Logging Best Practices
Expert guidance for production-grade logging based on Boris Tane's loggingsucks.com philosophy.
Core Philosophy
Stop logging "what your code is doing." Start logging "what happened to this request."
Traditional logging is optimized for *writing*, not *querying*. Developers emit logs for immediate debugging convenience without considering how they'll be searched later. This creates massive signal-to-noise ratios at scale.
The Wide Events Architecture
Instead of scattered log statements throughout your code, build one comprehensive event per request per service:
// ❌ Traditional scattered logging
logger.info("Request started");
logger.info(`User ${userId} found`);
logger.info("Fetching cart");
logger.debug(`Cart has ${items.length} items`);
logger.info("Processing payment");
logger.error(`Payment failed: ${error.message}`);
// ✅ Wide event - build throughout request, emit once
const event = {
request_id: req.id,
timestamp: Date.now(),
service: "checkout",
version: "2.3.1",
user: { id: userId, tier: "premium", account_age_days: 847 },
cart: { id: cartId, item_count: 3, total_cents: 15999 },
payment: { method: "card", provider: "stripe", latency_ms: 234 },
outcome: "failure",
error: { type: "PaymentDeclined", code: "card_declined", retriable: true }
};
logger.info(event);Key Concepts
| Concept | Definition |
|---|---|
| Wide Event | One comprehensive, context-rich log per request per service |
| Cardinality | Number of unique values (user IDs = high, HTTP methods = low) |
| Dimensionality | Count of fields per event (aim for 40+ meaningful fields) |
| Tail Sampling | Sample decisions after request completion based on outcomes |
When to Apply This Skill
- Implementing logging in new services
- Reviewing code with log statements
- Debugging production issues
- Designing observability strategy
- Migrating from printf-style to structured logging
- Reducing log volume while improving queryability
What This Skill Provides
- Wide event patterns for different frameworks and languages
- Field design guidance for high-cardinality debugging
- Sampling strategies that preserve signal
- Anti-pattern detection in existing logging code
- Query-first thinking for log architecture