ncklrs 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