What is the difference between logging and monitoring?

Logging records individual events (a request was made, an error occurred). Monitoring aggregates data over time to track trends and detect anomalies. Logs answer "what happened?" while monitoring answers "is the system healthy?"

What log levels should I use?

DEBUG for development details, INFO for normal operations (request received, job completed), WARN for recoverable issues (deprecated API used, retry succeeded), ERROR for failures requiring attention (unhandled exception, external service down). Keep production logs at INFO level or above.

Logging & Monitoring Explained

Capturing events, tracking metrics, and alerting on anomalies — the operational foundation for reliable software.

Logging & Monitoring

Logging records discrete events and data points from a running application (requests, errors, state changes). Monitoring collects, aggregates, and visualizes these logs and metrics over time to detect anomalies, trigger alerts, and provide operational visibility.

Explanation

Logging and monitoring are the eyes and ears of a production system. Logs capture what happened — a user made a request, a database query took 500ms, an exception was thrown. Structured logging (JSON format with consistent fields like timestamp, level, service, trace_id) makes logs searchable and parseable by tools like the ELK stack (Elasticsearch, Logstash, Kibana), Datadog, or CloudWatch. Monitoring goes beyond individual events to track system health over time. Key metrics include: request rate (throughput), error rate (percentage of failed requests), latency (p50, p95, p99 response times), and saturation (CPU, memory, disk, connection pool usage). These four metrics — known as the "four golden signals" from Google's SRE book — provide a comprehensive view of system health. Alerting closes the loop: when metrics cross predefined thresholds (error rate > 1%, p99 latency > 2s), the system notifies the team via Slack, PagerDuty, or email. Good alerting minimizes false positives (alert fatigue) while catching real issues quickly. Dashboards provide at-a-glance visibility into system health, and runbooks document the steps to diagnose and resolve common alerts.

Bookuvai Implementation

Every Bookuvai project includes structured logging (JSON format, consistent fields), centralized log aggregation, and a monitoring dashboard. We instrument applications with the four golden signals and configure alerts for anomalous behavior. Our AI PM reviews monitoring data during milestone rollouts and includes logging requirements in the technical design phase to ensure production readiness from day one.

Key Facts

The four golden signals: latency, traffic, errors, and saturation
Structured logging (JSON) enables searching, filtering, and automated analysis
Alert on symptoms (high error rate), not causes (server X is down)

Related Terms

Frequently Asked Questions

What is the difference between logging and monitoring?: Logging records individual events (a request was made, an error occurred). Monitoring aggregates data over time to track trends and detect anomalies. Logs answer "what happened?" while monitoring answers "is the system healthy?"
What log levels should I use?: DEBUG for development details, INFO for normal operations (request received, job completed), WARN for recoverable issues (deprecated API used, retry succeeded), ERROR for failures requiring attention (unhandled exception, external service down). Keep production logs at INFO level or above.