Real product. What tech probably powers it. And what you can learn from it.
Swiggy is one of India's largest food delivery platforms — handling millions of orders daily across real-time location tracking, dynamic pricing, restaurant discovery, and payment flows, all at once. Here's what likely powers it.
Component-based UI with Redux for global state and TypeScript for end-to-end type safety across the codebase.
Shared business logic across iOS and Android, with native Kotlin and Swift modules for performance-critical paths.
Fast, non-blocking API servers built for high-concurrency request volumes.
Powers ML models, data pipelines, and the recommendation engine for personalised feeds.
High-throughput microservices handling order processing and real-time delivery assignment.
Declarative, flexible data-fetching layer consumed directly by the client apps.
Low-latency binary protocol for internal microservice-to-microservice communication.
Primary cloud — EC2, S3, RDS, and Lambda for serverless compute at scale.
Container orchestration managing hundreds of microservices across availability zones.
Containerisation ensuring consistent, reproducible deployments at every environment.
Automated testing and deployment pipelines via Jenkins and GitHub Actions.
CloudFront and Akamai for edge-cached static asset delivery across geographies.
| Technology | Role |
|---|---|
| PostgreSQL | Structured data — user accounts, orders, restaurant records |
| MongoDB | Flexible schema data — menus, dynamic item metadata |
| Redis | Caching, session storage, and real-time leaderboard operations |
| Elasticsearch | Restaurant and food search — fast, ranked full-text queries |
| Apache Kafka | Event streaming — order lifecycle events and live location updates |
Even a small app benefits from caching frequent DB queries. Start with the most-read data.
If you're filtering in the database for every search query, you're doing it wrong. Elasticsearch is purpose-built for this.
An order triggers events that other services listen to. Design your app to think in events, not sequential calls.
When reads are 90% of traffic, read replicas and CQRS patterns change everything about your optimization strategy.
Calling an API every 2 seconds for updates is wasteful. WebSockets give real-time push with a fraction of the overhead.
| Swiggy's Stack | Your Version |
|---|---|
| React + Redux | React + Context API or Zustand |
| Node.js microservices | Single Node.js + Express REST API |
| PostgreSQL + MongoDB | PostgreSQL or MongoDB — pick one |
| Redis caching | Simple in-memory cache or basic Redis |
| Kafka event streaming | Skip — unnecessary at small scale |
| Kubernetes | Docker Compose is enough |
| Google Maps Platform | Leaflet.js (free) or Google Maps free tier |