Log Aggregation

Log aggregation is the practice of collecting logs from all application instances, services, and infrastructure components into a centralized platform for searching, analysis, and alerting. In distributed systems with multiple services running across many instances, logs are useless if they remain isolated on individual servers -- centralized aggregation enables cross-service correlation, pattern detection, and incident investigation.

The log aggregation pipeline typically involves three stages: collection (agents like Fluentd, Fluent Bit, Vector, or Logstash running on each host or as sidecar containers), transport (buffering and forwarding over the network, often via Kafka for high-volume environments), and storage/indexing (platforms like Elasticsearch/OpenSearch, Grafana Loki, Datadog Logs, or CloudWatch Logs). Loki takes a unique approach by indexing only labels (metadata) rather than full-text, drastically reducing storage costs while still enabling efficient querying.

Effective log aggregation requires consistent log formatting across services (structured JSON with shared field names), log retention policies balanced between cost and debugging needs (e.g., 7 days hot, 30 days warm, 1 year cold/archived), alert rules for error patterns and anomalies, and access controls limiting who can view production logs containing sensitive data. Sampling or filtering verbose debug logs before ingestion can significantly reduce costs without losing important signal.