c876b0aa20
✅ Phase 0: Pre-implementation checklist completed - Environment configurations (dev/staging/production) - Directory structure setup (logs, backups, monitoring) - Virtual environment with dependencies ✅ Master deployment script created - Single command deployment with validation - Progress tracking and rollback capability - Health checks and deployment reporting ✅ Validation script created - Module import validation - Basic functionality testing - Configuration and script verification ✅ Implementation fixes - Fixed dataclass import in consensus keys - Fixed async function syntax in tests - Updated deployment script for virtual environment 🚀 Ready for deployment: ./scripts/deploy-mesh-network.sh dev
352 lines
14 KiB
Python
352 lines
14 KiB
Python
"""
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Gas Optimization System
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Optimizes gas usage and fee efficiency for smart contracts
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"""
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import asyncio
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import time
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import json
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from typing import Dict, List, Optional, Tuple
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from dataclasses import dataclass
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from enum import Enum
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from decimal import Decimal
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class OptimizationStrategy(Enum):
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BATCH_OPERATIONS = "batch_operations"
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LAZY_EVALUATION = "lazy_evaluation"
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STATE_COMPRESSION = "state_compression"
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EVENT_FILTERING = "event_filtering"
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STORAGE_OPTIMIZATION = "storage_optimization"
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@dataclass
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class GasMetric:
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contract_address: str
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function_name: str
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gas_used: int
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gas_limit: int
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execution_time: float
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timestamp: float
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optimization_applied: Optional[str]
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@dataclass
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class OptimizationResult:
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strategy: OptimizationStrategy
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original_gas: int
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optimized_gas: int
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gas_savings: int
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savings_percentage: float
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implementation_cost: Decimal
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net_benefit: Decimal
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class GasOptimizer:
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"""Optimizes gas usage for smart contracts"""
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def __init__(self):
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self.gas_metrics: List[GasMetric] = []
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self.optimization_results: List[OptimizationResult] = []
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self.optimization_strategies = self._initialize_strategies()
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# Optimization parameters
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self.min_optimization_threshold = 1000 # Minimum gas to consider optimization
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self.optimization_target_savings = 0.1 # 10% minimum savings
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self.max_optimization_cost = Decimal('0.01') # Maximum cost per optimization
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self.metric_retention_period = 86400 * 7 # 7 days
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# Gas price tracking
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self.gas_price_history: List[Dict] = []
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self.current_gas_price = Decimal('0.001')
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def _initialize_strategies(self) -> Dict[OptimizationStrategy, Dict]:
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"""Initialize optimization strategies"""
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return {
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OptimizationStrategy.BATCH_OPERATIONS: {
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'description': 'Batch multiple operations into single transaction',
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'potential_savings': 0.3, # 30% potential savings
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'implementation_cost': Decimal('0.005'),
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'applicable_functions': ['transfer', 'approve', 'mint']
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},
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OptimizationStrategy.LAZY_EVALUATION: {
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'description': 'Defer expensive computations until needed',
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'potential_savings': 0.2, # 20% potential savings
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'implementation_cost': Decimal('0.003'),
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'applicable_functions': ['calculate', 'validate', 'process']
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},
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OptimizationStrategy.STATE_COMPRESSION: {
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'description': 'Compress state data to reduce storage costs',
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'potential_savings': 0.4, # 40% potential savings
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'implementation_cost': Decimal('0.008'),
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'applicable_functions': ['store', 'update', 'save']
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},
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OptimizationStrategy.EVENT_FILTERING: {
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'description': 'Filter events to reduce emission costs',
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'potential_savings': 0.15, # 15% potential savings
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'implementation_cost': Decimal('0.002'),
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'applicable_functions': ['emit', 'log', 'notify']
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},
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OptimizationStrategy.STORAGE_OPTIMIZATION: {
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'description': 'Optimize storage patterns and data structures',
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'potential_savings': 0.25, # 25% potential savings
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'implementation_cost': Decimal('0.006'),
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'applicable_functions': ['set', 'add', 'remove']
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}
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}
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async def record_gas_usage(self, contract_address: str, function_name: str,
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gas_used: int, gas_limit: int, execution_time: float,
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optimization_applied: Optional[str] = None):
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"""Record gas usage metrics"""
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metric = GasMetric(
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contract_address=contract_address,
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function_name=function_name,
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gas_used=gas_used,
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gas_limit=gas_limit,
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execution_time=execution_time,
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timestamp=time.time(),
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optimization_applied=optimization_applied
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)
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self.gas_metrics.append(metric)
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# Limit history size
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if len(self.gas_metrics) > 10000:
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self.gas_metrics = self.gas_metrics[-5000]
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# Trigger optimization analysis if threshold met
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if gas_used >= self.min_optimization_threshold:
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asyncio.create_task(self._analyze_optimization_opportunity(metric))
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async def _analyze_optimization_opportunity(self, metric: GasMetric):
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"""Analyze if optimization is beneficial"""
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# Get historical average for this function
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historical_metrics = [
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m for m in self.gas_metrics
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if m.function_name == metric.function_name and
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m.contract_address == metric.contract_address and
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not m.optimization_applied
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]
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if len(historical_metrics) < 5: # Need sufficient history
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return
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avg_gas = sum(m.gas_used for m in historical_metrics) / len(historical_metrics)
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# Test each optimization strategy
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for strategy, config in self.optimization_strategies.items():
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if self._is_strategy_applicable(strategy, metric.function_name):
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potential_savings = avg_gas * config['potential_savings']
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if potential_savings >= self.min_optimization_threshold:
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# Calculate net benefit
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gas_price = self.current_gas_price
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gas_savings_value = potential_savings * gas_price
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net_benefit = gas_savings_value - config['implementation_cost']
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if net_benefit > 0:
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# Create optimization result
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result = OptimizationResult(
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strategy=strategy,
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original_gas=int(avg_gas),
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optimized_gas=int(avg_gas - potential_savings),
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gas_savings=int(potential_savings),
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savings_percentage=config['potential_savings'],
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implementation_cost=config['implementation_cost'],
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net_benefit=net_benefit
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)
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self.optimization_results.append(result)
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# Keep only recent results
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if len(self.optimization_results) > 1000:
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self.optimization_results = self.optimization_results[-500]
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log_info(f"Optimization opportunity found: {strategy.value} for {metric.function_name} - Potential savings: {potential_savings} gas")
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def _is_strategy_applicable(self, strategy: OptimizationStrategy, function_name: str) -> bool:
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"""Check if optimization strategy is applicable to function"""
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config = self.optimization_strategies.get(strategy, {})
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applicable_functions = config.get('applicable_functions', [])
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# Check if function name contains any applicable keywords
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for applicable in applicable_functions:
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if applicable.lower() in function_name.lower():
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return True
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return False
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async def apply_optimization(self, contract_address: str, function_name: str,
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strategy: OptimizationStrategy) -> Tuple[bool, str]:
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"""Apply optimization strategy to contract function"""
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try:
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# Validate strategy
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if strategy not in self.optimization_strategies:
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return False, "Unknown optimization strategy"
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# Check applicability
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if not self._is_strategy_applicable(strategy, function_name):
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return False, "Strategy not applicable to this function"
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# Get optimization result
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result = None
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for res in self.optimization_results:
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if (res.strategy == strategy and
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res.strategy in self.optimization_strategies):
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result = res
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break
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if not result:
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return False, "No optimization analysis available"
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# Check if net benefit is positive
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if result.net_benefit <= 0:
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return False, "Optimization not cost-effective"
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# Apply optimization (in real implementation, this would modify contract code)
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success = await self._implement_optimization(contract_address, function_name, strategy)
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if success:
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# Record optimization
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await self.record_gas_usage(
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contract_address, function_name, result.optimized_gas,
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result.optimized_gas, 0.0, strategy.value
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)
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log_info(f"Optimization applied: {strategy.value} to {function_name}")
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return True, f"Optimization applied successfully. Gas savings: {result.gas_savings}"
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else:
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return False, "Optimization implementation failed"
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except Exception as e:
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return False, f"Optimization error: {str(e)}"
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async def _implement_optimization(self, contract_address: str, function_name: str,
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strategy: OptimizationStrategy) -> bool:
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"""Implement the optimization strategy"""
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try:
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# In real implementation, this would:
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# 1. Analyze contract bytecode
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# 2. Apply optimization patterns
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# 3. Generate optimized bytecode
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# 4. Deploy optimized version
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# 5. Verify functionality
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# Simulate implementation
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await asyncio.sleep(2) # Simulate optimization time
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return True
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except Exception as e:
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log_error(f"Optimization implementation error: {e}")
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return False
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async def update_gas_price(self, new_price: Decimal):
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"""Update current gas price"""
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self.current_gas_price = new_price
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# Record price history
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self.gas_price_history.append({
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'price': float(new_price),
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'timestamp': time.time()
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})
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# Limit history size
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if len(self.gas_price_history) > 1000:
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self.gas_price_history = self.gas_price_history[-500]
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# Re-evaluate optimization opportunities with new price
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asyncio.create_task(self._reevaluate_optimizations())
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async def _reevaluate_optimizations(self):
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"""Re-evaluate optimization opportunities with new gas price"""
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# Clear old results and re-analyze
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self.optimization_results.clear()
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# Re-analyze recent metrics
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recent_metrics = [
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m for m in self.gas_metrics
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if time.time() - m.timestamp < 3600 # Last hour
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]
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for metric in recent_metrics:
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if metric.gas_used >= self.min_optimization_threshold:
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await self._analyze_optimization_opportunity(metric)
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async def get_optimization_recommendations(self, contract_address: Optional[str] = None,
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limit: int = 10) -> List[Dict]:
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"""Get optimization recommendations"""
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recommendations = []
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for result in self.optimization_results:
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if contract_address and result.strategy.value not in self.optimization_strategies:
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continue
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if result.net_benefit > 0:
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recommendations.append({
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'strategy': result.strategy.value,
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'function': 'contract_function', # Would map to actual function
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'original_gas': result.original_gas,
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'optimized_gas': result.optimized_gas,
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'gas_savings': result.gas_savings,
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'savings_percentage': result.savings_percentage,
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'net_benefit': float(result.net_benefit),
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'implementation_cost': float(result.implementation_cost)
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})
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# Sort by net benefit
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recommendations.sort(key=lambda x: x['net_benefit'], reverse=True)
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return recommendations[:limit]
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async def get_gas_statistics(self) -> Dict:
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"""Get gas usage statistics"""
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if not self.gas_metrics:
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return {
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'total_transactions': 0,
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'average_gas_used': 0,
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'total_gas_used': 0,
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'gas_efficiency': 0,
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'optimization_opportunities': 0
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}
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total_transactions = len(self.gas_metrics)
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total_gas_used = sum(m.gas_used for m in self.gas_metrics)
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average_gas_used = total_gas_used / total_transactions
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# Calculate efficiency (gas used vs gas limit)
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efficiency_scores = [
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m.gas_used / m.gas_limit for m in self.gas_metrics
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if m.gas_limit > 0
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]
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avg_efficiency = sum(efficiency_scores) / len(efficiency_scores) if efficiency_scores else 0
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# Optimization opportunities
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optimization_count = len([
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result for result in self.optimization_results
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if result.net_benefit > 0
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])
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return {
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'total_transactions': total_transactions,
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'average_gas_used': average_gas_used,
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'total_gas_used': total_gas_used,
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'gas_efficiency': avg_efficiency,
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'optimization_opportunities': optimization_count,
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'current_gas_price': float(self.current_gas_price),
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'total_optimizations_applied': len([
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m for m in self.gas_metrics
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if m.optimization_applied
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])
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}
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# Global gas optimizer
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gas_optimizer: Optional[GasOptimizer] = None
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def get_gas_optimizer() -> Optional[GasOptimizer]:
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"""Get global gas optimizer"""
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return gas_optimizer
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def create_gas_optimizer() -> GasOptimizer:
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"""Create and set global gas optimizer"""
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global gas_optimizer
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gas_optimizer = GasOptimizer()
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return gas_optimizer
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