What Is Log Aggregation?

What Is Log Aggregation?

Log aggregation is the practice of collecting, centralizing, and analyzing logs from multiple sources in one place.

Why You Need It

Modern applications generate logs from many sources:

• Application servers (multiple instances)
• Web servers (nginx, Apache)
• Databases
• Load balancers
• Background job processors
• Third-party services

Without aggregation, debugging means SSH-ing into individual servers and grepping through files. With aggregation, you search all logs from a single interface.

How It Works

App Server 1 ─┐
App Server 2 ──┤
Worker 1 ──────┼──→ Log Collector ──→ Central Store ──→ Search/Dashboard
Database ──────┤
Nginx ─────────┘

Key Components

• Log shippers — agents that collect and forward logs (Fluentd, Filebeat, Vector)
• Central store — indexed storage for fast searching (Elasticsearch, Loki, ClickHouse)
• Visualization — dashboards and search interfaces (Grafana, Kibana)

Structured Logging Is Essential

Unstructured logs are hard to query:

# Bad: unstructured
User 123 placed order 456 for $99.99

# Good: structured JSON
{"event": "order_placed", "user_id": 123, "order_id": 456, "total": 99.99, "timestamp": "2025-03-20T10:30:00Z"}

Structured logs enable queries like "show all orders over $100 in the last hour."

Best Practices

• Use correlation IDs — trace a request across all services with a single ID
• Standardize log format — all services emit the same JSON structure
• Set appropriate retention — 30 days for debug logs, 1 year for audit logs
• Control log volume — don't log every successful health check
• Redact sensitive data — never log passwords, tokens, or PII

Log Levels

Logs vs. Error Tracking

Logs tell you what happened. Error tracking tools like Bugsly tell you what went wrong, with grouped exceptions, stack traces, and impact analysis. Use both: logs for the narrative, error tracking for the alerts and prioritization.