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235 commits

Author SHA1 Message Date
3a13bce468 fix(forecasting): savepoint db.execute failures to stop §22 session poisoning (#2464)
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One layer's caught DB error left the shared §22 report Session in Postgres's
"aborted transaction" state with no rollback, silently emptying every later
layer on the same report. Wrap each failure point in db.begin_nested()
(SAVEPOINT): orchestrator._safe_call, macro_series _query_key_rate_monthly /
_query_inflation_monthly / _query_mortgage_monthly (per indicator),
special_indices._run (per index), and sales_series _query_source_a /
_query_source_b — so a sibling builder's failure no longer cascades.

Also close the RELEASE-SAVEPOINT trap: inner helpers that swallowed a
db.execute failure without their own savepoint left the tx aborted, so the
outer builder's savepoint failed at RELEASE (illegal in an aborted tx) and
poisoning still cascaded. Add savepoints at the inner catch sites —
special_indices._query_parcel_centroid and district_resolver._admin_names /
resolve_objective_districts.

Refs #2464 (cluster A session-poisoning).
2026-07-07 17:50:17 +05:00
53b160ff0a fix(scrapers): rate-limit/backoff/circuit-breaker для ОКН EGRKN-скрапера (#2445-D3) (#2461)
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2026-07-05 20:09:18 +00:00
464519c69b fix(workers): izyatie per-doc isolation + newbuilding-crossload breadcrumb (#2445-D5/D6) (#2460)
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2026-07-05 20:09:12 +00:00
7315d04396 fix(scrapers): WAF-guard эндпоинтов + throttled catalog BrowserSession (#2445-D1/D2) (#2459)
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2026-07-05 20:09:06 +00:00
be4b2ee1fa fix(export): недостающая §13.7 «Уверенность» в PDF-экспортёре отчёта (#2445-C2) (#2456)
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2026-07-05 19:40:38 +00:00
f3988add0a fix(generative): устаревший VAT/office caveat в concept PDF (#2445-C1) (#2455)
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2026-07-05 19:40:30 +00:00
1bbd4c99e2 fix(generative): отклонять спроецированные координаты до pyproj CRSError → 422 (#2445-B1) (#2453)
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2026-07-05 19:20:19 +00:00
82e0b8b0a3 fix(site-finder): true-counts вместо молча-усечённых len(list) в connection-points/utility (#2445-A1/A2) (#2450)
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2026-07-05 18:54:49 +00:00
c8c96a87ee fix(site-finder): competitors avg-price по per-object снапшоту, не глобальному (#2445-A5) (#2447)
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2026-07-05 18:54:36 +00:00
9b6aba42dc fix(site-finder): L2 hidden-supply не обнуляет неизвестный flat_count (#2445-A3) (#2446)
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2026-07-05 18:54:14 +00:00
5b3799a23a feat(scrapers): wire per-flat DOM.РФ catalog scraper + propagate catalog_url_hash (#2442) (#2444)
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2026-07-05 17:00:58 +00:00
eea9c99e73 feat(scrapers): extract + download DOM.РФ flat plan images (#2440) (#2441)
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2026-07-05 16:23:29 +00:00
27868a4d2e feat(exporters): зеркалим permits_nearby (ГИСОГД 500м) в DOCX §6 (#2421)
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2026-07-04 13:28:00 +00:00
b3a59b7cf1 feat(gisogd66): permits_nearby — точный радиус-запрос РНС/РВЭ (#2367 PR-2, backend-half) (#2401)
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2026-07-04 09:59:49 +00:00
57d156973e feat(chat): tool get_parcel_info — курируемый градрегламент/ЗОУИТ/ЕГРН в контекст чата (#2366)
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2026-07-04 01:24:42 +00:00
6468bd6b37 fix(report-pdf): best-effort inline §22-форсайт на холодном участке — отчёт самодостаточен (#2357)
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2026-07-04 00:30:12 +00:00
27e21f37d3 fix(generative): MultiPolygon-участки — крупнейший контур в _parse_polygon (#2295) (#2315)
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2026-07-03 21:13:00 +00:00
c129515ab8 feat(exporters): DOCX-вариант полного отчёта ПТИЦА (#2259 R2) (#2301)
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2026-07-03 20:21:19 +00:00
b902edd44c fix(full-report): generic-маркеры областных админ-единиц в ЕКБ-фильтре тепла (#2297)
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2026-07-03 19:59:58 +00:00
e9e3cc7dcf fix(full-report): §7 на MultiPolygon + чистка печатного шума §3/§4 (вычитка живого отчёта) (#2296)
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2026-07-03 19:33:11 +00:00
034d9f9bca feat(reports): Celery-джоба полного PDF-отчёта + кэш + 3 endpoint'а (#2259 PR-D) (#2287)
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2026-07-03 11:13:06 +00:00
0b3ec7f91b feat(exporters): серверный статик-рендер карт для PDF-отчёта (#2259 PR-C Maps) (#2286)
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2026-07-03 10:29:23 +00:00
e7881cf45c feat(exporters): PURE HTML §4–§7 полного PDF-отчёта (#2259 PR-B Sections) (#2285)
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2026-07-03 10:10:43 +00:00
c0c18f4ecc feat(exporters): PURE HTML-агрегатор §1–§3 + альтернативы (#2259 PR-A Foundation) (#2284)
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2026-07-03 09:52:32 +00:00
99ee09e230 feat(site-finder): теплотрассы из OSM в слой инженерной инфраструктуры (Фаза C1) (#2280)
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2026-07-03 08:05:44 +00:00
dfc1ee8be0 feat: ревью-инструмент авто-маппингов Объектив↔ДОМ.РФ (/admin/mapping-review) (#2278)
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2026-07-03 07:59:13 +00:00
55b13e2b13 fix(dadata): фолбэк на suggest/address при отключённой фиче CLEAN (#2250)
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2026-07-03 03:42:40 +00:00
830d9f429a fix(etl): гео-проход — префикс «г Екатеринбург» адресам без города (#2177 остаток) (#2245)
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2026-07-03 01:41:19 +00:00
031bafde86 feat(etl): гео-проход auto_core_geo_v6 для tier_b/ambiguous mapping (#2177 остаток) (#2244)
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2026-07-03 01:19:59 +00:00
1bba3db7eb fix(best-layouts): lots-проекты в пуле core-матчинга — supply-only реально наполняется (#2177) (#2241)
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2026-07-03 00:14:19 +00:00
9cfefc0ca4 feat(site-finder): supply-only fallback в §4.2 — предложение без темпа продаж (#2177 шаг 3) (#2240)
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2026-07-02 23:18:42 +00:00
fc55c2c0b7 feat(best-layouts): атрибуция velocity по ядру комплекса (#2177 шаг 2b) (#2239)
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2026-07-02 22:41:25 +00:00
e9c8d05e1d feat(etl): auto_core_dev_v5 — core-name+dev backfill mapping'а (#2177 шаг 2) (#2237)
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2026-07-02 21:59:16 +00:00
ae483468c9 feat(site-finder): оптимизатор программ — «Как участок сходится» при отрицательном вердикте (#2181) (#2222)
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2026-07-02 20:57:12 +00:00
b028377584 fix(best-layouts): покрытие §4.2 в комплексах, не в сырых obj_id (#2177, шаг 1/3) (#2198)
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2026-07-02 19:34:11 +00:00
a3db6e4158 feat(site-finder): §4.3 тренд из цен предложения Объектива при устаревших сделках (#2178) (#2190)
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2026-07-02 18:33:16 +00:00
02b5f0b5d1 feat(site-finder): max-info попапы — конкурент / §3-таблица / польз.точка / соседи (#2111) (#2113)
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2026-06-30 13:45:43 +00:00
6d6e0ce0ca feat(site-finder): §3 «Сети и точки подключения» — попапы показывают характеристики (#2111) (#2112)
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2026-06-30 13:16:07 +00:00
53cfd3c3b8 feat(networks): человеко-язык типов §3 + реальные имена НСПД-точек (#1953) (#2107)
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2026-06-30 11:34:29 +00:00
975c400536 feat(section7): editable section footprint + корпус plural/program hint (#1953) (#2102)
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2026-06-30 09:48:11 +00:00
c8e7cb1c79 perf(objective_lots): inline DISTINCT ON for 3 view-fullscan consumers + cache landing stats (#1953) (#2067)
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2026-06-28 17:25:41 +00:00
94d01ed066 perf(objective_lots): inline-pushdown competitors._SOLD_COUNT_SQL + covering physflat index (#1953) (#2054)
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2026-06-28 14:07:49 +00:00
3421b21632 fix(scrapers): разблокировать domrf_kn flats — изоляция flats от poison-extras + throttle (#1945) (#2050)
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2026-06-28 13:03:43 +00:00
74f1ffb500 perf(objective): request-path consumers dedup inline, not via whole-table view (#1964)
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deep-review #1964: v_objective_lots_latest has NO premise/district filter inside,
so a consumer's outer WHERE cannot push below DISTINCT ON → the view materializes
the WHOLE table (Parallel Seq Scan + external Sort 1.76M rows, ~55MB spill) on
every query. For REQUEST-PATH consumers inside analyze_parcel this is a ~19x latency
regression vs the pre-#1964 raw-table plan.

DISPROVEN remedy (NOT applied): a full index on the physflat-key does NOT help —
DISTINCT ON selects ol.* (51 cols, width≈945) so index-only-unique is impossible;
the planner ignores the index (seq-scan+sort still cheaper) and even forced it is
~3.9 s. A 142MB index for zero request-path benefit + slower bulk-INSERT during
objective-scrape is wrong. Honors original #1964 decision "no new index".

Prod EXPLAIN (Академический / 3km radius, 2026-06-28):
  consumer            via view      inline (this commit)
  concepts median     5854 ms       1640 ms   (bitmap district + sort)
  parcels district    5854 ms       1640 ms
  parcels geo-median  6443 ms        122 ms   (NestedLoop geo->complex bitmap)
  parcels obj_pricing 5721 ms        441 ms   (project bitmap per nearby ЖК)

FIX: keep v_objective_lots_latest ONLY for batch/background/cached consumers
(supply_layers L1, competitors._SOLD_COUNT_SQL, special_indices [/forecast bg task
30-180s], admin, landing). Revert the 4 request-path consumers inside analyze_parcel
to inline DISTINCT ON (physflat-key, latest snapshot) with the filter pushed INTO the
CTE so the district/spatial/project index applies:
- concepts._OBJECTIVE_MEDIAN_SQL
- parcels.py district price block
- parcels.py geo-radius median (complex_id-scoped)
- parcels.py obj_pricing CTE (project_name-scoped; aggregates over deduped set)

Migration 175 header CORRECTED: accurately states the partial mig-173 index does NOT
serve the view (qual can't push below DISTINCT ON), the full index is disproven/not
added, and which consumers use the view vs inline. No DDL change (still view-only).

Tests: +guards (concepts/obj_pricing dedup inline, not view; obj_pricing physflat
DISTINCT ON; perf-pushdown scope preserved). 965 passed.
2026-06-28 05:00:47 +05:00
e67cb721bf fix(objective): physflat-dedup current-state consumers + honest n_sold window (#1964)
objective_lots — current-state UPSERT (UNIQUE objective_lot_id, 5 snapshot_date),
но Объектив присваивает ОДНОМУ физлоту (project,corpus,section,floor,lot_number)
несколько lot_id за пере-листинги → таблица раздута ~2.91× (прод: 1.75M квартир-
строк vs 603k физлотов). История — в отдельной objective_lots_history (не трогаем).

STEP 1: миграция 175 — VIEW v_objective_lots_latest (DISTINCT ON physflat-ключ,
последний снапшот). ol.* стабилен (51==51 колонок). DRY-RUN на проде: 603 049
квартир vs 1 753 283 raw.

STEP 2: репойнт current-state консьюмеров objective_lots → v_objective_lots_latest:
- supply_layers._L1_OPEN_SQL (L1 открытое предложение → дефицит-форсайт; прод:
  Юго-Западный комфорт 58 606 → 12 620)
- competitors._SOLD_COUNT_SQL (+ комментарий: COUNT(DISTINCT lot_id) СОХРАНЁН для
  fan-out-защиты маппинга, не COUNT(*) — view гарантирует physflat-дедуп, DISTINCT
  гарантирует mapping-fan-out-safety)
- parcels.py obj_pricing CTE (карточка конкурента units_sold/available)
- special_indices._ARTIFICIAL_DEMAND_SQL
- parcels.py district price block + geo-radius median (sample_size/n)
- concepts._OBJECTIVE_MEDIAN_SQL (гейт n≥10)
- landing KPI3 (% квартир с ценой)
- admin_scrape coverage: `lots` оставлен сырым (ETL-fidelity vs SQLite), добавлен
  `lots_physflat`
#1959 inline-дедуп в market_metrics НЕ рефакторим — добавлен комментарий об общем
physflat-ключе с view.

STEP 3: report_assembler._deal_count теперь = unit_velocity × window_months
(оконные продажи), НЕ кумулятивный n_sold. confidence_engine помечает фактор
«за 6 мес» и гейтит порогами окна (high≥50) — кумулятив (прод EKB ~380 921) делал
гейт бессмысленным и подпись лживой; оконное (~24 876 за 6 мес) честно.

Тесты: +guard'ы (L1/sold-count читают view, deal_count оконный). 963 passed.
2026-06-28 04:27:43 +05:00
c55eb4f4d4 feat(concept): фронт-пикер типовых домов (#1965 Stage 3b)
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Финальная часть эпика #1953: пользователь выбирает типовые дома
(тип × этажность × число секций) вместо авто max-FAR раскладки, формируя
building_program из Stage 3a.

Бэкенд:
- GET /api/v1/concepts/house-types — read-only каталог HOUSE_TYPES
  (section_type, label_ru, footprint w×d + sqm, default_floors, housing_class)
  как single source of truth; фронт ничего не хардкодит.
- Схема HouseTypeCatalog / HouseTypeCatalogItem в schemas/concept.py.
- Тесты эндпоинта: полнота каталога + совпадение ключей с available_section_types.

Кодген: api-types.ts перегенерён (dump OpenAPI → openapi-typescript →
project-local prettier 3.9.0); 2-й прогон без диффа.

Фронтенд:
- useHouseTypes() (TanStack useQuery, staleTime Infinity) в concept-api.ts;
  building_program в ConceptInput, placed_count/requested_count в ConceptVariant.
- HouseProgramPicker: toggle «Авто (max-FAR)» (default, program omit → greedy)
  vs «Выбрать дома» (список каталожных типов, count 1-50 / floors 1-40, дефолт
  из каталога; габариты/этажность/класс как подсказка). Смонтирован в
  Section7Concept и на странице /concept.
- Partial-fit заметка в ConceptVariantsResult: при placed<requested честное
  «Разместилось N из M секций — участок вмещает меньше» (нейтрально, не ошибка).
2026-06-28 02:49:44 +05:00
e8b05d821e test(concept): turn greedy backward-compat into a real golden pin (#1965 Stage 3a)
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The old test_no_program_reproduces_greedy_output_unchanged only compared
building_program=None (default) vs explicit None — both through the new
_Placer code — so it proved the two None branches agree but did NOT pin
the greedy geometry; it would still pass if the _Placer extraction had
drifted the output. test_placement.py only checks invariants, never
concrete counts/TEAP, so there was no anti-regression guard that the
greedy path is byte-identical after the refactor.

Replace it with two tests:
- test_greedy_output_matches_golden_pin: hard-coded literals per strategy
  on the fixed _BIG_PARCEL — (features, built_area_sqm, total_floor_area_sqm,
  apartments_count) — frozen from the current (== pre-refactor) output, so
  any future deterministic drift in greedy placement FAILS.
- test_explicit_none_program_equals_default_greedy: keeps the None-branch
  equivalence check (default vs explicit None go one greedy path).
2026-06-28 02:23:55 +05:00
94cf1f6217 feat(concept): house-type catalog + program-driven placement (#1965 Stage 3a)
Extend the concept generator so ConceptInput can carry an optional
building_program (list of typed houses from a catalog). When present,
placement lays out EXACTLY that program — for each item, place `count`
sections of the catalog footprint at the item's floors — instead of the
greedy max-FAR coverage-cap sweep. When absent, the existing greedy
behavior is unchanged (byte-for-byte backward-compatible).

- catalog.py: hardcoded HOUSE_TYPES (panel_econom, monolith_comfort,
  tower_business, lowrise_comfort, townhouse) — sane-default catalog,
  promote to DB later; get_house_type / available_section_types lookups.
- schema: additive BuildingProgramItem {section_type, floors, count} and
  ConceptInput.building_program (default None -> greedy). ConceptVariant
  gains optional placed_count / requested_count (partial-fit signal).
- placement: shared _Placer (collision/STRtree/setback machine extracted
  from greedy sweep, reused — no duplication); place_program +
  place_program_variant; branch in place_all_strategies on
  building_program. Mixed-floor TEAP via exact per-floor-group aggregation
  (GFA = sum(area_i * floors_i), no rounding drift).
- partial fit: when the parcel can't fit all sections, place as many as
  fit and report placed_count < requested_count (no hard-422); zero-fit
  still raises ParcelGeometryError (-> 422).
- API: validate program section_type keys against the catalog (unknown ->
  422) before placement.
- tests: catalog integrity, greedy backward-compat, exact 2-item program +
  TEAP reflection, over-packed partial placement, API program path.
- regenerate frontend api-types.ts (OpenAPI codegen gate stays green).
2026-06-28 01:29:04 +05:00
d38d5d43c5 feat(generative): LIVE financial recompute from massing program (#1965 Stage 2a)
Stage 2a of epic #1953: backend service + endpoint for live economic recompute
driven by the interactive 3D massing (Stage 2b debounced sliders).

- teap.py: add pure synthesize_teap_from_program(total_footprint_sqm, floors,
  site_area_sqm, housing_class, sections) — builds a TEAP from the SCALAR
  aggregate footprint × floors, mirroring synthesize_teap_from_buildability and
  reusing the same shared norm constants (_OFFICE_SHARE_OF_GFA / _EFFICIENCY_BY_CLASS
  / _AVG_APARTMENT_SQM / _PARKING_PER_APARTMENT) — single source of truth.
- schemas/concept.py: add MassingProgram (program contract, optional pre-resolved
  market_price_per_sqm + parcel_centroid_wkt) and MassingRecomputeOutput (teap + financial).
- api/v1/concepts.py: add POST /api/v1/concepts/recompute — synthesize TEAP → run
  the existing pure compute_financial. FAST path uses body market_price_per_sqm
  (no DB); else _lookup_market_price by centroid via run_in_threadpool; else class norm.
- tests: synthesize_teap_from_program (gfa math, parity with compute_teap, class
  efficiency, sections no-op) + endpoint (200, coherent output, price passthrough
  skips DB, DB fallback, class-norm default, floors validation).
2026-06-28 00:27:25 +05:00
0acd72a325 fix(best-layouts): per-object latest snapshot for supply (#1956)
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_SUPPLY_BATCH_SQL джойнил domrf_kn_flats по ОДНОЙ глобальной дате
(f.snapshot_date = MAX(snapshot_date) по всей таблице). Но domrf_kn_flats —
ПО-ОБЪЕКТНЫЙ time-series: каждый ЖК скрейпится в свой день. На единственной
глобал-max дате присутствует обычно 1 объект → у остальных 0 квартир →
supply_units_in_radius=0 для всех строк 4.2 Планировки → frontend показывал
«Срок продажи 0 мес» и «% продано —». Регрессия от #1944 (objects-first
дедуп snapshot'ов объектов, который сам по себе корректен).

Фикс: flats_latest CTE (DISTINCT ON (obj_id) ... ORDER BY obj_id,
snapshot_date DESC, id DESC) берёт для КАЖДОГО obj_id его собственный
последний снимок и джойнится к nearby. objects-first MATERIALIZED дедуп
(#1944) сохранён → fan-out по снимкам не возвращается. Глобальный
db.scalar(MAX(snapshot_date)) + :latest_snap bind удалены.

Прод (66:41:0205010:287, r=1км, 9 объектов): supply 0 (global-max) → 2675
(per-object, 4 объекта имеют flats на разных датах 2026-05-17/05-05; ни один
не на глобал-max 2026-06-22). Данные flats частично сломаны (#1945, отдельно),
но фикс корректно двигает supply с 0 к реальным per-object числам.

Тесты: новый guard test_supply_joins_flats_per_object_latest_snapshot;
обновлены mock-фабрики (db.scalar больше не вызывается).
2026-06-27 23:24:28 +05:00