Log Analysis

Implement structured, queryable logging with proper levels and correlation IDs to enable fast diagnosis of production issues.

When to Use

• Adding logging to an application or service
• Choosing appropriate log levels
• Debugging production issues via log output
• Setting up log aggregation (ELK, Datadog, CloudWatch)
• Tracing requests across multiple services

Core Patterns

Structured Logging (JSON)

Always log structured data, not interpolated strings:

// BAD: Unstructured string logs
console.log(`User ${userId} failed to login from ${ip} with error: ${err.message}`);
// Hard to parse, search, and aggregate

// GOOD: Structured JSON logging
import pino from "pino";

const logger = pino({ level: "info" });

logger.error({
  event: "login_failed",
  userId,
  ip,
  error: err.message,
  errorCode: err.code,
}, "User login failed");

# Python structured logging
import structlog

logger = structlog.get_logger()

logger.error(
    "login_failed",
    user_id=user_id,
    ip=ip,
    error=str(err),
    error_code=getattr(err, "code", None),
)

// Go structured logging with slog
import "log/slog"

slog.Error("login failed",
    "event", "login_failed",
    "userId", userId,
    "ip", ip,
    "error", err.Error(),
)

Log Levels

Use levels consistently across the entire application:

LEVEL    WHEN TO USE                              EXAMPLE
-----    -----------                              -------
error    Something failed, needs attention         Database connection lost
         Action: alert on-call, investigate        Payment processing failed

warn     Unexpected but handled, may need review   Rate limit approaching
         Action: review in daily triage            Deprecated API called

info     Normal but significant operations         User registered
         Action: none, useful for auditing         Order completed

debug    Detailed flow for development             Cache hit/miss
         Action: none, disabled in production      SQL query executed

const logger = pino({ level: process.env.LOG_LEVEL || "info" });

// ERROR: Operation failed, requires investigation
logger.error({ orderId, error: err.message }, "Payment charge failed");

// WARN: Handled but notable
logger.warn({ userId, attempts: 4, max: 5 }, "Login attempt threshold approaching");

// INFO: Normal business events
logger.info({ orderId, total, itemCount }, "Order placed successfully");

// DEBUG: Development-time detail (disabled in production)
logger.debug({ query, params, durationMs: 12 }, "Database query executed");

Correlation IDs for Request Tracing

Assign a unique ID to each request and propagate it through all logs and service calls:

import { randomUUID } from "crypto";
import { AsyncLocalStorage } from "async_hooks";

const requestContext = new AsyncLocalStorage<{ correlationId: string }>();

// Middleware: assign correlation ID
function correlationMiddleware(req: Request, res: Response, next: NextFunction) {
  const correlationId = req.headers["x-correlation-id"] as string || randomUUID();
  res.setHeader("x-correlation-id", correlationId);

  requestContext.run({ correlationId }, () => next());
}

// Logger: include correlation ID automatically
function getLogger() {
  const ctx = requestContext.getStore();
  return logger.child({ correlationId: ctx?.correlationId });
}

// Usage in any handler or service
function processOrder(order: Order) {
  const log = getLogger();
  log.info({ orderId: order.id }, "Processing order");
  // All logs from this request share the same correlationId
}

Output enables tracing a single request across services:

{"level":"info","correlationId":"abc-123","service":"api","msg":"Order received","orderId":"ord-1"}
{"level":"info","correlationId":"abc-123","service":"inventory","msg":"Stock reserved","orderId":"ord-1"}
{"level":"info","correlationId":"abc-123","service":"payment","msg":"Payment charged","orderId":"ord-1"}
{"level":"error","correlationId":"abc-123","service":"email","msg":"Notification failed","error":"SMTP timeout"}

Log Context Enrichment

Add persistent context to every log entry from a scope:

// Child loggers for adding scope context
const serviceLogger = logger.child({ service: "order-service", version: "2.1.0" });
const requestLogger = serviceLogger.child({ correlationId, userId });

// All logs from this request include service, version, correlationId, userId
requestLogger.info({ orderId }, "Order created");

# Python: bind context to logger
logger = structlog.get_logger()
log = logger.bind(service="order-service", correlation_id=correlation_id, user_id=user_id)

log.info("order_created", order_id=order_id)
# Output includes all bound context automatically

Analyzing Logs

# Search JSON logs with jq
# Find all errors for a specific correlation ID
cat app.log | jq 'select(.correlationId == "abc-123" and .level == "error")'

# Count errors by type in the last hour
cat app.log | jq 'select(.level == "error") | .event' | sort | uniq -c | sort -rn

# Find slow requests (> 1000ms)
cat app.log | jq 'select(.durationMs > 1000) | {path: .path, duration: .durationMs}'

# Trace a request across services
cat *.log | jq 'select(.correlationId == "abc-123")' | jq -s 'sort_by(.timestamp)'

Anti-Patterns

What NOT to Do

• Logging sensitive data: Never log passwords, tokens, credit card numbers, SSNs, or PII without masking.
• String interpolation in log messages:

  logger.info(\

  User ${id}`)` defeats structured search. Use fields.
• Logging inside tight loops: Thousands of log entries per second saturate I/O and storage.
• Inconsistent levels: Using

  error

  for non-errors or

  info

  for debug-level detail makes filtering useless.
• Missing context:

  logger.error("Failed")

  — failed what? Add the operation, entity ID, and error.

// BAD: Sensitive data in logs
logger.info({ password: user.password }, "User login");

// BAD: No context
logger.error("Something went wrong");

// BAD: Wrong level
logger.error("Cache miss for key: user:123");  // This is debug, not error

// GOOD: Masked sensitive data, full context, correct level
logger.info({ userId: user.id, email: maskEmail(user.email) }, "User login successful");
logger.error({ orderId, error: err.message, stack: err.stack }, "Payment processing failed");
logger.debug({ key: "user:123", hit: false }, "Cache lookup");

Quick Reference

Log Levels:
  error   Operation failed, needs attention
  warn    Unexpected but handled
  info    Normal significant events
  debug   Development detail

Structured Logging Checklist:
  - JSON format, not string interpolation
  - Consistent field names across services
  - Correlation ID on every request
  - Timestamps in ISO 8601 (UTC)
  - Service name and version in every entry
  - Error entries include message + stack
  - No sensitive data (mask PII, tokens)

Libraries:
  Node.js:  pino, winston
  Python:   structlog, python-json-logger
  Go:       slog (stdlib), zap, zerolog
  Java:     SLF4J + Logback, Log4j2

Analysis:
  jq        Query JSON logs from the command line
  grep -c   Count occurrences
  tail -f   Follow log output in real time