Real-Time Ad Bidding System
Low-latency bidding engine built with Kafka and distributed microservices.
Overview
Real-Time Distributed Ad Bidding Platform is a production-style cloud-native system built to simulate how modern real-time bidding platforms process ad auctions within milliseconds. The project focuses on microservices, Apache Kafka, Kubernetes, AWS EKS, and observability tooling to show how a low-latency ad-tech backend can be structured and operated at scale.
Problem Statement
Ad-tech systems must process bid requests, bidder responses, auction selection, and budget updates under extreme latency constraints. A monolithic backend makes it harder to scale these responsibilities independently and to keep the system observable. This project explores how a real-time bidding platform can be decomposed into distributed services that communicate through Kafka while remaining fault-tolerant, scalable, and cloud-native.
Features
- Event-driven real-time bidding flow
- Campaign management and budget tracking
- Bid request and bid response processing
- Auction winner selection
- Kafka-based asynchronous communication
- Idempotent event handling
- AWS EKS deployment
- Kubernetes ingress routing
- Prometheus monitoring
- Grafana dashboards
- Zipkin distributed tracing
- Dockerized microservices
Core Services
The system is composed of independently deployable services, each responsible for a single business capability.
Campaign Service
Creates campaigns and manages budgets.
Bidder Service
Consumes bid requests and generates bids asynchronously.
Auction Service
Processes bids and selects the auction winner.
Kafka
Acts as the distributed event backbone between services.
MySQL
Stores persistent campaign and auction data.
Technology Stack
Technologies, frameworks, infrastructure, and tooling used throughout the development of this project.
Backend
Java 21 • Spring Boot • Spring Kafka • Spring Data JPA • Maven
Database
MySQL • Persistent Volumes
Messaging
Apache Kafka • Event-Driven Architecture • Asynchronous Messaging • Consumer Groups • Partition-based Processing • Idempotent Event Handling
Infrastructure
Docker • Docker Compose • Kubernetes • AWS EKS • Helm • AWS Load Balancer Controller • EBS CSI Driver
Developer Tools
Prometheus • Grafana • Zipkin • Spring Boot Actuator • Micrometer
Engineering Decisions
Architectural choices made to improve scalability, maintainability, reliability, and long-term evolution of the system.
Event-Driven Architecture
The platform uses Kafka as the central communication layer so campaign updates, bid responses, and auction results can flow asynchronously between services.
Microservice Separation
Campaign, bidder, and auction responsibilities are split into distinct services so each part of the bidding pipeline can evolve independently.
Low-Latency Auction Flow
The system is designed around fast event propagation so auction-related work can happen within tight response windows.
Cloud-Native Deployment
The project is deployed on AWS EKS with Kubernetes, ingress routing, persistent volumes, and namespace isolation to simulate a production environment.
Observability First
Prometheus, Grafana, Zipkin, Actuator, and Micrometer are used to expose metrics, dashboards, and traces across the system.
Idempotent Processing
The design explicitly calls out idempotency and retry handling so duplicate event delivery does not break the bidding flow.
Challenges
The most significant engineering challenges encountered while building the system.
Designing a low-latency event pipeline for ad auctions.
Coordinating campaign, bidder, and auction services through Kafka.
Keeping the workflow fault-tolerant under retries and duplicate events.
Deploying and observing the system on AWS EKS.
Managing persistent storage and cloud-native networking in Kubernetes.
What I Learned
Key technical concepts and engineering practices gained during the implementation.
How real-time bidding systems are structured at a high level.
How Kafka can serve as the backbone for low-latency distributed workflows.
How Kubernetes and AWS EKS support production-style deployment.
How observability tools help debug distributed systems.
How idempotency and fault isolation matter in event-driven systems.
Future Improvements
Potential enhancements that could improve scalability, reliability, and maintainability.
Add dead-letter queue handling.
Expand analytics for auction and budget insights.
Improve retry and timeout management.
Add stronger automated test coverage.
Build a UI dashboard for campaign and auction monitoring.