naina-sriv/flash-sale-engine — explained in plain English
Analysis updated 2026-05-18
Build a checkout system that can survive a sudden traffic spike without crashing or overselling stock.
Study a working example of using Redis for atomic stock locking instead of relying on database row locks.
Load test a backend with Locust to see how it behaves under thousands of simultaneous purchase attempts.
Learn how to structure a write-behind pattern where fast operations happen first and slower database writes happen after.
| naina-sriv/flash-sale-engine | 0xallam/my-recipe | 0xhassaan/nn-from-scratch | |
|---|---|---|---|
| Stars | 0 | — | 0 |
| Language | Python | Python | Python |
| Last pushed | — | 2022-11-22 | — |
| Maintenance | — | Dormant | — |
| Setup difficulty | moderate | moderate | moderate |
| Complexity | 4/5 | 2/5 | 4/5 |
| Audience | developer | general | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires Docker Compose to run the full stack of Nginx, three API replicas, Postgres, and Redis together.
Flash Sale Engine is a backend system built to handle flash sales, the kind of event where thousands of people try to buy a heavily discounted item at the exact same moment. It is built with FastAPI (a Python web framework), Redis (a fast in-memory data store), and PostgreSQL (a database for long-term storage), and it runs behind Nginx, a tool that spreads incoming traffic across multiple copies of the application. The core idea is that the system never makes a shopper wait on the slower database during checkout. Instead, it uses Redis to instantly check and reduce stock counts the moment someone tries to buy an item, which prevents two people from accidentally buying the last unit at the same time. The actual order record is then saved to PostgreSQL afterward, in the background, so the checkout itself stays fast even under heavy load. To keep things fair and secure, the system includes a duplicate purchase lock so one person cannot submit the same order twice while it is still processing, plus a math challenge shoppers must solve before buying, meant to slow down bots. It also has rate limiting on login and purchase requests, and login is handled with short-lived JWT tokens paired with longer-lived refresh tokens, with password hashing done in a way that does not block other requests. The project ships with Docker Compose so the whole setup, three copies of the API, Nginx, Redis, and Postgres, can be started with one command. It also includes a Locust load test so a developer can simulate a thousand simultaneous buyers and see how the system holds up. The author says the project was inspired by real flash sale crashes, where cheap items caused so much simultaneous demand that ordinary database driven stores fell over. This project is essentially a hands-on demonstration of how to design a backend around that specific problem using fast, atomic operations instead of relying on the database to sort out the traffic under pressure.
A backend built to survive flash sales, using Redis to instantly and safely track stock so thousands of buyers can check out at once without overselling.
Mainly Python. The stack also includes Python, FastAPI, Redis.
No license information was found in the README.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
double-check against the repo, no cap.