Performance Reference

System Requirements

Minimum Requirements

• CPU: 2 cores
• Memory: 4GB RAM
• Storage: 20GB
• Network: 100Mbps

Recommended Requirements

• CPU: 4+ cores
• Memory: 8GB+ RAM
• Storage: 50GB SSD
• Network: 1Gbps

Performance Tuning

Database Optimization

# Connection Pool Configuration
engine = create_async_engine(
    settings.db_url,
    pool_size=settings.db_pool_size,
    max_overflow=settings.db_max_overflow,
    pool_timeout=settings.db_pool_timeout
)

# Query Optimization
from sqlalchemy import Index
Index('idx_process_instance_status', ProcessInstance.status)
Index('idx_process_instance_process_id', ProcessInstance.process_id)

Redis Caching

# Cache Configuration
cache = Redis(
    url=settings.redis_url,
    encoding="utf-8",
    decode_responses=True,
    pool_size=settings.redis_pool_size
)

# Cache Usage
@cached(ttl=300)  # 5 minutes
async def get_process_definition(id: str):
    return await db.get_process(id)

Process Engine Settings

# Token Batch Processing
settings = {
    'token_batch_size': 100,     # Process tokens in batches
    'max_parallel_tokens': 50,    # Maximum parallel token executions
    'state_check_interval': 5     # Seconds between state checks
}

# Variable Size Limits
settings = {
    'max_variable_size': 1048576,  # 1MB
    'max_total_variables': 10485760 # 10MB per instance
}

Scaling Strategies

Horizontal Scaling

# docker-compose.scale.yml
services:
  worker:
    build: ./backend
    command: python worker.py
    environment:
      - PYTHMATA_RABBITMQ_URL=amqp://guest:guest@rabbitmq:5672/
    deploy:
      replicas: 3
      resources:
        limits:
          cpus: '1'
          memory: 1G

Load Balancing

# nginx.conf
upstream backend {
    server backend1:8000;
    server backend2:8000;
    server backend3:8000;
}

server {
    listen 80;
    location / {
        proxy_pass http://backend;
    }
}

Monitoring

Metrics Collection

# Prometheus Metrics
from prometheus_client import Counter, Histogram

process_instances = Counter(
    'process_instances_total',
    'Total number of process instances'
)

execution_time = Histogram(
    'process_execution_seconds',
    'Time spent executing process instances'
)

Performance Dashboards

# Grafana Dashboard Configuration
dashboard = {
    'process_metrics': {
        'active_instances': 'pythmata_active_instances',
        'execution_time': 'pythmata_execution_time_seconds',
        'error_rate': 'pythmata_errors_total',
        'token_throughput': 'pythmata_tokens_processed_total'
    }
}

Optimization Tips

1. Database

• Use appropriate indexes
• Regular vacuum and analyze
• Monitor query performance
• Optimize connection pools

2. Caching

• Cache process definitions
• Cache frequently accessed data
• Use appropriate TTLs
• Monitor cache hit rates

3. Process Execution

• Batch token processing
• Parallel execution where possible
• Efficient variable handling
• Proper error handling

4. Network

• Use connection pooling
• Enable keepalive
• Compress responses
• Use WebSocket for real-time updates

Performance Testing

Load Testing

# locust test example
from locust import HttpUser, task, between

class ProcessUser(HttpUser):
    wait_time = between(1, 3)

    @task
    def start_process(self):
        self.client.post(
            "/api/v1/processes/OrderProcess/instances",
            json={"variables": {"amount": 100}}
        )

Benchmarking

# Apache Benchmark Example
ab -n 1000 -c 10 http://localhost:8000/api/v1/processes

# wrk Example
wrk -t12 -c400 -d30s http://localhost:8000/api/v1/processes

Common Issues

1. Memory Leaks

• Monitor memory usage
• Implement proper cleanup
• Use memory profiling
• Set appropriate limits

2. CPU Bottlenecks

• Profile CPU usage
• Optimize hot paths
• Use async where appropriate
• Scale horizontally

3. Database Performance

• Monitor slow queries
• Optimize indexes
• Use connection pooling
• Regular maintenance

4. Network Issues

• Monitor latency
• Use connection pooling
• Enable compression
• Implement retry logic

Performance Checklist

1. Database
2. [ ] Indexes optimized
3. [ ] Connection pools configured
4. [ ] Query performance monitored

5. [ ] Regular maintenance scheduled

6. Caching

7. [ ] Process definitions cached
8. [ ] Cache size configured
9. [ ] TTLs set appropriately

10. [ ] Hit rates monitored

11. Process Engine

12. [ ] Batch processing configured
13. [ ] Parallel execution enabled
14. [ ] Resource limits set

15. [ ] Error handling implemented

16. Monitoring

17. [ ] Metrics collection enabled
18. [ ] Dashboards configured
19. [ ] Alerts set up
20. [ ] Performance baselines established