strat-gameplay-webapp/.claude/implementation/02-game-engine.md
Cal Corum a287784328 CLAUDE: Complete Week 4 - State Management & Persistence
Implemented hybrid state management system with in-memory game states and async
PostgreSQL persistence. This provides the foundation for fast gameplay (<500ms
response) with complete state recovery capabilities.

## Components Implemented

### Production Code (3 files, 1,150 lines)
- app/models/game_models.py (492 lines)
  - Pydantic GameState with 20+ helper methods
  - RunnerState, LineupPlayerState, TeamLineupState
  - DefensiveDecision and OffensiveDecision models
  - Full Pydantic v2 validation with field validators

- app/core/state_manager.py (296 lines)
  - In-memory state management with O(1) lookups
  - State recovery from database
  - Idle game eviction mechanism
  - Statistics tracking

- app/database/operations.py (362 lines)
  - Async PostgreSQL operations
  - Game, lineup, and play persistence
  - Complete state loading for recovery
  - GameSession WebSocket state tracking

### Tests (4 files, 1,963 lines, 115 tests)
- tests/unit/models/test_game_models.py (60 tests, ALL PASSING)
- tests/unit/core/test_state_manager.py (26 tests, ALL PASSING)
- tests/integration/database/test_operations.py (21 tests)
- tests/integration/test_state_persistence.py (8 tests)
- pytest.ini (async test configuration)

### Documentation (6 files)
- backend/CLAUDE.md (updated with Week 4 patterns)
- .claude/implementation/02-week4-state-management.md (marked complete)
- .claude/status-2025-10-22-0113.md (planning session summary)
- .claude/status-2025-10-22-1147.md (implementation session summary)
- .claude/implementation/player-data-catalog.md (player data reference)
- Week 5 & 6 plans created

## Key Features

- Hybrid state: in-memory (fast) + PostgreSQL (persistent)
- O(1) state access via dictionary lookups
- Async database writes (non-blocking)
- Complete state recovery from database
- Pydantic validation on all models
- Helper methods for common game operations
- Idle game eviction with configurable timeout
- 86 unit tests passing (100%)

## Performance

- State access: O(1) via UUID lookup
- Memory per game: ~1KB (just state)
- Target response time: <500ms 
- Database writes: <100ms (async) 

## Testing

- Unit tests: 86/86 passing (100%)
- Integration tests: 29 written
- Test configuration: pytest.ini created
- Fixed Pydantic v2 config deprecation
- Fixed pytest-asyncio configuration

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-22 12:01:03 -05:00

210 lines
6.5 KiB
Markdown

# Phase 2: Game Engine Core
**Duration**: Weeks 4-6
**Status**: Not Started
**Prerequisites**: Phase 1 Complete
---
## Overview
Build the core game simulation engine with in-memory state management, play resolution logic, and database persistence. Implement the polymorphic player model system and league configuration framework.
## Key Objectives
By end of Phase 2, you should have:
- ✅ In-memory game state management working
- ✅ Play resolution engine with dice rolls
- ✅ League configuration system (SBA and PD configs)
- ✅ Polymorphic player models (BasePlayer, SbaPlayer, PdPlayer)
- ✅ Database persistence layer with async operations
- ✅ State recovery mechanism from database
- ✅ Basic game flow (start → plays → end)
## Major Components to Implement
### 1. Game Engine (`backend/app/core/game_engine.py`)
- Game session initialization
- Turn management (defensive → stolen base → offensive → resolution)
- Action processing and validation
- State update coordination
- Event emission to WebSocket clients
### 2. State Manager (`backend/app/core/state_manager.py`)
- In-memory game state dictionary
- State CRUD operations
- State lifecycle management
- Cache eviction for idle games
- State recovery from database
### 3. Play Resolver (`backend/app/core/play_resolver.py`)
- Cryptographic dice rolling
- Result chart lookup (league-specific)
- Play outcome determination
- Runner advancement logic
- Score calculation
### 4. Dice System (`backend/app/core/dice.py`)
- Secure random number generation
- Roll logging and verification
- Distribution testing
### 5. Polymorphic Player Models (`backend/app/models/player_models.py`)
- BasePlayer abstract class
- SbaPlayer implementation
- PdPlayer implementation
- Lineup factory method
- Type guards for league-specific logic
### 6. League Configuration (`backend/app/config/`)
- BaseGameConfig class
- SbaConfig and PdConfig subclasses
- Result charts (d20 tables) for each league
- Config loader and versioning
- Config validation
### 7. Database Operations (`backend/app/database/operations.py`)
- Async play persistence
- Game metadata updates
- Lineup operations
- State snapshot management
- Bulk recovery queries
### 8. API Client (`backend/app/data/api_client.py`)
- HTTP client for league REST APIs
- Team data fetching
- Roster data fetching
- Player/card data fetching
- Error handling and retries
- Response caching (optional)
## Implementation Order
1. **Week 4**: State Manager + Database Operations
- [ ] In-memory state structure
- [ ] Basic CRUD operations
- [ ] Database persistence layer
- [ ] State recovery mechanism
2. **Week 5**: Game Engine + Play Resolver
- [ ] Game initialization flow
- [ ] Turn management
- [ ] Dice rolling system
- [ ] Basic play resolution (simplified charts)
3. **Week 6**: League Configs + Player Models
- [ ] Polymorphic player architecture
- [ ] League configuration system
- [ ] Complete result charts
- [ ] API client integration
- [ ] End-to-end testing
## Testing Strategy
### Unit Tests
- State manager operations
- Dice roll distribution
- Play resolver outcomes
- Player model instantiation
- Config loading
### Integration Tests
- Full game flow from start to end
- State recovery from database
- Multi-turn sequences
- API client with mocked responses
### E2E Tests
- Play a complete 9-inning game
- Verify database persistence
- Test state recovery mid-game
## Key Files to Create
```
backend/app/
├── core/
│ ├── game_engine.py # Main game logic
│ ├── state_manager.py # In-memory state
│ ├── play_resolver.py # Play outcomes
│ ├── dice.py # Random generation
│ └── validators.py # Rule validation
├── config/
│ ├── base_config.py # Base configuration
│ ├── league_configs.py # SBA/PD configs
│ ├── result_charts.py # d20 tables
│ └── loader.py # Config utilities
├── models/
│ ├── player_models.py # Polymorphic players
│ └── game_models.py # Pydantic game models
└── data/
└── api_client.py # League API client
```
## Reference Documents
- [Backend Architecture](./backend-architecture.md) - Complete backend structure
- [Database Design](./database-design.md) - Schema and queries
- [WebSocket Protocol](./websocket-protocol.md) - Event specifications
- [PRD Lines 378-551](../prd-web-scorecard-1.1.md) - Polymorphic player architecture
- [PRD Lines 780-846](../prd-web-scorecard-1.1.md) - League configuration system
## Deliverable
A working game backend that can:
- Initialize a game with teams from league APIs
- Process a complete at-bat (decisions → dice roll → outcome)
- Update game state in memory and persist to database
- Recover game state after backend restart
- Handle basic substitutions
## Notes
- Focus on getting one at-bat working perfectly before expanding
- Test dice roll distribution extensively
- Validate all state transitions
- Use simplified result charts initially, expand in Phase 3
- Don't implement UI yet - test via WebSocket events or Python scripts
---
## Implementation Approach
### Key Decisions (2025-10-22)
1. **Development Order**: SBA First → PD Second
- Build each component for SBA league first
- Learn lessons and apply to PD implementation
- Ensures simpler case works before tackling complexity
2. **Testing Strategy**: Automated Python Tests
- Unit tests for each component
- Integration tests for full workflows
- No WebSocket UI testing in Phase 2
- Python scripts to simulate game flows
3. **Result Selection Models**:
- **SBA**: Players see dice roll, then select outcome from available results
- **PD**: Flexible approach
- Human players can manually select results
- AI/Auto mode uses scouting model to determine results automatically
- Initial implementation uses placeholder/simplified charts
4. **Build Philosophy**: One Perfect At-Bat
- Focus on completing a single defensive decision → dice roll → offensive decision → resolution flow
- Validate all state transitions work correctly
- Expand features in Phase 3
### Detailed Weekly Plans
Detailed implementation instructions for each week:
- [Week 4: State Management & Persistence](./02-week4-state-management.md)
- [Week 5: Game Logic & Play Resolution](./02-week5-game-logic.md)
- [Week 6: League Features & Integration](./02-week6-league-features.md)
---
**Status**: In Progress - Planning Complete (2025-10-22)
**Next Phase**: [03-gameplay-features.md](./03-gameplay-features.md)