fix: add rcon materialized leaderboard snapshots
This commit is contained in:
devRaGonSa
403
backend/app/rcon_historical_leaderboards.py
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403
backend/app/rcon_historical_leaderboards.py
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@@ -0,0 +1,403 @@
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"""Leaderboard read model over materialized RCON/AdminLog match stats."""
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from __future__ import annotations
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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 .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
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LeaderboardTimeframe = Literal["weekly", "monthly"]
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LeaderboardMetric = Literal["kills", "deaths", "matches_over_100_kills", "support"]
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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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) -> 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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window = _build_window(normalized_timeframe)
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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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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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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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counts = _fetch_match_counts(
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connection,
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server_key=server_key,
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timeframe=normalized_timeframe,
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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": "rcon-materialized-current-window",
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"current_week_start": _to_iso(_week_start(window["end"])),
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"current_week_closed_matches": counts["current_week_closed_matches"],
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"previous_week_closed_matches": counts["previous_week_closed_matches"],
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"current_month_start": _to_iso(_month_start(window["end"])),
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"current_month_closed_matches": counts["current_month_closed_matches"],
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"previous_month_closed_matches": counts["previous_month_closed_matches"],
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"sufficient_sample": {
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"minimum_closed_matches": 1,
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"current_week_closed_matches": counts["current_week_closed_matches"],
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"current_week_has_sufficient_sample": counts["current_week_closed_matches"] >= 1,
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"is_early_week": False,
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"fallback_max_weekday": 2,
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},
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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 _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 = {
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"kills": "SUM(COALESCE(stats.kills, 0))",
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"deaths": "SUM(COALESCE(stats.deaths, 0))",
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"matches_over_100_kills": "SUM(CASE WHEN COALESCE(stats.kills, 0) >= 100 THEN 1 ELSE 0 END)",
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}[metric]
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having_sql = f"HAVING {metric_sql} > 0"
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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 metric_value DESC, matches_considered DESC, stats.player_name ASC
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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,
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server_key=server_key,
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start=previous_month_start,
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end=current_month_start,
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),
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}
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def _fetch_source_range(
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connection: object,
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*,
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server_key: str | None,
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window_start: datetime,
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window_end: datetime,
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) -> tuple[datetime | None, datetime | None]:
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scope_sql, scope_params = _build_scope_sql(server_key, table_alias="matches")
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row = connection.execute(
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f"""
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SELECT
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MIN(COALESCE(CAST(matches.ended_at AS TEXT), CAST(matches.started_at AS TEXT))) AS source_range_start,
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MAX(COALESCE(CAST(matches.ended_at AS TEXT), CAST(matches.started_at AS TEXT))) AS source_range_end
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FROM rcon_materialized_matches AS matches
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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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""",
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[MATCH_RESULT_SOURCE, _to_iso(window_start), _to_iso(window_end), *scope_params],
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).fetchone()
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if not row:
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return None, None
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return _parse_datetime(row["source_range_start"]), _parse_datetime(row["source_range_end"])
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def _count_matches(
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connection: object,
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*,
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server_key: str | None,
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start: datetime,
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end: datetime,
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) -> int:
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scope_sql, scope_params = _build_scope_sql(server_key, table_alias="matches")
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row = connection.execute(
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f"""
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SELECT COUNT(*) AS count
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FROM rcon_materialized_matches AS matches
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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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""",
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[MATCH_RESULT_SOURCE, _to_iso(start), _to_iso(end), *scope_params],
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).fetchone()
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return int(row["count"] or 0) if row else 0
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def _build_item(row: dict[str, object], *, index: int) -> dict[str, object]:
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kills = _coerce_int(row.get("kills"))
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deaths = _coerce_int(row.get("deaths"))
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return {
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"ranking_position": index,
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"player": {
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"id": row.get("player_id"),
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"name": row.get("player_name"),
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},
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"player_id": row.get("player_id"),
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"player_name": row.get("player_name"),
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"metric_value": _coerce_int(row.get("metric_value")),
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"matches_considered": _coerce_int(row.get("matches_considered")),
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"kills": kills,
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"deaths": deaths,
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"teamkills": _coerce_int(row.get("teamkills")),
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"kd_ratio": round(kills / deaths, 2) if deaths else float(kills),
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}
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def _build_scope_sql(
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server_key: str | None,
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*,
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table_alias: str = "matches",
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) -> tuple[str, list[object]]:
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if not server_key or server_key == ALL_SERVERS_SLUG:
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return "", []
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return f"AND ({table_alias}.target_key = ? OR {table_alias}.external_server_id = ?)", [
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server_key,
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server_key,
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]
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def _connect_scope(resolved_path: Path, *, db_path: Path | None):
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if use_postgres_rcon_storage(explicit_sqlite_path=db_path):
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from .postgres_rcon_storage import connect_postgres_compat
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return connect_postgres_compat()
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return closing(connect_sqlite_readonly(resolved_path))
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def _empty_payload(
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*,
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server_key: str | None,
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timeframe: str,
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metric: str,
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limit: int,
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window: dict[str, object],
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reason: str,
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) -> dict[str, object]:
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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": metric,
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"limit": 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": reason,
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"current_week_start": _to_iso(_week_start(window["end"])),
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"current_week_closed_matches": 0,
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"previous_week_closed_matches": 0,
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"current_month_start": _to_iso(_month_start(window["end"])),
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"current_month_closed_matches": 0,
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"previous_month_closed_matches": 0,
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"sufficient_sample": {
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"minimum_closed_matches": 1,
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"current_week_closed_matches": 0,
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"current_week_has_sufficient_sample": False,
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"is_early_week": False,
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"fallback_max_weekday": 2,
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},
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"source_range_start": None,
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"source_range_end": None,
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"items": [],
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}
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def _build_window(timeframe: str) -> dict[str, object]:
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now = datetime.now(timezone.utc)
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if timeframe == "monthly":
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start = _month_start(now)
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return {
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"start": start,
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"end": now,
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"days": max(1, (now.date() - start.date()).days + 1),
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"kind": "current-month",
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"label": "Mes actual",
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}
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start = _week_start(now)
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return {
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"start": start,
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"end": now,
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"days": max(1, (now.date() - start.date()).days + 1),
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"kind": "current-week",
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"label": "Semana actual",
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}
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def _week_start(value: datetime) -> datetime:
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point = value.astimezone(timezone.utc)
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start = point - timedelta(days=point.weekday())
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return start.replace(hour=0, minute=0, second=0, microsecond=0)
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def _month_start(value: datetime) -> datetime:
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point = value.astimezone(timezone.utc)
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return point.replace(day=1, hour=0, minute=0, second=0, microsecond=0)
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def _previous_month_start(current_month_start: datetime) -> datetime:
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previous_month_end = current_month_start - timedelta(days=1)
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return _month_start(previous_month_end)
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def _normalize_timeframe(value: str) -> LeaderboardTimeframe:
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return "monthly" if str(value or "").strip().lower() == "monthly" else "weekly"
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def _normalize_metric(value: str) -> LeaderboardMetric:
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normalized = str(value or "kills").strip().lower()
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if normalized in {"kills", "deaths", "matches_over_100_kills", "support"}:
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return normalized # type: ignore[return-value]
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return "kills"
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def _coerce_int(value: object) -> int:
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try:
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return int(value or 0)
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except (TypeError, ValueError):
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return 0
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def _parse_datetime(value: object) -> datetime | None:
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if isinstance(value, datetime):
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parsed = value
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elif isinstance(value, str) and value.strip():
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try:
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parsed = datetime.fromisoformat(value.strip().replace("Z", "+00:00"))
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except ValueError:
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return None
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else:
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return None
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if parsed.tzinfo is None:
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parsed = parsed.replace(tzinfo=timezone.utc)
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return parsed.astimezone(timezone.utc)
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def _to_iso(value: object) -> str:
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parsed = _parse_datetime(value)
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if parsed is None:
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parsed = datetime.now(timezone.utc)
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return parsed.astimezone(timezone.utc).isoformat().replace("+00:00", "Z")
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