PROJECT-CONTORL/backend/app/services/workload_cache.py

"""Workload cache service using Redis.

Provides caching for workload calculations to improve API response times.
"""
import json
from datetime import date
from decimal import Decimal
from typing import Optional, List

from app.core.redis import redis_client
from app.schemas.workload import UserWorkloadSummary, LoadLevel

# Cache TTL in seconds (1 hour)
WORKLOAD_CACHE_TTL = 3600


def _make_heatmap_cache_key(
    week_start: date,
    department_id: Optional[str] = None,
    user_ids: Optional[List[str]] = None,
) -> str:
    """Generate cache key for heatmap query."""
    parts = ["workload", "heatmap", str(week_start)]
    if department_id:
        parts.append(f"dept:{department_id}")
    if user_ids:
        parts.append(f"users:{','.join(sorted(user_ids))}")
    return ":".join(parts)


def _make_user_cache_key(user_id: str, week_start: date) -> str:
    """Generate cache key for user workload."""
    return f"workload:user:{user_id}:{week_start}"


def _serialize_workload_summary(summary: UserWorkloadSummary) -> dict:
    """Serialize UserWorkloadSummary for JSON storage."""
    return {
        "user_id": summary.user_id,
        "user_name": summary.user_name,
        "department_id": summary.department_id,
        "department_name": summary.department_name,
        "capacity_hours": str(summary.capacity_hours),
        "allocated_hours": str(summary.allocated_hours),
        "load_percentage": (
            str(summary.load_percentage) if summary.load_percentage is not None else None
        ),
        "load_level": summary.load_level.value,
        "task_count": summary.task_count,
    }


def _deserialize_workload_summary(data: dict) -> UserWorkloadSummary:
    """Deserialize UserWorkloadSummary from JSON."""
    return UserWorkloadSummary(
        user_id=data["user_id"],
        user_name=data["user_name"],
        department_id=data["department_id"],
        department_name=data["department_name"],
        capacity_hours=Decimal(data["capacity_hours"]),
        allocated_hours=Decimal(data["allocated_hours"]),
        load_percentage=Decimal(data["load_percentage"]) if data["load_percentage"] else None,
        load_level=LoadLevel(data["load_level"]),
        task_count=data["task_count"],
    )


def get_cached_heatmap(
    week_start: date,
    department_id: Optional[str] = None,
    user_ids: Optional[List[str]] = None,
) -> Optional[List[UserWorkloadSummary]]:
    """
    Get cached heatmap data.

    Args:
        week_start: Start of week
        department_id: Department filter
        user_ids: User IDs filter

    Returns:
        List of UserWorkloadSummary or None if not cached
    """
    cache_key = _make_heatmap_cache_key(week_start, department_id, user_ids)
    cached = redis_client.get(cache_key)

    if cached:
        data = json.loads(cached)
        return [_deserialize_workload_summary(item) for item in data]

    return None


def set_cached_heatmap(
    week_start: date,
    summaries: List[UserWorkloadSummary],
    department_id: Optional[str] = None,
    user_ids: Optional[List[str]] = None,
) -> None:
    """
    Cache heatmap data.

    Args:
        week_start: Start of week
        summaries: List of workload summaries
        department_id: Department filter
        user_ids: User IDs filter
    """
    cache_key = _make_heatmap_cache_key(week_start, department_id, user_ids)
    data = [_serialize_workload_summary(s) for s in summaries]
    redis_client.setex(cache_key, WORKLOAD_CACHE_TTL, json.dumps(data))


def get_cached_user_workload(
    user_id: str,
    week_start: date,
) -> Optional[UserWorkloadSummary]:
    """
    Get cached user workload.

    Args:
        user_id: User ID
        week_start: Start of week

    Returns:
        UserWorkloadSummary or None if not cached
    """
    cache_key = _make_user_cache_key(user_id, week_start)
    cached = redis_client.get(cache_key)

    if cached:
        data = json.loads(cached)
        return _deserialize_workload_summary(data)

    return None


def set_cached_user_workload(
    user_id: str,
    week_start: date,
    summary: UserWorkloadSummary,
) -> None:
    """
    Cache user workload.

    Args:
        user_id: User ID
        week_start: Start of week
        summary: Workload summary
    """
    cache_key = _make_user_cache_key(user_id, week_start)
    data = _serialize_workload_summary(summary)
    redis_client.setex(cache_key, WORKLOAD_CACHE_TTL, json.dumps(data))


def invalidate_user_workload_cache(user_id: str) -> None:
    """
    Invalidate all cached workload data for a user.

    This clears:
    1. User-specific workload cache (workload:user:{user_id}:*)
    2. All heatmap caches (workload:heatmap:*) since heatmap aggregates may include this user

    Note: This uses pattern matching which may be slow for large datasets.
    For Phase 1, we rely on TTL expiration instead of active invalidation.
    """
    # Clear user-specific workload cache
    user_pattern = f"workload:user:{user_id}:*"
    for key in redis_client.scan_iter(match=user_pattern):
        redis_client.delete(key)

    # Clear all heatmap caches since they aggregate user data
    heatmap_pattern = "workload:heatmap:*"
    for key in redis_client.scan_iter(match=heatmap_pattern):
        redis_client.delete(key)