observability-patterns

name	observability-patterns
description	Use this skill when implementing logging, monitoring, timing, or diagnostics. Triggers on: logging setup, performance profiling, timing, stderr output, or mentions of "logging", "monitoring", "timing", "profiling", "observability", or "diagnostics".

Observability Patterns — DoclingOptimisation

Current Logging

• Python logging module with custom format (%(message)s)
• All logs go to stderr (stdout reserved for markdown output)
• Timing helper _step() logs elapsed time since process start
• Pipeline profiling enabled: settings.debug.profile_pipeline_timings = True

Logging Standards

• Use logging.getLogger("docling-processor") for the main logger
• Include elapsed time in all step messages
• Log CPU detection results, accelerator config, batch sizes
• Log input file metadata (name, size)
• Log conversion timing (convert duration, export duration, total)

For Azure Deployment

• Container App Job logs go to Azure Log Analytics
• Consider structured JSON logging for machine-parseable output
• Track: conversion time, file size, thread count, model used
• Set up alerts for: job failures, timeouts, excessive memory usage

Health Monitoring

• Container App Jobs don't need health check endpoints
• Monitor job execution status via Azure Container Apps API
• Track job duration trends to detect performance regressions