# ⚡ AI Performance Optimization Guide

Supercharge your Odoo operations with AI-powered performance analysis and optimization!

## 🎯 Overview

Zenoo RPC's AI Performance Optimization provides:
- **Intelligent query analysis** with bottleneck identification
- **Automated optimization suggestions** based on real performance data
- **Proactive performance monitoring** with AI insights
- **Best practice recommendations** for scalable Odoo development

## 🚀 Basic Usage

### Query Performance Analysis

```python
import asyncio
import time
from zenoo_rpc import ZenooClient

async def analyze_query_performance():
    async with ZenooClient("http://localhost:8069") as client:
        await client.login("demo", "admin", "admin")
        await client.setup_ai(provider="gemini", api_key="your-key")
        
        # Measure query performance
        start_time = time.time()
        
        partners = await client.search_read(
            "res.partner",
            [("customer_rank", ">", 0)],
            ["name", "email", "phone"]
        )
        
        execution_time = time.time() - start_time
        
        # Get AI optimization suggestions
        query_stats = {
            "execution_time": execution_time,
            "record_count": len(partners),
            "model": "res.partner",
            "domain": [("customer_rank", ">", 0)],
            "fields": ["name", "email", "phone"]
        }
        
        suggestions = await client.ai.suggest_optimization(query_stats)
        
        print(f"Query took {execution_time:.2f}s for {len(partners)} records")
        print("\n🚀 AI Optimization Suggestions:")
        for i, suggestion in enumerate(suggestions, 1):
            print(f"{i}. {suggestion}")

asyncio.run(analyze_query_performance())
```

### Automatic Performance Monitoring

```python
class PerformanceMonitor:
    """AI-powered performance monitoring."""
    
    def __init__(self, client):
        self.client = client
        self.query_history = []
    
    async def monitor_query(self, model, domain, fields=None, **kwargs):
        """Monitor and analyze query performance."""
        start_time = time.time()
        
        try:
            if fields:
                result = await self.client.search_read(model, domain, fields, **kwargs)
            else:
                result = await self.client.search(model, domain, **kwargs)
            
            execution_time = time.time() - start_time
            
            # Store performance data
            query_data = {
                "model": model,
                "domain": domain,
                "fields": fields,
                "execution_time": execution_time,
                "record_count": len(result),
                "timestamp": time.time()
            }
            
            self.query_history.append(query_data)
            
            # Analyze if query is slow
            if execution_time > 2.0:  # Threshold for slow queries
                await self._analyze_slow_query(query_data)
            
            return result
            
        except Exception as e:
            print(f"Query failed: {e}")
            raise
    
    async def _analyze_slow_query(self, query_data):
        """Analyze slow query with AI."""
        suggestions = await self.client.ai.suggest_optimization(query_data)
        
        print(f"⚠️ Slow query detected ({query_data['execution_time']:.2f}s)")
        print(f"Model: {query_data['model']}")
        print(f"Records: {query_data['record_count']}")
        print("\n💡 AI Suggestions:")
        for suggestion in suggestions:
            print(f"  • {suggestion}")

# Usage
monitor = PerformanceMonitor(client)
partners = await monitor.monitor_query(
    "res.partner",
    [("customer_rank", ">", 0)],
    ["name", "email"]
)
```

## 🎨 Optimization Categories

### 1. Query Optimization

```python
async def query_optimization_examples():
    # Analyze different query patterns
    
    # Large dataset query
    large_query_stats = {
        "execution_time": 5.2,
        "record_count": 50000,
        "model": "res.partner",
        "domain": [("active", "=", True)],
        "operation": "search_read"
    }
    
    suggestions = await client.ai.suggest_optimization(large_query_stats)
    print("Large Dataset Optimization:")
    for suggestion in suggestions:
        print(f"  • {suggestion}")
    
    # Complex domain query
    complex_query_stats = {
        "execution_time": 3.1,
        "record_count": 1200,
        "model": "sale.order",
        "domain": [
            ("state", "in", ["sale", "done"]),
            ("date_order", ">=", "2024-01-01"),
            ("partner_id.country_id.code", "=", "US")
        ],
        "operation": "search"
    }
    
    suggestions = await client.ai.suggest_optimization(complex_query_stats)
    print("\nComplex Domain Optimization:")
    for suggestion in suggestions:
        print(f"  • {suggestion}")
```

### 2. Batch Operation Optimization

```python
async def batch_optimization():
    # Analyze batch operations
    batch_stats = {
        "operation": "bulk_create",
        "model": "product.product",
        "record_count": 1000,
        "execution_time": 15.3,
        "batch_size": 100,
        "memory_usage": "high"
    }
    
    suggestions = await client.ai.suggest_optimization(batch_stats)
    print("Batch Operation Optimization:")
    for suggestion in suggestions:
        print(f"  • {suggestion}")
```

### 3. Memory Usage Optimization

```python
async def memory_optimization():
    # Analyze memory-intensive operations
    memory_stats = {
        "operation": "large_export",
        "model": "account.move.line",
        "record_count": 100000,
        "execution_time": 45.0,
        "memory_usage": "critical",
        "fields": ["name", "debit", "credit", "account_id", "partner_id"]
    }
    
    suggestions = await client.ai.suggest_optimization(memory_stats)
    print("Memory Usage Optimization:")
    for suggestion in suggestions:
        print(f"  • {suggestion}")
```

## 🔧 Advanced Performance Features

### Performance Profiling

```python
class AdvancedProfiler:
    """Advanced performance profiling with AI analysis."""
    
    def __init__(self, client):
        self.client = client
        self.profiles = {}
    
    async def profile_operation(self, operation_name, operation_func, *args, **kwargs):
        """Profile any operation with detailed metrics."""
        import psutil
        import tracemalloc
        
        # Start memory tracking
        tracemalloc.start()
        process = psutil.Process()
        
        # Measure initial state
        initial_memory = process.memory_info().rss / 1024 / 1024  # MB
        start_time = time.time()
        
        try:
            # Execute operation
            result = await operation_func(*args, **kwargs)
            
            # Measure final state
            execution_time = time.time() - start_time
            final_memory = process.memory_info().rss / 1024 / 1024  # MB
            current, peak = tracemalloc.get_traced_memory()
            tracemalloc.stop()
            
            # Create performance profile
            profile = {
                "operation": operation_name,
                "execution_time": execution_time,
                "memory_used": final_memory - initial_memory,
                "peak_memory": peak / 1024 / 1024,  # MB
                "result_size": len(result) if hasattr(result, '__len__') else 1,
                "cpu_percent": process.cpu_percent()
            }
            
            self.profiles[operation_name] = profile
            
            # Get AI analysis
            await self._analyze_profile(profile)
            
            return result
            
        except Exception as e:
            tracemalloc.stop()
            raise
    
    async def _analyze_profile(self, profile):
        """Analyze performance profile with AI."""
        if profile["execution_time"] > 1.0 or profile["memory_used"] > 100:
            suggestions = await self.client.ai.suggest_optimization(profile)
            
            print(f"\n📊 Performance Profile: {profile['operation']}")
            print(f"⏱️ Time: {profile['execution_time']:.2f}s")
            print(f"💾 Memory: {profile['memory_used']:.1f}MB")
            print(f"📈 Peak Memory: {profile['peak_memory']:.1f}MB")
            print(f"🔢 Results: {profile['result_size']}")
            
            if suggestions:
                print("\n💡 AI Optimization Suggestions:")
                for suggestion in suggestions:
                    print(f"  • {suggestion}")

# Usage
profiler = AdvancedProfiler(client)

# Profile a search operation
partners = await profiler.profile_operation(
    "partner_search",
    client.search_read,
    "res.partner",
    [("customer_rank", ">", 0)],
    ["name", "email"]
)
```

### Comparative Analysis

```python
async def comparative_analysis():
    """Compare different approaches and get AI recommendations."""
    
    # Approach 1: Single large query
    start_time = time.time()
    all_partners = await client.search_read(
        "res.partner",
        [("customer_rank", ">", 0)],
        ["name", "email", "phone", "country_id"]
    )
    approach1_time = time.time() - start_time
    
    # Approach 2: Paginated queries
    start_time = time.time()
    paginated_partners = []
    offset = 0
    limit = 100
    
    while True:
        batch = await client.search_read(
            "res.partner",
            [("customer_rank", ">", 0)],
            ["name", "email", "phone", "country_id"],
            offset=offset,
            limit=limit
        )
        
        if not batch:
            break
            
        paginated_partners.extend(batch)
        offset += limit
    
    approach2_time = time.time() - start_time
    
    # Compare approaches
    comparison_data = {
        "operation": "large_dataset_retrieval",
        "approaches": [
            {
                "name": "single_query",
                "execution_time": approach1_time,
                "record_count": len(all_partners),
                "memory_pattern": "high_peak"
            },
            {
                "name": "paginated_query",
                "execution_time": approach2_time,
                "record_count": len(paginated_partners),
                "memory_pattern": "steady_low"
            }
        ]
    }
    
    # Get AI recommendation
    suggestions = await client.ai.suggest_optimization(comparison_data)
    
    print("🔍 Approach Comparison:")
    print(f"Single Query: {approach1_time:.2f}s")
    print(f"Paginated: {approach2_time:.2f}s")
    print("\n💡 AI Recommendations:")
    for suggestion in suggestions:
        print(f"  • {suggestion}")
```

## 📊 Performance Metrics and KPIs

### Key Performance Indicators

```python
class PerformanceKPIs:
    """Track and analyze key performance indicators."""
    
    def __init__(self, client):
        self.client = client
        self.metrics = {
            "query_times": [],
            "memory_usage": [],
            "error_rates": [],
            "throughput": []
        }
    
    async def track_kpis(self, operation_stats):
        """Track KPIs and get AI insights."""
        
        # Calculate KPIs
        avg_query_time = sum(self.metrics["query_times"]) / len(self.metrics["query_times"]) if self.metrics["query_times"] else 0
        
        kpi_data = {
            "average_query_time": avg_query_time,
            "current_query_time": operation_stats.get("execution_time", 0),
            "memory_trend": "increasing" if len(self.metrics["memory_usage"]) > 1 and self.metrics["memory_usage"][-1] > self.metrics["memory_usage"][-2] else "stable",
            "performance_trend": "degrading" if operation_stats.get("execution_time", 0) > avg_query_time * 1.5 else "stable"
        }
        
        # Get AI analysis of trends
        if kpi_data["performance_trend"] == "degrading":
            suggestions = await self.client.ai.suggest_optimization({
                **operation_stats,
                "performance_context": "degrading_performance_trend",
                "historical_average": avg_query_time
            })
            
            print("⚠️ Performance Degradation Detected!")
            print(f"Current: {operation_stats.get('execution_time', 0):.2f}s")
            print(f"Average: {avg_query_time:.2f}s")
            print("\n💡 AI Suggestions:")
            for suggestion in suggestions:
                print(f"  • {suggestion}")
```

## 🎯 Real-World Optimization Scenarios

### E-commerce Integration Optimization

```python
async def ecommerce_optimization():
    """Optimize e-commerce data synchronization."""
    
    # Product sync performance
    sync_stats = {
        "operation": "product_sync",
        "source": "ecommerce_api",
        "record_count": 5000,
        "execution_time": 120.0,
        "sync_type": "full_sync",
        "data_size": "large",
        "network_latency": "high"
    }
    
    suggestions = await client.ai.suggest_optimization(sync_stats)
    
    print("🛒 E-commerce Sync Optimization:")
    for suggestion in suggestions:
        print(f"  • {suggestion}")
```

### Reporting Performance

```python
async def reporting_optimization():
    """Optimize report generation performance."""
    
    report_stats = {
        "operation": "financial_report",
        "date_range": "12_months",
        "record_count": 25000,
        "execution_time": 45.0,
        "complexity": "high",
        "aggregations": ["sum", "group_by", "join"]
    }
    
    suggestions = await client.ai.suggest_optimization(report_stats)
    
    print("📊 Report Generation Optimization:")
    for suggestion in suggestions:
        print(f"  • {suggestion}")
```

### Migration Performance

```python
async def migration_optimization():
    """Optimize data migration performance."""
    
    migration_stats = {
        "operation": "data_migration",
        "source_system": "legacy_erp",
        "record_count": 100000,
        "execution_time": 300.0,
        "data_transformation": "complex",
        "validation_rules": "strict"
    }
    
    suggestions = await client.ai.suggest_optimization(migration_stats)
    
    print("🔄 Migration Optimization:")
    for suggestion in suggestions:
        print(f"  • {suggestion}")
```

## 🛠️ Best Practices

### 1. Continuous Monitoring

```python
async def setup_continuous_monitoring():
    """Set up continuous performance monitoring."""
    
    class ContinuousMonitor:
        def __init__(self, client):
            self.client = client
            self.alert_threshold = 2.0  # seconds
        
        async def monitor_all_operations(self):
            """Monitor all operations continuously."""
            
            # Wrap client methods with monitoring
            original_search = self.client.search
            original_search_read = self.client.search_read
            original_create = self.client.create
            
            async def monitored_search(*args, **kwargs):
                return await self._monitor_operation("search", original_search, *args, **kwargs)
            
            async def monitored_search_read(*args, **kwargs):
                return await self._monitor_operation("search_read", original_search_read, *args, **kwargs)
            
            async def monitored_create(*args, **kwargs):
                return await self._monitor_operation("create", original_create, *args, **kwargs)
            
            # Replace methods
            self.client.search = monitored_search
            self.client.search_read = monitored_search_read
            self.client.create = monitored_create
        
        async def _monitor_operation(self, operation_name, operation_func, *args, **kwargs):
            """Monitor individual operation."""
            start_time = time.time()
            
            try:
                result = await operation_func(*args, **kwargs)
                execution_time = time.time() - start_time
                
                if execution_time > self.alert_threshold:
                    await self._handle_slow_operation(operation_name, execution_time, args, kwargs)
                
                return result
                
            except Exception as e:
                execution_time = time.time() - start_time
                await self._handle_failed_operation(operation_name, execution_time, e, args, kwargs)
                raise
        
        async def _handle_slow_operation(self, operation, execution_time, args, kwargs):
            """Handle slow operations."""
            stats = {
                "operation": operation,
                "execution_time": execution_time,
                "args": str(args)[:100],  # Truncate for logging
                "performance_issue": "slow_execution"
            }
            
            suggestions = await self.client.ai.suggest_optimization(stats)
            
            print(f"⚠️ Slow {operation}: {execution_time:.2f}s")
            for suggestion in suggestions[:3]:  # Top 3 suggestions
                print(f"  💡 {suggestion}")
    
    # Setup monitoring
    monitor = ContinuousMonitor(client)
    await monitor.monitor_all_operations()
```

### 2. Performance Budgets

```python
class PerformanceBudget:
    """Set and monitor performance budgets."""
    
    def __init__(self, client):
        self.client = client
        self.budgets = {
            "search": 1.0,      # 1 second max
            "search_read": 2.0, # 2 seconds max
            "create": 0.5,      # 0.5 seconds max
            "write": 0.5,       # 0.5 seconds max
            "unlink": 0.3       # 0.3 seconds max
        }
    
    async def check_budget(self, operation, execution_time, context=None):
        """Check if operation exceeds performance budget."""
        budget = self.budgets.get(operation, 1.0)
        
        if execution_time > budget:
            overage = execution_time - budget
            overage_percent = (overage / budget) * 100
            
            print(f"💸 Budget exceeded for {operation}!")
            print(f"Budget: {budget}s, Actual: {execution_time:.2f}s")
            print(f"Overage: {overage:.2f}s ({overage_percent:.1f}%)")
            
            # Get AI suggestions for budget compliance
            budget_stats = {
                "operation": operation,
                "execution_time": execution_time,
                "budget": budget,
                "overage_percent": overage_percent,
                "context": context or {}
            }
            
            suggestions = await self.client.ai.suggest_optimization(budget_stats)
            
            print("💡 Budget Compliance Suggestions:")
            for suggestion in suggestions:
                print(f"  • {suggestion}")
```

## 🚨 Troubleshooting Performance Issues

### Common Performance Problems

```python
async def diagnose_performance_problems():
    """Diagnose common performance issues."""
    
    # Problem 1: N+1 Query Problem
    n_plus_one_stats = {
        "operation": "order_processing",
        "execution_time": 15.0,
        "query_count": 1001,  # 1 + 1000 queries
        "pattern": "n_plus_one",
        "model": "sale.order"
    }
    
    suggestions = await client.ai.suggest_optimization(n_plus_one_stats)
    print("🔍 N+1 Query Problem:")
    for suggestion in suggestions:
        print(f"  • {suggestion}")
    
    # Problem 2: Large Result Set
    large_result_stats = {
        "operation": "data_export",
        "execution_time": 30.0,
        "record_count": 100000,
        "memory_usage": "critical",
        "timeout_risk": "high"
    }
    
    suggestions = await client.ai.suggest_optimization(large_result_stats)
    print("\n📊 Large Result Set:")
    for suggestion in suggestions:
        print(f"  • {suggestion}")
```

## 🎯 Next Steps

- **[Advanced AI Features](./advanced-ai-features.md)** - Explore advanced optimization techniques
- **[AI Configuration](./ai-configuration.md)** - Fine-tune performance analysis
- **[Error Diagnosis](./error-diagnosis.md)** - Combine performance and error analysis
- **[AI Chat Assistant](./ai-chat-assistant.md)** - Get interactive optimization help

---

**💡 Pro Tip**: Performance optimization is an ongoing process. Use AI suggestions as starting points and continuously monitor your improvements!
