orion/app/services/capacity_forecast_service.py

# app/services/capacity_forecast_service.py
"""
Capacity forecasting service for growth trends and scaling recommendations.

Provides:
- Historical capacity trend analysis
- Growth rate calculations
- Days-until-threshold projections
- Scaling recommendations based on growth patterns
"""

import logging
from datetime import UTC, datetime, timedelta
from decimal import Decimal

from sqlalchemy import func
from sqlalchemy.orm import Session

from app.modules.catalog.models import Product
from app.modules.billing.models import (
    CapacitySnapshot,
    SubscriptionStatus,
    VendorSubscription,
)
from models.database.vendor import Vendor, VendorUser

logger = logging.getLogger(__name__)


# Scaling thresholds based on capacity-planning.md
INFRASTRUCTURE_SCALING = [
    {"name": "Starter", "max_vendors": 50, "max_products": 10_000, "cost_monthly": 30},
    {"name": "Small", "max_vendors": 100, "max_products": 30_000, "cost_monthly": 80},
    {"name": "Medium", "max_vendors": 300, "max_products": 100_000, "cost_monthly": 150},
    {"name": "Large", "max_vendors": 500, "max_products": 250_000, "cost_monthly": 350},
    {"name": "Scale", "max_vendors": 1000, "max_products": 500_000, "cost_monthly": 700},
    {"name": "Enterprise", "max_vendors": None, "max_products": None, "cost_monthly": 1500},
]


class CapacityForecastService:
    """Service for capacity forecasting and trend analysis."""

    def capture_daily_snapshot(self, db: Session) -> CapacitySnapshot:
        """
        Capture a daily snapshot of platform capacity metrics.

        Should be called by a daily background job.
        """
        from app.services.image_service import image_service
        from app.services.platform_health_service import platform_health_service

        now = datetime.now(UTC)
        today = now.replace(hour=0, minute=0, second=0, microsecond=0)

        # Check if snapshot already exists for today
        existing = (
            db.query(CapacitySnapshot)
            .filter(CapacitySnapshot.snapshot_date == today)
            .first()
        )
        if existing:
            logger.info(f"Snapshot already exists for {today}")
            return existing

        # Gather metrics
        total_vendors = db.query(func.count(Vendor.id)).scalar() or 0
        active_vendors = (
            db.query(func.count(Vendor.id))
            .filter(Vendor.is_active == True)  # noqa: E712
            .scalar()
            or 0
        )

        # Subscription metrics
        total_subs = db.query(func.count(VendorSubscription.id)).scalar() or 0
        active_subs = (
            db.query(func.count(VendorSubscription.id))
            .filter(VendorSubscription.status.in_(["active", "trial"]))
            .scalar()
            or 0
        )
        trial_vendors = (
            db.query(func.count(VendorSubscription.id))
            .filter(VendorSubscription.status == SubscriptionStatus.TRIAL.value)
            .scalar()
            or 0
        )

        # Resource metrics
        total_products = db.query(func.count(Product.id)).scalar() or 0
        total_team = (
            db.query(func.count(VendorUser.id))
            .filter(VendorUser.is_active == True)  # noqa: E712
            .scalar()
            or 0
        )

        # Orders this month
        start_of_month = now.replace(day=1, hour=0, minute=0, second=0, microsecond=0)
        total_orders = sum(
            s.orders_this_period
            for s in db.query(VendorSubscription).all()
        )

        # Storage metrics
        try:
            image_stats = image_service.get_storage_stats()
            storage_gb = image_stats.get("total_size_gb", 0)
        except Exception:
            storage_gb = 0

        try:
            db_size = platform_health_service._get_database_size(db)
        except Exception:
            db_size = 0

        # Theoretical capacity from subscriptions
        capacity = platform_health_service.get_subscription_capacity(db)
        theoretical_products = capacity["products"].get("theoretical_limit", 0)
        theoretical_orders = capacity["orders_monthly"].get("theoretical_limit", 0)
        theoretical_team = capacity["team_members"].get("theoretical_limit", 0)

        # Tier distribution
        tier_distribution = capacity.get("tier_distribution", {})

        # Create snapshot
        snapshot = CapacitySnapshot(
            snapshot_date=today,
            total_vendors=total_vendors,
            active_vendors=active_vendors,
            trial_vendors=trial_vendors,
            total_subscriptions=total_subs,
            active_subscriptions=active_subs,
            total_products=total_products,
            total_orders_month=total_orders,
            total_team_members=total_team,
            storage_used_gb=Decimal(str(storage_gb)),
            db_size_mb=Decimal(str(db_size)),
            theoretical_products_limit=theoretical_products,
            theoretical_orders_limit=theoretical_orders,
            theoretical_team_limit=theoretical_team,
            tier_distribution=tier_distribution,
        )

        db.add(snapshot)
        db.flush()
        db.refresh(snapshot)

        logger.info(f"Captured capacity snapshot for {today}")
        return snapshot

    def get_growth_trends(self, db: Session, days: int = 30) -> dict:
        """
        Calculate growth trends over the specified period.

        Returns growth rates and projections for key metrics.
        """
        now = datetime.now(UTC)
        start_date = now - timedelta(days=days)

        # Get snapshots for the period
        snapshots = (
            db.query(CapacitySnapshot)
            .filter(CapacitySnapshot.snapshot_date >= start_date)
            .order_by(CapacitySnapshot.snapshot_date)
            .all()
        )

        if len(snapshots) < 2:
            return {
                "period_days": days,
                "snapshots_available": len(snapshots),
                "trends": {},
                "message": "Insufficient data for trend analysis",
            }

        first = snapshots[0]
        last = snapshots[-1]
        period_days = (last.snapshot_date - first.snapshot_date).days or 1

        def calc_growth(metric: str) -> dict:
            start_val = getattr(first, metric) or 0
            end_val = getattr(last, metric) or 0
            change = end_val - start_val

            if start_val > 0:
                growth_rate = (change / start_val) * 100
                daily_rate = growth_rate / period_days
                monthly_rate = daily_rate * 30
            else:
                growth_rate = 0 if end_val == 0 else 100
                daily_rate = 0
                monthly_rate = 0

            return {
                "start_value": start_val,
                "current_value": end_val,
                "change": change,
                "growth_rate_percent": round(growth_rate, 2),
                "daily_growth_rate": round(daily_rate, 3),
                "monthly_projection": round(end_val * (1 + monthly_rate / 100), 0),
            }

        trends = {
            "vendors": calc_growth("active_vendors"),
            "products": calc_growth("total_products"),
            "orders": calc_growth("total_orders_month"),
            "team_members": calc_growth("total_team_members"),
            "storage_gb": {
                "start_value": float(first.storage_used_gb or 0),
                "current_value": float(last.storage_used_gb or 0),
                "change": float((last.storage_used_gb or 0) - (first.storage_used_gb or 0)),
            },
        }

        return {
            "period_days": period_days,
            "snapshots_available": len(snapshots),
            "start_date": first.snapshot_date.isoformat(),
            "end_date": last.snapshot_date.isoformat(),
            "trends": trends,
        }

    def get_scaling_recommendations(self, db: Session) -> list[dict]:
        """
        Generate scaling recommendations based on current capacity and growth.

        Returns prioritized list of recommendations.
        """
        from app.services.platform_health_service import platform_health_service

        recommendations = []

        # Get current capacity
        capacity = platform_health_service.get_subscription_capacity(db)
        health = platform_health_service.get_full_health_report(db)
        trends = self.get_growth_trends(db, days=30)

        # Check product capacity
        products = capacity["products"]
        if products.get("utilization_percent") and products["utilization_percent"] > 80:
            recommendations.append({
                "category": "capacity",
                "severity": "warning",
                "title": "Product capacity approaching limit",
                "description": f"Currently at {products['utilization_percent']:.0f}% of theoretical product capacity",
                "action": "Consider upgrading vendor tiers or adding capacity",
            })

        # Check infrastructure tier
        current_tier = health.get("infrastructure_tier", {})
        next_trigger = health.get("next_tier_trigger")
        if next_trigger:
            recommendations.append({
                "category": "infrastructure",
                "severity": "info",
                "title": f"Current tier: {current_tier.get('name', 'Unknown')}",
                "description": f"Next upgrade trigger: {next_trigger}",
                "action": "Monitor growth and plan for infrastructure scaling",
            })

        # Check growth rate
        if trends.get("trends"):
            vendor_growth = trends["trends"].get("vendors", {})
            if vendor_growth.get("monthly_projection", 0) > 0:
                monthly_rate = vendor_growth.get("growth_rate_percent", 0)
                if monthly_rate > 20:
                    recommendations.append({
                        "category": "growth",
                        "severity": "info",
                        "title": "High vendor growth rate",
                        "description": f"Vendor base growing at {monthly_rate:.1f}% over last 30 days",
                        "action": "Ensure infrastructure can scale to meet demand",
                    })

        # Check storage
        storage_percent = health.get("image_storage", {}).get("total_size_gb", 0)
        if storage_percent > 800:  # 80% of 1TB
            recommendations.append({
                "category": "storage",
                "severity": "warning",
                "title": "Storage usage high",
                "description": f"Image storage at {storage_percent:.1f} GB",
                "action": "Plan for storage expansion or implement cleanup policies",
            })

        # Sort by severity
        severity_order = {"critical": 0, "warning": 1, "info": 2}
        recommendations.sort(key=lambda r: severity_order.get(r["severity"], 3))

        return recommendations

    def get_days_until_threshold(
        self, db: Session, metric: str, threshold: int
    ) -> int | None:
        """
        Calculate days until a metric reaches a threshold based on current growth.

        Returns None if insufficient data or no growth.
        """
        trends = self.get_growth_trends(db, days=30)

        if not trends.get("trends") or metric not in trends["trends"]:
            return None

        metric_data = trends["trends"][metric]
        current = metric_data.get("current_value", 0)
        daily_rate = metric_data.get("daily_growth_rate", 0)

        if daily_rate <= 0 or current >= threshold:
            return None

        remaining = threshold - current
        days = remaining / (current * daily_rate / 100) if current > 0 else None

        return int(days) if days else None


# Singleton instance
capacity_forecast_service = CapacityForecastService()