Scalable Log Processing System
Designing a high-throughput logging system capable of handling millions of events per day with real-time monitoring and alerting.
Problem
Modern applications generate massive volumes of logs. Traditional systems fail to scale, leading to delayed insights and poor incident response.
- High log volume (1M+ events/day)
- Need real-time processing
- Reliable storage and search
Architecture
The system is designed using a distributed pipeline to ensure scalability and fault tolerance.
- Producers: Applications generate logs
- Kafka: Handles ingestion and buffering
- Logstash: Processes and transforms logs
- Elasticsearch: Stores and indexes logs
- Kibana: Visualization and monitoring
Architecture Diagram
Kafka → Handles ingestion & buffering
Logstash → Transforms logs
Elasticsearch → Index & search
Kibana → Visualization dashboard
Scaling Strategy
- Kafka partitions for horizontal scaling
- Elasticsearch sharding for distributed storage
- Stateless log processors for auto-scaling
Trade-offs
- Kafka adds complexity but ensures durability
- Elasticsearch is powerful but resource-intensive
- Real-time processing increases infrastructure cost