Project
Elastic Auction Room Sharding for Peak Traffic
Revmax Technologies
Apr 2024 – Aug 2024
PythonFlaskRedisWebSockets
Auto-partitioned bidders into isolated rooms when demand exceeded capacity; scaled to N rooms to sustain peak events.
Problem
Scheduled auctions caused sudden surges where a single room could not handle all users without performance degradation.
Solution
Implemented dynamic room partitioning: users were placed into multiple isolated rooms with separate bid visibility, scaling room count based on load.
Impact
- Sustained peak-time events by distributing load across rooms (no cross-room bid/user visibility).
- Enabled scaling to N rooms based on participant count, improving stability at peak.