name: logging-best-practices
description: Logging best practices focused on wide events for powerful debugging and analytics
license: MIT
metadata:
author: [email protected]
version: "0.0.1"

Logging Best Practices Skill

Version: 0.0.1

Purpose

This skill provides guidelines for implementing effective logging in applications. It focuses on wide events (also called canonical log lines) - a pattern where you emit a single, context-rich event per request per service, enabling powerful debugging and analytics.

When to Apply

Apply these guidelines when:
- Writing or updating logging code
- Adding logger.info, print, println!, info!, console.log, developer.log or similar depending on the programming language
- Designing logging strategy for new services
- Setting up logging infrastructure

Core Principles

1. Wide Events (CRITICAL)

Emit one context-rich event per request per service. Instead of scattering log lines throughout your handler, consolidate everything into a single structured event emitted at request completion.

import time
from datetime import datetime, timezone

wide_event = {
    "method": "POST",
    "path": "/checkout",
    "request_id": request.state.request_id,
    "timestamp": datetime.now(timezone.utc).isoformat(),
}

try:
    user = get_user(request.state.user_id)
    wide_event["user"] = {"id": user.id, "subscription": user.subscription}

    cart = get_cart(user.id)
    wide_event["cart"] = {"total_cents": cart.total, "item_count": len(cart.items)}

    wide_event["status_code"] = 200
    wide_event["outcome"] = "success"
    return {"success": True}
except Exception as e:
    wide_event["status_code"] = 500
    wide_event["outcome"] = "error"
    wide_event["error"] = {"message": str(e), "type": type(e).__name__}
    raise
finally:
    wide_event["duration_ms"] = (time.time() - start_time) * 1000
    logger.info(wide_event)

2. High Cardinality & Dimensionality (CRITICAL)

Include fields with high cardinality (user IDs, request IDs - millions of unique values) and high dimensionality (many fields per event). This enables querying by specific users and answering questions you haven't anticipated yet.

3. Business Context (CRITICAL)

Always include business context: user subscription tier, cart value, feature flags, account age. The goal is to know "a premium customer couldn't complete a $2,499 purchase" not just "checkout failed."

4. Environment Characteristics (CRITICAL)

Include environment and deployment info in every event: commit hash, service version, region, instance ID. This enables correlating issues with deployments and identifying region-specific problems.

5. Single Logger (HIGH)

Use one logger instance configured at startup and import it everywhere. This ensures consistent formatting and automatic environment context.

6. Middleware Pattern (HIGH)

Use middleware to handle wide event infrastructure (timing, status, environment, emission). Handlers should only add business context.

7. Structure & Consistency (HIGH)

• Use JSON format consistently
• Maintain consistent field names across services
• Simplify to two log levels: info and error
• Never log unstructured strings

Anti-Patterns to Avoid

1. Scattered logs: Multiple logger.info calls per request
2. Multiple loggers: Different logger instances in different files
3. Missing environment context: No commit hash or deployment info
4. Missing business context: Logging technical details without user/business data
5. Unstructured strings: print('something wrong happened') instead of structured data
6. Inconsistent schemas: Different field names across services

Guidelines

Wide Events

• Emit one wide event per service hop
• Include all relevant context
• Connect events with request ID
• Emit at request completion in finally block

Context

• Support high cardinality fields (user_id, request_id)
• Include high dimensionality (many fields)
• Always include business context
• Always include environment characteristics (commit_hash, version, region)

Structure

• Use a single logger throughout the codebase
• Use middleware for consistent wide events
• Use JSON format for formatting
• Use key-value for rendering
• Maintain consistent schema (use consistent field names across all services. If one service uses user_id and another uses userId, querying becomes painful.)
• Limit yourself to four log levels: debug, info, warning, error
• Never log unstructured strings (every log must be structured with queryable fields)

Common Pitfalls

• Avoid multiple log lines per request. The only exception is temporary verbose logging during AI-assisted debugging sessions.
• Design for unknown unknowns
• Always propagate request IDs across services