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.