azure-monitor-ingestion-py

Topics in the source

• Azure Monitor Ingestion SDK for Python
• Installation
• Environment Variables
• Prerequisites
• Authentication & Lifecycle
• Upload Custom Logs
• Upload from JSON File
• Custom Error Handling

Send custom logs to Azure Monitor Log Analytics workspace using the Logs Ingestion API. Before using this SDK, you need: 1. Log Analytics Workspace — Target for your logs 2. Data Collection Endpoint (DCE) — Ingestion endpoint 3. Data Collection Rule (DCR) — Defines schema and destination 4. Custom Table — In Log Analytics (created via DCR or manually) 🔑 Two rules apply to every code sample below: 1. Prefer DefaultAzureCredential. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation. - Local dev: DefaultAzureCredential works as-is. - Production: set AZURETOKENCREDENTIALS=prod (or AZURETOKENCREDENTIALS= ) to constrain the credential chain to production-safe credentials. 2. Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically: - Sync: with (...) as client: - Async: async with (...) as client: and async with DefaultAzureCredential() as credential: (from azure.identity.aio) Snippets may abbreviate this setup, but production code should always follow both rules. Handle pa

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