lekss361
2a38561175
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193 lines
8.4 KiB
Python
193 lines
8.4 KiB
Python
"""SQL-билдеры страницы «доля квартир дома в продаже» (мигр. 143; окно 45д мигр. 146).
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Читаем готовый view v_building_sale_share — логику доли НЕ пересчитываем.
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Два процента на дом:
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- sale_share_pct — active_secondary / flat_count_effective (только активные);
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- sale_share_pct_45d — distinct vtorichka за последние 45д (активные ИЛИ недавно
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закрытые) / flat_count_effective.
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Дом проходит фильтр, если ЛЮБОЙ из двух процентов в диапазоне [min_pct, max_pct].
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Все запросы параметризованы (SQLAlchemy text + bind params, CAST(:x AS type) —
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psycopg v3). Сортировка/колонка гистограммы — строго по whitelist, user-ввод в SQL
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не интерполируется.
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"""
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from __future__ import annotations
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# share_desc / share_45d_desc / active_desc / count_45d_desc / exposure_desc /
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# price_asc / price_desc → безопасный ORDER BY. house_id как стабильный tiebreaker
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# для детерминированной пагинации.
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_SORT_SQL: dict[str, str] = {
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"share_desc": "sale_share_pct DESC NULLS LAST, house_id",
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"share_45d_desc": "sale_share_pct_45d DESC NULLS LAST, house_id",
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"active_desc": "active_secondary DESC NULLS LAST, house_id",
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"count_45d_desc": "listings_45d DESC NULLS LAST, house_id",
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"exposure_desc": "avg_days_on_market DESC NULLS LAST, house_id",
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"price_asc": "median_price_rub ASC NULLS LAST, house_id",
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"price_desc": "median_price_rub DESC NULLS LAST, house_id",
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}
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ALLOWED_SORTS: frozenset[str] = frozenset(_SORT_SQL)
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# Гистограмма распределения процента — фиксированный порядок корзин.
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HISTOGRAM_BUCKETS: tuple[str, ...] = (
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"0-5",
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"5-10",
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"10-20",
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"20-30",
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"30-50",
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"50-100",
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"100+",
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)
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# Колонки view, по которым можно строить гистограмму (whitelist против инъекции).
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_HISTOGRAM_COLUMNS: frozenset[str] = frozenset({"sale_share_pct", "sale_share_pct_45d"})
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# Колонки view, отдаваемые в список (в порядке SELECT). median_* округляем до
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# bigint — percentile_cont отдаёт double precision (может быть x.5), а Pydantic
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# int-поле на дробном float падает. over_100 — implausible-флаг по любому из %.
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_LIST_COLUMNS = (
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"house_id, "
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"COALESCE(short_address, full_address, address) AS address, "
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"lat, lon, "
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"sale_share_pct, "
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"sale_share_pct_45d, "
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"(sale_share_pct > 100 OR sale_share_pct_45d > 100) AS over_100, "
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"active_secondary, "
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"listings_45d, "
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"flat_count_effective, "
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"gar_match_method, "
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"CAST(round(median_price_rub) AS bigint) AS median_price_rub, "
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"CAST(round(median_price_per_m2) AS bigint) AS median_price_per_m2, "
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"avg_days_on_market, "
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"year_built, house_type, total_floors, series_name, is_emergency"
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)
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def build_sale_share_query(
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*,
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min_pct: float = 5.0,
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max_pct: float | None = None,
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city: str | None = None,
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price_min: int | None = None,
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price_max: int | None = None,
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year_min: int | None = None,
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year_max: int | None = None,
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house_type: str | None = None,
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sort: str = "share_desc",
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limit: int = 200,
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) -> tuple[str, dict[str, object]]:
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"""SELECT домов из v_building_sale_share по фильтрам.
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Дом проходит, если ЛЮБОЙ из двух процентов (now / 45д) попадает в диапазон
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[min_pct, max_pct]. NULL-процент (неправдоподобный знаменатель) не проходит
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сравнение, поэтому отдельный `IS NOT NULL` не нужен — рядки без обоих % выпадают.
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"""
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where: list[str] = []
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args: dict[str, object] = {}
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# Каждая ветка требует, чтобы ИМЕННО её % был в диапазоне; building qualifies
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# on either. Без max_pct сводится к `(pct_now>=min OR pct_45d>=min)`.
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args["min_pct"] = min_pct
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qual_now = "sale_share_pct >= CAST(:min_pct AS numeric)"
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qual_45d = "sale_share_pct_45d >= CAST(:min_pct AS numeric)"
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if max_pct is not None:
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args["max_pct"] = max_pct
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qual_now += " AND sale_share_pct <= CAST(:max_pct AS numeric)"
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qual_45d += " AND sale_share_pct_45d <= CAST(:max_pct AS numeric)"
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where.append(f"(({qual_now}) OR ({qual_45d}))")
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if city:
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# case-insensitive substring по всем адресным полям (город может быть в любом).
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where.append(
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"(COALESCE(short_address, '') || ' ' || COALESCE(full_address, '') "
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"|| ' ' || COALESCE(address, '')) ILIKE CAST(:city_like AS text)"
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)
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args["city_like"] = f"%{city}%"
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if price_min is not None:
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where.append("median_price_rub >= CAST(:price_min AS bigint)")
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args["price_min"] = price_min
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if price_max is not None:
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where.append("median_price_rub <= CAST(:price_max AS bigint)")
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args["price_max"] = price_max
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if year_min is not None:
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where.append("year_built >= CAST(:year_min AS integer)")
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args["year_min"] = year_min
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if year_max is not None:
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where.append("year_built <= CAST(:year_max AS integer)")
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args["year_max"] = year_max
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if house_type:
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where.append("house_type = CAST(:house_type AS text)")
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args["house_type"] = house_type
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order_sql = _SORT_SQL.get(sort, _SORT_SQL["share_desc"])
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args["limit"] = max(1, min(int(limit), 1000))
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sql = (
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f"SELECT {_LIST_COLUMNS} "
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"FROM v_building_sale_share "
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f"WHERE {' AND '.join(where)} "
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f"ORDER BY {order_sql} "
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"LIMIT CAST(:limit AS integer)"
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)
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return sql, args
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def build_listings_query(house_id: int) -> tuple[str, dict[str, object]]:
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"""Активные вторичные объявления одного дома (ORDER BY price_rub)."""
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sql = (
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"SELECT id AS listing_id, source, source_url, price_rub, price_per_m2, "
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"rooms, CAST(area_m2 AS double precision) AS area_m2, floor, total_floors, "
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"days_on_market, listing_date, address "
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"FROM listings "
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"WHERE house_id_fk = CAST(:house_id AS bigint) "
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"AND is_active AND listing_segment = 'vtorichka' "
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"ORDER BY price_rub ASC NULLS LAST, id"
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)
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return sql, {"house_id": house_id}
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def build_summary_scalars_query() -> str:
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"""Скаляры сводки: всего домов, с знаменателем (любой %), coverage, max/p95 обоих %."""
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return (
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"SELECT "
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"count(*) AS total_secondary_buildings, "
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"count(*) FILTER (WHERE sale_share_pct IS NOT NULL OR sale_share_pct_45d IS NOT NULL) "
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"AS buildings_with_denominator, "
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"COALESCE(round("
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"100.0 * count(*) FILTER "
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"(WHERE sale_share_pct IS NOT NULL OR sale_share_pct_45d IS NOT NULL) "
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"/ NULLIF(count(*), 0), 1), 0) AS coverage_pct, "
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"max(sale_share_pct) AS max_pct, "
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"percentile_cont(0.95) WITHIN GROUP (ORDER BY sale_share_pct) AS p95_pct, "
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"max(sale_share_pct_45d) AS max_pct_45d, "
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"percentile_cont(0.95) WITHIN GROUP (ORDER BY sale_share_pct_45d) AS p95_pct_45d "
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"FROM v_building_sale_share"
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)
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def build_summary_histogram_query(column: str = "sale_share_pct") -> str:
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"""Гистограмма по фиксированным корзинам (только non-null) для одного из двух %.
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column — whitelist {sale_share_pct, sale_share_pct_45d}; чужое → fallback на
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sale_share_pct (user-ввод в SQL не интерполируется).
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"""
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col = column if column in _HISTOGRAM_COLUMNS else "sale_share_pct"
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return (
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"SELECT "
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"CASE "
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f"WHEN {col} < 5 THEN '0-5' "
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f"WHEN {col} < 10 THEN '5-10' "
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f"WHEN {col} < 20 THEN '10-20' "
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f"WHEN {col} < 30 THEN '20-30' "
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f"WHEN {col} < 50 THEN '30-50' "
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f"WHEN {col} <= 100 THEN '50-100' "
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"ELSE '100+' "
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"END AS bucket, "
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"count(*) AS count "
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"FROM v_building_sale_share "
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f"WHERE {col} IS NOT NULL "
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"GROUP BY bucket"
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)
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