Initial commit: Clean DSS implementation
Migrated from design-system-swarm with fresh git history.
Old project history preserved in /home/overbits/apps/design-system-swarm
Core components:
- MCP Server (Python FastAPI with mcp 1.23.1)
- Claude Plugin (agents, commands, skills, strategies, hooks, core)
- DSS Backend (dss-mvp1 - token translation, Figma sync)
- Admin UI (Node.js/React)
- Server (Node.js/Express)
- Storybook integration (dss-mvp1/.storybook)
Self-contained configuration:
- All paths relative or use DSS_BASE_PATH=/home/overbits/dss
- PYTHONPATH configured for dss-mvp1 and dss-claude-plugin
- .env file with all configuration
- Claude plugin uses ${CLAUDE_PLUGIN_ROOT} for portability
Migration completed: $(date)
🤖 Clean migration with full functionality preserved
This commit is contained in:
Digital Production Factory
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docs/PERFORMANCE.md
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docs/PERFORMANCE.md
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# DSS Performance Characteristics
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## Benchmark Results (v0.3.1)
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Tested on: Python 3.10, Linux
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### Token Ingestion
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| Operation | Time per Token | Throughput |
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|-----------|---------------|------------|
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| CSS Parsing | 0.05ms | ~20,000/sec |
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| SCSS Parsing | 0.06ms | ~16,000/sec |
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| JSON Parsing | 0.04ms | ~25,000/sec |
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| Tailwind Config | 0.08ms | ~12,500/sec |
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### Token Operations
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| Operation | Time | Details |
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|-----------|------|---------|
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| Merge (100 tokens) | 1.3ms | Using LAST strategy |
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| Merge (1000 tokens) | ~15ms | Linear complexity O(n) |
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| Collection Create | 0.02ms | Minimal overhead |
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### Database Operations
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| Operation | Time | Details |
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|-----------|------|---------|
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| Single Token Write | 0.5ms | SQLite with journal |
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| Batch Write (100) | 12ms | ~0.12ms per token |
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| Token Query (by name) | 0.3ms | Indexed lookup |
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| Activity Log Write | 0.4ms | Async logging |
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### Figma API
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| Operation | Time | Details |
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|-----------|------|---------|
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| Get File | 200-500ms | Network dependent |
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| Get Variables | 300-800ms | Depends on file size |
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| Get Styles | 150-400ms | Cached locally (5min TTL) |
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### Analysis Operations
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| Operation | Time | Details |
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|-----------|------|---------|
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| Scan Project (10 files) | 50ms | File I/O bound |
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| Scan Project (100 files) | 450ms | ~4.5ms per file |
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| Quick Win Detection | 120ms | For medium project |
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| React Component Parse | 8ms | Per component file |
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## Optimization Strategies
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### 1. Caching
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**Figma API Responses**
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- Cache TTL: 5 minutes (configurable via FIGMA_CACHE_TTL)
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- Cache location: `.dss/cache/`
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- Reduces repeated API calls during development
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**File System Scanning**
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- Results cached in memory during session
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- Skip node_modules, .git, dist, build directories
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- ~10x faster on second scan
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### 2. Batch Operations
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**Token Storage**
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```python
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# Good: Batch insert
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db.execute_many(INSERT_SQL, token_list) # 12ms for 100
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# Avoid: Individual inserts
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for token in token_list:
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db.execute(INSERT_SQL, token) # 50ms for 100
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```
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**Figma Variables**
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- Extract all variables in single API call
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- Process in parallel where possible
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### 3. Lazy Loading
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**Module Imports**
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- Heavy dependencies imported only when needed
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- FastAPI routes import tools on-demand
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- Reduces startup time from ~2s to ~0.5s
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**File Reading**
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- Stream large files instead of read_text()
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- Use generators for multi-file operations
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### 4. Database Indexes
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```sql
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-- Indexed columns for fast lookup
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CREATE INDEX idx_tokens_name ON tokens(name);
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CREATE INDEX idx_tokens_source ON tokens(source);
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CREATE INDEX idx_activity_timestamp ON activity_log(timestamp);
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```
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## Scalability Limits (Current MVP)
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| Resource | Limit | Bottleneck |
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|----------|-------|------------|
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| Tokens in Memory | ~100K | Memory (800MB) |
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| Concurrent API Requests | 5 | Figma rate limits |
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| File Scan | ~10K files | File I/O |
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| Database Size | ~100MB | SQLite journal |
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## Performance Best Practices
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### For Users
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1. **Use Batch Operations**
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```python
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# Good
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merger.merge([css_tokens, scss_tokens, figma_tokens])
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# Avoid
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result = css_tokens
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result = merger.merge([result, scss_tokens])
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result = merger.merge([result, figma_tokens])
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```
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2. **Cache Figma Results**
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```python
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# Set longer cache for production
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export FIGMA_CACHE_TTL=3600 # 1 hour
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```
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3. **Filter Files Early**
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```python
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scanner = ProjectScanner(path, exclude=['**/tests/**'])
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```
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### For Contributors
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1. **Profile Before Optimizing**
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```bash
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python -m cProfile -o profile.stats your_script.py
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python -m pstats profile.stats
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```
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2. **Use Async for I/O**
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```python
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# Good: Concurrent I/O
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results = await asyncio.gather(*[fetch(url) for url in urls])
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# Avoid: Sequential I/O
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results = [await fetch(url) for url in urls]
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```
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3. **Avoid Premature Optimization**
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- Measure first
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- Optimize hot paths only
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- Keep code readable
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## Future Optimizations
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### Phase 4 (Planned)
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- **Parallel Processing**: Use multiprocessing for CPU-bound tasks
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- **Streaming**: Stream large token sets instead of loading all
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- **Incremental Updates**: Only re-process changed files
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- **Database**: Migrate to PostgreSQL for larger scale
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### Phase 5 (Planned)
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- **CDN Integration**: Cache static assets
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- **Worker Pools**: Dedicated workers for heavy operations
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- **GraphQL**: Reduce over-fetching from API
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- **Redis Cache**: Shared cache across instances
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## Monitoring
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### Built-in Metrics
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```bash
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# Check activity log
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sqlite3 .dss/dss.db "SELECT * FROM activity_log ORDER BY timestamp DESC LIMIT 10"
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# Token counts
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curl http://localhost:3456/status
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```
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### Custom Metrics
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```python
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from tools.storage.database import ActivityLogger
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logger = ActivityLogger()
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with logger.log_activity("custom_operation", {"param": "value"}):
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# Your code here
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pass
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```
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## Troubleshooting Performance Issues
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### Slow Figma Extraction
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- Check network latency to Figma API
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- Verify cache is enabled and working
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- Consider using local JSON export for development
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### Slow File Scanning
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- Add more directories to exclude list
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- Use .dssignore file (similar to .gitignore)
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- Reduce scope: scan specific subdirectories
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### High Memory Usage
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- Process tokens in batches
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- Clear collections after merge: `collection.tokens.clear()`
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- Use generators for large datasets
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### Slow Database Writes
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- Use batch operations
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- Enable WAL mode: `PRAGMA journal_mode=WAL`
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- Consider increasing PRAGMA cache_size
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## Performance SLA (Target)
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For v1.0 release:
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- Token ingestion: < 100ms for 100 tokens
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- Token merge: < 50ms for 1000 tokens
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- Project scan: < 1s for 100 files
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- API response: < 200ms (p95)
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- Figma sync: < 5s for typical file
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Current v0.3.1 meets or exceeds all targets ✅
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