Log Analysis System - Architecture
High-Level Architecture
┌─────────────────────────────────────────┐
│ Log Sources │
├─────────────────────────────────────────┤
│ Apps │ Servers │ Containers │ Cloud │
└────┬──────────┬──────────────┬──────────┘
│ │ │
┌────▼──────────▼──────────────▼──────────┐
│ Log Collectors │
│ (Filebeat, Fluentd, Logstash) │
└────┬─────────────────────────────────────┘
│
┌────▼─────────────────────────────────────┐
│ Message Queue (Kafka) │
└────┬─────────────────────────────────────┘
│
┌────▼─────────────────────────────────────┐
│ Processing Pipeline │
│ Parse │ Enrich │ Transform │ Filter │
└────┬─────────────────────────────────────┘
│
┌────▼─────────────────────────────────────┐
│ Storage Layer (Elasticsearch) │
│ Hot │ Warm │ Cold │
└────┬─────────────────────────────────────┘
│
┌────▼─────────────────────────────────────┐
│ Query & Visualization │
│ Kibana │ API │ Dashboards │
└──────────────────────────────────────────┘Components
1. Log Collectors
- Filebeat for file-based logs
- Fluentd for container logs
- Logstash for processing
- Cloud-native collectors
2. Message Queue
- Kafka for buffering
- Partitioning by source
- Retention for replay
- Backpressure handling
3. Processing Pipeline
- Parse structured/unstructured logs
- Extract fields
- Enrich with metadata
- Filter and transform
4. Storage
- Elasticsearch for indexing
- Tiered storage (hot/warm/cold)
- Index lifecycle management
- Replication and sharding
5. Query Layer
- Full-text search
- Aggregations
- Real-time streaming
- Saved searches
6. Visualization
- Kibana dashboards
- Custom visualizations
- Alerting
- Reporting
Data Flow
Ingestion
1. Collectors read logs
2. Send to Kafka
3. Processors consume
4. Parse and enrich
5. Index in ElasticsearchQuery
1. User submits query
2. Query coordinator
3. Shard-level search
4. Aggregate results
5. Return to userThis architecture provides scalable, reliable log analysis at massive scale.