Dynamic difficulty curve adjustment
A backend architecture for a system designed to dynamically update the difficulty of an online game in real time. The sytem collects game telemetry from the main game server, analyzes player behavior and triggers adjustments based on this analysis. The adjustment service then communicates with the game server tio implement the required difficulty adjustments. The processed player data is also stored in a database for historical analysis.
Main project components
Data Ingestion
Game events and player actions are captured in real-time.
Data is streamed to Apache Kafka.
Stream Processing
A stream processing engine (e.g., Quix) consumes data from the queue.
Initial data cleaning and aggregation are performed.
Real-time Analytics
Processed data is fed into a real-time analytics engine.
Machine learning models or statistical algorithms analyze player performance.
Decision Engine
Based on analytics output, a decision engine determines necessary difficulty adjustments.
Feedback Loop
Adjustment decisions are sent back to the game server for implementation.
Data Storage
Raw and processed data are stored in a database for historical analysis and model training.
Technologies used
Several technologies could be employed to build this system:
- Apache Kafka: For high-throughput, low-latency data streaming.
- Quix: For real-time stream processing and complex event processing.
- Redis: As an in-memory data store for rapid access to player profiles and game state.
- PostGreSQL: For storing unstructured player data and game events.
- TensorFlow or PyTorch: For implementing machine learning models for player skill prediction.