910 lines
33 KiB
Python
910 lines
33 KiB
Python
"""Leaderboard read model over materialized RCON/AdminLog match stats."""
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from __future__ import annotations
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import os
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from contextlib import closing
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from datetime import datetime, timedelta, timezone
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from pathlib import Path
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from typing import Literal
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from .config import get_storage_path, use_postgres_rcon_storage
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from .config import get_historical_weekly_fallback_min_matches
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from .historical_storage import ALL_SERVERS_SLUG
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from .rcon_admin_log_materialization import (
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MATCH_RESULT_SOURCE,
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initialize_rcon_materialized_storage,
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)
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from .sqlite_utils import connect_sqlite_readonly, connect_sqlite_writer
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LeaderboardTimeframe = Literal["weekly", "monthly"]
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LeaderboardMetric = Literal[
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"kills",
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"deaths",
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"teamkills",
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"matches_considered",
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"kd_ratio",
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"kills_per_match",
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"matches_over_100_kills",
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"support",
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]
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RANKING_SNAPSHOT_SQLITE_SCHEMA_SQL = """
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CREATE TABLE IF NOT EXISTS ranking_snapshots (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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timeframe TEXT NOT NULL,
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server_id TEXT NOT NULL,
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metric TEXT NOT NULL,
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window_start TEXT NOT NULL,
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window_end TEXT NOT NULL,
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generated_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP,
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source TEXT NOT NULL DEFAULT 'rcon-materialized-admin-log',
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snapshot_status TEXT NOT NULL DEFAULT 'ready',
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item_count INTEGER NOT NULL DEFAULT 0,
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limit_size INTEGER NOT NULL DEFAULT 20,
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source_matches_count INTEGER NOT NULL DEFAULT 0,
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freshness TEXT NOT NULL DEFAULT 'fresh',
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window_kind TEXT,
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window_label TEXT,
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error_message TEXT,
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UNIQUE(timeframe, server_id, metric, window_start, window_end)
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);
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CREATE INDEX IF NOT EXISTS idx_ranking_snapshots_lookup
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ON ranking_snapshots(timeframe, server_id, metric, snapshot_status, window_end DESC, generated_at DESC);
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CREATE TABLE IF NOT EXISTS ranking_snapshot_items (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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snapshot_id INTEGER NOT NULL REFERENCES ranking_snapshots(id) ON DELETE CASCADE,
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ranking_position INTEGER NOT NULL,
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player_id TEXT NOT NULL,
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player_name TEXT NOT NULL,
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metric_value REAL NOT NULL DEFAULT 0,
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matches_considered INTEGER NOT NULL DEFAULT 0,
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kills INTEGER NOT NULL DEFAULT 0,
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deaths INTEGER NOT NULL DEFAULT 0,
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teamkills INTEGER NOT NULL DEFAULT 0,
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kd_ratio REAL NOT NULL DEFAULT 0.0,
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kills_per_match REAL NOT NULL DEFAULT 0.0,
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UNIQUE(snapshot_id, ranking_position),
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UNIQUE(snapshot_id, player_id)
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);
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CREATE INDEX IF NOT EXISTS idx_ranking_snapshot_items_snapshot
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ON ranking_snapshot_items(snapshot_id, ranking_position);
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CREATE INDEX IF NOT EXISTS idx_ranking_snapshot_items_player
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ON ranking_snapshot_items(snapshot_id, player_id);
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"""
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def initialize_ranking_snapshot_storage(*, db_path: Path | None = None) -> Path:
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"""Create ranking snapshot tables used by weekly/monthly public ranking reads."""
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resolved_path = initialize_rcon_materialized_storage(db_path=db_path)
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if use_postgres_rcon_storage(explicit_sqlite_path=db_path):
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from .postgres_rcon_storage import initialize_postgres_rcon_storage
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initialize_postgres_rcon_storage()
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return resolved_path
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with closing(connect_sqlite_writer(resolved_path)) as connection:
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with connection:
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connection.executescript(RANKING_SNAPSHOT_SQLITE_SCHEMA_SQL)
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return resolved_path
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def is_ranking_runtime_fallback_enabled() -> bool:
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"""Return whether `/api/ranking` may fall back to runtime reads when snapshot is missing."""
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normalized = os.getenv(
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"HLL_BACKEND_RANKING_RUNTIME_FALLBACK_ENABLED",
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"true",
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).strip().lower()
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return normalized in {"1", "true", "yes", "on"}
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def get_latest_ranking_snapshot(
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*,
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server_key: str | None = None,
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timeframe: str = "weekly",
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metric: str = "kills",
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limit: int = 10,
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db_path: Path | None = None,
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) -> dict[str, object]:
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"""Return the latest ready weekly/monthly ranking snapshot for the requested scope."""
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normalized_server_key = _normalize_snapshot_server_key(server_key)
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normalized_timeframe = _normalize_timeframe(timeframe)
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normalized_metric = _normalize_metric(metric)
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normalized_limit = max(1, int(limit or 10))
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resolved_path = initialize_ranking_snapshot_storage(db_path=db_path)
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connection_scope = _connect_scope(resolved_path, db_path=db_path)
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with connection_scope as connection:
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snapshot = _find_latest_snapshot(
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connection=connection,
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timeframe=normalized_timeframe,
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server_key=normalized_server_key,
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metric=normalized_metric,
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)
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if snapshot is None:
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return {
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"snapshot_status": "missing",
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"timeframe": normalized_timeframe,
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"server_id": normalized_server_key,
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"metric": normalized_metric,
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"limit": normalized_limit,
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"requested_limit": normalized_limit,
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"effective_limit": 0,
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"snapshot_limit": None,
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"item_count": 0,
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"generated_at": None,
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"window_start": None,
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"window_end": None,
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"window_kind": None,
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"window_label": None,
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"source": "ranking-snapshot",
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"freshness": "missing",
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"source_matches_count": 0,
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"items": [],
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}
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snapshot_limit = max(1, int(snapshot.get("limit_size") or normalized_limit))
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item_count = int(snapshot.get("item_count") or 0)
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effective_limit = max(0, min(normalized_limit, snapshot_limit, item_count))
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items = _list_snapshot_items(
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connection=connection,
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snapshot_id=int(snapshot["id"]),
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limit=effective_limit if effective_limit > 0 else None,
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)
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return {
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"snapshot_status": "ready",
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"timeframe": normalized_timeframe,
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"server_id": normalized_server_key,
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"metric": normalized_metric,
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"limit": effective_limit,
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"requested_limit": normalized_limit,
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"effective_limit": effective_limit,
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"snapshot_limit": snapshot_limit,
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"item_count": item_count,
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"generated_at": snapshot.get("generated_at"),
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"window_start": snapshot.get("window_start"),
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"window_end": snapshot.get("window_end"),
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"window_kind": snapshot.get("window_kind"),
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"window_label": snapshot.get("window_label"),
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"source": snapshot.get("source") or "ranking-snapshot",
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"freshness": snapshot.get("freshness") or "fresh",
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"source_matches_count": int(snapshot.get("source_matches_count") or 0),
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"items": items,
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}
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def build_rcon_materialized_leaderboard_snapshot_payload(
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*,
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server_id: str | None = None,
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timeframe: str = "weekly",
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metric: str = "kills",
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limit: int = 10,
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) -> dict[str, object]:
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"""Return an API payload for RCON-backed leaderboard snapshots.
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This is a runtime fast read over the materialized AdminLog tables. It intentionally
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avoids the old public-scoreboard fallback because the UI is running in RCON mode.
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"""
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normalized_timeframe = _normalize_timeframe(timeframe)
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normalized_metric = _normalize_metric(metric)
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result = list_rcon_materialized_leaderboard(
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server_key=server_id,
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timeframe=normalized_timeframe,
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metric=normalized_metric,
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limit=limit,
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)
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items = list(result.get("items") or [])[:limit]
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return {
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"status": "ok",
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"data": {
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"title": _build_title(
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metric=normalized_metric,
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timeframe=normalized_timeframe,
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server_id=server_id,
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),
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"context": f"historical-{normalized_timeframe}-leaderboard-snapshot",
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"source": "rcon-materialized-admin-log-leaderboard",
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"server_slug": server_id,
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"timeframe": normalized_timeframe,
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"metric": normalized_metric,
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"found": True,
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"snapshot_status": "ready",
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"missing_reason": None,
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"request_path_policy": "runtime-rcon-materialized-fast-path",
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"generation_policy": "runtime-materialized-read",
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"generated_at": _to_iso(datetime.now(timezone.utc)),
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"source_range_start": result.get("source_range_start"),
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"source_range_end": result.get("source_range_end"),
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"is_stale": False,
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"freshness": "runtime",
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"window_days": result.get("window_days"),
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"window_start": result.get("window_start"),
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"window_end": result.get("window_end"),
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"window_kind": result.get("window_kind"),
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"window_label": result.get("window_label"),
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"uses_fallback": False,
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"selection_reason": result.get("selection_reason"),
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"current_week_start": result.get("current_week_start"),
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"current_week_closed_matches": result.get("current_week_closed_matches"),
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"previous_week_closed_matches": result.get("previous_week_closed_matches"),
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"current_month_start": result.get("current_month_start"),
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"selected_month_start": result.get("selected_month_start"),
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"selected_month_end": result.get("selected_month_end"),
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"current_month_closed_matches": result.get("current_month_closed_matches"),
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"previous_month_closed_matches": result.get("previous_month_closed_matches"),
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"sufficient_sample": result.get("sufficient_sample"),
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"snapshot_limit": result.get("limit"),
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"limit": limit,
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"runtime_enrichment": {
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"applied": False,
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"reason": None,
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},
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"primary_source": "rcon",
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"selected_source": "rcon",
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"fallback_used": False,
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"fallback_reason": None,
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"source_attempts": [
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{
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"source": "rcon",
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"role": "primary",
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"status": "success",
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"reason": "leaderboard-served-by-rcon-materialized-admin-log",
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"message": None,
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}
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],
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"items": items,
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},
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}
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def list_rcon_materialized_leaderboard(
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*,
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server_key: str | None = None,
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timeframe: str = "weekly",
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metric: str = "kills",
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limit: int = 10,
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db_path: Path | None = None,
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now: datetime | None = None,
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) -> dict[str, object]:
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"""Return a leaderboard built from materialized RCON/AdminLog player stats.
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RCON/AdminLog materialization currently has reliable kill/death/teamkill counters,
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but not public-scoreboard support points. For support, return an explicitly empty
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supported payload rather than falling back to unrelated public scoreboard storage.
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"""
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normalized_timeframe = _normalize_timeframe(timeframe)
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normalized_metric = _normalize_metric(metric)
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normalized_limit = max(1, int(limit or 10))
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anchor = _as_utc(now or datetime.now(timezone.utc))
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resolved_path = initialize_rcon_materialized_storage(db_path=db_path)
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connection_scope = _connect_scope(resolved_path, db_path=db_path)
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with connection_scope as connection:
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window = select_leaderboard_window(
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connection=connection,
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server_key=server_key,
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timeframe=normalized_timeframe,
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now=anchor,
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)
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if normalized_metric == "support":
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return _empty_payload(
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server_key=server_key,
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timeframe=normalized_timeframe,
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metric=normalized_metric,
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limit=normalized_limit,
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window=window,
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reason="rcon-materialized-stats-do-not-include-support-score",
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)
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rows = _fetch_leaderboard_rows(
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connection,
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server_key=server_key,
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metric=normalized_metric,
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limit=normalized_limit,
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window_start=window["start"],
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window_end=window["end"],
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)
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source_range = _fetch_source_range(
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connection,
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server_key=server_key,
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window_start=window["start"],
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window_end=window["end"],
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)
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items = [_build_item(row, index=index + 1) for index, row in enumerate(rows)]
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return {
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"source": "rcon-materialized-admin-log-leaderboard",
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"server_key": server_key,
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"metric": normalized_metric,
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"limit": normalized_limit,
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"window_days": window["days"],
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"window_start": _to_iso(window["start"]),
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"window_end": _to_iso(window["end"]),
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"window_kind": window["kind"],
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"window_label": window["label"],
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"uses_fallback": False,
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"selection_reason": window["selection_reason"],
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"current_week_start": _to_iso(window["current_week_start"]),
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"current_week_closed_matches": window["current_week_closed_matches"],
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"previous_week_closed_matches": window["previous_week_closed_matches"],
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"current_month_start": _to_iso(window["current_month_start"]),
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"selected_month_start": _to_iso(window["selected_month_start"]),
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"selected_month_end": _to_iso(window["selected_month_end"]),
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"current_month_closed_matches": window["current_month_closed_matches"],
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"previous_month_closed_matches": window["previous_month_closed_matches"],
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"sufficient_sample": window["sufficient_sample"],
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"source_range_start": _to_iso(source_range[0]) if source_range[0] else None,
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"source_range_end": _to_iso(source_range[1]) if source_range[1] else None,
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"items": items,
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}
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def _find_latest_snapshot(
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*,
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connection: object,
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timeframe: str,
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server_key: str,
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metric: str,
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) -> dict[str, object] | None:
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row = connection.execute(
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"""
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SELECT
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id,
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timeframe,
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server_id,
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metric,
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window_start,
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window_end,
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generated_at,
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source,
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snapshot_status,
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item_count,
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limit_size,
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source_matches_count,
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freshness,
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window_kind,
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window_label
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FROM ranking_snapshots
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WHERE timeframe = ?
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AND server_id = ?
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AND metric = ?
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AND snapshot_status = 'ready'
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ORDER BY window_end DESC, generated_at DESC
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LIMIT 1
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""",
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[timeframe, server_key, metric],
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).fetchone()
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return dict(row) if row else None
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def _list_snapshot_items(
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*,
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connection: object,
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snapshot_id: int,
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limit: int | None = None,
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) -> list[dict[str, object]]:
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params: list[object] = [snapshot_id]
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query = """
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SELECT
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ranking_position,
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player_id,
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player_name,
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metric_value,
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matches_considered,
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kills,
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deaths,
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teamkills,
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kd_ratio,
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kills_per_match
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FROM ranking_snapshot_items
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WHERE snapshot_id = ?
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ORDER BY ranking_position ASC
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"""
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if limit is not None:
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query = f"{query}\n LIMIT ?"
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params.append(limit)
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rows = connection.execute(query, params).fetchall()
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return [dict(row) for row in rows]
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def _fetch_leaderboard_rows(
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connection: object,
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*,
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server_key: str | None,
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metric: str,
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limit: int,
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window_start: datetime,
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window_end: datetime,
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) -> list[dict[str, object]]:
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scope_sql, scope_params = _build_scope_sql(server_key)
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metric_sql, having_sql, order_by_sql = _resolve_metric_sql(metric)
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params: list[object] = [
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_to_iso(window_start),
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_to_iso(window_end),
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*scope_params,
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limit,
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]
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rows = connection.execute(
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f"""
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SELECT
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stats.player_id,
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stats.player_name,
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{metric_sql} AS metric_value,
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COUNT(DISTINCT stats.match_key) AS matches_considered,
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SUM(COALESCE(stats.kills, 0)) AS kills,
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SUM(COALESCE(stats.deaths, 0)) AS deaths,
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SUM(COALESCE(stats.teamkills, 0)) AS teamkills
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FROM rcon_match_player_stats AS stats
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INNER JOIN rcon_materialized_matches AS matches
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ON matches.target_key = stats.target_key
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AND matches.match_key = stats.match_key
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WHERE matches.source_basis = ?
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AND COALESCE(CAST(matches.ended_at AS TEXT), CAST(matches.started_at AS TEXT)) >= ?
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AND COALESCE(CAST(matches.ended_at AS TEXT), CAST(matches.started_at AS TEXT)) <= ?
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{scope_sql}
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AND TRIM(COALESCE(stats.player_name, '')) != ''
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GROUP BY stats.player_id, stats.player_name
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{having_sql}
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ORDER BY {order_by_sql}
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LIMIT ?
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""",
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[MATCH_RESULT_SOURCE, *params],
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).fetchall()
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return [dict(row) for row in rows]
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def _fetch_match_counts(
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connection: object,
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*,
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server_key: str | None,
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timeframe: str,
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window_start: datetime,
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window_end: datetime,
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) -> dict[str, int]:
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current_week_start = _week_start(window_end)
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previous_week_start = current_week_start - timedelta(days=7)
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current_month_start = _month_start(window_end)
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previous_month_start = _previous_month_start(current_month_start)
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return {
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"current_week_closed_matches": _count_matches(
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connection,
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server_key=server_key,
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start=current_week_start,
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end=window_end,
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),
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"previous_week_closed_matches": _count_matches(
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connection,
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server_key=server_key,
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start=previous_week_start,
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end=current_week_start,
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),
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"current_month_closed_matches": _count_matches(
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connection,
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server_key=server_key,
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start=current_month_start,
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end=window_end,
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),
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"previous_month_closed_matches": _count_matches(
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connection,
|
|
server_key=server_key,
|
|
start=previous_month_start,
|
|
end=current_month_start,
|
|
),
|
|
}
|
|
|
|
|
|
def select_leaderboard_window(
|
|
*,
|
|
connection: object,
|
|
server_key: str | None,
|
|
timeframe: str,
|
|
now: datetime | None = None,
|
|
) -> dict[str, object]:
|
|
"""Select the RCON leaderboard window using weekly/monthly fallback policy."""
|
|
anchor = _as_utc(now or datetime.now(timezone.utc))
|
|
current_week_start = _week_start(anchor)
|
|
previous_week_start = current_week_start - timedelta(days=7)
|
|
current_month_start = _month_start(anchor)
|
|
previous_month_start = _previous_month_start(current_month_start)
|
|
minimum_week_matches = get_historical_weekly_fallback_min_matches()
|
|
current_week_count = _count_matches(
|
|
connection,
|
|
server_key=server_key,
|
|
start=current_week_start,
|
|
end=anchor,
|
|
)
|
|
previous_week_count = _count_matches(
|
|
connection,
|
|
server_key=server_key,
|
|
start=previous_week_start,
|
|
end=current_week_start,
|
|
)
|
|
current_month_count = _count_matches(
|
|
connection,
|
|
server_key=server_key,
|
|
start=current_month_start,
|
|
end=anchor,
|
|
)
|
|
previous_month_count = _count_matches(
|
|
connection,
|
|
server_key=server_key,
|
|
start=previous_month_start,
|
|
end=current_month_start,
|
|
)
|
|
|
|
if timeframe == "monthly":
|
|
use_previous_month = anchor.day <= 7
|
|
start = previous_month_start if use_previous_month else current_month_start
|
|
end = current_month_start if use_previous_month else anchor
|
|
return {
|
|
"start": start,
|
|
"end": end,
|
|
"days": max(1, (end.date() - start.date()).days),
|
|
"kind": "previous-month" if use_previous_month else "current-month",
|
|
"label": "Mes anterior" if use_previous_month else "Mes actual",
|
|
"selection_reason": (
|
|
"monthly-uses-previous-month-until-day-8"
|
|
if use_previous_month
|
|
else "monthly-uses-current-month-after-day-7"
|
|
),
|
|
"current_week_start": current_week_start,
|
|
"current_week_closed_matches": current_week_count,
|
|
"previous_week_closed_matches": previous_week_count,
|
|
"current_month_start": current_month_start,
|
|
"selected_month_start": start,
|
|
"selected_month_end": end,
|
|
"current_month_closed_matches": current_month_count,
|
|
"previous_month_closed_matches": previous_month_count,
|
|
"sufficient_sample": {
|
|
"minimum_closed_matches": 1,
|
|
"current_month_closed_matches": current_month_count,
|
|
"previous_month_closed_matches": previous_month_count,
|
|
"current_month_has_sufficient_sample": current_month_count >= 1,
|
|
"uses_previous_month_until_day": 7,
|
|
},
|
|
}
|
|
|
|
current_week_has_sample = current_week_count >= minimum_week_matches
|
|
start = current_week_start if current_week_has_sample else previous_week_start
|
|
end = anchor if current_week_has_sample else current_week_start
|
|
return {
|
|
"start": start,
|
|
"end": end,
|
|
"days": max(1, (end.date() - start.date()).days),
|
|
"kind": "current-week" if current_week_has_sample else "previous-week",
|
|
"label": "Semana actual" if current_week_has_sample else "Semana anterior",
|
|
"selection_reason": (
|
|
"weekly-current-week-has-sufficient-closed-matches"
|
|
if current_week_has_sample
|
|
else "weekly-fallback-previous-week-insufficient-current-week-data"
|
|
),
|
|
"current_week_start": current_week_start,
|
|
"current_week_closed_matches": current_week_count,
|
|
"previous_week_closed_matches": previous_week_count,
|
|
"current_month_start": current_month_start,
|
|
"selected_month_start": current_month_start,
|
|
"selected_month_end": anchor,
|
|
"current_month_closed_matches": current_month_count,
|
|
"previous_month_closed_matches": previous_month_count,
|
|
"sufficient_sample": {
|
|
"minimum_closed_matches": minimum_week_matches,
|
|
"current_week_closed_matches": current_week_count,
|
|
"current_week_has_sufficient_sample": current_week_has_sample,
|
|
"previous_week_closed_matches": previous_week_count,
|
|
},
|
|
}
|
|
|
|
|
|
def _fetch_source_range(
|
|
connection: object,
|
|
*,
|
|
server_key: str | None,
|
|
window_start: datetime,
|
|
window_end: datetime,
|
|
) -> tuple[datetime | None, datetime | None]:
|
|
scope_sql, scope_params = _build_scope_sql(server_key, table_alias="matches")
|
|
row = connection.execute(
|
|
f"""
|
|
SELECT
|
|
MIN(COALESCE(CAST(matches.ended_at AS TEXT), CAST(matches.started_at AS TEXT))) AS source_range_start,
|
|
MAX(COALESCE(CAST(matches.ended_at AS TEXT), CAST(matches.started_at AS TEXT))) AS source_range_end
|
|
FROM rcon_materialized_matches AS matches
|
|
WHERE matches.source_basis = ?
|
|
AND COALESCE(CAST(matches.ended_at AS TEXT), CAST(matches.started_at AS TEXT)) >= ?
|
|
AND COALESCE(CAST(matches.ended_at AS TEXT), CAST(matches.started_at AS TEXT)) <= ?
|
|
{scope_sql}
|
|
""",
|
|
[MATCH_RESULT_SOURCE, _to_iso(window_start), _to_iso(window_end), *scope_params],
|
|
).fetchone()
|
|
if not row:
|
|
return None, None
|
|
return _parse_datetime(row["source_range_start"]), _parse_datetime(row["source_range_end"])
|
|
|
|
|
|
def _count_matches(
|
|
connection: object,
|
|
*,
|
|
server_key: str | None,
|
|
start: datetime,
|
|
end: datetime,
|
|
) -> int:
|
|
scope_sql, scope_params = _build_scope_sql(server_key, table_alias="matches")
|
|
row = connection.execute(
|
|
f"""
|
|
SELECT COUNT(*) AS count
|
|
FROM rcon_materialized_matches AS matches
|
|
WHERE matches.source_basis = ?
|
|
AND COALESCE(CAST(matches.ended_at AS TEXT), CAST(matches.started_at AS TEXT)) >= ?
|
|
AND COALESCE(CAST(matches.ended_at AS TEXT), CAST(matches.started_at AS TEXT)) < ?
|
|
{scope_sql}
|
|
""",
|
|
[MATCH_RESULT_SOURCE, _to_iso(start), _to_iso(end), *scope_params],
|
|
).fetchone()
|
|
return int(row["count"] or 0) if row else 0
|
|
|
|
|
|
def _build_item(row: dict[str, object], *, index: int) -> dict[str, object]:
|
|
kills = _coerce_int(row.get("kills"))
|
|
deaths = _coerce_int(row.get("deaths"))
|
|
matches_considered = _coerce_int(row.get("matches_considered"))
|
|
kd_ratio = round(kills / deaths, 2) if deaths else float(kills)
|
|
kills_per_match = round(kills / matches_considered, 2) if matches_considered else 0.0
|
|
return {
|
|
"ranking_position": index,
|
|
"player": {
|
|
"id": row.get("player_id"),
|
|
"name": row.get("player_name"),
|
|
},
|
|
"player_id": row.get("player_id"),
|
|
"player_name": row.get("player_name"),
|
|
"metric_value": _coerce_metric_value(row.get("metric_value")),
|
|
"matches_considered": matches_considered,
|
|
"kills": kills,
|
|
"deaths": deaths,
|
|
"teamkills": _coerce_int(row.get("teamkills")),
|
|
"kd_ratio": kd_ratio,
|
|
"kills_per_match": kills_per_match,
|
|
}
|
|
|
|
|
|
def _build_scope_sql(
|
|
server_key: str | None,
|
|
*,
|
|
table_alias: str = "matches",
|
|
) -> tuple[str, list[object]]:
|
|
if not server_key or server_key == ALL_SERVERS_SLUG:
|
|
return "", []
|
|
return f"AND ({table_alias}.target_key = ? OR {table_alias}.external_server_id = ?)", [
|
|
server_key,
|
|
server_key,
|
|
]
|
|
|
|
|
|
def _normalize_snapshot_server_key(server_key: str | None) -> str:
|
|
normalized = str(server_key or "").strip()
|
|
normalized_lower = normalized.lower()
|
|
if not normalized or normalized_lower in {ALL_SERVERS_SLUG, "all"}:
|
|
return ALL_SERVERS_SLUG
|
|
return normalized
|
|
|
|
|
|
def _connect_scope(resolved_path: Path, *, db_path: Path | None):
|
|
if use_postgres_rcon_storage(explicit_sqlite_path=db_path):
|
|
from .postgres_rcon_storage import connect_postgres_compat
|
|
|
|
return connect_postgres_compat()
|
|
return closing(connect_sqlite_readonly(resolved_path))
|
|
|
|
|
|
def _empty_payload(
|
|
*,
|
|
server_key: str | None,
|
|
timeframe: str,
|
|
metric: str,
|
|
limit: int,
|
|
window: dict[str, object],
|
|
reason: str,
|
|
) -> dict[str, object]:
|
|
return {
|
|
"source": "rcon-materialized-admin-log-leaderboard",
|
|
"server_key": server_key,
|
|
"metric": metric,
|
|
"limit": limit,
|
|
"window_days": window["days"],
|
|
"window_start": _to_iso(window["start"]),
|
|
"window_end": _to_iso(window["end"]),
|
|
"window_kind": window["kind"],
|
|
"window_label": window["label"],
|
|
"uses_fallback": False,
|
|
"selection_reason": reason,
|
|
"current_week_start": _to_iso(window["current_week_start"]),
|
|
"current_week_closed_matches": window["current_week_closed_matches"],
|
|
"previous_week_closed_matches": window["previous_week_closed_matches"],
|
|
"current_month_start": _to_iso(window["current_month_start"]),
|
|
"selected_month_start": _to_iso(window["selected_month_start"]),
|
|
"selected_month_end": _to_iso(window["selected_month_end"]),
|
|
"current_month_closed_matches": window["current_month_closed_matches"],
|
|
"previous_month_closed_matches": window["previous_month_closed_matches"],
|
|
"sufficient_sample": window["sufficient_sample"],
|
|
"source_range_start": None,
|
|
"source_range_end": None,
|
|
"items": [],
|
|
}
|
|
|
|
|
|
def _build_window(timeframe: str) -> dict[str, object]:
|
|
now = datetime.now(timezone.utc)
|
|
if timeframe == "monthly":
|
|
start = _month_start(now)
|
|
return {
|
|
"start": start,
|
|
"end": now,
|
|
"days": max(1, (now.date() - start.date()).days + 1),
|
|
"kind": "current-month",
|
|
"label": "Mes actual",
|
|
}
|
|
start = _week_start(now)
|
|
return {
|
|
"start": start,
|
|
"end": now,
|
|
"days": max(1, (now.date() - start.date()).days + 1),
|
|
"kind": "current-week",
|
|
"label": "Semana actual",
|
|
}
|
|
|
|
|
|
def _as_utc(value: datetime) -> datetime:
|
|
if value.tzinfo is None:
|
|
return value.replace(tzinfo=timezone.utc)
|
|
return value.astimezone(timezone.utc)
|
|
|
|
|
|
def _week_start(value: datetime) -> datetime:
|
|
point = value.astimezone(timezone.utc)
|
|
start = point - timedelta(days=point.weekday())
|
|
return start.replace(hour=0, minute=0, second=0, microsecond=0)
|
|
|
|
|
|
def _month_start(value: datetime) -> datetime:
|
|
point = value.astimezone(timezone.utc)
|
|
return point.replace(day=1, hour=0, minute=0, second=0, microsecond=0)
|
|
|
|
|
|
def _previous_month_start(current_month_start: datetime) -> datetime:
|
|
previous_month_end = current_month_start - timedelta(days=1)
|
|
return _month_start(previous_month_end)
|
|
|
|
|
|
def _normalize_timeframe(value: str) -> LeaderboardTimeframe:
|
|
return "monthly" if str(value or "").strip().lower() == "monthly" else "weekly"
|
|
|
|
|
|
def _normalize_metric(value: str) -> LeaderboardMetric:
|
|
normalized = str(value or "kills").strip().lower()
|
|
if normalized in {
|
|
"kills",
|
|
"deaths",
|
|
"teamkills",
|
|
"matches_considered",
|
|
"kd_ratio",
|
|
"kills_per_match",
|
|
"matches_over_100_kills",
|
|
"support",
|
|
}:
|
|
return normalized # type: ignore[return-value]
|
|
return "kills"
|
|
|
|
|
|
def _build_title(*, metric: str, timeframe: str, server_id: str | None) -> str:
|
|
timeframe_label = "mensual" if timeframe == "monthly" else "semanal"
|
|
scope = "totales" if server_id == ALL_SERVERS_SLUG else "por servidor"
|
|
metric_label = {
|
|
"kills": "Top kills",
|
|
"deaths": "Top muertes",
|
|
"teamkills": "Top teamkills",
|
|
"matches_considered": "Top partidas jugadas",
|
|
"kd_ratio": "Top K/D",
|
|
"kills_per_match": "Top kills por partida",
|
|
"matches_over_100_kills": "Partidas 100+ kills",
|
|
"support": "Top soporte",
|
|
}.get(metric, "Top kills")
|
|
return f"Snapshot {metric_label} {timeframe_label} {scope}"
|
|
|
|
|
|
def _coerce_int(value: object) -> int:
|
|
try:
|
|
return int(value or 0)
|
|
except (TypeError, ValueError):
|
|
return 0
|
|
|
|
|
|
def _coerce_metric_value(value: object) -> int | float:
|
|
numeric = _coerce_float(value)
|
|
if numeric.is_integer():
|
|
return int(numeric)
|
|
return round(numeric, 2)
|
|
|
|
|
|
def _coerce_float(value: object) -> float:
|
|
try:
|
|
return float(value or 0)
|
|
except (TypeError, ValueError):
|
|
return 0.0
|
|
|
|
|
|
def _resolve_metric_sql(metric: str) -> tuple[str, str, str]:
|
|
metric_sql_by_metric = {
|
|
"kills": "SUM(COALESCE(stats.kills, 0))",
|
|
"deaths": "SUM(COALESCE(stats.deaths, 0))",
|
|
"teamkills": "SUM(COALESCE(stats.teamkills, 0))",
|
|
"matches_considered": "COUNT(DISTINCT stats.match_key)",
|
|
"kd_ratio": (
|
|
"CASE "
|
|
"WHEN SUM(COALESCE(stats.deaths, 0)) > 0 "
|
|
"THEN ROUND(CAST(SUM(COALESCE(stats.kills, 0)) AS REAL) / SUM(COALESCE(stats.deaths, 0)), 2) "
|
|
"ELSE CAST(SUM(COALESCE(stats.kills, 0)) AS REAL) "
|
|
"END"
|
|
),
|
|
"kills_per_match": (
|
|
"CASE "
|
|
"WHEN COUNT(DISTINCT stats.match_key) > 0 "
|
|
"THEN ROUND(CAST(SUM(COALESCE(stats.kills, 0)) AS REAL) / COUNT(DISTINCT stats.match_key), 2) "
|
|
"ELSE 0.0 "
|
|
"END"
|
|
),
|
|
"matches_over_100_kills": "SUM(CASE WHEN COALESCE(stats.kills, 0) >= 100 THEN 1 ELSE 0 END)",
|
|
}
|
|
having_sql_by_metric = {
|
|
"kills": "HAVING SUM(COALESCE(stats.kills, 0)) > 0",
|
|
"deaths": "HAVING SUM(COALESCE(stats.deaths, 0)) > 0",
|
|
"teamkills": "HAVING SUM(COALESCE(stats.teamkills, 0)) > 0",
|
|
"matches_considered": "HAVING COUNT(DISTINCT stats.match_key) > 0",
|
|
"kd_ratio": "HAVING SUM(COALESCE(stats.kills, 0)) > 0",
|
|
"kills_per_match": "HAVING COUNT(DISTINCT stats.match_key) > 0 AND SUM(COALESCE(stats.kills, 0)) > 0",
|
|
"matches_over_100_kills": "HAVING SUM(CASE WHEN COALESCE(stats.kills, 0) >= 100 THEN 1 ELSE 0 END) > 0",
|
|
}
|
|
order_by_sql_by_metric = {
|
|
"kills": "metric_value DESC, matches_considered DESC, stats.player_name ASC",
|
|
"deaths": "metric_value DESC, matches_considered DESC, stats.player_name ASC",
|
|
"teamkills": "metric_value DESC, matches_considered DESC, stats.player_name ASC",
|
|
"matches_considered": "metric_value DESC, kills DESC, stats.player_name ASC",
|
|
"kd_ratio": "metric_value DESC, kills DESC, matches_considered DESC, stats.player_name ASC",
|
|
"kills_per_match": "metric_value DESC, kills DESC, matches_considered DESC, stats.player_name ASC",
|
|
"matches_over_100_kills": "metric_value DESC, matches_considered DESC, stats.player_name ASC",
|
|
}
|
|
if metric == "support":
|
|
raise ValueError("support is handled separately")
|
|
return (
|
|
metric_sql_by_metric[metric],
|
|
having_sql_by_metric[metric],
|
|
order_by_sql_by_metric[metric],
|
|
)
|
|
|
|
|
|
def _parse_datetime(value: object) -> datetime | None:
|
|
if isinstance(value, datetime):
|
|
parsed = value
|
|
elif isinstance(value, str) and value.strip():
|
|
try:
|
|
parsed = datetime.fromisoformat(value.strip().replace("Z", "+00:00"))
|
|
except ValueError:
|
|
return None
|
|
else:
|
|
return None
|
|
if parsed.tzinfo is None:
|
|
parsed = parsed.replace(tzinfo=timezone.utc)
|
|
return parsed.astimezone(timezone.utc)
|
|
|
|
|
|
def _to_iso(value: object) -> str:
|
|
parsed = _parse_datetime(value)
|
|
if parsed is None:
|
|
parsed = datetime.now(timezone.utc)
|
|
return parsed.astimezone(timezone.utc).isoformat().replace("+00:00", "Z")
|