Project Overview
A high-throughput distributed event streaming platform built as an architectural prototype to demonstrate 40,000+ telemetry events/sec ingestion. (Concept / Dummy product for system design showcase).
The Problem & Operational Challenges
High-volume operational telemetry streams choke traditional monolithic relational databases, creating severe ingestion lag and dropped messages under burst load.
Handling sudden traffic surges of 50,000+ events per second without dropping packets.
Ensuring exactly-once or at-least-once message processing semantics across consumer workers.
Maintaining real-time cluster health and latency telemetry dashboards.
Engineering Solution & Architecture
Constructed an event ingestion pipeline utilizing Go concurrency routines, Apache Kafka message partitions, and Redis distributed caching, packaged in lightweight Docker containers.
Key Technical Decisions
Implemented worker pool buffering in Go to batch database writes efficiently.
Leveraged Kafka topic partitioning to scale consumer groups horizontally.
Used Prometheus and Grafana for continuous latency and consumer lag observability.
Measurable Results & Impact
Eliminated database write bottlenecks and provided continuous real-time stream observability.
48,000+ Events Processed Per Second at Peak Ingestion
0.84ms Median Latency for In-Memory State Updates
Zero Consumer Lag with Horizontal Auto-Scaling
100% Event Delivery Reliability with Dead-Letter Queues