"""Operational alerting for Stonks Oracle pipeline health.

Evaluates alert rules against PostgreSQL operational state and emits
structured log events and Prometheus metrics when thresholds are breached.

Alert rules:
- source_failures: sustained source retrieval failures per source
- schema_failure_spike: extraction validation failure rate exceeds threshold
- analytical_lag: lake publication has not completed within threshold
- broker_issues: consecutive broker submission errors

Requirements: 12.3
Design: Section 12 (Observability and Operations)
"""
from __future__ import annotations

import logging
from dataclasses import dataclass, field
from datetime import datetime, timezone
from typing import Any

import asyncpg

from services.shared.config import AlertingConfig
from services.shared.metrics import (
    ALERT_ACTIVE,
    ALERT_CHECK_DURATION,
    ALERTS_FIRED,
    ALERTS_RESOLVED,
)

logger = logging.getLogger("alerting")


@dataclass
class Alert:
    """A single alert instance."""

    rule: str
    severity: str  # "warning" | "critical"
    summary: str
    details: dict[str, Any] = field(default_factory=dict)
    fired_at: datetime = field(default_factory=lambda: datetime.now(timezone.utc))


@dataclass
class AlertState:
    """Tracks which rules are currently firing to detect transitions."""

    active: dict[str, Alert] = field(default_factory=dict)

    def fire(self, alert: Alert) -> bool:
        """Record an alert firing. Returns True if this is a new firing."""
        key = f"{alert.rule}:{alert.details.get('key', '')}"
        is_new = key not in self.active
        self.active[key] = alert
        return is_new

    def resolve(self, rule: str, key: str = "") -> bool:
        """Resolve an alert. Returns True if it was previously active."""
        full_key = f"{rule}:{key}"
        if full_key in self.active:
            del self.active[full_key]
            return True
        return False

    def is_firing(self, rule: str, key: str = "") -> bool:
        return f"{rule}:{key}" in self.active


async def check_source_failures(
    pool: asyncpg.Pool,
    config: AlertingConfig,
) -> list[Alert]:
    """Check for sources with sustained consecutive failures.

    Queries ingestion_runs for sources where the last N runs all failed
    within the lookback window.
    """
    rows = await pool.fetch(
        """WITH recent_runs AS (
            SELECT source_id, status,
                   ROW_NUMBER() OVER (PARTITION BY source_id ORDER BY started_at DESC) AS rn
            FROM ingestion_runs
            WHERE started_at >= NOW() - INTERVAL '1 hour' * $1
        ),
        failure_streaks AS (
            SELECT source_id,
                   COUNT(*) FILTER (WHERE status = 'failed') AS consecutive_failures,
                   COUNT(*) AS total_runs
            FROM recent_runs
            WHERE rn <= $2
            GROUP BY source_id
            HAVING COUNT(*) FILTER (WHERE status = 'failed') = COUNT(*)
               AND COUNT(*) >= $2
        )
        SELECT fs.source_id, fs.consecutive_failures,
               s.source_type, s.source_name, c.ticker
        FROM failure_streaks fs
        JOIN sources s ON s.id = fs.source_id
        JOIN companies c ON c.id = s.company_id""",
        config.source_failure_window_hours,
        config.source_failure_threshold,
    )

    alerts = []
    for row in rows:
        alerts.append(Alert(
            rule="source_failures",
            severity="warning",
            summary=(
                f"Source {row['source_name']} ({row['source_type']}) for "
                f"{row['ticker']} has {row['consecutive_failures']} consecutive failures"
            ),
            details={
                "key": str(row["source_id"]),
                "source_id": str(row["source_id"]),
                "source_type": row["source_type"],
                "source_name": row["source_name"],
                "ticker": row["ticker"],
                "consecutive_failures": row["consecutive_failures"],
            },
        ))
    return alerts


async def check_schema_failure_spike(
    pool: asyncpg.Pool,
    config: AlertingConfig,
) -> list[Alert]:
    """Check if extraction schema validation failure rate exceeds threshold.

    Queries model_performance_metrics for the recent window and computes
    the failure rate.
    """
    row = await pool.fetchrow(
        """SELECT
            COUNT(*) AS total,
            COUNT(*) FILTER (WHERE NOT success) AS failed
        FROM model_performance_metrics
        WHERE recorded_at >= NOW() - INTERVAL '1 hour' * $1""",
        config.schema_failure_window_hours,
    )

    if not row or row["total"] == 0:
        return []

    total = row["total"]
    failed = row["failed"]
    failure_rate = failed / total

    if failure_rate >= config.schema_failure_rate_threshold:
        return [Alert(
            rule="schema_failure_spike",
            severity="critical" if failure_rate >= 0.5 else "warning",
            summary=(
                f"Extraction schema failure rate is {failure_rate:.1%} "
                f"({failed}/{total}) in the last {config.schema_failure_window_hours}h"
            ),
            details={
                "key": "global",
                "total_extractions": total,
                "failed_extractions": failed,
                "failure_rate": round(failure_rate, 4),
                "threshold": config.schema_failure_rate_threshold,
                "window_hours": config.schema_failure_window_hours,
            },
        )]
    return []


async def check_analytical_lag(
    pool: asyncpg.Pool,
    config: AlertingConfig,
) -> list[Alert]:
    """Check if lake publication is lagging beyond threshold.

    Looks at the audit_events table for the most recent successful
    lake_publish events per table, and alerts if any are stale.
    """
    rows = await pool.fetch(
        """SELECT
            details->>'table_name' AS table_name,
            MAX(created_at) AS last_publish
        FROM audit_events
        WHERE event_type = 'lake_publish'
          AND details->>'status' = 'success'
          AND details->>'table_name' IS NOT NULL
        GROUP BY details->>'table_name'
        HAVING MAX(created_at) < NOW() - INTERVAL '1 minute' * $1""",
        config.lake_lag_threshold_minutes,
    )

    alerts = []
    now = datetime.now(timezone.utc)
    for row in rows:
        table_name = row["table_name"]
        last_publish = row["last_publish"]
        if last_publish.tzinfo is None:
            last_publish = last_publish.replace(tzinfo=timezone.utc)
        lag_minutes = (now - last_publish).total_seconds() / 60

        alerts.append(Alert(
            rule="analytical_lag",
            severity="warning",
            summary=(
                f"Lake table '{table_name}' last published {lag_minutes:.0f}m ago "
                f"(threshold: {config.lake_lag_threshold_minutes}m)"
            ),
            details={
                "key": table_name,
                "table_name": table_name,
                "last_publish": last_publish.isoformat(),
                "lag_minutes": round(lag_minutes, 1),
                "threshold_minutes": config.lake_lag_threshold_minutes,
            },
        ))
    return alerts


async def check_broker_issues(
    pool: asyncpg.Pool,
    config: AlertingConfig,
) -> list[Alert]:
    """Check for consecutive broker submission errors.

    Queries order_events for recent broker-level errors (rejections,
    timeouts, connection failures) within the lookback window.
    """
    rows = await pool.fetch(
        """WITH recent_events AS (
            SELECT order_id, event_type, created_at,
                   ROW_NUMBER() OVER (ORDER BY created_at DESC) AS rn
            FROM order_events
            WHERE created_at >= NOW() - INTERVAL '1 hour' * $1
              AND event_type IN ('broker_error', 'broker_timeout', 'connection_failed')
        )
        SELECT COUNT(*) AS error_count
        FROM recent_events
        WHERE rn <= $2""",
        config.broker_error_window_hours,
        config.broker_error_threshold,
    )

    if not rows:
        return []

    error_count = rows[0]["error_count"]
    if error_count >= config.broker_error_threshold:
        return [Alert(
            rule="broker_issues",
            severity="critical",
            summary=(
                f"{error_count} broker errors in the last "
                f"{config.broker_error_window_hours}h"
            ),
            details={
                "key": "global",
                "error_count": error_count,
                "threshold": config.broker_error_threshold,
                "window_hours": config.broker_error_window_hours,
            },
        )]
    return []


async def evaluate_alerts(
    pool: asyncpg.Pool,
    config: AlertingConfig,
    state: AlertState,
) -> list[Alert]:
    """Run all alert rules and return newly fired alerts.

    Updates AlertState to track firing/resolved transitions and emits
    structured log events and Prometheus metrics for each transition.
    """
    all_alerts: list[Alert] = []

    with ALERT_CHECK_DURATION.time():
        # Collect alerts from all rules
        try:
            all_alerts.extend(await check_source_failures(pool, config))
        except Exception:
            logger.exception("Error checking source failures")

        try:
            all_alerts.extend(await check_schema_failure_spike(pool, config))
        except Exception:
            logger.exception("Error checking schema failure spike")

        try:
            all_alerts.extend(await check_analytical_lag(pool, config))
        except Exception:
            logger.exception("Error checking analytical lag")

        try:
            all_alerts.extend(await check_broker_issues(pool, config))
        except Exception:
            logger.exception("Error checking broker issues")

    # Track which rule+key combos are currently firing
    current_keys: set[str] = set()
    newly_fired: list[Alert] = []

    for alert in all_alerts:
        key = f"{alert.rule}:{alert.details.get('key', '')}"
        current_keys.add(key)

        if state.fire(alert):
            # New alert firing
            ALERTS_FIRED.labels(rule=alert.rule, severity=alert.severity).inc()
            ALERT_ACTIVE.labels(rule=alert.rule).set(1)
            newly_fired.append(alert)
            logger.warning(
                "ALERT FIRING: [%s] %s",
                alert.rule,
                alert.summary,
                extra={
                    "alert_rule": alert.rule,
                    "alert_severity": alert.severity,
                    "alert_details": alert.details,
                },
            )

    # Check for resolved alerts
    resolved_keys = set(state.active.keys()) - current_keys
    for key in resolved_keys:
        rule = key.split(":")[0]
        detail_key = key[len(rule) + 1:]
        if state.resolve(rule, detail_key):
            ALERTS_RESOLVED.labels(rule=rule).inc()
            # Only set gauge to 0 if no more alerts for this rule
            still_firing = any(k.startswith(f"{rule}:") for k in state.active)
            if not still_firing:
                ALERT_ACTIVE.labels(rule=rule).set(0)
            logger.info(
                "ALERT RESOLVED: [%s] key=%s",
                rule,
                detail_key,
            )

    return newly_fired