Logs are essential for debugging, auditing, and understanding system behavior. At scale, you need centralized logging that's searchable, analyzable, and cost-effective.
Log Aggregation Architecture#
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Service A │ │ Service B │ │ Service C │
└──────┬──────┘ └──────┬──────┘ └──────┬──────┘
│ │ │
└────────────────┼────────────────┘
│
▼
┌─────────────────┐
│ Log Shipper │
│ (Fluentd/ │
│ Filebeat) │
└────────┬────────┘
│
▼
┌─────────────────┐
│ Message Queue │
│ (Kafka) │
└────────┬────────┘
│
▼
┌─────────────────┐
│ Log Storage │
│ (Elasticsearch/ │
│ Loki/Cloud) │
└────────┬────────┘
│
▼
┌─────────────────┐
│ Visualization │
│(Kibana/Grafana) │
└─────────────────┘
Structured Logging#
Log Format#
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Implementation#
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Log Collection#
Fluentd Configuration#
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Kubernetes Logging#
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Storage Solutions#
Elasticsearch#
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Grafana Loki#
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Log Retention#
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Best Practices#
What to Log#
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Log Sampling#
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Conclusion#
Effective log aggregation requires structured logging, efficient collection, and smart retention. Use structured JSON logs for parseability, centralize logs for searchability, and implement retention policies for cost control.
Logs complement metrics and traces—together they provide complete observability into your systems.