How to Design a “Cut‑the‑Price” Giveaway Without Going Broke – Interview‑Winning Solution

1. Core Algorithm: “Pricing per User” Instead of Random

Cutting is not random; it is a dynamic allocation based on user value. Assuming a phone cost of 2000 CNY and an acquisition budget of 200 CNY, the algorithm’s sole goal is to let only users who bring at least 200 CNY of effective value obtain the phone.

1.1 Allocate weight by user value

New users receive high weight and can cut tens of yuan, encouraging referrals; old users receive low weight, typically only a few cents; bots or machine accounts receive near‑zero weight (0.01 CNY), effectively useless assistance.

1.2 “Last 0.01 CNY” never reached

When progress approaches 99 %, the algorithm enters an extreme convergence mode, repeatedly halving the remaining amount (Zeno’s paradox: 1 → 0.5 → 0.2 → 0.1 → 0.05 …). The value asymptotically approaches zero but never equals it, explaining why users get stuck on the final fraction.

2. Architecture for High Concurrency and Anti‑Fraud

2.1 Money storage without floating‑point errors

Storing amounts as integer “micro‑units” (1 CNY = 1 000 000 µ) in Redis Long avoids precision issues. Atomic deductions are performed with Lua scripts, delivering extreme speed and absolute accuracy.

2.2 Mitigating “hot key” from viral influencers

When a popular influencer triggers a million simultaneous cuts, a single Redis key can be overwhelmed. The solution combines a local cache that filters 90 % of invalid requests, a message‑queue (MQ) to smooth spikes, and a front‑end “cutting in progress” indicator that improves user experience while protecting the service.

3. Core Anti‑Fraud Measures

3.1 Device fingerprint + behavioral biometrics

Beyond IP, the system examines operation habits. Real users generate variable press‑area changes and slight phone shake; scripted bots produce rigid, pattern‑like actions lacking physical feedback, leading to weight reduction and no actual monetary deduction.

3.2 Final‑node Turing test

When the remaining amount reaches the critical nodes (1 CNY or 0.01 CNY), the system forces a sliding‑puzzle or character‑selection CAPTCHA, disabling 99 % of automated tools.

4. Why the Final Amount Turns into “Coins/Fragments”

The database stores amounts to 0.000001 CNY, but the front‑end can display only two decimal places. To keep the game flowing, the system switches units—CNY → coins → fragments—when precision limits appear, making users feel they are always “just a little more” away from winning.

5. Interview Answer Template

Interviewer, the core of this system is a dynamic pricing model based on acquisition cost. Algorithm layer: user‑value weight + extreme convergence logic to keep cost controllable. Storage layer: Redis Long micro‑unit with Lua for speed and precision. Risk layer: device‑behavior biometrics + key‑node CAPTCHAs to block bots. Product layer: unit conversion to coins/fragments when precision is insufficient, extending retention and ensuring compliance.

Conclusion

The essence of “cut‑the‑price” is not luck but precise calculation of human behavior; technology guarantees the platform does not lose money, while psychology makes users feel they can win.