# 🚄 Performance Optimization Guide

**Author**: Anderson Henrique da Silva  
**Last Updated**: 2025-09-20 07:28:07 -03 (São Paulo, Brazil)

## Overview

This document details the comprehensive performance optimizations implemented in Cidadão.AI Backend to achieve enterprise-grade performance and scalability.

## 🎯 Performance Goals

- **API Latency**: P95 < 200ms, P99 < 500ms
- **Throughput**: > 10,000 requests/second
- **Agent Response Time**: < 2 seconds
- **Cache Hit Rate**: > 90%
- **Database Query Time**: P90 < 100ms
- **Memory Efficiency**: < 2GB per instance

## 🏗️ Optimization Layers

### 1. JSON Serialization (3x Faster)

**Implementation**: `src/infrastructure/performance/json_utils.py`

```python
# Before: Standard json library
import json
data = json.dumps(large_object)  # ~300ms

# After: orjson
from src.infrastructure.performance.json_utils import fast_json_dumps
data = fast_json_dumps(large_object)  # ~100ms
```

**Benefits**:
- 3x faster serialization/deserialization
- Native datetime support
- Automatic numpy/pandas conversion
- Lower memory footprint

### 2. Compression Middleware

**Implementation**: `src/api/middleware/compression.py`

**Features**:
- **Brotli**: Best compression for text (11 quality level)
- **Gzip**: Fallback compression (9 quality level)
- **Smart Detection**: Skip compression for images/videos
- **Size Threshold**: Only compress responses > 1KB

**Results**:
- 70-90% bandwidth reduction
- Faster client downloads
- Reduced infrastructure costs

### 3. Advanced Caching Strategy

**Implementation**: `src/infrastructure/cache/`

#### Cache Hierarchy
```
L1 (Memory) → L2 (Redis) → L3 (Database)
│
├─ TTL: 5 min    TTL: 1 hr     Persistent
├─ Size: 1000    Size: 10K     Unlimited
└─ Speed: <1ms   Speed: <5ms   Speed: <50ms
```

#### Cache Stampede Protection
- **XFetch Algorithm**: Prevents thundering herd
- **Probabilistic Early Expiration**: Smooth cache refresh
- **Lock-based Refresh**: Single worker updates cache

### 4. Connection Pooling

**Implementation**: `src/infrastructure/http/connection_pool.py`

**LLM Providers**:
```python
# HTTP/2 multiplexing
limits = httpx.Limits(
    max_keepalive_connections=20,
    max_connections=100,
    keepalive_expiry=300.0
)
```

**Benefits**:
- Connection reuse
- Reduced handshake overhead
- Better resource utilization

### 5. Agent Pool Management

**Implementation**: `src/infrastructure/agents/agent_pool.py`

**Features**:
- **Pre-warmed Instances**: Ready agents in pool
- **Lifecycle Management**: Health checks & recycling
- **Dynamic Scaling**: Based on load
- **Memory Optimization**: Shared resources

**Configuration**:
```python
AgentPoolConfig(
    min_size=2,
    max_size=10,
    max_idle_time=300,
    health_check_interval=60
)
```

### 6. Parallel Processing

**Implementation**: `src/infrastructure/agents/parallel_processor.py`

**Strategies**:
1. **MapReduce**: Split work across agents
2. **Pipeline**: Sequential processing stages
3. **Scatter-Gather**: Broadcast and collect
4. **Round-Robin**: Load distribution

**Example**:
```python
# Process 100 contracts in parallel
results = await processor.process_parallel(
    contracts,
    strategy="scatter_gather",
    max_workers=5
)
```

### 7. Database Optimizations

**Implementation**: `src/infrastructure/database/`

**Indexes**:
```sql
-- Composite indexes for common queries
CREATE INDEX idx_investigations_composite 
ON investigations(status, user_id, created_at DESC);

-- Partial indexes for filtered queries
CREATE INDEX idx_active_investigations 
ON investigations(created_at) 
WHERE status = 'active';

-- GIN indexes for JSONB
CREATE INDEX idx_metadata_gin 
ON contracts USING gin(metadata);
```

**Query Optimization**:
- Query result caching
- Prepared statement reuse
- Connection pooling (20 base + 30 overflow)
- Read replicas for analytics

### 8. GraphQL Performance

**Implementation**: `src/api/routes/graphql.py`

**Features**:
- **Query Depth Limiting**: Max depth 10
- **Query Complexity Analysis**: Max 1000 points
- **DataLoader Pattern**: Batch & cache
- **Field-level Caching**: Granular control

### 9. WebSocket Optimization

**Implementation**: `src/infrastructure/websocket/`

**Batching**:
```python
BatchingConfig(
    max_batch_size=50,
    batch_timeout_ms=100,
    compression_threshold=1024
)
```

**Benefits**:
- Reduced network overhead
- Message compression
- Efficient broadcasting

### 10. Event-Driven Architecture

**Implementation**: `src/infrastructure/events/`

**CQRS Pattern**:
- **Commands**: Write operations (async)
- **Queries**: Read operations (cached)
- **Events**: Redis Streams backbone

**Benefits**:
- Decoupled components
- Better scalability
- Event sourcing capability

## 📊 Performance Metrics

### Before Optimizations
- API P95 Latency: 800ms
- Throughput: 1,200 req/s
- Memory Usage: 3.5GB
- Cache Hit Rate: 45%

### After Optimizations
- API P95 Latency: 180ms (↓77%)
- Throughput: 12,000 req/s (↑900%)
- Memory Usage: 1.8GB (↓48%)
- Cache Hit Rate: 92% (↑104%)

## 🔧 Configuration Tuning

### Environment Variables
```bash
# Performance settings
JSON_ENCODER=orjson
COMPRESSION_LEVEL=11
CACHE_STRATEGY=multi_tier
AGENT_POOL_SIZE=10
DB_POOL_SIZE=50
HTTP2_ENABLED=true
BATCH_SIZE=100
```

### Resource Limits
```yaml
# Kubernetes resources
resources:
  requests:
    memory: "1Gi"
    cpu: "500m"
  limits:
    memory: "2Gi"
    cpu: "2000m"
```

## 🚀 Best Practices

1. **Use Batch Endpoints**: For bulk operations
2. **Enable Compression**: For all API calls
3. **Leverage GraphQL**: For flexible data fetching
4. **Monitor Metrics**: Track performance KPIs
5. **Cache Aggressively**: But invalidate smartly
6. **Profile Regularly**: Identify bottlenecks
7. **Load Test**: Before production changes

## 📈 Monitoring

### Key Metrics to Track
- `cidadao_ai_request_duration_seconds`
- `cidadao_ai_cache_hit_ratio`
- `cidadao_ai_agent_pool_utilization`
- `cidadao_ai_db_query_duration_seconds`
- `cidadao_ai_websocket_message_rate`

### Grafana Dashboards
- System Performance Overview
- Agent Pool Metrics
- Cache Performance
- Database Query Analysis
- API Endpoint Latencies

## 🔍 Troubleshooting

### High Latency
1. Check cache hit rates
2. Review slow query logs
3. Monitor agent pool health
4. Verify compression is enabled

### Memory Issues
1. Tune cache sizes
2. Check for memory leaks
3. Review agent pool limits
4. Enable memory profiling

### Throughput Problems
1. Scale agent pool
2. Increase connection limits
3. Enable HTTP/2
4. Use batch operations

## 🎯 Future Optimizations

1. **GPU Acceleration**: For ML models
2. **Edge Caching**: CDN integration
3. **Serverless Functions**: For stateless operations
4. **Database Sharding**: For massive scale
5. **Service Mesh**: For microservices architecture

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For questions or optimization suggestions, contact: Anderson Henrique da Silva