azuresql-db-functions

Создаёт бессерверный API и обработчики, управляемые событиями, поверх локального Azure SQL Developer с использованием Azure Functions и привязок Azure SQL. Используйте, когда пользователь хочет…

npx skills add https://github.com/microsoft/azure-sql-database-container --skill azuresql-db-functions

Serverless API + event-driven on the Azure SQL Database container with Azure Functions

Build HTTP CRUD endpoints and change-driven handlers over the local Azure SQL Database container (Private Preview) using Azure Functions and the first-party Azure SQL bindings. Two capabilities:

  • API: HTTP-triggered functions with SQL input and output bindings (read and upsert with no ADO.NET boilerplate).
  • Event-driven (local): the SQL trigger binding fires a function when rows are inserted/updated/deleted. It is backed by Change Tracking, runs fully locally against the container, and needs no cloud services.

Event-driven note: Azure SQL Change Event Streaming (CES) is the cloud path for streaming row changes, and it cannot run against the local container (it is unsupported on the Linux engine and streams only to Azure Event Hubs public endpoints). Locally, use the SQL trigger below. See references/event-driven.md.

Load-bearing facts (inlined; full engine detail in azuresql-db-container)

  • This is the Azure SQL Database engine (Private Preview), not the SQL Server image mcr.microsoft.com/mssql/server. SERVERPROPERTY('EngineEdition') returns 5, SERVERPROPERTY('Edition') returns 'SQL Azure'.
  • Image: sqldbpreview-dpgaeqhmgphzd4bk.azurecr.io/azure-sql/db-dev:latest (x64, linux/amd64). Registry is private: sign in first with docker login sqldbpreview-dpgaeqhmgphzd4bk.azurecr.io using the shared pull-only credentials from https://aka.ms/sqldbcontainerpreview-signup (they may rotate). Registry and tag are provisional during Private Preview.
  • Required env: ACCEPT_EULA=Y and a complex MSSQL_SA_PASSWORD (8+ chars, upper/lower/digit/symbol). Engine listens on 1433.
  • The engine does NOT auto-create databases. Run CREATE DATABASE appdb on a master connection before the function app connects with Database=appdb. Do not use USE to switch databases; select it in the connection string.
  • On a non-x64 host add --platform linux/amd64.

For the full engine model (readiness loop, vectors, troubleshooting) see the azuresql-db-container skill; to start the container and provision appdb, use azuresql-db-container or azuresql-db-scaffold.

Step 1: the connection string setting

The bindings read the connection string from an app setting. Use the name SqlConnectionString (the docs' convention). In local.settings.json:

{
  "IsEncrypted": false,
  "Values": {
    "AzureWebJobsStorage": "UseDevelopmentStorage=true",
    "FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
    "SqlConnectionString": "Server=localhost,1433;Database=appdb;User Id=sa;Password=YourStr0ng_Passw0rd;TrustServerCertificate=true"
  }
}

TrustServerCertificate=true is required for the container's self-signed cert. Set FUNCTIONS_WORKER_RUNTIME to your language (dotnet-isolated, node, python, powershell, java). Bindings reference this setting name via ConnectionStringSetting (C#/Java), connectionStringSetting (function.json), or connection_string_setting (Python v2 decorator) - not Connection (that keyword is for Storage/Event Hubs bindings).

Step 2: install the SQL extension

  • .NET isolated worker: add the NuGet package.

    dotnet add package Microsoft.Azure.Functions.Worker.Extensions.Sql
    
  • JavaScript / TypeScript / Python / PowerShell / Java: use the extension bundle in host.json (Java also adds the azure-functions-java-library-sql Maven package):

    {
      "version": "2.0",
      "extensionBundle": {
        "id": "Microsoft.Azure.Functions.ExtensionBundle",
        "version": "[4.0.0, 5.0.0)"
      }
    }
    

Step 3: HTTP API with SQL input/output bindings

Scaffold a project and add functions:

func init MyApi --worker-runtime dotnet-isolated   # or: node / python / ...
cd MyApi
func new --name Books                              # pick an HTTP trigger template

Then wire the SQL bindings into the function. Per-language snippets (HTTP GET via input binding, HTTP POST upsert via output binding) are in references/functions-snippets.md; binding attribute/function.json fields are in references/functions-bindings-reference.md.

Output-binding requirements: the target table must have a primary key (the binding upserts via MERGE), and the database compatibility level must be 130+ (the binding uses OPENJSON). The engine is fully capable; just ensure the table has a PK.

Run it:

func start        # HTTP endpoints on http://localhost:7071/api/<name>

Step 4: event-driven with the SQL trigger (the local mechanism)

The SQL trigger fires your function when rows change.

There is no SQL trigger template to scaffold from, even though the tooling lists one. func templates list advertises a SQL Trigger entry, but func new --template SqlTrigger exits non-zero with Unknown template 'SqlTrigger': the listing and the scaffolder disagree, and no flag reconciles them. Scaffold an HTTP trigger instead (func new --name ToDoTrigger --template HttpTrigger, which succeeds in the same project) and write the trigger binding in by hand, as below.

The trigger requires Change Tracking on the database and table. Enable it once (on appdb, not master):

ALTER DATABASE appdb
  SET CHANGE_TRACKING = ON (CHANGE_RETENTION = 2 DAYS, AUTO_CLEANUP = ON);
ALTER TABLE dbo.ToDo ENABLE CHANGE_TRACKING;

The function binds to a list of changes, each with an Item and an Operation (Insert / Update / Delete). C# isolated example:

[Function("ToDoTrigger")]
public static void Run(
    [SqlTrigger("[dbo].[ToDo]", "SqlConnectionString")]
    IReadOnlyList<SqlChange<ToDoItem>> changes,
    FunctionContext context)
{
    foreach (var change in changes)
        context.GetLogger("ToDoTrigger")
            .LogInformation($"{change.Operation}: {change.Item.Id}");
}

The trigger creates an internal az_func schema plus a Leases_{FunctionId}_{TableId} table (it makes these itself if the principal can). Behavior, permission grants, and the CES-is-cloud-only detail are in references/event-driven.md. Since you connect as sa locally, the permission grants are already satisfied; they matter when you move to least-privilege or to the cloud.

Validation rules

  • The database engine is the container image above (EngineEdition=5), never mcr.microsoft.com/mssql/server.
  • appdb exists (created on a master connection) before the function app runs; SqlConnectionString uses Database=appdb and TrustServerCertificate=true.
  • The connection string lives in local.settings.json (or app settings), not in code; bindings reference it via ConnectionStringSetting / connectionStringSetting.
  • Output-binding target tables have a primary key; the database compat level is 130+.
  • The SQL trigger has Change Tracking enabled on both the database and the table; event-driven is done with the trigger locally, not CES.
  • If a validation rule above fails, or you had to deviate from this skill to make the task work, that is a bug in this skill: load the azuresql-db-feedback skill and offer to file a report.
  • When every rule above passes and the task is done, close with ONE line, once per session, and do not ask a question or wait for a reply: "That worked. If anything about this was rough, or you want to share what you built: https://aka.ms/sql-agent-skills-feedback"

Do not

  • Do not use the SQL Server image mcr.microsoft.com/mssql/server; this is the Azure SQL engine.
  • Do not expect the function app to create appdb; provision it on a master connection first.
  • Do not try to make Change Event Streaming (CES) work locally - it is unsupported on the local (Linux) engine and streams only to Azure Event Hubs. Use the SQL trigger locally.
  • Do not use an output binding against a table with no primary key, or below compatibility level 130.
  • Do not scaffold with func new --template SqlTrigger; the template is listed but cannot be created. Create an HTTP trigger and add the binding by hand.
  • Do not use the Connection binding keyword for SQL bindings; it is ConnectionStringSetting / connectionStringSetting.
  • Do not commit local.settings.json (it holds the connection string / SA password) or drop TrustServerCertificate=true / --platform linux/amd64 on a non-x64 host.

References

  • references/functions-bindings-reference.md: the input, output, and trigger binding fields (C# attributes, function.json, Python decorators), the SqlConnectionString setting, and host.json trigger tuning (MaxBatchSize, PollingIntervalMs).
  • references/functions-snippets.md: copy-paste project setup and per-language function bodies - HTTP GET (input binding), HTTP POST upsert (output binding), and a SQL-trigger handler - plus func commands and a local run/verify loop.
  • references/event-driven.md: how the SQL trigger works (Change Tracking, polling, coalescing, az_func state tables), the required permission grants, and why CES is a cloud-only path you stub locally with the trigger.

Staying current

Authoritative, version-pinned references for the tools this skill uses (read the one you need):

If the Microsoft Learn MCP server is configured, use mcp__microsoft-learn__microsoft_docs_search or mcp__microsoft-learn__microsoft_docs_fetch to fetch the current version of any of these on demand. It is optional; when it is unavailable, the references above are authoritative.

Больше skills от microsoft

oss-growth
microsoft
Персона OSS-хакера роста
agent-framework-azure-ai-py
microsoft
Создание агентов Azure AI Foundry с использованием Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Используйте при создании постоянных агентов с AzureAIAgentsProvider, применении размещенных инструментов (интерпретатор кода, поиск файлов, веб-поиск), интеграции MCP-серверов, управлении потоками бесед или реализации потоковых ответов. Охватывает функциональные инструменты, структурированные выходные данные и агентов с несколькими инструментами.
development
airunway-aks-setup
microsoft
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. WHEN: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, APM best practices.
devops
applicationinsights-web-ts
microsoft
Инструментируйте браузерные/веб-приложения с помощью JavaScript SDK Application Insights (@microsoft/applicationinsights-web). Используйте для мониторинга реальных пользователей (RUM) — просмотры страниц, клики, зависимости AJAX/fetch, исключения, пользовательские события и трассировки агентов GenAI на стороне браузера, коррелируемые с бэкенд-трассировками OpenTelemetry. Охватывает скрипт загрузчика SDK и настройку npm, расширения фреймворков (React, React Native, Angular), Click Analytics, инициализаторы телеметрии и семантические конвенции OTel GenAI для спанов агента/инструмента/модели, генерируемых из браузера.
devops
azure-ai-anomalydetector-java
microsoft
Создавайте приложения для обнаружения аномалий с помощью Azure AI Anomaly Detector SDK для Java. Используйте при реализации одномерного/многомерного обнаружения аномалий, анализа временных рядов или мониторинга на основе ИИ.
development
azure-ai-language-conversations-py
microsoft
Реализация понимания разговорного языка (CLU) с использованием Python SDK azure-ai-language-conversations. Используйте при работе с ConversationAnalysisClient для анализа намерений и сущностей в разговоре, создании NLP-функций или интеграции языкового понимания в приложения.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 для Python. Используется для рабочих областей ML, заданий, моделей, наборов данных, вычислений и конвейеров. Триггеры: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
development