利润率异常修复

Pyspark_job/dim/dim_asin_profit_rate_info.py

 """
 author: CT
-description: 利润率数据增量同步 + 去重整合 — 一站式 PG → Hive → Doris
+description: 利润率数据增量整合 — ODS → Hive → Doris
+             前置：ods_asin_profit_rate.py 已将当日 PG 增量 sqoop 到 ods_asin_profit_rate
              步骤：
-               1) sqoop 从 PG {site_name}_asin_profit_rate_calc 增量拉取
-                  时间窗 [last_date_info, 当天 00:00:00)，先写入 Hive
-                  dim_asin_profit_rate_info (site_name, date_info=今日) 分区作为"原始增量"
-               2) Spark 读当日 sqoop 增量 + Hive 历史所有分区，按 (asin, price) 去重
-                  排序键：updated_time desc 保留最新一行
-               3) 覆盖当日分区为整合后全量快照；写入校验通过后删除所有 < 今日 的历史分区
-               4) 当日 sqoop 增量（不含历史回灌）写入 Doris dwd.dwd_asin_profit_rate_latest
+               1) 读 ods_asin_profit_rate 当日分区（原始增量）+ dim_asin_profit_rate_info 历史分区
+               2) 按 (asin, price) 去重，updated_time desc 保留最新一行
+               3) 覆盖 dim_asin_profit_rate_info 当日分区为整合后全量快照；校验后删除历史分区
+               4) 当日 ODS 增量写入 Doris dwd.dwd_asin_profit_rate_latest
                   Doris UNIQUE KEY(site_name, asin, price) + sequence_col=update_time 自动取最新
 执行示例: spark-submit dim_asin_profit_rate_info.py us 2026-05-15
 """
 import os
 import sys
-from datetime import datetime, timedelta
 
 sys.path.append(os.path.dirname(sys.path[0]))
 
@@ -22,7 +19,6 @@ from pyspark.sql import functions as F, Window
 
 from utils.spark_util import SparkUtil
 from utils.common_util import CommonUtil
-from utils.secure_db_client import get_remote_engine
 from utils.hdfs_utils import HdfsUtils
 from utils.DorisHelper import DorisHelper
 
@@ -30,14 +26,12 @@ from utils.DorisHelper import DorisHelper
 class DimAsinProfitRateInfo(object):
 
     def __init__(self, site_name, date_info):
-        self.site_name      = site_name
-        self.date_info      = date_info
-        self.last_date_info = (datetime.strptime(date_info, "%Y-%m-%d").date() - timedelta(days=1)).strftime("%Y-%m-%d")
-        # 上限：程序运行当天 00:00:00（拉昨天及之前完整数据，今天还在写不拉）
-        self.upper_bound = datetime.now().strftime("%Y-%m-%d") + " 00:00:00"
+        self.site_name   = site_name
+        self.date_info   = date_info
         self.spark = SparkUtil.get_spark_session(
             f"{self.__class__.__name__}: {self.site_name} {self.date_info}"
         )
+        self.ods_table   = "ods_asin_profit_rate"
         self.hive_table  = "dim_asin_profit_rate_info"
         self.doris_db    = "dwd"
         self.doris_table = "dwd_asin_profit_rate_latest"
@@ -46,49 +40,25 @@ class DimAsinProfitRateInfo(object):
         self.df_save     = None
 
     def run(self):
-        self.sqoop_to_hive()
         self.read_data()
         self.handle_data()
         self.save_data()
         self.write_to_doris()
 
-    def sqoop_to_hive(self):
-        """sqoop 从 PG 增量拉到 Hive dim_asin_profit_rate_info 当日分区（原始增量）"""
-        partition_dict = {"site_name": self.site_name, "date_info": self.date_info}
-        hdfs_path = CommonUtil.build_hdfs_path(self.hive_table, partition_dict=partition_dict)
-
-        engine = get_remote_engine(site_name=self.site_name, db_type='postgresql_cluster')
-        import_table = f"{self.site_name}_asin_profit_rate_calc"
-        # 增量区间：[last_date_info, 程序运行当天 00:00:00)
-        # 上限用 self.upper_bound（当天 00:00:00），避免拉到 PG 当日正在写入的数据
-        query = f"""
-        SELECT asin, price, category, ocean_profit, air_profit,
-               package_length, package_width, package_height, weight,
-               updated_time, asin_crawl_date
-        FROM {import_table}
-        WHERE updated_time >= '{self.last_date_info}' AND updated_time < '{self.upper_bound}' AND \\$CONDITIONS
-        """
-        print(f"sqoop query:\n{query}")
-        engine.sqoop_raw_import(
-            query=query,
-            hive_table=self.hive_table,
-            hdfs_path=hdfs_path,
-            partitions=partition_dict,
-            check_count=True,
-        )
-
     def read_data(self):
-        """读当日 sqoop 增量 + 历史所有分区"""
+        """读当日 ODS 增量（ods_asin_profit_rate）+ dim 历史所有分区"""
         sql_today = f"""
             SELECT asin, price, category, ocean_profit, air_profit,
                    package_length, package_width, package_height, weight,
                    asin_crawl_date, updated_time
-            FROM {self.hive_table}
+            FROM {self.ods_table}
             WHERE site_name = '{self.site_name}' AND date_info = '{self.date_info}'
         """
         print(f"sql_today =\n{sql_today}")
         # cache：save_data 会 DROP PARTITION 当日分区，write_to_doris 还要复用此 df，必须先物化
+        # count() 强制立即物化，确保 cache 在 DROP/saveAsTable 之前已填充到 BlockManager
         self.df_today = self.spark.sql(sqlQuery=sql_today).repartition(40, 'asin', 'price').cache()
+        self.df_today.count()
 
         sql_history = f"""
             SELECT asin, price, category, ocean_profit, air_profit,

Pyspark_job/sqoop_import/ods_asin_profit_rate.py

+"""
+author: CT
+description: sqoop 从 PG {site_name}_asin_profit_rate_calc 增量拉取利润率数据
+             写入 Hive ods_asin_profit_rate 分区 (site_name, date_info)
+             增量时间窗：[date_info-1天 00:00:00, 当天运行时刻的 00:00:00)
+执行示例: python ods_asin_profit_rate.py us 2026-05-15
+"""
+import os
+import sys
+from datetime import datetime, timedelta
+
+sys.path.append(os.path.dirname(sys.path[0]))
+
+from utils.common_util import CommonUtil
+from utils.secure_db_client import get_remote_engine
+
+if __name__ == '__main__':
+    site_name = CommonUtil.get_sys_arg(1, None)
+    date_info = CommonUtil.get_sys_arg(2, None)
+    assert site_name is not None, "site_name 不能为空！"
+    assert date_info is not None, "date_info 不能为空！"
+
+    last_date_info = (datetime.strptime(date_info, "%Y-%m-%d").date() - timedelta(days=1)).strftime("%Y-%m-%d")
+    # 上限：程序运行当天 00:00:00，避免拉到 PG 当日正在写入的数据
+    upper_bound = datetime.now().strftime("%Y-%m-%d") + " 00:00:00"
+
+    hive_table    = "ods_asin_profit_rate"
+    import_table  = f"{site_name}_asin_profit_rate_calc"
+    partition_dict = {"site_name": site_name, "date_info": date_info}
+    hdfs_path     = CommonUtil.build_hdfs_path(hive_table, partition_dict=partition_dict)
+
+    engine = get_remote_engine(site_name=site_name, db_type='postgresql_cluster')
+
+    query = f"""
+    SELECT asin, price, category, ocean_profit, air_profit,
+           package_length, package_width, package_height, weight,
+           updated_time, asin_crawl_date
+    FROM {import_table}
+    WHERE updated_time >= '{last_date_info}' AND updated_time < '{upper_bound}' AND \\$CONDITIONS
+    """
+    print(f"sqoop query:\n{query}")
+
+    engine.sqoop_raw_import(
+        query=query,
+        hive_table=hive_table,
+        hdfs_path=hdfs_path,
+        partitions=partition_dict,
+        check_count=True,
+    )
+    print("success!")