1f6d15fa
 tangwang
重构：SPU级别索引、统一索引架构...
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"""
SPU data transformer for Shoplazza products.
Transforms SPU and SKU data from MySQL into SPU-level ES documents with nested variants.
"""
import pandas as pd
import numpy as np
from typing import Dict, Any, List, Optional
from sqlalchemy import create_engine, text
from utils.db_connector import create_db_connection
class SPUTransformer:
"""Transform SPU and SKU data into SPU-level ES documents."""
def __init__(
self,
db_engine: Any,
tenant_id: str
):
"""
Initialize SPU transformer.
Args:
db_engine: SQLAlchemy database engine
tenant_id: Tenant ID for filtering data
"""
self.db_engine = db_engine
self.tenant_id = tenant_id
def load_spu_data(self) -> pd.DataFrame:
"""
Load SPU data from MySQL.
Returns:
DataFrame with SPU data
"""
query = text("""
SELECT
id, shop_id, shoplazza_id, handle, title, brief, description,
spu, vendor, vendor_url, seo_title, seo_description, seo_keywords,
image_src, image_width, image_height, image_path, image_alt,
tags, note, category,
shoplazza_created_at, shoplazza_updated_at, tenant_id,
creator, create_time, updater, update_time, deleted
FROM shoplazza_product_spu
WHERE tenant_id = :tenant_id AND deleted = 0
""")
with self.db_engine.connect() as conn:
df = pd.read_sql(query, conn, params={"tenant_id": self.tenant_id})
return df
def load_sku_data(self) -> pd.DataFrame:
"""
Load SKU data from MySQL.
Returns:
DataFrame with SKU data
"""
query = text("""
SELECT
id, spu_id, shop_id, shoplazza_id, shoplazza_product_id,
shoplazza_image_id, title, sku, barcode, position,
price, compare_at_price, cost_price,
option1, option2, option3,
inventory_quantity, weight, weight_unit, image_src,
wholesale_price, note, extend,
shoplazza_created_at, shoplazza_updated_at, tenant_id,
creator, create_time, updater, update_time, deleted
FROM shoplazza_product_sku
WHERE tenant_id = :tenant_id AND deleted = 0
""")
with self.db_engine.connect() as conn:
df = pd.read_sql(query, conn, params={"tenant_id": self.tenant_id})
return df
def transform_batch(self) -> List[Dict[str, Any]]:
"""
Transform SPU and SKU data into ES documents.
Returns:
List of SPU-level ES documents
"""
# Load data
spu_df = self.load_spu_data()
sku_df = self.load_sku_data()
if spu_df.empty:
return []
# Group SKUs by SPU
sku_groups = sku_df.groupby('spu_id')
documents = []
for _, spu_row in spu_df.iterrows():
spu_id = spu_row['id']
# Get SKUs for this SPU
skus = sku_groups.get_group(spu_id) if spu_id in sku_groups.groups else pd.DataFrame()
# Transform to ES document
doc = self._transform_spu_to_doc(spu_row, skus)
if doc:
documents.append(doc)
return documents
def _transform_spu_to_doc(
self,
spu_row: pd.Series,
skus: pd.DataFrame
) -> Optional[Dict[str, Any]]:
"""
Transform a single SPU row and its SKUs into an ES document.
Args:
spu_row: SPU row from database
skus: DataFrame with SKUs for this SPU
Returns:
ES document or None if transformation fails
"""
doc = {}
# Tenant ID (required)
doc['tenant_id'] = str(self.tenant_id)
# Product ID
doc['product_id'] = str(spu_row['id'])
# Handle
if pd.notna(spu_row.get('handle')):
doc['handle'] = str(spu_row['handle'])
# Title
if pd.notna(spu_row.get('title')):
doc['title'] = str(spu_row['title'])
# Brief
if pd.notna(spu_row.get('brief')):
doc['brief'] = str(spu_row['brief'])
# Description
if pd.notna(spu_row.get('description')):
doc['description'] = str(spu_row['description'])
# SEO fields
if pd.notna(spu_row.get('seo_title')):
doc['seo_title'] = str(spu_row['seo_title'])
if pd.notna(spu_row.get('seo_description')):
doc['seo_description'] = str(spu_row['seo_description'])
if pd.notna(spu_row.get('seo_keywords')):
doc['seo_keywords'] = str(spu_row['seo_keywords'])
# Vendor
if pd.notna(spu_row.get('vendor')):
doc['vendor'] = str(spu_row['vendor'])
doc['vendor_keyword'] = str(spu_row['vendor'])
# Product type (from category or tags)
if pd.notna(spu_row.get('category')):
doc['product_type'] = str(spu_row['category'])
doc['product_type_keyword'] = str(spu_row['category'])
# Tags
if pd.notna(spu_row.get('tags')):
tags_str = str(spu_row['tags'])
doc['tags'] = tags_str
doc['tags_keyword'] = tags_str
# Category
if pd.notna(spu_row.get('category')):
doc['category'] = str(spu_row['category'])
doc['category_keyword'] = str(spu_row['category'])
# Image URL
if pd.notna(spu_row.get('image_src')):
image_src = str(spu_row['image_src'])
if not image_src.startswith('http'):
image_src = f"//{image_src}" if image_src.startswith('//') else image_src
doc['image_url'] = image_src
# Process variants
variants = []
prices = []
compare_prices = []
for _, sku_row in skus.iterrows():
variant = self._transform_sku_to_variant(sku_row)
if variant:
variants.append(variant)
if 'price' in variant and variant['price'] is not None:
try:
prices.append(float(variant['price']))
except (ValueError, TypeError):
pass
if 'compare_at_price' in variant and variant['compare_at_price'] is not None:
try:
compare_prices.append(float(variant['compare_at_price']))
except (ValueError, TypeError):
pass
doc['variants'] = variants
# Calculate price ranges
if prices:
doc['min_price'] = float(min(prices))
doc['max_price'] = float(max(prices))
else:
doc['min_price'] = 0.0
doc['max_price'] = 0.0
if compare_prices:
doc['compare_at_price'] = float(max(compare_prices))
else:
doc['compare_at_price'] = None
return doc
def _transform_sku_to_variant(self, sku_row: pd.Series) -> Optional[Dict[str, Any]]:
"""
Transform a SKU row into a variant object.
Args:
sku_row: SKU row from database
Returns:
Variant dictionary or None
"""
variant = {}
# Variant ID
variant['variant_id'] = str(sku_row['id'])
# Title
if pd.notna(sku_row.get('title')):
variant['title'] = str(sku_row['title'])
# Price
if pd.notna(sku_row.get('price')):
try:
variant['price'] = float(sku_row['price'])
except (ValueError, TypeError):
variant['price'] = None
else:
variant['price'] = None
# Compare at price
if pd.notna(sku_row.get('compare_at_price')):
try:
variant['compare_at_price'] = float(sku_row['compare_at_price'])
except (ValueError, TypeError):
variant['compare_at_price'] = None
else:
variant['compare_at_price'] = None
# SKU
if pd.notna(sku_row.get('sku')):
variant['sku'] = str(sku_row['sku'])
# Stock
if pd.notna(sku_row.get('inventory_quantity')):
try:
variant['stock'] = int(sku_row['inventory_quantity'])
except (ValueError, TypeError):
variant['stock'] = 0
else:
variant['stock'] = 0
# Options (from option1, option2, option3)
options = {}
if pd.notna(sku_row.get('option1')):
options['option1'] = str(sku_row['option1'])
if pd.notna(sku_row.get('option2')):
options['option2'] = str(sku_row['option2'])
if pd.notna(sku_row.get('option3')):
options['option3'] = str(sku_row['option3'])
if options:
variant['options'] = options
return variant
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