Dashflow – Reactive Order Assignment System

Dashflow – Reactive Order Assignment System

A DoorDash-style real-time backend for batching and assigning food delivery orders using Spring Boot WebFlux, Kafka, and Project Reactor.

---

🧩 Problem Statement

Food delivery platforms like DoorDash need to efficiently group and assign high volumes of incoming orders while minimizing delays and driver overhead.

However:

• Orders arrive continuously and unpredictably
• Naive one-order-per-runner logic creates inefficiencies
• Delays in assignment hurt both customers and restaurants

---

❓ Why Are We Solving This?

Let’s say:

• 🧍‍♂️ Person A (in ZIP 85719) orders from Domino’s
• 🧍‍♀️ One minute later, Person B (same ZIP) orders from Burger King — close to Domino’s

Instead of dispatching two drivers, we batch the orders and assign them to one runner, saving time and cost.

---

🎯 Goal

Design a backend system that simulates real-time:

• Order ingestion
• Batching by delivery zone
• Assignment to delivery runners
• Routing logic for efficient chunked dispatching

---

🛠 Tech Stack

Component	Tool/Framework
Reactive Backend	Spring Boot WebFlux
Event Streaming	Apache Kafka
Reactive Streams	Project Reactor
Batching & Assignment	Custom Zone-Aware Logic
Deployment	Localhost via Docker/Kafka

---

🧪 Working

1. Orders are sent in real time via REST (/orders)
2. Orders are streamed through Kafka
3. Orders are buffered and batched by ZIP code (zone)
4. Assignment logic runs on a schedule (e.g., every 60 seconds)
5. Chunked orders are assigned to available runners

---

✅ Result

• Achieved 2× faster dispatch times by reducing latency
• Enabled zone-based batching for grouped deliveries
• Simulated real-world delivery routing scenarios
• Laid the foundation for a scalable food delivery backend

---

▶️ Running the Project

1. Clone the repo
2. Start Kafka with Docker:

docker-compose up -d

./gradlew bootRun

curl -X POST http://localhost:8080/orders \
  -H "Content-Type: application/json" \
  -d '{"restaurantId": "r1", "customerAddress": "123 Main St", "zone": "85719", "estimatedPrepTime": 10}'