored functions, parameterized plant and line filters, dashboard integration, and performance review with execution statistics, then validated the change with read-only CLI checks and portal evidence. The rollout used a pilot scope first, with diagnostic logging enabled before wider release. Support teams received a runbook explaining expected output, common failure modes, and the safest rollback path. Security reviewers checked access boundaries and data-handling assumptions before the change moved to production.

Potential outcomes
  • reduced report variance between teams by 62 percent
  • kept queries under the five-second target
  • lowered analyst rework by 40 percent
  • gave plant managers one agreed calculation
What to learn

KQL function is valuable when teams connect the Azure setting to measurable security, reliability, operational, cost, and performance outcomes.

Azure CLI

CLI checks are useful for KQL function because they capture live Azure state, reduce guesswork, and separate safe inspection from approved changes.

Useful for

  • Confirm the live Azure resource or configuration related to KQL function before approving a production change.
  • Capture read-only evidence for KQL function during incident response, audit review, or release validation.
  • Compare CLI output with infrastructure-as-code, portal settings, and runbook expectations for KQL function.
  • Validate graph-connected dependencies for KQL function before changing production scope.

Before you run a command

  • Confirm tenant, subscription, resource group, service name, and environment before trusting command output.
  • Run list or show commands first, then save evidence before any create, update, delete, restore, or deploy action.
  • Check whether the command exposes secrets, customer data, training examples, file paths, keys, or private endpoints.
  • Have an approved rollback path and owner contact ready before changing production configuration.

What the output tells you

  • Whether the expected Azure resource exists and whether KQL function is configured at the intended scope.
  • Which names, IDs, locations, states, tiers, policies, identities, and dependent resources are active right now.
  • Whether live Azure state differs from the design document, deployment template, release ticket, or support runbook.
  • Which metric, log query, portal page, or application test should be checked before closing the issue.

Mapped commands

KQL function operational checks

direct 
az kusto cluster show --name <cluster-name> --resource-group <resource-group>
az kusto clusterdiscoverAnalytics
read-only Microsoft Learn 
az kusto database show --cluster-name <cluster-name> --database-name <database-name> --resource-group <resource-group>
az kusto databasediscoverAnalytics
read-only Microsoft Learn 
az rest --method POST --url https://<cluster-uri>/v1/rest/mgmt --body @create-kql-function.json
az restoperateMonitoring and Observability
modifies-state Microsoft Learn 
az monitor log-analytics query --workspace <workspace-id> --analytics-query "let RecentErrors = () { AppExceptions | where TimeGenerated > ago(1h) }; RecentErrors() | take 10"
az monitor log-analyticsdiscoverMonitoring and Observability
read-only Microsoft Learn 
az monitor scheduled-query show --name <rule-name> --resource-group <resource-group>
az monitor scheduled-querydiscoverMonitoring and Observability
read-only Microsoft Learn

Architecture context

Technically, KQL function sits in Azure Data Explorer databases, Microsoft Sentinel hunting queries, Log Analytics saved content, function definitions. Key fields include function name, parameters, folder, docstring. Operators verify it with stored function metadata, query text, invocation results, execution errors. In production reviews, connect the term to resource scope, identity, network path, diagnostics, cost ownership, and rollback. Confirm subscription, resource group, service tier, dependent workload, and current Azure evidence before changing it.

Security
Security for KQL function starts with database permissions, function ownership, query exposure, sensitive table access, Sentinel rule dependencies. Review who can read, create, update, delete, restore, deploy, or invoke the related resource, and verify that privileged changes create audit evidence. Prefer Microsoft Entra ID, managed identities, private endpoints, key rotation, customer-managed keys, and policy controls where the service supports them. Keep secrets, credentials, personal data, and regulated content out of scripts and examples unless the data-handling design explicitly allows it. During approval, check tenant boundaries, network exposure, diagnostic logs, and break-glass procedures so a configuration mistake does not become an incident.
Cost
Cost for KQL function is driven by query scan volume, repeated expensive joins, ADX compute, Log Analytics query cost pressure, alert evaluation frequency. The common mistake is treating the term as free because it is a setting, schema choice, job, or child resource instead of a cost influence. Check whether charges come from storage, requests, tokens, replicas, retention, backups, training, data transfer, diagnostics, or engineer time spent recovering from bad configuration. Use tags, budgets, Azure Cost Management, and owner reviews to connect usage to a workload. When reducing cost, confirm the change will not remove recovery evidence, security controls, or needed performance headroom.
Reliability
Reliability for KQL function depends on schema compatibility, parameter validation, alert dependencies, versioned function changes, test queries. A resource can exist and still fail the business workflow when permissions, network paths, limits, schema settings, or downstream services are wrong. Define the health signal before production use, then test the expected failure mode with a controlled change. Monitor platform metrics, application traces, deployment history, and user symptoms in the same time window during incidents. Recovery plans should include owner contact, safe rollback, validation queries, and customer-impact checks, not just proof that the Azure resource exists. Confirm this behavior is tested before the workload depends on it.
Performance
Performance for KQL function depends on function inlining behavior, filter placement, join strategy, parameter selectivity, materialized views. Measure the real workload instead of assuming the default configuration is enough. Look at latency, throughput, concurrency, request size, metadata operations, query complexity, token counts, or recovery duration depending on the service. Compare production metrics with load tests and with the limits of the selected tier or model. Tuning should be incremental and reversible, because a change that improves one path can hurt another. Always verify user-facing behavior after configuration, schema, deployment, or data-layout changes. Capture before-and-after metrics so tuning is based on evidence rather than assumptions.
Operations
Operations for KQL function require function cataloging, owner tags, docstrings, dependency review, performance checks. Treat the term as something support teams must inspect quickly, not only as a design-time concept. Keep a runbook with portal locations, CLI commands, expected output, known dependencies, approval rules, and rollback steps. Review it during releases, migrations, incidents, access changes, and cost investigations. Good operations practice also means tagging owners, enabling diagnostics, storing evidence from read-only checks, and documenting exceptions. When the term changes, update handoff notes so future operators know what normal looks like. Keep the same evidence available to the next on-call engineer.

Common mistakes

  • Treating KQL function as a harmless label instead of checking the live resource, scope, owner, and dependencies.
  • Running a mutating command in the wrong subscription, resource group, account, service, index, share, or deployment.
  • Assuming a successful deployment proves the feature works without checking logs, metrics, access, and rollback evidence.
  • Ignoring cost, retention, quotas, network exposure, or data classification until an incident forces emergency cleanup.