#!/usr/bin/env python3
r"""
generate_total_cleaned_volume_chunked.py

Processes EXACTLY ONE next incomplete month per run, then exits cleanly.

Why this version is different:
- Uses a 50GB DuckDB memory limit
- Does NOT build a full-history TEMP VIEW across all months
- Only queries the single next month that has not yet been completed
- Writes monthly result immediately
- Saves final/partial JSON immediately
- Closes DuckDB and exits, so the next launch starts fresh and fast

Outputs:
    A:\TrafficAnalytics\PROJECTS\reports\deduped\chunked_total_cleaned_volume_monthly.csv
    A:\TrafficAnalytics\PROJECTS\reports\deduped\chunked_total_cleaned_volume_final.json
"""

from __future__ import annotations

import csv
import json
import math
import sys
import time
from dataclasses import dataclass
from datetime import date
from pathlib import Path

import duckdb


MAIN_DB = r"A:\TrafficAnalytics\DATA\SCATS\scats.duckdb"
CONT_DB = r"A:\TrafficAnalytics\DATA\SCATS\scats_continuation.duckdb"
REC_DB = r"A:\TrafficAnalytics\DATA\SCATS\scats_recovery.duckdb"

TEMP_DIR = r"G:\DuckDBTemp"
MEMORY_LIMIT = "50GB"
MAX_TEMP_DIRECTORY_SIZE = "1000GiB"
THREADS = 10

REPORT_DIR = Path(r"A:\TrafficAnalytics\PROJECTS\reports\deduped")
MONTHLY_CSV = REPORT_DIR / "chunked_total_cleaned_volume_monthly.csv"
FINAL_JSON = REPORT_DIR / "chunked_total_cleaned_volume_final.json"

DATE_RANGE_START = date(2014, 1, 1)
DATE_RANGE_END = date(2026, 4, 7)


@dataclass(frozen=True)
class MonthWindow:
    month_start: date
    next_month_start: date
    label: str


def fmt_int(value) -> str:
    if value is None:
        return "N/A"
    return f"{int(value):,}"


def fmt_seconds(seconds: float) -> str:
    seconds = max(0, int(seconds))
    hours, rem = divmod(seconds, 3600)
    minutes, secs = divmod(rem, 60)
    if hours > 0:
        return f"{hours}h {minutes}m {secs}s"
    if minutes > 0:
        return f"{minutes}m {secs}s"
    return f"{secs}s"


def safe_float(value: float | None) -> float:
    if value is None:
        return 0.0
    if isinstance(value, float) and (math.isnan(value) or math.isinf(value)):
        return 0.0
    return float(value)


def month_iter(start: date, end: date) -> list[MonthWindow]:
    months: list[MonthWindow] = []
    y, m = start.year, start.month

    while True:
        month_start = date(y, m, 1)
        if m == 12:
            next_month_start = date(y + 1, 1, 1)
        else:
            next_month_start = date(y, m + 1, 1)

        months.append(
            MonthWindow(
                month_start=month_start,
                next_month_start=next_month_start,
                label=f"{month_start.year:04d}-{month_start.month:02d}",
            )
        )

        if month_start.year == end.year and month_start.month == end.month:
            break

        if m == 12:
            y += 1
            m = 1
        else:
            m += 1

    return months


def connect_db() -> duckdb.DuckDBPyConnection:
    Path(TEMP_DIR).mkdir(parents=True, exist_ok=True)
    REPORT_DIR.mkdir(parents=True, exist_ok=True)

    con = duckdb.connect(MAIN_DB, read_only=False)
    con.execute(f"SET memory_limit='{MEMORY_LIMIT}'")
    con.execute(f"SET temp_directory='{TEMP_DIR.replace(chr(92), '/')}'")
    con.execute(f"SET max_temp_directory_size='{MAX_TEMP_DIRECTORY_SIZE}'")
    con.execute(f"SET threads={THREADS}")
    con.execute("SET preserve_insertion_order=false")

    # These can help long-running / large temp workloads behave better
    try:
        con.execute("SET enable_progress_bar=true")
    except Exception:
        pass

    con.execute(f"ATTACH '{CONT_DB.replace(chr(92), '/')}' AS cont")
    con.execute(f"ATTACH '{REC_DB.replace(chr(92), '/')}' AS rec")
    return con


def preflight_check(con: duckdb.DuckDBPyConnection) -> None:
    checks = [
        "scats_clean",
        "cont.scats_clean",
        "rec.scats_clean",
    ]
    print("Running preflight checks...")
    for obj in checks:
        con.execute(f"SELECT COUNT(*) FROM {obj} LIMIT 1").fetchone()
        print(f"  OK: {obj}")


def ensure_monthly_csv() -> None:
    if not MONTHLY_CSV.exists():
        with MONTHLY_CSV.open("w", newline="", encoding="utf-8") as f:
            writer = csv.writer(f)
            writer.writerow(
                [
                    "month_label",
                    "month_start",
                    "next_month_start",
                    "month_total_cleaned_volume",
                    "month_elapsed_seconds",
                    "completed_at_epoch",
                ]
            )


def load_completed_months() -> dict[str, dict]:
    ensure_monthly_csv()
    completed: dict[str, dict] = {}

    with MONTHLY_CSV.open("r", newline="", encoding="utf-8") as f:
        reader = csv.DictReader(f)
        for row in reader:
            completed[row["month_label"]] = row

    return completed


def append_month_result(
    month: MonthWindow,
    month_total: float | int | None,
    month_elapsed_seconds: float,
) -> None:
    ensure_monthly_csv()
    with MONTHLY_CSV.open("a", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(
            [
                month.label,
                month.month_start.isoformat(),
                month.next_month_start.isoformat(),
                int(month_total) if month_total is not None else "",
                round(month_elapsed_seconds, 3),
                round(time.time(), 3),
            ]
        )


def current_temp_usage_bytes(temp_dir: str) -> int:
    total = 0
    root = Path(temp_dir)
    if not root.exists():
        return 0
    for p in root.rglob("*"):
        try:
            if p.is_file():
                total += p.stat().st_size
        except OSError:
            pass
    return total


def fmt_gb(num_bytes: int) -> str:
    return f"{num_bytes / (1024 ** 3):,.1f} GB"


def find_next_incomplete_month(months: list[MonthWindow], completed: dict[str, dict]) -> MonthWindow | None:
    for month in months:
        if month.label not in completed:
            return month
    return None


def query_month_total(con: duckdb.DuckDBPyConnection, month: MonthWindow) -> float | None:
    start_s = month.month_start.isoformat()
    end_s = month.next_month_start.isoformat()

    # Restrict each source to the target month BEFORE union/dedup.
    # This is the big change that keeps each run smaller and fresher.
    sql = f"""
    WITH unified AS (
        SELECT
            scats_site,
            count_date,
            detector,
            region_code,
            source_file_id,
            interval_index,
            time_bin,
            volume_15m
        FROM scats_clean
        WHERE count_date >= DATE '{start_s}'
          AND count_date <  DATE '{end_s}'

        UNION ALL

        SELECT
            scats_site,
            count_date,
            detector,
            region_code,
            source_file_id,
            interval_index,
            time_bin,
            volume_15m
        FROM cont.scats_clean
        WHERE count_date >= DATE '{start_s}'
          AND count_date <  DATE '{end_s}'

        UNION ALL

        SELECT
            scats_site,
            count_date,
            detector,
            region_code,
            source_file_id,
            interval_index,
            time_bin,
            volume_15m
        FROM rec.scats_clean
        WHERE count_date >= DATE '{start_s}'
          AND count_date <  DATE '{end_s}'
    ),
    ranked AS (
        SELECT
            scats_site,
            count_date,
            detector,
            region_code,
            source_file_id,
            interval_index,
            time_bin,
            volume_15m,
            ROW_NUMBER() OVER (
                PARTITION BY scats_site, count_date, detector, interval_index
                ORDER BY source_file_id
            ) AS rn
        FROM unified
    )
    SELECT SUM(volume_15m)
    FROM ranked
    WHERE rn = 1
    """
    row = con.execute(sql).fetchone()
    return row[0] if row else None


def build_final_json(completed_months: dict[str, dict], months: list[MonthWindow]) -> dict:
    month_labels = [m.label for m in months]
    done_labels = [label for label in month_labels if label in completed_months]

    running_total = 0
    total_elapsed = 0.0

    for label in done_labels:
        row = completed_months[label]
        value = row.get("month_total_cleaned_volume", "")
        if value not in ("", None):
            running_total += int(value)
        elapsed = row.get("month_elapsed_seconds", "")
        if elapsed not in ("", None):
            total_elapsed += float(elapsed)

    final = {
        "metric_name": "total_cleaned_volume",
        "date_range_start": DATE_RANGE_START.isoformat(),
        "date_range_end": DATE_RANGE_END.isoformat(),
        "months_total": len(months),
        "months_completed": len(done_labels),
        "is_complete": len(done_labels) == len(months),
        "total_cleaned_volume": running_total if len(done_labels) == len(months) else None,
        "partial_total_cleaned_volume": running_total,
        "total_elapsed_seconds": round(total_elapsed, 3),
        "generated_at_epoch": round(time.time(), 3),
        "monthly_csv": str(MONTHLY_CSV),
    }
    return final


def save_final_json(payload: dict) -> None:
    with FINAL_JSON.open("w", encoding="utf-8") as f:
        json.dump(payload, f, indent=2)


def main() -> None:
    overall_start = time.time()
    months = month_iter(DATE_RANGE_START, DATE_RANGE_END)
    total_months = len(months)

    completed_before = load_completed_months()
    completed_count_before = sum(1 for m in months if m.label in completed_before)
    next_month = find_next_incomplete_month(months, completed_before)

    print("=" * 90)
    print("CHUNKED TOTAL CLEANED VOLUME RUN - ONE MONTH PER EXECUTION")
    print("=" * 90)
    print(f"Date range           : {DATE_RANGE_START} to {DATE_RANGE_END}")
    print(f"Months total         : {total_months}")
    print(f"Already completed    : {completed_count_before}/{total_months}")
    print(f"Main DB              : {MAIN_DB}")
    print(f"Continuation DB      : {CONT_DB}")
    print(f"Recovery DB          : {REC_DB}")
    print(f"Temp directory       : {TEMP_DIR}")
    print(f"Monthly CSV output   : {MONTHLY_CSV}")
    print(f"Final JSON output    : {FINAL_JSON}")
    print(f"Memory limit         : {MEMORY_LIMIT}")
    print(f"Temp max             : {MAX_TEMP_DIRECTORY_SIZE}")
    print(f"Threads              : {THREADS}")
    print("=" * 90)

    partial_before = build_final_json(completed_before, months)
    print(f"Existing partial sum : {fmt_int(partial_before['partial_total_cleaned_volume'])}")

    if next_month is None:
        save_final_json(partial_before)
        print("All months are already complete. Nothing to do.")
        return

    next_idx = next(i for i, m in enumerate(months, start=1) if m.label == next_month.label)
    print(f"Next month to run    : {next_month.label} ({next_idx}/{total_months})")
    print(
        f"Date window          : {next_month.month_start.isoformat()} "
        f"to < {next_month.next_month_start.isoformat()}"
    )

    con = connect_db()
    try:
        preflight_check(con)

        temp_before = current_temp_usage_bytes(TEMP_DIR)
        print(f"Temp usage before    : {fmt_gb(temp_before)}")

        month_start_ts = time.time()
        month_total = query_month_total(con, next_month)
        month_elapsed = time.time() - month_start_ts

        append_month_result(next_month, month_total, month_elapsed)

        completed_after = load_completed_months()
        final = build_final_json(completed_after, months)
        save_final_json(final)

        temp_after = current_temp_usage_bytes(TEMP_DIR)
        temp_delta = temp_after - temp_before

        print("-" * 90)
        print(f"Finished month       : {next_month.label}")
        print(f"Month subtotal       : {fmt_int(month_total)}")
        print(f"Month elapsed        : {fmt_seconds(month_elapsed)}")
        print(f"Temp usage after     : {fmt_gb(temp_after)}")
        print(f"Temp delta           : {fmt_gb(temp_delta)}")
        print(f"Months completed     : {final['months_completed']}/{final['months_total']}")
        print(f"Running total volume : {fmt_int(final['partial_total_cleaned_volume'])}")
        print(f"Overall elapsed      : {fmt_seconds(time.time() - overall_start)}")
        print(f"Monthly CSV          : {MONTHLY_CSV}")
        print(f"Final JSON           : {FINAL_JSON}")
        print("-" * 90)

        if final["is_complete"]:
            print("ARCHIVE COMPLETE.")
        else:
            upcoming = find_next_incomplete_month(months, completed_after)
            if upcoming is not None:
                print(f"Next launch will start at month: {upcoming.label}")

    finally:
        con.close()


if __name__ == "__main__":
    try:
        main()
    except KeyboardInterrupt:
        print("\nInterrupted by user.")
        sys.exit(130)