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Use for sending custom logs to Log Analytics workspace via Logs Ingestion API.\n  Triggers: \"azure-monitor-ingestion\", \"LogsIngestionClient\", \"custom logs\", \"DCR\", \"data collection rule\", \"Log Analytics\".\nlicense: MIT\nmetadata:\n  author: Microsoft\n  version: \"1.0.0\"\n  package: azure-monitor-ingestion\n---\n\n# Azure Monitor Ingestion SDK for Python\n\nSend custom logs to Azure Monitor Log Analytics workspace using the Logs Ingestion API.\n\n## Installation\n\n```bash\npip install azure-monitor-ingestion\npip install azure-identity\n```\n\n## Environment Variables\n\n```bash\n# Data Collection Endpoint (DCE)\nAZURE_DCE_ENDPOINT=https:\u002F\u002F\u003Cdce-name>.\u003Cregion>.ingest.monitor.azure.com  # Required for all auth methods\n\n# Data Collection Rule (DCR) immutable ID\nAZURE_DCR_RULE_ID=dcr-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx  # Required for all auth methods\n\n# Stream name from DCR\nAZURE_DCR_STREAM_NAME=Custom-MyTable_CL  # Required for all auth methods\nAZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production\n```\n\n## Prerequisites\n\nBefore using this SDK, you need:\n\n1. **Log Analytics Workspace** — Target for your logs\n2. **Data Collection Endpoint (DCE)** — Ingestion endpoint\n3. **Data Collection Rule (DCR)** — Defines schema and destination\n4. **Custom Table** — In Log Analytics (created via DCR or manually)\n\n## Authentication & Lifecycle\n\n> **? Two rules apply to every code sample below:**\n>\n> 1. **Prefer `DefaultAzureCredential`.** It works locally (Azure CLI \u002F VS Code \u002F Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account\u002FAPI keys — they bypass Entra audit and rotation.\n>    - Local dev: `DefaultAzureCredential` works as-is.\n>    - Production: set `AZURE_TOKEN_CREDENTIALS=prod` (or `AZURE_TOKEN_CREDENTIALS=\u003Cspecific_credential>`) to constrain the credential chain to production-safe credentials.\n> 2. **Wrap every client in a context manager** so HTTP transports, sockets, and token caches are released deterministically:\n>    - Sync: `with \u003CClient>(...) as client:`\n>    - Async: `async with \u003CClient>(...) as client:` **and** `async with DefaultAzureCredential() as credential:` (from `azure.identity.aio`)\n>\n> Snippets may abbreviate this setup, but production code should always follow both rules.\n\n```python\nfrom azure.monitor.ingestion import LogsIngestionClient\nfrom azure.identity import DefaultAzureCredential, ManagedIdentityCredential\nimport os\n\n# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=\u003Cspecific_credential>\ncredential = DefaultAzureCredential(require_envvar=True)\n# Or use a specific credential directly in production:\n# See https:\u002F\u002Flearn.microsoft.com\u002Fpython\u002Fapi\u002Foverview\u002Fazure\u002Fidentity-readme?view=azure-python#credential-classes\n# credential = ManagedIdentityCredential()\n\nwith LogsIngestionClient(\n    endpoint=os.environ[\"AZURE_DCE_ENDPOINT\"],\n    credential=credential\n) as client:\n    # Use `client.upload(...)` for all subsequent operations (see examples below)\n    ...\n```\n\n## Upload Custom Logs\n\n```python\nfrom azure.monitor.ingestion import LogsIngestionClient\nfrom azure.identity import DefaultAzureCredential\nimport os\n\nrule_id = os.environ[\"AZURE_DCR_RULE_ID\"]\nstream_name = os.environ[\"AZURE_DCR_STREAM_NAME\"]\n\nlogs = [\n    {\"TimeGenerated\": \"2024-01-15T10:00:00Z\", \"Computer\": \"server1\", \"Message\": \"Application started\"},\n    {\"TimeGenerated\": \"2024-01-15T10:01:00Z\", \"Computer\": \"server1\", \"Message\": \"Processing request\"},\n    {\"TimeGenerated\": \"2024-01-15T10:02:00Z\", \"Computer\": \"server2\", \"Message\": \"Connection established\"}\n]\n\nwith LogsIngestionClient(\n    endpoint=os.environ[\"AZURE_DCE_ENDPOINT\"],\n    credential=DefaultAzureCredential()\n) as client:\n    client.upload(rule_id=rule_id, stream_name=stream_name, logs=logs)\n```\n\n## Upload from JSON File\n\n```python\nimport json\n\nwith open(\"logs.json\", \"r\") as f:\n    logs = json.load(f)\n\nclient.upload(rule_id=rule_id, stream_name=stream_name, logs=logs)\n```\n\n## Custom Error Handling\n\nHandle partial failures with a callback:\n\n```python\nfailed_logs = []\n\ndef on_error(error):\n    print(f\"Upload failed: {error.error}\")\n    failed_logs.extend(error.failed_logs)\n\nclient.upload(\n    rule_id=rule_id,\n    stream_name=stream_name,\n    logs=logs,\n    on_error=on_error\n)\n\n# Retry failed logs\nif failed_logs:\n    print(f\"Retrying {len(failed_logs)} failed logs...\")\n    client.upload(rule_id=rule_id, stream_name=stream_name, logs=failed_logs)\n```\n\n## Ignore Errors\n\n```python\ndef ignore_errors(error):\n    pass  # Silently ignore upload failures\n\nclient.upload(\n    rule_id=rule_id,\n    stream_name=stream_name,\n    logs=logs,\n    on_error=ignore_errors\n)\n```\n\n## Async Client\n\n```python\nimport asyncio\nfrom azure.monitor.ingestion.aio import LogsIngestionClient\nfrom azure.identity.aio import DefaultAzureCredential\n\nasync def upload_logs():\n    async with LogsIngestionClient(\n        endpoint=endpoint,\n        credential=DefaultAzureCredential()\n    ) as client:\n        await client.upload(\n            rule_id=rule_id,\n            stream_name=stream_name,\n            logs=logs\n        )\n\nasyncio.run(upload_logs())\n```\n\n## Sovereign Clouds\n\n```python\nfrom azure.identity import AzureAuthorityHosts, DefaultAzureCredential\nfrom azure.monitor.ingestion import LogsIngestionClient\n\n# Azure Government\ncredential = DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_GOVERNMENT)\nwith LogsIngestionClient(\n    endpoint=\"https:\u002F\u002Fexample.ingest.monitor.azure.us\",\n    credential=credential,\n    credential_scopes=[\"https:\u002F\u002Fmonitor.azure.us\u002F.default\"]\n) as client:\n    # client.upload(...)\n    ...\n```\n\n## Batching Behavior\n\nThe SDK automatically:\n- Splits logs into chunks of 1MB or less\n- Compresses each chunk with gzip\n- Uploads chunks in parallel\n\nNo manual batching needed for large log sets.\n\n## Client Types\n\n| Client | Purpose |\n|--------|---------|\n| `LogsIngestionClient` | Sync client for uploading logs |\n| `LogsIngestionClient` (aio) | Async client for uploading logs |\n\n## Key Concepts\n\n| Concept | Description |\n|---------|-------------|\n| **DCE** | Data Collection Endpoint — ingestion URL |\n| **DCR** | Data Collection Rule — defines schema, transformations, destination |\n| **Stream** | Named data flow within a DCR |\n| **Custom Table** | Target table in Log Analytics (ends with `_CL`) |\n\n## DCR Stream Name Format\n\nStream names follow patterns:\n- `Custom-\u003CTableName>_CL` — For custom tables\n- `Microsoft-\u003CTableName>` — For built-in tables\n\n## Best Practices\n\n1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.\n2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async) to ensure proper cleanup. For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.\n3. **Use `DefaultAzureCredential`** for code that runs locally. Use a specific token credential for code that runs in Azure.\n4. **Handle errors gracefully** — use `on_error` callback for partial failures\n5. **Include TimeGenerated** — Required field for all logs\n6. **Match DCR schema** — Log fields must match DCR column definitions\n7. **Use async client** for high-throughput scenarios\n8. **Batch uploads** — SDK handles batching, but send reasonable chunks\n9. **Monitor ingestion** — Check Log Analytics for ingestion status\n\n## Reference Files\n\n| File | Contents |\n|------|----------|\n| [references\u002Fcapabilities.md](references\u002Fcapabilities.md) | Additional non-hero capabilities, operation-group coverage, and production checklists. |\n| [references\u002Fnon-hero-scenarios.md](references\u002Fnon-hero-scenarios.md) | Dedicated non-hero examples for secondary\u002Fadvanced scenarios. |\n","# Azure Monitor Ingestion SDK for Python\n\nSend custom logs to Azure Monitor Log Analytics workspace using the Logs Ingestion API.\n\n## Installation\n\n```bash\npip install azure-monitor-ingestion\npip install azure-identity\n```\n\n## Environment Variables\n\n```bash\n# Data Collection Endpoint (DCE)\nAZURE_DCE_ENDPOINT=https:\u002F\u002F\u003Cdce-name>.\u003Cregion>.ingest.monitor.azure.com  # Required for all auth methods\n\n# Data Collection Rule (DCR) immutable ID\nAZURE_DCR_RULE_ID=dcr-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx  # Required for all auth methods\n\n# Stream name from DCR\nAZURE_DCR_STREAM_NAME=Custom-MyTable_CL  # Required for all auth methods\nAZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production\n```\n\n## Prerequisites\n\nBefore using this SDK, you need:\n\n1. **Log Analytics Workspace** — Target for your logs\n2. **Data Collection Endpoint (DCE)** — Ingestion endpoint\n3. **Data Collection Rule (DCR)** — Defines schema and destination\n4. **Custom Table** — In Log Analytics (created via DCR or manually)\n\n## Authentication & Lifecycle\n\n> **? Two rules apply to every code sample below:**\n>\n> 1. **Prefer `DefaultAzureCredential`.** It works locally (Azure CLI \u002F VS Code \u002F Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account\u002FAPI keys — they bypass Entra audit and rotation.\n>    - Local dev: `DefaultAzureCredential` works as-is.\n>    - Production: set `AZURE_TOKEN_CREDENTIALS=prod` (or `AZURE_TOKEN_CREDENTIALS=\u003Cspecific_credential>`) to constrain the credential chain to production-safe credentials.\n> 2. **Wrap every client in a context manager** so HTTP transports, sockets, and token caches are released deterministically:\n>    - Sync: `with \u003CClient>(...) as client:`\n>    - Async: `async with \u003CClient>(...) as client:` **and** `async with DefaultAzureCredential() as credential:` (from `azure.identity.aio`)\n>\n> Snippets may abbreviate this setup, but production code should always follow both rules.\n\n```python\nfrom azure.monitor.ingestion import LogsIngestionClient\nfrom azure.identity import DefaultAzureCredential, ManagedIdentityCredential\nimport os\n\n# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=\u003Cspecific_credential>\ncredential = DefaultAzureCredential(require_envvar=True)\n# Or use a specific credential directly in production:\n# See https:\u002F\u002Flearn.microsoft.com\u002Fpython\u002Fapi\u002Foverview\u002Fazure\u002Fidentity-readme?view=azure-python#credential-classes\n# credential = ManagedIdentityCredential()\n\nwith LogsIngestionClient(\n    endpoint=os.environ[\"AZURE_DCE_ENDPOINT\"],\n    credential=credential\n) as client:\n    # Use `client.upload(...)` for all subsequent operations (see examples below)\n    ...\n```\n\n## Upload Custom Logs\n\n```python\nfrom azure.monitor.ingestion import LogsIngestionClient\nfrom azure.identity import DefaultAzureCredential\nimport os\n\nrule_id = os.environ[\"AZURE_DCR_RULE_ID\"]\nstream_name = os.environ[\"AZURE_DCR_STREAM_NAME\"]\n\nlogs = [\n    {\"TimeGenerated\": \"2024-01-15T10:00:00Z\", \"Computer\": \"server1\", \"Message\": \"Application started\"},\n    {\"TimeGenerated\": \"2024-01-15T10:01:00Z\", \"Computer\": \"server1\", \"Message\": \"Processing request\"},\n    {\"TimeGenerated\": \"2024-01-15T10:02:00Z\", \"Computer\": \"server2\", \"Message\": \"Connection established\"}\n]\n\nwith LogsIngestionClient(\n    endpoint=os.environ[\"AZURE_DCE_ENDPOINT\"],\n    credential=DefaultAzureCredential()\n) as client:\n    client.upload(rule_id=rule_id, stream_name=stream_name, logs=logs)\n```\n\n## Upload from JSON File\n\n```python\nimport json\n\nwith open(\"logs.json\", \"r\") as f:\n    logs = json.load(f)\n\nclient.upload(rule_id=rule_id, stream_name=stream_name, logs=logs)\n```\n\n## Custom Error Handling\n\nHandle partial failures with a callback:\n\n```python\nfailed_logs = []\n\ndef on_error(error):\n    print(f\"Upload failed: {error.error}\")\n    failed_logs.extend(error.failed_logs)\n\nclient.upload(\n    rule_id=rule_id,\n    stream_name=stream_name,\n    logs=logs,\n    on_error=on_error\n)\n\n# Retry failed logs\nif failed_logs:\n    print(f\"Retrying {len(failed_logs)} failed logs...\")\n    client.upload(rule_id=rule_id, stream_name=stream_name, logs=failed_logs)\n```\n\n## Ignore Errors\n\n```python\ndef ignore_errors(error):\n    pass  # Silently ignore upload failures\n\nclient.upload(\n    rule_id=rule_id,\n    stream_name=stream_name,\n    logs=logs,\n    on_error=ignore_errors\n)\n```\n\n## Async Client\n\n```python\nimport asyncio\nfrom azure.monitor.ingestion.aio import LogsIngestionClient\nfrom azure.identity.aio import DefaultAzureCredential\n\nasync def upload_logs():\n    async with LogsIngestionClient(\n        endpoint=endpoint,\n        credential=DefaultAzureCredential()\n    ) as client:\n        await client.upload(\n            rule_id=rule_id,\n            stream_name=stream_name,\n            logs=logs\n        )\n\nasyncio.run(upload_logs())\n```\n\n## Sovereign Clouds\n\n```python\nfrom azure.identity import AzureAuthorityHosts, DefaultAzureCredential\nfrom azure.monitor.ingestion import LogsIngestionClient\n\n# Azure Government\ncredential = DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_GOVERNMENT)\nwith LogsIngestionClient(\n    endpoint=\"https:\u002F\u002Fexample.ingest.monitor.azure.us\",\n    credential=credential,\n    credential_scopes=[\"https:\u002F\u002Fmonitor.azure.us\u002F.default\"]\n) as client:\n    # client.upload(...)\n    ...\n```\n\n## Batching Behavior\n\nThe SDK automatically:\n- Splits logs into chunks of 1MB or less\n- Compresses each chunk with gzip\n- Uploads chunks in parallel\n\nNo manual batching needed for large log sets.\n\n## Client Types\n\n| Client | Purpose |\n|--------|---------|\n| `LogsIngestionClient` | Sync client for uploading logs |\n| `LogsIngestionClient` (aio) | Async client for uploading logs |\n\n## Key Concepts\n\n| Concept | Description |\n|---------|-------------|\n| **DCE** | Data Collection Endpoint — ingestion URL |\n| **DCR** | Data Collection Rule — defines schema, transformations, destination |\n| **Stream** | Named data flow within a DCR |\n| **Custom Table** | Target table in Log Analytics (ends with `_CL`) |\n\n## DCR Stream Name Format\n\nStream names follow patterns:\n- `Custom-\u003CTableName>_CL` — For custom tables\n- `Microsoft-\u003CTableName>` — For built-in tables\n\n## Best Practices\n\n1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. 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Use when building text-to-speech features, audio narrative generation, podcast creation from content, or integrating with Azure OpenAI Realtime API for real audio output. Covers full-stack implementation from React frontend to Python FastAPI backend with WebSocket streaming.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fpodcast-generation\u002FSKILL.md",[137,140,143,146],{"categoryNo":138,"categoryName":139,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":141,"categoryName":142,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":144,"categoryName":145,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":111,"categoryName":112,"taxonomyType":113,"taxonomyTypeDesc":114},[148,149,150,151,38,152,153,24,154,155,156,37,157,158,159,160,161,162,163,164,165,129,166],"podcast generation","podcast","generation","frontend","react","realtime","azure openai","generate","streaming","Audio","stack","text","model","fastapi","creation","python fastapi","narrative","features","fastapi backend",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":174,"publishedAt":44},"01M69B4CYPM5SESY5ZWEFDXEQZ","microsoft-docs","Understand Microsoft technologies by querying official documentation. Use whenever the user asks how something works, wants tutorials, needs configuration options, limits, quotas, or best practices for any Microsoft technology (Azure, .NET, M365, Windows, Power Platform, etc.)—even if they don't mention \"docs.\" If the question is about understanding a concept rather than writing code, this is the right skill.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fmicrosoft-docs\u002FSKILL.md",[173],{"categoryNo":111,"categoryName":112,"taxonomyType":113,"taxonomyTypeDesc":114},[175,9,176,177,178,179,180,181,182,183,184,24,185,186,187,36,188,66,189,190,191,192,193,31],"docs","microsoft docs","MCP","writing","cli","mcp server","windows","power platform","official","documentation","learn","best practices","question","power","back","technology","platform","learn mcp","concept",{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":198,"categories":199,"keywords":201,"publishedAt":44},"01M69B49C3YPD1ZJ3NC0HPCY58","mcp-builder","Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP), Node\u002FTypeScript (MCP SDK), or C#\u002F.NET (Microsoft MCP SDK).","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fmcp-builder\u002FSKILL.md",[200],{"categoryNo":111,"categoryName":112,"taxonomyType":113,"taxonomyTypeDesc":114},[177,202,203,204,38,205,43,206,9,207,208,160,209,210,124,36,211,212,213,126,31,129],"mcp builder","builder","typescript","llms","model context protocol","fastmcp","Tools","node","context","integrate","mcp sdk","building mcp servers",{"skillNo":215,"name":216,"nameZh":6,"description":217,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":218,"categories":219,"keywords":228,"publishedAt":44},"01M69B467A7CDMFQ0T6Q0ZA12Q","kql","KQL language expertise for writing correct, efficient Kusto Query Language queries. Covers syntax gotchas, join patterns, dynamic types, datetime pitfalls, regex patterns, serialization, memory management, result-size discipline, and advanced functions (geo, vector, graph). USE THIS SKILL whenever writing, debugging, or reviewing KQL queries — even simple ones — because the gotchas section prevents the most common errors that waste tool calls and cause expensive retry cascades. Trigger on: KQL, Kusto, ADX, Azure Data Explorer, Fabric Real-Time Intelligence, EventHouse, Log Analytics, log analysis, data exploration, time series, anomaly detection, summarize, where clause, join, extend, project, let statement, parse operator, extract function, any mention of pipe-forward query syntax.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fkql\u002FSKILL.md",[220,223,224,225],{"categoryNo":221,"categoryName":222,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":111,"categoryName":112,"taxonomyType":113,"taxonomyTypeDesc":114},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":226,"categoryName":227,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000036","内容创作者",[216,178,229,230,42,24,231,232,233,234,35,235,236,237,238,239,240,241,242,31,243,244,245],"summarize","memory","vector","time series","tool","discipline","geo","exploration","pipe","time","project","regex","join","datetime","section","anomaly","parse",{"skillNo":247,"name":248,"nameZh":6,"description":249,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":250,"categories":251,"keywords":252,"publishedAt":44},"01M69B44HJQ7897EZ7NE7DWQX3","github-issue-creator","Convert raw notes, error logs, voice dictation, or screenshots into crisp GitHub-flavored markdown issue reports. Use when the user pastes bug info, error messages, or informal descriptions and wants a structured GitHub issue. Supports images\u002FGIFs for visual evidence.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fgithub-issue-creator\u002FSKILL.md",[],[253,254,255,256,117,257,258,259,41,260,261,262,36,263,264,265,266,267,268,31,129],"github","github issue creator","github issue","issue","issue creator","screenshots","notes","error messages","messages","convert","evidence","reports","images","info","visual","error"]