Oracle Operations and Maintenance

# Data quality monitoring for price feeds
def monitor_price_feed(symbol, current_price, historical_prices):
    # Check for extreme deviations
    recent_avg = np.mean(historical_prices[-10:])
    deviation_pct = abs(current_price - recent_avg) / recent_avg
    
    if deviation_pct > 0.15:  # 15% deviation threshold
        alert_severity = "HIGH" if deviation_pct > 0.25 else "MEDIUM"
        send_alert(f"Price anomaly detected for {symbol}: {deviation_pct:.2%} deviation")
    
    # Check data freshness
    last_update = get_last_update_time(symbol)
    staleness = time.now() - last_update
    
    if staleness > expected_update_interval * 2:
        send_alert(f"Stale data for {symbol}: {staleness} seconds old")

class OptimizedDataRetriever:
    def __init__(self):
        self.cache = {}
        self.request_queue = asyncio.Queue()
        self.rate_limiter = RateLimiter(calls_per_second=10)
    
    async def get_price_data(self, symbol, max_age_seconds=30):
        # Check cache first
        if symbol in self.cache:
            data, timestamp = self.cache[symbol]
            if time.now() - timestamp < max_age_seconds:
                return data
        
        # Batch multiple requests to same provider
        await self.request_queue.put((symbol, max_age_seconds))
        return await self.process_batched_requests()

class OptimizedTransactionManager {
    constructor(account, xrplClient) {
        this.account = account;
        this.client = xrplClient;
        this.noncePool = new NoncePool(account);
        this.pendingTxs = new Map();
    }
    
    async submitOracleUpdate(oracleData, priority = 'normal') {
        const baseFee = await this.estimateNetworkFee();
        const adjustedFee = this.adjustFeeForPriority(baseFee, priority);
        
        const tx = {
            TransactionType: 'Payment',
            Account: this.account.address,
            Destination: this.account.address,
            Amount: '1', // Minimal self-payment
            Memos: this.encodeOracleData(oracleData),
            Fee: adjustedFee.toString(),
            Sequence: await this.noncePool.getNext()
        };
        
        return await this.client.submitAndWait(this.account.sign(tx));
    }
}

class OracleCapacityPlanner:
    def __init__(self):
        self.historical_metrics = {}
        self.growth_models = {}
        
    def forecast_demand(self, service_type, forecast_horizon_days):
        # Base demand from historical trends
        base_demand = self.calculate_trend_demand(service_type, forecast_horizon_days)
        
        # Seasonal adjustments
        seasonal_multiplier = self.get_seasonal_multiplier(service_type)
        
        # Event-driven spike probability
        spike_probability = self.calculate_spike_probability(service_type)
        
        # Market volatility impact for financial oracles
        if service_type == 'financial':
            volatility_multiplier = self.get_volatility_multiplier()
        else:
            volatility_multiplier = 1.0
        
        # Calculate capacity requirements with safety margin
        expected_demand = base_demand * seasonal_multiplier * volatility_multiplier
        capacity_requirement = expected_demand * 1.5  # 50% safety margin
        
        if spike_probability > 0.3:  # 30% spike probability threshold
            capacity_requirement *= 2.0  # Double capacity for spike protection
        
        return {
            'expected_demand': expected_demand,
            'recommended_capacity': capacity_requirement,
            'spike_probability': spike_probability,
            'confidence_interval': self.calculate_confidence_interval(expected_demand)
        }

# Kubernetes auto-scaling configuration for oracle services
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: financial-oracle-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: financial-oracle
  minReplicas: 3
  maxReplicas: 20
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Pods
    pods:
      metric:
        name: oracle_requests_per_second
      target:
        type: AverageValue
        averageValue: "100"

class TechnicalDebtAssessment:
    def __init__(self, codebase_path):
        self.codebase_path = codebase_path
        self.debt_metrics = {}
    
    def assess_code_debt(self):
        # Cyclomatic complexity analysis
        complexity_scores = self.analyze_complexity()
        
        # Test coverage analysis
        coverage_report = self.generate_coverage_report()
        
        # Documentation coverage
        doc_coverage = self.assess_documentation_coverage()
        
        # Dependency analysis
        outdated_deps = self.check_dependency_freshness()
        
        return {
            'complexity_debt': self.score_complexity_debt(complexity_scores),
            'test_debt': self.score_test_debt(coverage_report),
            'documentation_debt': self.score_documentation_debt(doc_coverage),
            'dependency_debt': self.score_dependency_debt(outdated_deps),
            'overall_score': self.calculate_overall_debt_score()
        }

class KeyRotationManager:
    def __init__(self, key_store, notification_service):
        self.key_store = key_store
        self.notifications = notification_service
        self.rotation_schedule = {}
    
    def plan_key_rotation(self, key_id, rotation_frequency_days):
        current_key = self.key_store.get_key(key_id)
        last_rotation = current_key.created_date
        next_rotation = last_rotation + timedelta(days=rotation_frequency_days)
        
        self.rotation_schedule[key_id] = {
            'next_rotation': next_rotation,
            'frequency_days': rotation_frequency_days,
            'key_type': current_key.key_type,
            'dependent_services': self.identify_dependent_services(key_id)
        }
    
    async def execute_key_rotation(self, key_id):
        # Generate new key
        new_key = await self.key_store.generate_key(
            key_type=self.rotation_schedule[key_id]['key_type']
        )
        
        # Update dependent services with new key
        dependent_services = self.rotation_schedule[key_id]['dependent_services']
        for service in dependent_services:
            await service.update_key(key_id, new_key)
        
        # Verify new key functionality
        verification_result = await self.verify_key_functionality(key_id, new_key)
        
        if verification_result.success:
            # Archive old key with retention policy
            await self.key_store.archive_key(key_id, retention_days=90)
            await self.notifications.send_notification(
                f"Key rotation completed successfully for {key_id}"
            )
        else:
            # Rollback on failure
            await self.rollback_key_rotation(key_id, verification_result.error)

# Upgrade testing pipeline configuration
upgrade_testing:
  blockchain_compatibility:
    - test_name: "XRPL Amendment Compatibility"
      test_scenarios:
        - current_protocol_with_new_amendment
        - mixed_validator_versions
        - transaction_format_changes
    
  api_compatibility:
    - test_name: "External API Version Compatibility"
      test_scenarios:
        - old_api_version_deprecation
        - new_field_additions
        - response_format_changes
        - rate_limit_changes

upgrade_rollback:
  automated_rollback_triggers:
    - error_rate_threshold: 5%
    - latency_degradation: 200%
    - consumer_application_failures: 3
  
  rollback_procedures:
    - database_schema_rollback
    - configuration_rollback
    - dependency_version_rollback
    - external_communication_procedures