azuresql-db-connections

作者: microsoft

使应用的数据库连接在本地 Azure SQL Developer(私有预览版)和云端 Azure SQL Database 上保持可靠,无需更改:……

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

Reliable connections on the Azure SQL Database container (pooling + retry)

Make the app's database connections reliable with connection pooling and retry / transient-fault handling. This is the Azure SQL engine (Private Preview), not the SQL Server image.

Why do this locally (local-to-cloud parity)

The local container rarely drops a connection, so it is tempting to skip pooling and retry. Do not. Azure SQL Database in the cloud throttles and drops connections during failovers, scaling, and load; a client with no retry surfaces those as hard errors. Build pooling and retry now, against the local container, and the same code survives in the cloud with no rewrite. For the full promote-to-cloud story see the azuresql-db-local-to-cloud skill.

Verify identity once running: SELECT SERVERPROPERTY('EngineEdition') returns 5 and SERVERPROPERTY('Edition') returns 'SQL Azure'. For full engine detail see the azuresql-db-container skill.

The engine and the connection contract

  • Image: sqldbpreview-dpgaeqhmgphzd4bk.azurecr.io/azure-sql/db-dev:latest (x64 / linux/amd64, Private Preview registry). Sign in first: docker login sqldbpreview-dpgaeqhmgphzd4bk.azurecr.io with the shared pull-only credentials from https://aka.ms/sqldbcontainerpreview-signup (they may rotate). On a non-x64 host add --platform linux/amd64 (Docker) or platform: linux/amd64 (compose).
  • Do NOT use mcr.microsoft.com/mssql/server (the SQL Server image).
  • Required env: ACCEPT_EULA=Y and a complex MSSQL_SA_PASSWORD (example literal: YourStr0ng_Passw0rd). The engine listens on 1433.
  • The engine does NOT auto-create databases. CREATE DATABASE appdb on a master connection first. Do not USE to switch databases: a user-database session returns Msg 40508. Select the database in the connection string (Database=appdb).
  • Apps read one env var, SQL_CONNECTION_STRING. Strings use User Id= / Password= / Database= and TrustServerCertificate=true. sqlcmd uses -C.

Start the container and provision appdb

HOST_PORT=1433; while lsof -nP -iTCP:"$HOST_PORT" -sTCP:LISTEN >/dev/null 2>&1; do HOST_PORT=$((HOST_PORT+1)); done
PLATFORM=(); case "$(docker info -f '{{.Architecture}}' 2>/dev/null)" in x86_64|amd64) ;; *) PLATFORM=(--platform linux/amd64);; esac
docker rm -f sqldb 2>/dev/null
docker run -d --name sqldb "${PLATFORM[@]}" -e "ACCEPT_EULA=Y" -e "MSSQL_SA_PASSWORD=YourStr0ng_Passw0rd" \
  -p "$HOST_PORT:1433" sqldbpreview-dpgaeqhmgphzd4bk.azurecr.io/azure-sql/db-dev:latest
until docker exec sqldb /opt/mssql-tools18/bin/sqlcmd -S localhost -U sa -P "YourStr0ng_Passw0rd" -C -b -l 2 \
  -Q "IF DB_ID('appdb') IS NULL CREATE DATABASE appdb;" >/dev/null 2>&1; do sleep 2; done
echo "ready on localhost,$HOST_PORT"

The canonical string the app consumes (replace 1433 with the chosen HOST_PORT if 1433 was occupied):

Server=localhost,1433;Database=appdb;User Id=sa;Password=YourStr0ng_Passw0rd;TrustServerCertificate=true

Pooling: reuse connections, do not reopen per query

A connection pool keeps a set of open connections and hands one back on each Open(). Opening a pooled connection is cheap; opening a brand-new physical connection per query is not, and it exhausts server resources under load.

  • Keep pooling on (it is on by default in most drivers) and let one pool serve the app.
  • Set a bounded Max Pool Size (default 100 in .NET) so a spike cannot open unlimited connections. Size it to real concurrency, not a guess.
  • A small Min Pool Size keeps a few connections warm and cuts cold-start latency.
  • One connection string means one pool. Do not build strings dynamically per request (each distinct string is a separate pool) and do not open a fresh, unpooled connection per call.
  • Always close/dispose connections (or use using / with / context managers) so they return to the pool instead of leaking.

Retry: only for transient faults, with backoff

A transient fault is a temporary condition (throttling, a brief failover, a dropped idle connection) that succeeds on a retry. In Azure SQL these arrive as specific error numbers (for example 40501 throttling, 40613 database unavailable, 49918/49919/49920 busy, 4060, 10928, 10929, 40197, 233, and connection-timeout / broken-pipe socket errors).

  • Retry only transient errors. Retrying a non-transient error (login failure 18456, syntax error, constraint violation, permission denied) just fails slower and hides the real bug.
  • Use exponential backoff with a cap and a small jitter, and a bounded attempt count (for example 5 attempts). Do not hammer a throttled server.
  • Be careful with non-idempotent writes. A retry can double-apply an INSERT if the first attempt actually committed before the connection dropped. Make writes idempotent (natural or client-generated keys, MERGE, or wrap the unit of work in a transaction that a retry can safely re-run as a whole). The built-in EF Core execution strategy handles this for you when work is wrapped in its Execute/transaction API.
  • Prefer a framework retry policy over hand-rolled loops where one exists (EF Core EnableRetryOnFailure for .NET). Hand-roll only for raw drivers.

Per-stack

Copy-pasteable pooling config and transient-only retry for each stack live in references/retry-snippets.md:

  • .NET (Microsoft.Data.SqlClient): pooling keywords (Max Pool Size, Min Pool Size, Pooling=true) and connection-string retry keywords (ConnectRetryCount, ConnectRetryInterval); plus EF Core EnableRetryOnFailure (the SqlServer execution strategy).
  • Node (mssql / tedious): pool config (max / min / idleTimeoutMillis) and a transient-error retry wrapper.
  • Python (pyodbc): connection reuse and a tenacity retry decorator that retries only transient ODBC errors.

Keep the single SQL_CONNECTION_STRING contract: pooling and retry are tuned in code and in driver-specific keywords, not by inventing new env vars.

Validation rules

  • Retry fires only on transient errors; non-transient errors (auth, syntax, constraint) surface immediately.
  • Retry uses bounded attempts with exponential backoff, and non-idempotent writes are made safe to re-run (keys, MERGE, or a retriable transaction).
  • Pooling is on with a bounded Max Pool Size; connections are disposed and returned to the pool, never opened per query.
  • One connection string / one pool; the app still reads a single SQL_CONNECTION_STRING.
  • Runs against the engine image with EngineEdition 5; appdb was created on a master connection before the app connected.
  • 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 retry non-transient errors (auth, syntax, constraint); they will just fail slower.
  • Do not retry non-idempotent writes without idempotency (keys, MERGE, or a retriable transaction).
  • Do not set an unbounded pool; do not open a new connection per query instead of pooling.
  • Do not invent extra env vars; keep the single SQL_CONNECTION_STRING contract.
  • Do not use the mcr.microsoft.com/mssql/server SQL Server image, and do not call a non-x64 host "supported".

References

  • references/retry-snippets.md: copy-pasteable pooling config and transient-only retry for .NET (Microsoft.Data.SqlClient + EF Core EnableRetryOnFailure), Node (mssql/tedious pool + retry wrapper), and Python (pyodbc reuse + tenacity decorator). Read the section for your stack.

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.

来自 microsoft 的更多技能

oss-growth
microsoft
OSS增长黑客角色
agent-framework-azure-ai-py
microsoft
使用Microsoft Agent Framework Python SDK(agent-framework-azure-ai)构建Azure AI Foundry代理。在创建使用AzureAIAgentsProvider的持久化代理、使用托管工具(代码解释器、文件搜索、网络搜索)、集成MCP服务器、管理对话线程或实现流式响应时使用。涵盖函数工具、结构化输出和多工具代理。
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
使用Azure Application Insights对Web应用进行插桩的指南。提供遥测模式、SDK设置和配置参考。适用场景:如何对应用进行插桩、App Insights SDK、遥测模式、什么是App Insights、Application Insights指南、插桩示例、APM最佳实践。
devops
applicationinsights-web-ts
microsoft
使用Application Insights JavaScript SDK(@microsoft/applicationinsights-web)为浏览器/Web应用添加检测。用于真实用户监控(RUM)——页面视图、点击、AJAX/fetch依赖项、异常、自定义事件,以及与后端OpenTelemetry追踪关联的浏览器端GenAI代理追踪。涵盖SDK加载器脚本和npm设置、框架扩展(React、React Native、Angular)、点击分析、遥测初始化器,以及从浏览器发出的代理/工具/模型跨度所遵循的OTel GenAI语义约定。
devops
azure-ai-anomalydetector-java
microsoft
使用适用于 Java 的 Azure AI 异常检测器 SDK 构建异常检测应用程序。在实现单变量/多变量异常检测、时间序列分析或 AI 驱动的监控时使用。
development
azure-ai-language-conversations-py
microsoft
使用azure-ai-language-conversations Python SDK实现对话语言理解(CLU)。当使用ConversationAnalysisClient分析对话意图和实体、构建NLP功能或将语言理解集成到应用程序中时使用。
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python。用于机器学习工作区、作业、模型、数据集、计算资源和管道。 触发词:“azure-ai-ml”、“MLClient”、“工作区”、“模型注册表”、“训练作业”、“数据集”。
development