# Optimization Planning

> Module: Comprehensive optimization plan generation
> Complexity: Advanced
> Time: 15+ minutes
> Dependencies: asyncio

## Core Implementation

### Optimization Planning System

```python
from typing import Dict, List
from dataclasses import dataclass
import re
from collections import defaultdict

@dataclass
class OptimizationPlan:
    """Comprehensive optimization plan with prioritized actions."""
    bottlenecks: List[PerformanceBottleneck]
    execution_order: List[int]
    estimated_total_improvement: str
    implementation_complexity: str
    risk_level: str
    prerequisites: List[str]
    validation_strategy: str

class OptimizationPlanner:
    """Create comprehensive optimization plans."""

    def __init__(self, detector):
        self.detector = detector

    async def create_optimization_plan(
        self, bottlenecks: List[PerformanceBottleneck],
        context7_patterns: Dict[str, Any] = None
    ) -> OptimizationPlan:
        """Create comprehensive optimization plan."""

        # Prioritize bottlenecks by impact and severity
        prioritized_bottlenecks = self._prioritize_bottlenecks(bottlenecks)

        # Create execution order
        execution_order = self._create_optimization_execution_order(prioritized_bottlenecks)

        # Estimate total improvement
        total_improvement = self._estimate_total_improvement(prioritized_bottlenecks)

        # Assess implementation complexity
        complexity = self._assess_implementation_complexity(prioritized_bottlenecks)

        # Assess risk level
        risk_level = self._assess_optimization_risk(prioritized_bottlenecks)

        # Identify prerequisites
        prerequisites = self._identify_optimization_prerequisites(prioritized_bottlenecks)

        # Create validation strategy
        validation_strategy = self._create_validation_strategy(prioritized_bottlenecks)

        return OptimizationPlan(
            bottlenecks=prioritized_bottlenecks,
            execution_order=execution_order,
            estimated_total_improvement=total_improvement,
            implementation_complexity=complexity,
            risk_level=risk_level,
            prerequisites=prerequisites,
            validation_strategy=validation_strategy
        )

    def _prioritize_bottlenecks(
        self, bottlenecks: List[PerformanceBottleneck]
    ) -> List[PerformanceBottleneck]:
        """Prioritize bottlenecks by impact and implementation complexity."""

        # Sort by severity, impact score, and optimization type
        severity_order = {'critical': 4, 'high': 3, 'medium': 2, 'low': 1}

        return sorted(
            bottlenecks,
            key=lambda x: (
                severity_order.get(x.severity, 0),
                x.impact_score,
                self._get_optimization_priority(x.optimization_type)
            ),
            reverse=True
        )

    def _get_optimization_priority(self, opt_type: OptimizationType) -> int:
        """Get priority weight for optimization type."""
        priorities = {
            OptimizationType.ALGORITHM_IMPROVEMENT: 4,
            OptimizationType.CACHING: 3,
            OptimizationType.CONCURRENCY: 3,
            OptimizationType.MEMORY_OPTIMIZATION: 2,
            OptimizationType.DATA_STRUCTURE_CHANGE: 2,
            OptimizationType.I_O_OPTIMIZATION: 2,
            OptimizationType.DATABASE_OPTIMIZATION: 1
        }
        return priorities.get(opt_type, 1)

    def _create_optimization_execution_order(
        self, bottlenecks: List[PerformanceBottleneck]
    ) -> List[int]:
        """Create optimal execution order for optimizations."""

        # Group by optimization type
        type_groups = defaultdict(list)
        for i, bottleneck in enumerate(bottlenecks):
            type_groups[bottleneck.optimization_type].append(i)

        # Define execution order by type
        execution_order = []
        type_order = [
            OptimizationType.ALGORITHM_IMPROVEMENT,
            OptimizationType.DATA_STRUCTURE_CHANGE,
            OptimizationType.CACHING,
            OptimizationType.MEMORY_OPTIMIZATION,
            OptimizationType.CONCURRENCY,
            OptimizationType.I_O_OPTIMIZATION,
            OptimizationType.DATABASE_OPTIMIZATION
        ]

        for opt_type in type_order:
            if opt_type in type_groups:
                execution_order.extend(type_groups[opt_type])

        return execution_order

    def _estimate_total_improvement(
        self, bottlenecks: List[PerformanceBottleneck]
    ) -> str:
        """Estimate total performance improvement."""

        if not bottlenecks:
            return "No significant improvement expected"

        # Calculate weighted improvement
        total_weighted_improvement = 0
        total_weight = 0

        for bottleneck in bottlenecks:
            # Extract improvement percentage from description
            improvement_range = self._parse_improvement_estimate(bottleneck.estimated_improvement)
            if improvement_range:
                avg_improvement = (improvement_range[0] + improvement_range[1]) / 2
                weight = bottleneck.impact_score
                total_weighted_improvement += avg_improvement * weight
                total_weight += weight

        if total_weight > 0:
            avg_improvement = total_weighted_improvement / total_weight
            return f"{avg_improvement:.0f}% average performance improvement"

        return "Performance improvement depends on implementation"

    def _parse_improvement_estimate(self, estimate: str) -> tuple:
        """Parse improvement percentage from estimate string."""

        # Look for percentage ranges like "20-50%" or "30%"
        match = re.search(r'(\d+)-?(\d+)?%', estimate)
        if match:
            start = int(match.group(1))
            end = int(match.group(2)) if match.group(2) else start
            return (start, end)

        return None

    def _assess_implementation_complexity(
        self, bottlenecks: List[PerformanceBottleneck]
    ) -> str:
        """Assess overall implementation complexity."""

        complexity_scores = {
            OptimizationType.ALGORITHM_IMPROVEMENT: 3,
            OptimizationType.DATA_STRUCTURE_CHANGE: 3,
            OptimizationType.CONCURRENCY: 4,
            OptimizationType.DATABASE_OPTIMIZATION: 3,
            OptimizationType.CACHING: 2,
            OptimizationType.MEMORY_OPTIMIZATION: 2,
            OptimizationType.I_O_OPTIMIZATION: 2
        }

        if not bottlenecks:
            return "low"

        avg_complexity = sum(
            complexity_scores.get(b.optimization_type, 2) * b.impact_score
            for b in bottlenecks
        ) / sum(b.impact_score for b in bottlenecks)

        if avg_complexity > 3.5:
            return "high"
        elif avg_complexity > 2.5:
            return "medium"
        else:
            return "low"

    def _assess_optimization_risk(
        self, bottlenecks: List[PerformanceBottleneck]
    ) -> str:
        """Assess risk level of optimizations."""

        high_risk_types = {
            OptimizationType.ALGORITHM_IMPROVEMENT,
            OptimizationType.DATA_STRUCTURE_CHANGE,
            OptimizationType.CONCURRENCY
        }

        high_risk_count = sum(
            1 for b in bottlenecks
            if b.optimization_type in high_risk_types and b.impact_score > 0.3
        )

        if high_risk_count > 3:
            return "high"
        elif high_risk_count > 1:
            return "medium"
        else:
            return "low"

    def _identify_optimization_prerequisites(
        self, bottlenecks: List[PerformanceBottleneck]
    ) -> List[str]:
        """Identify prerequisites for safe optimization."""

        prerequisites = [
            "Create comprehensive performance benchmarks",
            "Ensure version control with current implementation",
            "Set up performance testing environment"
        ]

        # Add specific prerequisites based on bottleneck types
        optimization_types = set(b.optimization_type for b in bottlenecks)

        if OptimizationType.CONCURRENCY in optimization_types:
            prerequisites.extend([
                "Review thread safety and shared resource access",
                "Implement proper synchronization mechanisms"
            ])

        if OptimizationType.DATABASE_OPTIMIZATION in optimization_types:
            prerequisites.extend([
                "Create database backup before optimization",
                "Set up database performance monitoring"
            ])

        if OptimizationType.ALGORITHM_IMPROVEMENT in optimization_types:
            prerequisites.extend([
                "Verify algorithm correctness with test suite",
                "Compare against known reference implementations"
            ])

        return prerequisites

    def _create_validation_strategy(
        self, bottlenecks: List[PerformanceBottleneck]
    ) -> str:
        """Create validation strategy for optimizations."""

        strategy = """
Validation Strategy:
1. Baseline Performance Measurement
   - Record current performance metrics
   - Establish performance regression thresholds

2. Incremental Testing
   - Apply optimizations one at a time
   - Measure performance impact after each change

3. Automated Performance Testing
   - Implement performance regression tests
   - Set up continuous performance monitoring

4. Functional Validation
   - Run complete test suite after each optimization
   - Verify no functional regressions introduced

5. Production Monitoring
   - Monitor performance in staging environment
   - Gradual rollout with performance validation
"""

        return strategy
```

## Usage Examples

```python
# Create optimization plan
planner = OptimizationPlanner(detector)
optimization_plan = await planner.create_optimization_plan(bottlenecks)

print(f"\nOptimization Plan:")
print(f"  Estimated improvement: {optimization_plan.estimated_total_improvement}")
print(f"  Implementation complexity: {optimization_plan.implementation_complexity}")
print(f"  Risk level: {optimization_plan.risk_level}")
print(f"  Prerequisites: {len(optimization_plan.prerequisites)} items")
print(f"  Execution order: {optimization_plan.execution_order}")
print(f"\nValidation Strategy:")
print(optimization_plan.validation_strategy)
```

## Best Practices

1. **Prioritization**: Adddess high-impact, low-complexity optimizations first
2. **Risk Assessment**: Understand and mitigate optimization risks
3. **Incremental Approach**: Apply optimizations one at a time
4. **Baseline Measurement**: Establish performance baselines before optimization
5. **Validation Strategy**: Comprehensive testing prevents regressions

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Related: [Bottleneck Detection](./bottleneck-detection.md) | [AI-Powered Optimization](./ai-optimization.md)
