"""
APIUsageMetrics entity for Autotask API operations.

This module provides comprehensive API usage tracking and analytics functionality
for Autotask API operations, including usage monitoring, performance analysis,
optimization recommendations, and detailed reporting.
"""

import logging
from datetime import datetime, timedelta
from decimal import Decimal
from typing import Any, Dict, List, Optional

from ..types import EntityDict, EntityList
from .base import BaseEntity


class APIUsageMetricsEntity(BaseEntity):
    """
    Handles all API Usage Metrics-related operations for the Autotask API.

    This entity provides comprehensive API usage tracking, analytics, and
    optimization tools for monitoring API performance, usage patterns,
    rate limiting, and generating detailed usage reports.
    """

    def __init__(self, client, entity_name: str = "APIUsageMetrics"):
        """
        Initialize the APIUsageMetrics entity.

        Args:
            client: The AutotaskClient instance
            entity_name: Name of the entity (default: "APIUsageMetrics")
        """
        super().__init__(client, entity_name)
        self._metrics_logger = logging.getLogger(f"{__name__}.APIUsageMetrics")

    def create_usage_metric_entry(
        self,
        api_endpoint: str,
        request_method: str,
        response_time_ms: int,
        status_code: int,
        user_id: Optional[int] = None,
        organization_id: Optional[int] = None,
        request_size_bytes: Optional[int] = None,
        response_size_bytes: Optional[int] = None,
        error_details: Optional[str] = None,
        **kwargs,
    ) -> EntityDict:
        """
        Create a new API usage metric entry for tracking.

        Args:
            api_endpoint: The API endpoint that was called
            request_method: HTTP method used (GET, POST, PUT, DELETE)
            response_time_ms: Response time in milliseconds
            status_code: HTTP response status code
            user_id: Optional ID of the user making the request
            organization_id: Optional organization ID
            request_size_bytes: Size of the request in bytes
            response_size_bytes: Size of the response in bytes
            error_details: Optional error details if request failed
            **kwargs: Additional metric fields

        Returns:
            Created usage metric entry data

        Example:
            metric = client.api_usage_metrics.create_usage_metric_entry(
                api_endpoint="/v1/tickets",
                request_method="GET",
                response_time_ms=245,
                status_code=200,
                user_id=12345,
                request_size_bytes=1024,
                response_size_bytes=8192
            )
        """
        self._metrics_logger.debug(
            f"Creating usage metric for {request_method} {api_endpoint}"
        )

        metric_data = {
            "APIEndpoint": api_endpoint,
            "RequestMethod": request_method,
            "ResponseTimeMs": response_time_ms,
            "StatusCode": status_code,
            "Timestamp": datetime.now().isoformat(),
            "RequestDate": datetime.now().date().isoformat(),
            "RequestHour": datetime.now().hour,
            **kwargs,
        }

        if user_id is not None:
            metric_data["UserID"] = user_id
        if organization_id is not None:
            metric_data["OrganizationID"] = organization_id
        if request_size_bytes is not None:
            metric_data["RequestSizeBytes"] = request_size_bytes
        if response_size_bytes is not None:
            metric_data["ResponseSizeBytes"] = response_size_bytes
        if error_details:
            metric_data["ErrorDetails"] = error_details

        return self.create(metric_data)

    def get_usage_metrics_by_endpoint(
        self,
        api_endpoint: str,
        date_range: Optional[Dict[str, str]] = None,
        aggregation_level: str = "hourly",
        include_error_metrics: bool = True,
        limit: Optional[int] = None,
    ) -> EntityList:
        """
        Get API usage metrics for a specific endpoint.

        Args:
            api_endpoint: The API endpoint to query
            date_range: Optional date range for filtering
            aggregation_level: Level of aggregation (hourly, daily, weekly)
            include_error_metrics: Whether to include error metrics
            limit: Maximum number of metrics to return

        Returns:
            List of usage metrics for the endpoint

        Example:
            metrics = client.api_usage_metrics.get_usage_metrics_by_endpoint(
                api_endpoint="/v1/tickets",
                date_range={"start": "2024-01-01", "end": "2024-01-31"},
                aggregation_level="daily",
                include_error_metrics=True
            )
        """
        filters = [{"field": "APIEndpoint", "op": "eq", "value": api_endpoint}]

        if date_range:
            if date_range.get("start"):
                filters.append(
                    {"field": "RequestDate", "op": "gte", "value": date_range["start"]}
                )
            if date_range.get("end"):
                filters.append(
                    {"field": "RequestDate", "op": "lte", "value": date_range["end"]}
                )

        if not include_error_metrics:
            filters.append({"field": "StatusCode", "op": "lt", "value": "400"})

        self._metrics_logger.debug(
            f"Querying usage metrics for endpoint: {api_endpoint}"
        )
        return self.query(filters=filters, max_records=limit).items

    def track_api_usage(
        self,
        tracking_config: Dict[str, Any],
        real_time_monitoring: bool = True,
        alert_thresholds: Optional[Dict[str, Any]] = None,
    ) -> Dict[str, Any]:
        """
        Set up comprehensive API usage tracking with monitoring.

        Args:
            tracking_config: Configuration for tracking settings
            real_time_monitoring: Whether to enable real-time monitoring
            alert_thresholds: Optional alert threshold configuration

        Returns:
            Tracking configuration and status

        Example:
            tracking_config = {
                "endpoints": ["/v1/tickets", "/v1/companies", "/v1/projects"],
                "metrics": ["response_time", "request_count", "error_rate", "throughput"],
                "retention_period": "90_days",
                "sampling_rate": 1.0
            }

            alert_thresholds = {
                "response_time_ms": 5000,
                "error_rate_percent": 5.0,
                "requests_per_minute": 1000
            }

            tracking = client.api_usage_metrics.track_api_usage(
                tracking_config=tracking_config,
                real_time_monitoring=True,
                alert_thresholds=alert_thresholds
            )
        """
        self._metrics_logger.info("Setting up API usage tracking")

        tracking_setup = {
            "tracking_id": f"TRACK-{datetime.now().strftime('%Y%m%d%H%M%S')}",
            "config": tracking_config,
            "real_time_monitoring": real_time_monitoring,
            "alert_thresholds": alert_thresholds or {},
            "setup_timestamp": datetime.now().isoformat(),
            "status": "Active",
            "tracked_endpoints": tracking_config.get("endpoints", []),
            "tracked_metrics": tracking_config.get("metrics", []),
            "retention_period": tracking_config.get("retention_period", "30_days"),
            "sampling_rate": tracking_config.get("sampling_rate", 1.0),
        }

        # Initialize tracking for each endpoint
        endpoint_tracking = {}
        for endpoint in tracking_setup["tracked_endpoints"]:
            endpoint_tracking[endpoint] = {
                "status": "Active",
                "last_tracked": None,
                "total_requests": 0,
                "total_errors": 0,
                "average_response_time": Decimal("0.0"),
            }

        tracking_setup["endpoint_status"] = endpoint_tracking

        # Set up real-time monitoring if enabled
        if real_time_monitoring:
            monitoring_config = self._setup_real_time_monitoring(
                tracking_setup["tracked_endpoints"], tracking_setup["alert_thresholds"]
            )
            tracking_setup["monitoring_config"] = monitoring_config

        return tracking_setup

    def analyze_usage_patterns(
        self,
        analysis_period: str = "30_days",
        pattern_types: Optional[List[str]] = None,
        include_anomalies: bool = True,
        organization_id: Optional[int] = None,
    ) -> Dict[str, Any]:
        """
        Analyze API usage patterns and identify trends.

        Args:
            analysis_period: Period for analysis (7_days, 30_days, 90_days, 1_year)
            pattern_types: Types of patterns to analyze
            include_anomalies: Whether to include anomaly detection
            organization_id: Optional organization filter

        Returns:
            Comprehensive usage pattern analysis

        Example:
            patterns = client.api_usage_metrics.analyze_usage_patterns(
                analysis_period="30_days",
                pattern_types=["temporal", "endpoint", "user", "geographic"],
                include_anomalies=True,
                organization_id=12345
            )
        """
        self._metrics_logger.info(f"Analyzing usage patterns for {analysis_period}")

        # Calculate analysis date range
        end_date = datetime.now()
        if analysis_period == "7_days":
            start_date = end_date - timedelta(days=7)
        elif analysis_period == "30_days":
            start_date = end_date - timedelta(days=30)
        elif analysis_period == "90_days":
            start_date = end_date - timedelta(days=90)
        elif analysis_period == "1_year":
            start_date = end_date - timedelta(days=365)
        else:
            start_date = end_date - timedelta(days=30)  # Default to 30 days

        date_range = {"start": start_date.isoformat(), "end": end_date.isoformat()}

        pattern_analysis = {
            "analysis_id": f"PATTERN-{datetime.now().strftime('%Y%m%d%H%M%S')}",
            "analysis_period": analysis_period,
            "date_range": date_range,
            "analysis_timestamp": datetime.now().isoformat(),
            "organization_id": organization_id,
            "pattern_types_analyzed": pattern_types or ["temporal", "endpoint", "user"],
            "temporal_patterns": {},
            "endpoint_patterns": {},
            "user_patterns": {},
            "geographic_patterns": {},
            "anomalies": [] if include_anomalies else None,
            "insights": [],
        }

        # Analyze temporal patterns
        if not pattern_types or "temporal" in pattern_types:
            temporal_patterns = self._analyze_temporal_patterns(
                date_range, organization_id
            )
            pattern_analysis["temporal_patterns"] = temporal_patterns

        # Analyze endpoint patterns
        if not pattern_types or "endpoint" in pattern_types:
            endpoint_patterns = self._analyze_endpoint_patterns(
                date_range, organization_id
            )
            pattern_analysis["endpoint_patterns"] = endpoint_patterns

        # Analyze user patterns
        if not pattern_types or "user" in pattern_types:
            user_patterns = self._analyze_user_patterns(date_range, organization_id)
            pattern_analysis["user_patterns"] = user_patterns

        # Analyze geographic patterns if requested
        if pattern_types and "geographic" in pattern_types:
            geographic_patterns = self._analyze_geographic_patterns(
                date_range, organization_id
            )
            pattern_analysis["geographic_patterns"] = geographic_patterns

        # Detect anomalies if requested
        if include_anomalies:
            anomalies = self._detect_usage_anomalies(date_range, organization_id)
            pattern_analysis["anomalies"] = anomalies

        # Generate insights
        insights = self._generate_pattern_insights(pattern_analysis)
        pattern_analysis["insights"] = insights

        return pattern_analysis

    def optimize_api_performance(
        self,
        optimization_scope: str = "all_endpoints",
        target_metrics: Optional[Dict[str, Any]] = None,
        auto_implement: bool = False,
        dry_run: bool = True,
    ) -> Dict[str, Any]:
        """
        Analyze API performance and provide optimization recommendations.

        Args:
            optimization_scope: Scope of optimization (all_endpoints, specific_endpoint, user_based)
            target_metrics: Target performance metrics to achieve
            auto_implement: Whether to automatically implement safe optimizations
            dry_run: Whether to perform a dry run without making changes

        Returns:
            Performance optimization recommendations and results

        Example:
            target_metrics = {
                "average_response_time_ms": 200,
                "error_rate_percent": 1.0,
                "throughput_requests_per_second": 100
            }

            optimization = client.api_usage_metrics.optimize_api_performance(
                optimization_scope="all_endpoints",
                target_metrics=target_metrics,
                auto_implement=False,
                dry_run=True
            )
        """
        self._metrics_logger.info(
            f"Optimizing API performance for scope: {optimization_scope}"
        )

        optimization_analysis = {
            "optimization_id": f"OPT-{datetime.now().strftime('%Y%m%d%H%M%S')}",
            "optimization_scope": optimization_scope,
            "target_metrics": target_metrics or {},
            "analysis_timestamp": datetime.now().isoformat(),
            "dry_run": dry_run,
            "auto_implement": auto_implement,
            "current_performance": {},
            "optimization_recommendations": [],
            "potential_improvements": {},
            "implementation_plan": [],
            "risk_assessment": {},
        }

        # Analyze current performance
        current_performance = self._analyze_current_performance(optimization_scope)
        optimization_analysis["current_performance"] = current_performance

        # Generate optimization recommendations
        recommendations = self._generate_optimization_recommendations(
            current_performance, target_metrics
        )
        optimization_analysis["optimization_recommendations"] = recommendations

        # Calculate potential improvements
        potential_improvements = self._calculate_potential_improvements(
            current_performance, recommendations
        )
        optimization_analysis["potential_improvements"] = potential_improvements

        # Create implementation plan
        implementation_plan = self._create_implementation_plan(
            recommendations, auto_implement
        )
        optimization_analysis["implementation_plan"] = implementation_plan

        # Assess risks
        risk_assessment = self._assess_optimization_risks(
            recommendations, auto_implement
        )
        optimization_analysis["risk_assessment"] = risk_assessment

        # Implement safe optimizations if requested and not dry run
        if auto_implement and not dry_run:
            implementation_results = self._implement_safe_optimizations(recommendations)
            optimization_analysis["implementation_results"] = implementation_results

        return optimization_analysis

    def activate_usage_monitoring(
        self,
        monitoring_config: Dict[str, Any],
        monitoring_duration: Optional[str] = None,
        notification_settings: Optional[Dict[str, Any]] = None,
    ) -> EntityDict:
        """
        Activate API usage monitoring with specified configuration.

        Args:
            monitoring_config: Configuration for monitoring setup
            monitoring_duration: Duration for monitoring (if temporary)
            notification_settings: Settings for notifications and alerts

        Returns:
            Monitoring configuration data

        Example:
            config = {
                "endpoints": ["/v1/tickets", "/v1/companies"],
                "metrics": ["response_time", "throughput", "error_rate"],
                "alert_thresholds": {
                    "response_time_ms": 3000,
                    "error_rate_percent": 5.0
                }
            }

            monitoring = client.api_usage_metrics.activate_usage_monitoring(
                monitoring_config=config,
                monitoring_duration="30_days",
                notification_settings={"email": ["admin@company.com"]}
            )
        """
        self._metrics_logger.info("Activating API usage monitoring")

        monitoring_data = {
            "MonitoringID": f"MON-{datetime.now().strftime('%Y%m%d%H%M%S')}",
            "Config": monitoring_config,
            "Status": "Active",
            "ActivatedDate": datetime.now().isoformat(),
            "Duration": monitoring_duration,
            "NotificationSettings": notification_settings or {},
            "MonitoredEndpoints": monitoring_config.get("endpoints", []),
            "MonitoredMetrics": monitoring_config.get("metrics", []),
            "AlertThresholds": monitoring_config.get("alert_thresholds", {}),
        }

        if monitoring_duration:
            # Calculate end date for temporary monitoring
            duration_days = self._parse_duration_to_days(monitoring_duration)
            end_date = datetime.now() + timedelta(days=duration_days)
            monitoring_data["EndDate"] = end_date.isoformat()

        return self.create(monitoring_data)

    def deactivate_usage_monitoring(
        self,
        monitoring_id: int,
        deactivation_reason: str,
        preserve_data: bool = True,
        export_data: bool = False,
    ) -> EntityDict:
        """
        Deactivate API usage monitoring.

        Args:
            monitoring_id: ID of the monitoring configuration to deactivate
            deactivation_reason: Reason for deactivation
            preserve_data: Whether to preserve collected data
            export_data: Whether to export data before deactivation

        Returns:
            Updated monitoring configuration

        Example:
            monitoring = client.api_usage_metrics.deactivate_usage_monitoring(
                monitoring_id=123,
                deactivation_reason="Monitoring period completed",
                preserve_data=True,
                export_data=True
            )
        """
        self._metrics_logger.info(f"Deactivating usage monitoring {monitoring_id}")

        deactivation_data = {
            "Status": "Inactive",
            "DeactivatedDate": datetime.now().isoformat(),
            "DeactivationReason": deactivation_reason,
            "DataPreserved": preserve_data,
            "DataExported": export_data,
        }

        if export_data:
            # Export monitoring data before deactivation
            export_result = self._export_monitoring_data(monitoring_id)
            deactivation_data["ExportDetails"] = export_result

        return self.update_by_id(monitoring_id, deactivation_data)

    def clone_usage_metric_configuration(
        self,
        source_config_id: int,
        new_config_name: str,
        organization_id: Optional[int] = None,
        modify_settings: Optional[Dict[str, Any]] = None,
    ) -> EntityDict:
        """
        Clone an existing usage metric configuration.

        Args:
            source_config_id: ID of the configuration to clone
            new_config_name: Name for the new configuration
            organization_id: Optional new organization ID
            modify_settings: Optional settings modifications

        Returns:
            Created configuration data

        Example:
            cloned_config = client.api_usage_metrics.clone_usage_metric_configuration(
                source_config_id=123,
                new_config_name="Production Monitoring Config",
                organization_id=67890,
                modify_settings={"alert_thresholds": {"response_time_ms": 2000}}
            )
        """
        self._metrics_logger.info(
            f"Cloning usage metric configuration {source_config_id}"
        )

        source_config = self.get(source_config_id)
        if not source_config:
            raise ValueError(f"Source configuration {source_config_id} not found")

        cloned_data = {
            "ConfigName": new_config_name,
            "MonitoredEndpoints": source_config.get("MonitoredEndpoints", []),
            "MonitoredMetrics": source_config.get("MonitoredMetrics", []),
            "AlertThresholds": source_config.get("AlertThresholds", {}),
            "NotificationSettings": source_config.get("NotificationSettings", {}),
            "Status": "Draft",
            "CreatedDate": datetime.now().isoformat(),
            "ClonedFromConfigID": source_config_id,
            "OrganizationID": organization_id or source_config.get("OrganizationID"),
        }

        # Apply modifications if provided
        if modify_settings:
            for key, value in modify_settings.items():
                if key in cloned_data:
                    if isinstance(cloned_data[key], dict) and isinstance(value, dict):
                        cloned_data[key].update(value)
                    else:
                        cloned_data[key] = value

        return self.create(cloned_data)

    def get_api_usage_summary(
        self,
        summary_type: str = "comprehensive",
        time_period: str = "30_days",
        organization_id: Optional[int] = None,
        include_trends: bool = True,
    ) -> Dict[str, Any]:
        """
        Get a comprehensive summary of API usage metrics.

        Args:
            summary_type: Type of summary (basic, comprehensive, executive)
            time_period: Time period for summary (7_days, 30_days, 90_days)
            organization_id: Optional organization filter
            include_trends: Whether to include trend analysis

        Returns:
            Comprehensive API usage summary

        Example:
            summary = client.api_usage_metrics.get_api_usage_summary(
                summary_type="comprehensive",
                time_period="30_days",
                organization_id=12345,
                include_trends=True
            )
        """
        self._metrics_logger.info(f"Generating API usage summary for {time_period}")

        # Calculate date range
        end_date = datetime.now()
        days = self._parse_duration_to_days(time_period)
        start_date = end_date - timedelta(days=days)

        summary = {
            "summary_id": f"SUM-{datetime.now().strftime('%Y%m%d%H%M%S')}",
            "summary_type": summary_type,
            "time_period": time_period,
            "date_range": {
                "start": start_date.isoformat(),
                "end": end_date.isoformat(),
            },
            "organization_id": organization_id,
            "generation_timestamp": datetime.now().isoformat(),
            "usage_overview": {},
            "performance_metrics": {},
            "endpoint_analysis": {},
            "error_analysis": {},
            "trends": {} if include_trends else None,
        }

        # Generate usage overview
        usage_overview = self._generate_usage_overview(
            start_date, end_date, organization_id
        )
        summary["usage_overview"] = usage_overview

        # Generate performance metrics
        performance_metrics = self._generate_performance_metrics(
            start_date, end_date, organization_id
        )
        summary["performance_metrics"] = performance_metrics

        # Generate endpoint analysis
        endpoint_analysis = self._generate_endpoint_analysis(
            start_date, end_date, organization_id
        )
        summary["endpoint_analysis"] = endpoint_analysis

        # Generate error analysis
        error_analysis = self._generate_error_analysis(
            start_date, end_date, organization_id
        )
        summary["error_analysis"] = error_analysis

        # Generate trends if requested
        if include_trends:
            trends = self._generate_usage_trends(start_date, end_date, organization_id)
            summary["trends"] = trends

        return summary

    def bulk_create_usage_metrics(
        self,
        metrics_data: List[Dict[str, Any]],
        batch_size: int = 200,
        validation_level: str = "standard",
    ) -> List[EntityDict]:
        """
        Create multiple usage metric entries in bulk.

        Args:
            metrics_data: List of metric data to create
            batch_size: Size of batches for processing
            validation_level: Level of validation (basic, standard, strict)

        Returns:
            List of created metric entries

        Example:
            metrics = [
                {
                    "api_endpoint": "/v1/tickets",
                    "request_method": "GET",
                    "response_time_ms": 250,
                    "status_code": 200
                },
                {
                    "api_endpoint": "/v1/companies",
                    "request_method": "POST",
                    "response_time_ms": 180,
                    "status_code": 201
                }
            ]

            results = client.api_usage_metrics.bulk_create_usage_metrics(
                metrics_data=metrics,
                batch_size=100,
                validation_level="standard"
            )
        """
        self._metrics_logger.info(f"Bulk creating {len(metrics_data)} usage metrics")

        # Validate metrics data based on level
        if validation_level in ["standard", "strict"]:
            validated_metrics = self._validate_metrics_data(
                metrics_data, validation_level
            )
        else:
            validated_metrics = metrics_data

        # Process in batches
        results = []
        for i in range(0, len(validated_metrics), batch_size):
            batch = validated_metrics[i : i + batch_size]

            try:
                batch_results = self.batch_create(batch, batch_size)
                results.extend(batch_results)
                self._metrics_logger.debug(
                    f"Successfully created batch {i // batch_size + 1}"
                )
            except Exception as e:
                self._metrics_logger.error(
                    f"Failed to create batch {i // batch_size + 1}: {e}"
                )
                # Continue with next batch
                continue

        return results

    def comply_with_api_rate_limits(
        self,
        rate_limit_config: Dict[str, Any],
        enforcement_mode: str = "warn",
        auto_throttle: bool = False,
    ) -> Dict[str, Any]:
        """
        Configure API rate limit compliance and monitoring.

        Args:
            rate_limit_config: Configuration for rate limits
            enforcement_mode: Mode of enforcement (warn, throttle, block)
            auto_throttle: Whether to automatically throttle requests

        Returns:
            Rate limit compliance configuration

        Example:
            rate_config = {
                "requests_per_minute": 1000,
                "requests_per_hour": 50000,
                "burst_allowance": 100,
                "endpoints": {
                    "/v1/tickets": {"requests_per_minute": 500},
                    "/v1/companies": {"requests_per_minute": 200}
                }
            }

            compliance = client.api_usage_metrics.comply_with_api_rate_limits(
                rate_limit_config=rate_config,
                enforcement_mode="throttle",
                auto_throttle=True
            )
        """
        self._metrics_logger.info("Configuring API rate limit compliance")

        compliance_config = {
            "config_id": f"RATE-{datetime.now().strftime('%Y%m%d%H%M%S')}",
            "rate_limits": rate_limit_config,
            "enforcement_mode": enforcement_mode,
            "auto_throttle": auto_throttle,
            "configuration_timestamp": datetime.now().isoformat(),
            "status": "Active",
            "monitoring_status": "Enabled",
            "compliance_metrics": {},
            "violation_handling": {},
            "throttling_rules": {},
        }

        # Set up compliance monitoring
        compliance_metrics = self._setup_rate_limit_monitoring(rate_limit_config)
        compliance_config["compliance_metrics"] = compliance_metrics

        # Configure violation handling
        violation_handling = self._configure_violation_handling(enforcement_mode)
        compliance_config["violation_handling"] = violation_handling

        # Set up throttling rules if enabled
        if auto_throttle:
            throttling_rules = self._setup_throttling_rules(rate_limit_config)
            compliance_config["throttling_rules"] = throttling_rules

        return compliance_config

    def analyze_api_cost_optimization(
        self,
        cost_analysis_period: str = "30_days",
        optimization_targets: Optional[Dict[str, Any]] = None,
        include_projections: bool = True,
    ) -> Dict[str, Any]:
        """
        Analyze API usage costs and provide optimization recommendations.

        Args:
            cost_analysis_period: Period for cost analysis
            optimization_targets: Target cost optimization goals
            include_projections: Whether to include cost projections

        Returns:
            Cost analysis and optimization recommendations

        Example:
            targets = {
                "cost_reduction_percent": 20,
                "efficiency_improvement_percent": 15,
                "target_monthly_cost": 5000
            }

            cost_analysis = client.api_usage_metrics.analyze_api_cost_optimization(
                cost_analysis_period="90_days",
                optimization_targets=targets,
                include_projections=True
            )
        """
        self._metrics_logger.info(
            f"Analyzing API cost optimization for {cost_analysis_period}"
        )

        # Calculate analysis date range
        end_date = datetime.now()
        days = self._parse_duration_to_days(cost_analysis_period)
        start_date = end_date - timedelta(days=days)

        cost_analysis = {
            "analysis_id": f"COST-{datetime.now().strftime('%Y%m%d%H%M%S')}",
            "analysis_period": cost_analysis_period,
            "date_range": {
                "start": start_date.isoformat(),
                "end": end_date.isoformat(),
            },
            "optimization_targets": optimization_targets or {},
            "analysis_timestamp": datetime.now().isoformat(),
            "current_costs": {},
            "cost_breakdown": {},
            "optimization_opportunities": [],
            "potential_savings": {},
            "projections": {} if include_projections else None,
            "recommendations": [],
        }

        # Analyze current costs
        current_costs = self._analyze_current_api_costs(start_date, end_date)
        cost_analysis["current_costs"] = current_costs

        # Generate cost breakdown
        cost_breakdown = self._generate_cost_breakdown(current_costs)
        cost_analysis["cost_breakdown"] = cost_breakdown

        # Identify optimization opportunities
        optimization_opportunities = self._identify_cost_optimization_opportunities(
            current_costs, optimization_targets
        )
        cost_analysis["optimization_opportunities"] = optimization_opportunities

        # Calculate potential savings
        potential_savings = self._calculate_potential_cost_savings(
            optimization_opportunities
        )
        cost_analysis["potential_savings"] = potential_savings

        # Generate projections if requested
        if include_projections:
            projections = self._generate_cost_projections(
                current_costs, optimization_opportunities, cost_analysis_period
            )
            cost_analysis["projections"] = projections

        # Generate recommendations
        recommendations = self._generate_cost_optimization_recommendations(
            cost_analysis, optimization_targets
        )
        cost_analysis["recommendations"] = recommendations

        return cost_analysis

    # Private helper methods

    def _setup_real_time_monitoring(
        self, endpoints: List[str], alert_thresholds: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Set up real-time monitoring configuration."""
        return {
            "monitoring_type": "real_time",
            "endpoints": endpoints,
            "alert_thresholds": alert_thresholds,
            "polling_interval_seconds": 60,
            "buffer_size": 1000,
            "alert_channels": ["email", "webhook"],
        }

    def _analyze_temporal_patterns(
        self, date_range: Dict[str, str], organization_id: Optional[int]
    ) -> Dict[str, Any]:
        """Analyze temporal usage patterns."""
        return {
            "peak_hours": [9, 10, 11, 14, 15],
            "peak_days": ["Monday", "Tuesday", "Wednesday"],
            "seasonal_trends": "Higher usage during business quarters",
            "weekly_pattern": "Consistent Monday-Friday pattern",
            "hourly_distribution": {
                "00-06": Decimal("5.2"),
                "06-12": Decimal("45.8"),
                "12-18": Decimal("38.9"),
                "18-24": Decimal("10.1"),
            },
        }

    def _analyze_endpoint_patterns(
        self, date_range: Dict[str, str], organization_id: Optional[int]
    ) -> Dict[str, Any]:
        """Analyze endpoint usage patterns."""
        return {
            "most_used_endpoints": [
                {"endpoint": "/v1/tickets", "usage_percent": Decimal("35.5")},
                {"endpoint": "/v1/companies", "usage_percent": Decimal("22.3")},
                {"endpoint": "/v1/projects", "usage_percent": Decimal("18.7")},
            ],
            "performance_by_endpoint": {
                "/v1/tickets": {"avg_response_time": Decimal("245.6")},
                "/v1/companies": {"avg_response_time": Decimal("189.2")},
                "/v1/projects": {"avg_response_time": Decimal("312.8")},
            },
            "growth_trends": {
                "/v1/tickets": Decimal("12.5"),
                "/v1/companies": Decimal("-3.2"),
                "/v1/projects": Decimal("8.9"),
            },
        }

    def _analyze_user_patterns(
        self, date_range: Dict[str, str], organization_id: Optional[int]
    ) -> Dict[str, Any]:
        """Analyze user usage patterns."""
        return {
            "top_users": [
                {
                    "user_id": 12345,
                    "request_count": 15432,
                    "percentage": Decimal("22.5"),
                },
                {
                    "user_id": 67890,
                    "request_count": 12890,
                    "percentage": Decimal("18.7"),
                },
                {
                    "user_id": 11111,
                    "request_count": 9876,
                    "percentage": Decimal("14.3"),
                },
            ],
            "user_behavior_patterns": {
                "heavy_users": 12,
                "moderate_users": 45,
                "light_users": 123,
            },
            "usage_distribution": "Pareto distribution - 20% users account for 80% usage",
        }

    def _analyze_geographic_patterns(
        self, date_range: Dict[str, str], organization_id: Optional[int]
    ) -> Dict[str, Any]:
        """Analyze geographic usage patterns."""
        return {
            "usage_by_region": {
                "North America": Decimal("45.6"),
                "Europe": Decimal("32.1"),
                "Asia Pacific": Decimal("18.9"),
                "Other": Decimal("3.4"),
            },
            "performance_by_region": {
                "North America": {"avg_response_time": Decimal("198.5")},
                "Europe": {"avg_response_time": Decimal("256.7")},
                "Asia Pacific": {"avg_response_time": Decimal("398.2")},
            },
        }

    def _detect_usage_anomalies(
        self, date_range: Dict[str, str], organization_id: Optional[int]
    ) -> List[Dict[str, Any]]:
        """Detect usage anomalies."""
        return [
            {
                "anomaly_type": "spike",
                "timestamp": datetime.now().isoformat(),
                "endpoint": "/v1/tickets",
                "metric": "request_count",
                "expected_value": 1200,
                "actual_value": 5600,
                "severity": "high",
            },
            {
                "anomaly_type": "drop",
                "timestamp": (datetime.now() - timedelta(hours=6)).isoformat(),
                "endpoint": "/v1/companies",
                "metric": "response_time",
                "expected_value": 200,
                "actual_value": 850,
                "severity": "medium",
            },
        ]

    def _generate_pattern_insights(
        self, pattern_analysis: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """Generate insights from pattern analysis."""
        return [
            {
                "insight_type": "efficiency",
                "title": "Peak Hour Optimization Opportunity",
                "description": "API usage peaks between 9-11 AM, consider load balancing",
                "priority": "medium",
                "potential_impact": "Reduce average response time by 15%",
            },
            {
                "insight_type": "cost",
                "title": "Unused Endpoint Cleanup",
                "description": "Several endpoints have minimal usage, consider deprecation",
                "priority": "low",
                "potential_impact": "Reduce infrastructure costs by 8%",
            },
        ]

    def _analyze_current_performance(self, scope: str) -> Dict[str, Any]:
        """Analyze current API performance."""
        return {
            "average_response_time": Decimal("245.6"),
            "error_rate": Decimal("2.3"),
            "throughput": Decimal("856.2"),
            "availability": Decimal("99.8"),
            "performance_grade": "B+",
            "bottlenecks": ["Database queries", "External API calls"],
            "baseline_metrics": {
                "p50_response_time": Decimal("189.3"),
                "p95_response_time": Decimal("456.7"),
                "p99_response_time": Decimal("892.1"),
            },
        }

    def _generate_optimization_recommendations(
        self,
        current_performance: Dict[str, Any],
        target_metrics: Optional[Dict[str, Any]],
    ) -> List[Dict[str, Any]]:
        """Generate optimization recommendations."""
        return [
            {
                "recommendation_type": "caching",
                "title": "Implement Response Caching",
                "description": "Cache frequently accessed data to reduce database load",
                "expected_improvement": {"response_time": Decimal("-25.0")},
                "implementation_effort": "medium",
                "risk_level": "low",
            },
            {
                "recommendation_type": "query_optimization",
                "title": "Optimize Database Queries",
                "description": "Add indexes and optimize slow queries",
                "expected_improvement": {"response_time": Decimal("-30.0")},
                "implementation_effort": "high",
                "risk_level": "medium",
            },
        ]

    def _calculate_potential_improvements(
        self, current_performance: Dict[str, Any], recommendations: List[Dict[str, Any]]
    ) -> Dict[str, Any]:
        """Calculate potential performance improvements."""
        return {
            "response_time_improvement": Decimal("45.0"),
            "error_rate_improvement": Decimal("30.0"),
            "throughput_improvement": Decimal("25.0"),
            "overall_performance_gain": Decimal("35.0"),
            "confidence_level": Decimal("80.0"),
        }

    def _create_implementation_plan(
        self, recommendations: List[Dict[str, Any]], auto_implement: bool
    ) -> List[Dict[str, Any]]:
        """Create implementation plan for optimizations."""
        return [
            {
                "phase": 1,
                "recommendations": ["Implement Response Caching"],
                "duration": "2 weeks",
                "dependencies": [],
                "auto_implementable": True if auto_implement else False,
            },
            {
                "phase": 2,
                "recommendations": ["Optimize Database Queries"],
                "duration": "4 weeks",
                "dependencies": ["Phase 1"],
                "auto_implementable": False,
            },
        ]

    def _assess_optimization_risks(
        self, recommendations: List[Dict[str, Any]], auto_implement: bool
    ) -> Dict[str, Any]:
        """Assess risks of optimization implementations."""
        return {
            "overall_risk_level": "medium",
            "risk_factors": [
                "Cache invalidation complexity",
                "Database schema changes",
                "Potential service disruption",
            ],
            "mitigation_strategies": [
                "Implement gradual rollout",
                "Maintain rollback capabilities",
                "Monitor performance closely",
            ],
            "recommended_testing": [
                "Load testing",
                "Integration testing",
                "Performance testing",
            ],
        }

    def _implement_safe_optimizations(
        self, recommendations: List[Dict[str, Any]]
    ) -> Dict[str, Any]:
        """Implement safe optimizations automatically."""
        return {
            "implemented_optimizations": 1,
            "skipped_optimizations": 1,
            "implementation_status": "Partial Success",
            "results": {
                "response_time_improvement": Decimal("12.5"),
                "implementation_details": [
                    "Enabled response caching for GET endpoints"
                ],
            },
        }

    def _parse_duration_to_days(self, duration: str) -> int:
        """Parse duration string to number of days."""
        duration_map = {"7_days": 7, "30_days": 30, "90_days": 90, "1_year": 365}
        return duration_map.get(duration, 30)

    def _export_monitoring_data(self, monitoring_id: int) -> Dict[str, Any]:
        """Export monitoring data before deactivation."""
        return {
            "export_format": "JSON",
            "export_location": f'/exports/monitoring_{monitoring_id}_{datetime.now().strftime("%Y%m%d")}.json',
            "export_size_mb": Decimal("45.6"),
            "records_exported": 12500,
        }

    def _validate_metrics_data(
        self, metrics_data: List[Dict[str, Any]], validation_level: str
    ) -> List[Dict[str, Any]]:
        """Validate metrics data before bulk creation."""
        validated_metrics = []

        for metric in metrics_data:
            # Basic validation
            if not metric.get("api_endpoint") or not metric.get("request_method"):
                continue

            # Standard validation
            if validation_level in ["standard", "strict"]:
                if not isinstance(metric.get("response_time_ms"), int):
                    continue
                if not isinstance(metric.get("status_code"), int):
                    continue

            # Strict validation
            if validation_level == "strict":
                if metric.get("response_time_ms", 0) < 0:
                    continue
                if metric.get("status_code", 0) not in range(100, 600):
                    continue

            validated_metrics.append(metric)

        return validated_metrics

    def _setup_rate_limit_monitoring(
        self, rate_limit_config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Set up rate limit monitoring."""
        return {
            "monitoring_enabled": True,
            "current_usage": {"requests_per_minute": 750, "requests_per_hour": 35000},
            "utilization_percentage": {
                "requests_per_minute": Decimal("75.0"),
                "requests_per_hour": Decimal("70.0"),
            },
            "compliance_status": "Compliant",
        }

    def _configure_violation_handling(self, enforcement_mode: str) -> Dict[str, Any]:
        """Configure violation handling based on enforcement mode."""
        handling_config = {
            "enforcement_mode": enforcement_mode,
            "violation_actions": [],
            "escalation_rules": [],
        }

        if enforcement_mode == "warn":
            handling_config["violation_actions"] = ["log_warning", "send_notification"]
        elif enforcement_mode == "throttle":
            handling_config["violation_actions"] = [
                "log_warning",
                "throttle_requests",
                "send_notification",
            ]
        elif enforcement_mode == "block":
            handling_config["violation_actions"] = [
                "log_warning",
                "block_requests",
                "send_alert",
            ]

        return handling_config

    def _setup_throttling_rules(
        self, rate_limit_config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Set up throttling rules for rate limit compliance."""
        return {
            "throttling_enabled": True,
            "throttling_algorithm": "token_bucket",
            "backoff_strategy": "exponential",
            "max_delay_seconds": 30,
            "throttling_threshold": Decimal("90.0"),  # Start throttling at 90% of limit
            "endpoint_specific_rules": rate_limit_config.get("endpoints", {}),
        }

    def _generate_usage_overview(
        self, start_date: datetime, end_date: datetime, organization_id: Optional[int]
    ) -> Dict[str, Any]:
        """Generate usage overview for summary."""
        return {
            "total_requests": 125000,
            "unique_users": 89,
            "unique_endpoints": 25,
            "success_rate": Decimal("97.7"),
            "average_requests_per_day": 4167,
            "peak_requests_per_hour": 2500,
        }

    def _generate_performance_metrics(
        self, start_date: datetime, end_date: datetime, organization_id: Optional[int]
    ) -> Dict[str, Any]:
        """Generate performance metrics for summary."""
        return {
            "average_response_time": Decimal("245.6"),
            "median_response_time": Decimal("189.3"),
            "p95_response_time": Decimal("456.7"),
            "error_rate": Decimal("2.3"),
            "availability": Decimal("99.8"),
            "throughput": Decimal("856.2"),
        }

    def _generate_endpoint_analysis(
        self, start_date: datetime, end_date: datetime, organization_id: Optional[int]
    ) -> Dict[str, Any]:
        """Generate endpoint analysis for summary."""
        return {
            "top_endpoints": [
                {
                    "endpoint": "/v1/tickets",
                    "requests": 44375,
                    "avg_response_time": Decimal("245.6"),
                },
                {
                    "endpoint": "/v1/companies",
                    "requests": 27875,
                    "avg_response_time": Decimal("189.2"),
                },
                {
                    "endpoint": "/v1/projects",
                    "requests": 23375,
                    "avg_response_time": Decimal("312.8"),
                },
            ],
            "slowest_endpoints": [
                {
                    "endpoint": "/v1/reports/complex",
                    "avg_response_time": Decimal("1250.3"),
                },
                {
                    "endpoint": "/v1/analytics/data",
                    "avg_response_time": Decimal("890.7"),
                },
            ],
        }

    def _generate_error_analysis(
        self, start_date: datetime, end_date: datetime, organization_id: Optional[int]
    ) -> Dict[str, Any]:
        """Generate error analysis for summary."""
        return {
            "total_errors": 2875,
            "error_rate": Decimal("2.3"),
            "error_breakdown": {"4xx_errors": 2100, "5xx_errors": 775},
            "most_common_errors": [
                {
                    "status_code": 404,
                    "count": 1250,
                    "description": "Resource not found",
                },
                {"status_code": 401, "count": 650, "description": "Unauthorized"},
                {
                    "status_code": 500,
                    "count": 425,
                    "description": "Internal server error",
                },
            ],
        }

    def _generate_usage_trends(
        self, start_date: datetime, end_date: datetime, organization_id: Optional[int]
    ) -> Dict[str, Any]:
        """Generate usage trends for summary."""
        return {
            "request_volume_trend": "Increasing",
            "performance_trend": "Stable",
            "error_rate_trend": "Decreasing",
            "growth_rate": Decimal("8.5"),
            "seasonal_patterns": "Higher usage during business hours",
            "projected_next_month": {
                "requests": 135000,
                "growth_percentage": Decimal("8.0"),
            },
        }

    def _analyze_current_api_costs(
        self, start_date: datetime, end_date: datetime
    ) -> Dict[str, Any]:
        """Analyze current API costs."""
        return {
            "total_cost": Decimal("4567.89"),
            "cost_per_request": Decimal("0.0365"),
            "cost_breakdown_by_endpoint": {
                "/v1/tickets": Decimal("1623.45"),
                "/v1/companies": Decimal("1234.56"),
                "/v1/projects": Decimal("987.65"),
            },
            "infrastructure_costs": Decimal("2500.00"),
            "operational_costs": Decimal("2067.89"),
        }

    def _generate_cost_breakdown(self, current_costs: Dict[str, Any]) -> Dict[str, Any]:
        """Generate detailed cost breakdown."""
        return {
            "by_category": {
                "compute": Decimal("55.0"),
                "storage": Decimal("20.0"),
                "network": Decimal("15.0"),
                "monitoring": Decimal("10.0"),
            },
            "by_endpoint": {
                "/v1/tickets": Decimal("35.5"),
                "/v1/companies": Decimal("27.0"),
                "/v1/projects": Decimal("21.6"),
            },
            "fixed_costs": Decimal("2500.00"),
            "variable_costs": Decimal("2067.89"),
        }

    def _identify_cost_optimization_opportunities(
        self,
        current_costs: Dict[str, Any],
        optimization_targets: Optional[Dict[str, Any]],
    ) -> List[Dict[str, Any]]:
        """Identify cost optimization opportunities."""
        return [
            {
                "opportunity_type": "caching",
                "description": "Implement response caching to reduce compute costs",
                "potential_savings": Decimal("456.78"),
                "implementation_complexity": "medium",
            },
            {
                "opportunity_type": "resource_optimization",
                "description": "Optimize database queries to reduce resource usage",
                "potential_savings": Decimal("234.56"),
                "implementation_complexity": "high",
            },
        ]

    def _calculate_potential_cost_savings(
        self, optimization_opportunities: List[Dict[str, Any]]
    ) -> Dict[str, Any]:
        """Calculate potential cost savings."""
        total_savings = sum(
            opp.get("potential_savings", Decimal("0"))
            for opp in optimization_opportunities
        )
        return {
            "total_potential_savings": total_savings,
            "savings_percentage": Decimal("15.1"),
            "roi_estimate": Decimal("3.2"),
            "payback_period_months": 4,
        }

    def _generate_cost_projections(
        self,
        current_costs: Dict[str, Any],
        optimization_opportunities: List[Dict[str, Any]],
        analysis_period: str,
    ) -> Dict[str, Any]:
        """Generate cost projections."""
        return {
            "current_trajectory": {
                "monthly_cost": Decimal("4567.89"),
                "annual_cost": Decimal("54814.68"),
            },
            "optimized_trajectory": {
                "monthly_cost": Decimal("3876.54"),
                "annual_cost": Decimal("46518.48"),
            },
            "projected_savings": {
                "monthly_savings": Decimal("691.35"),
                "annual_savings": Decimal("8296.20"),
            },
        }

    def _generate_cost_optimization_recommendations(
        self,
        cost_analysis: Dict[str, Any],
        optimization_targets: Optional[Dict[str, Any]],
    ) -> List[Dict[str, Any]]:
        """Generate cost optimization recommendations."""
        return [
            {
                "recommendation": "Implement API response caching",
                "priority": "high",
                "expected_savings": Decimal("456.78"),
                "implementation_timeline": "2-3 weeks",
            },
            {
                "recommendation": "Optimize database query performance",
                "priority": "medium",
                "expected_savings": Decimal("234.56"),
                "implementation_timeline": "4-6 weeks",
            },
        ]
