azuresql-db-connections

作成者: microsoft

アプリのデータベース接続を、ローカルのAzure SQL Developer(Private Preview)および、変更なしでクラウド上のAzure SQL Databaseに対して信頼性の高いものにします:…

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.

microsoftのその他のスキル

oss-growth
microsoft
OSS成長ハッカーのペルソナ
agent-framework-azure-ai-py
microsoft
Microsoft Agent Framework Python SDK(agent-framework-azure-ai)を使用してAzure AI Foundryエージェントを構築します。AzureAIAgentsProviderを使用した永続的なエージェントの作成、ホスト型ツール(コードインタープリター、ファイル検索、ウェブ検索)の使用、MCPサーバーの統合、会話スレッドの管理、ストリーミング応答の実装時に使用します。関数ツール、構造化出力、マルチツールエージェントをカバーします。
development
airunway-aks-setup
microsoft
AKS上でAI Runwayをセットアップ — ベアクラスターからモデル実行まで。クラスター検証、コントローラーインストール、GPU評価、プロバイダー設定、初回デプロイをカバー。対象: 「AI Runwayのセットアップ」「AKSクラスターのオンボード」「AI Runwayのインストール」「airunway setup」「AKSへのモデルデプロイ」「AKSでのGPU推論」「AKSでのKAITOセットアップ」「AKSでのLLM実行」「AKSでのvLLM」「AKSでのモデルサービング設定」「AI Runwayコントローラー」。
devops
appinsights-instrumentation
microsoft
Azure Application Insightsを使用したWebアプリのインストルメンテーションに関するガイダンス。テレメトリパターン、SDKセットアップ、構成リファレンスを提供します。対象: アプリのインストルメンテーション方法、App Insights SDK、テレメトリパターン、App Insightsとは何か、Application Insightsガイダンス、インストルメンテーション例、APMベストプラクティス。
devops
applicationinsights-web-ts
microsoft
Application Insights JavaScript SDK(@microsoft/applicationinsights-web)を使用してブラウザ/Webアプリを計測します。Real User Monitoring(RUM)— ページビュー、クリック、AJAX/fetch依存関係、例外、カスタムイベント、およびバックエンドのOpenTelemetryトレースに関連付けられたブラウザ側のGenAIエージェントトレースに使用します。SDKローダースクリプトとnpmセットアップ、フレームワーク拡張機能(React、React Native、Angular)、Click Analytics、テレメトリ初期化子、およびブラウザから生成されるエージェント/ツール/モデルスパンのOTel GenAIセマンティック規約をカバーします。
devops
azure-ai-anomalydetector-java
microsoft
Azure AI Anomaly Detector SDK for Javaを使用して異常検出アプリケーションを構築します。単変量/多変量異常検出、時系列分析、またはAIを活用したモニタリングを実装する際に使用します。
development
azure-ai-language-conversations-py
microsoft
azure-ai-language-conversations Python SDKを使用して会話言語理解(CLU)を実装します。ConversationAnalysisClientを使用して会話の意図とエンティティを分析する場合、NLP機能を構築する場合、またはアプリケーションに言語理解を統合する場合に使用します。
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python。MLワークスペース、ジョブ、モデル、データセット、コンピュート、パイプラインに使用します。 トリガー: 「azure-ai-ml」、「MLClient」、「ワークスペース」、「モデルレジストリ」、「トレーニングジョブ」、「データセット」。
development