euclid-boy¶
Algorithmic optimization and computational performance
Euclid-boy analyzes code for bottlenecks, suggests algorithmic optimizations, profiles database queries, and helps you scale. Your performance engineer obsessed with speed.
What It Does¶
- Performance Profiling - Find slow code and expensive queries
- Algorithmic Analysis - Suggest better algorithms
- Query Optimization - Improve database performance
- Caching Strategies - Reduce redundant computation
- Load Testing - Understand system limits
Key Capabilities¶
Performance Profiling¶
- CPU/Memory Profiling
- Query Profiling
- API Latency Tracking
- Benchmark Results
Algorithmic Optimization¶
- Complexity Analysis
- Algorithm Suggestions
- Search Optimization
- Graph/Numeric Algorithms
Database Optimization¶
- Query Analysis and N+1 detection
- Index Recommendations
- Query Rewriting
- Connection Pooling
Caching Strategies¶
- Cache Analysis
- Cache Implementation Patterns
- Cache Invalidation
- Cache Hit Ratios
Load Testing¶
- Concurrent User Simulation
- Stress/Soak Testing
- Spike Testing
- Scaling Analysis
Accessing euclid-boy¶
URL: http://127.0.0.1:8011
Commands:
python manage.py profile --function view_name
python manage.py analyze-queries --realm herald
python manage.py load-test --concurrent 100 --duration 60
Common Use Cases¶
Find Performance Bottleneck¶
Profile slow code, identify functions consuming time.
Optimize Slow Query¶
Discover missing indexes, apply fixes.
Load Test Before Launch¶
Determine concurrent user capacity.
Implement Caching¶
Recommend Redis caching patterns.
Troubleshooting¶
Profiling shows no bottleneck¶
Run longer or enable detailed profiling.
Index creation slows writes¶
Normal overhead, worth it for reads.
Cache hit ratio is low¶
Check if caching right data and TTL appropriate.
Related Realms¶
- pipeline-go - Runs performance tests before deployment
- test-master - Performance testing integration
- worker-bee - Production metrics monitoring