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Distributed Event Platform

High-throughput event streaming architecture using Kafka and microservices for real-time data processing.

Key Features

  • High-throughput event ingestion (100k+ events/sec)
  • Real-time stream processing with Kafka Streams
  • Event sourcing and CQRS patterns
  • Dead letter queue for failed events
  • Schema registry for event versioning
  • Monitoring and alerting dashboard

Challenge

Ensuring exactly-once processing semantics while maintaining high throughput and handling partition rebalancing gracefully.

Solution

Implemented idempotent consumers with deduplication, used Kafka transactions for exactly-once semantics, and designed graceful rebalancing with state store backups.

Technical Architecture

Frontend

React dashboard for monitoring event flows, consumer lag, and system health metrics.

Backend

Spring Boot microservices with Kafka Streams for stream processing. Event-driven architecture with domain events and sagas for distributed transactions.

Database

Apache Kafka for event storage with log compaction. PostgreSQL for materialized views and query optimization.

Deployment

Kubernetes on AWS EKS with Strimzi Kafka operator. Prometheus and Grafana for monitoring.

Development Process

Methodology

Event storming for domain modeling. Chaos engineering for resilience testing.

Timeline

6 months: 2 months architecture design, 3 months development, 1 month performance tuning.

Team

Team of 3: 2 backend developers and 1 DevOps engineer.

Tools

IntelliJ IDEA, Kafka CLI, Conduktor for Kafka management, JMeter for load testing.

Performance & Analytics

Key Metrics

100,000+ events/second throughput, 99.99% delivery guarantee, sub-100ms end-to-end latency.

Optimization

Partition tuning, batch processing, consumer group optimization, and efficient serialization with Avro.

Results

Real-time metrics on consumer lag, throughput, and error rates with automated alerting.

Lessons Learned

  • Mastered event-driven architecture patterns
  • Learned Kafka internals and optimization strategies
  • Gained expertise in distributed systems and fault tolerance

Future Enhancements

  • Add Kafka Connect for external system integration
  • Implement stream processing with Apache Flink
  • Add multi-region replication for disaster recovery
  • Build self-service event catalog for developers