forked from cartsnitch/api
feat: merge cartsnitch/api into api/ subdirectory
Consolidate API gateway service into monorepo. Squashed from https://github.com/cartsnitch/api main (89bacb1). Co-Authored-By: Paperclip <noreply@paperclip.ing>
This commit is contained in:
Coupon Carl
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"""Price service — trends, increases, comparison."""
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from uuid import UUID
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from sqlalchemy import and_, func, select
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy.orm import selectinload
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from cartsnitch_api.services.queries import latest_price_per_store
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class PriceService:
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def __init__(self, db: AsyncSession) -> None:
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self.db = db
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async def get_trends(self, category: str | None = None) -> list[dict]:
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from cartsnitch_api.models import NormalizedProduct, PriceHistory
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query = (
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select(PriceHistory)
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.join(NormalizedProduct)
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.options(
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selectinload(PriceHistory.store),
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selectinload(PriceHistory.normalized_product),
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)
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.order_by(PriceHistory.observed_date)
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)
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if category:
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query = query.where(NormalizedProduct.category == category)
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result = await self.db.execute(query)
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prices = result.scalars().all()
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# Group by product
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by_product: dict[UUID, dict] = {}
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for ph in prices:
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pid = ph.normalized_product_id
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if pid not in by_product:
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by_product[pid] = {
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"product_id": pid,
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"product_name": ph.normalized_product.canonical_name,
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"data_points": [],
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}
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by_product[pid]["data_points"].append(
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{
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"date": ph.observed_date,
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"price": float(ph.regular_price),
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"store_id": ph.store_id,
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"store_name": ph.store.name,
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}
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)
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return list(by_product.values())
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async def get_increases(self) -> list[dict]:
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"""Find products with recent significant price increases.
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Uses a window function (lag) to compare each price observation with the
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previous one per product+store, avoiding the N+1 query pattern.
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"""
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from cartsnitch_api.models import NormalizedProduct, PriceHistory, Store
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# Use lag() window function to get previous price in a single query
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prev_price = (
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func.lag(PriceHistory.regular_price)
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.over(
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partition_by=[PriceHistory.normalized_product_id, PriceHistory.store_id],
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order_by=PriceHistory.observed_date,
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)
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.label("prev_price")
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)
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row_num = (
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func.row_number()
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.over(
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partition_by=[PriceHistory.normalized_product_id, PriceHistory.store_id],
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order_by=PriceHistory.observed_date.desc(),
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)
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.label("rn")
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)
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inner = select(
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PriceHistory.normalized_product_id,
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PriceHistory.store_id,
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PriceHistory.regular_price,
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PriceHistory.observed_date,
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prev_price,
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row_num,
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).subquery()
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# Only keep the latest row (rn=1) where price increased
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result = await self.db.execute(
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select(
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inner.c.normalized_product_id,
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inner.c.store_id,
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inner.c.regular_price,
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inner.c.observed_date,
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inner.c.prev_price,
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NormalizedProduct.canonical_name,
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Store.name.label("store_name"),
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)
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.join(NormalizedProduct, NormalizedProduct.id == inner.c.normalized_product_id)
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.join(Store, Store.id == inner.c.store_id)
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.where(
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inner.c.rn == 1,
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inner.c.prev_price.isnot(None),
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inner.c.regular_price > inner.c.prev_price,
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)
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)
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increases = []
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for row in result.all():
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old = float(row.prev_price)
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new = float(row.regular_price)
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increases.append(
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{
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"product_id": row.normalized_product_id,
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"product_name": row.canonical_name,
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"store_name": row.store_name,
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"old_price": old,
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"new_price": new,
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"increase_pct": round((new - old) / old * 100, 2),
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"detected_at": row.observed_date,
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}
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)
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increases.sort(key=lambda x: x["increase_pct"], reverse=True)
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return increases
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async def get_comparison(self, product_ids: list[UUID]) -> list[dict]:
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from cartsnitch_api.models import NormalizedProduct, PriceHistory
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if not product_ids:
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return []
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# Fetch all requested products in one query
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prod_result = await self.db.execute(
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select(NormalizedProduct).where(NormalizedProduct.id.in_(product_ids))
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)
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products_by_id = {p.id: p for p in prod_result.scalars().all()}
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# Latest prices for all requested products in one query
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subq = latest_price_per_store(product_ids)
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prices_result = await self.db.execute(
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select(PriceHistory)
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.join(
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subq,
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and_(
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PriceHistory.store_id == subq.c.store_id,
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PriceHistory.observed_date == subq.c.max_date,
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PriceHistory.normalized_product_id == subq.c.normalized_product_id,
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),
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)
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.where(PriceHistory.normalized_product_id.in_(product_ids))
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.options(selectinload(PriceHistory.store))
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)
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all_prices = prices_result.scalars().all()
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# Group prices by product
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prices_by_product: dict[UUID, list] = {pid: [] for pid in product_ids}
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for ph in all_prices:
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prices_by_product.setdefault(ph.normalized_product_id, []).append(ph)
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comparisons = []
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for pid in product_ids:
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product = products_by_id.get(pid)
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if not product:
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continue
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comparisons.append(
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{
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"product_id": pid,
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"product_name": product.canonical_name,
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"prices": [
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{
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"store_id": ph.store_id,
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"store_name": ph.store.name,
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"current_price": float(ph.regular_price),
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"last_seen_at": ph.observed_date,
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}
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for ph in prices_by_product.get(pid, [])
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],
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}
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)
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return comparisons
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