asin利润率相关链路整合优化

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
+             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) 整合后的当日数据写入 Doris dwd.dwd_asin_profit_rate_latest
+                  Doris UNIQUE KEY(site_name, asin, price) + sequence_col=asin_crawl_date 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]))
 
-from utils.spark_util import SparkUtil
 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 datetime import datetime, timedelta
+from utils.DorisHelper import DorisHelper
 
 
 class DimAsinProfitRateInfo(object):
@@ -16,90 +33,183 @@ class DimAsinProfitRateInfo(object):
         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")
-        self.spark = SparkUtil.get_spark_session(f"{self.__class__.__name__}: {self.site_name} {self.date_info}")
-
-        self.df_asin_profit = self.spark.sql(f"select 1+1;")
-        self.df_keepa_asin = self.spark.sql(f"select 1+1;")
-        self.df_save = self.spark.sql(f"select 1+1;")
+        # 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.spark = SparkUtil.get_spark_session(
+            f"{self.__class__.__name__}: {self.site_name} {self.date_info}"
+        )
+        self.hive_table  = "dim_asin_profit_rate_info"
+        self.doris_db    = "dwd"
+        self.doris_table = "dwd_asin_profit_rate_latest"
+        self.df_today    = None
+        self.df_history  = None
+        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):
-        # 清洗整合利润率表
-        sql = f"""
-            select asin, price, category, ocean_profit, air_profit, package_length, package_width, package_height, weight, updated_time, date_info
-            from dim_asin_profit_rate_info where site_name = '{self.site_name}';
+        """读当日 sqoop 增量 + 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}
+            WHERE site_name = '{self.site_name}' AND date_info = '{self.date_info}'
         """
-        self.df_asin_profit = self.spark.sql(sqlQuery=sql).repartition(40, 'asin').cache()
-
-        # 读取keepa数据
-        sql = f"""
-            select asin, package_length, package_width, package_height, item_weight as weight 
-            from dim_keepa_asin_info where site_name = '{self.site_name}';
+        print(f"sql_today =\n{sql_today}")
+        self.df_today = self.spark.sql(sqlQuery=sql_today).repartition(40, 'asin', 'price')
+
+        sql_history = 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}
+            WHERE site_name = '{self.site_name}' AND date_info != '{self.date_info}'
         """
-        self.df_keepa_asin = self.spark.sql(sqlQuery=sql).repartition(40, 'asin')
+        print(f"sql_history =\n{sql_history}")
+        self.df_history = self.spark.sql(sqlQuery=sql_history).repartition(40, 'asin', 'price').cache()
 
     def handle_data(self):
-        # 因为keepa数据存在更新的情况，保留与keepa最新数据所对应的数据行
-        self.df_asin_profit = self.df_asin_profit.join(
-            self.df_keepa_asin, on=['asin', 'package_length', 'package_width', 'package_height', 'weight'], how='inner'
+        """union 当日 + 历史，按 (asin, price) 去重保留 updated_time desc 最新一行"""
+        df_all = self.df_today.unionByName(self.df_history)
+        window = Window.partitionBy('asin', 'price').orderBy(
+            F.col('updated_time').desc_nulls_last(),
         )
-        # 去重
-        window = Window.partitionBy(['asin', 'price']).orderBy(
-            self.df_asin_profit.updated_time.desc_nulls_last()
-        )
-        self.df_asin_profit = self.df_asin_profit.withColumn(
-            'rank', F.row_number().over(window=window)
-        ).filter('rank = 1').drop('rank', 'date_info')
+        self.df_save = df_all.withColumn('rk', F.row_number().over(window)) \
+                             .filter('rk = 1') \
+                             .drop('rk')
 
     def save_data(self):
-        self.df_save = self.df_asin_profit.withColumn(
-            "site_name", F.lit(self.site_name)
-        ).withColumn(
-            "date_info", F.lit(self.date_info)
-        ).repartition(10).cache()
-
+        """覆盖当日分区为整合后全量快照；写入校验通过后删除历史分区"""
+        self.df_save = self.df_save \
+            .withColumn('site_name', F.lit(self.site_name)) \
+            .withColumn('date_info', F.lit(self.date_info)) \
+            .repartition(40).cache()
         new_count = self.df_save.count()
-        print(f"最新数据量：{new_count}")
+        old_count = self.df_history.count()
+        print(f"整合后 new_count={new_count:,}，历史 old_count={old_count:,}")
 
-        hive_tb = "dim_asin_profit_rate_info"
-        partition_dict = {
-            "site_name": self.site_name,
-            "date_info": self.date_info
-        }
+        partition_dict = {"site_name": self.site_name, "date_info": self.date_info}
         partition_by   = list(partition_dict.keys())
-        hdfs_path = CommonUtil.build_hdfs_path(hive_tb, partition_dict)
+        hdfs_path      = CommonUtil.build_hdfs_path(self.hive_table, partition_dict)
+
+        # 先清掉 sqoop 写入的当日分区（hdfs + metastore），避免重复 append
+        print(f"清空当日分区 hdfs：{hdfs_path}")
         HdfsUtils.delete_hdfs_file(hdfs_path)
-        print(f"正在进行数据存储，当前存储的表名为：{hive_tb}，存储路径：{hdfs_path}")
-        self.df_save.write.saveAsTable(name=hive_tb, format='hive', mode='append', partitionBy=partition_by)
-
-        # 验证实际写入数量，确保写入成功后再删除历史分区
-        written_count = self.spark.sql(f"""
-            select count(1) as cnt from {hive_tb}
-            where site_name='{self.site_name}' and date_info='{self.date_info}'
-        """).collect()[0]['cnt']
-        print(f"实际写入数量：{written_count}，预期：{new_count}")
+        self.spark.sql(
+            f"ALTER TABLE {self.hive_table} DROP IF EXISTS "
+            f"PARTITION (site_name='{self.site_name}', date_info='{self.date_info}')"
+        )
+        # 写入整合后的当日分区（try-except 保护：写入失败立即抛出，绝不进入删除历史分区流程）
+        print(f"写入 Hive 当日分区：{hdfs_path}")
+        try:
+            self.df_save.write.saveAsTable(
+                name=self.hive_table, format='hive', mode='append', partitionBy=partition_by
+            )
+        except Exception as e:
+            print(f"❌ Hive 当日分区写入失败：{e}；保留所有历史分区不删，请人工排查！")
+            self.df_history.unpersist()
+            raise
+
+        # 验证当日分区实际写入行数与预期一致（防止 saveAsTable 静默丢数据）
+        written_count = self.spark.sql(
+            f"SELECT COUNT(1) AS cnt FROM {self.hive_table} "
+            f"WHERE site_name='{self.site_name}' AND date_info='{self.date_info}'"
+        ).collect()[0]['cnt']
+        print(f"实际写入行数 written_count={written_count:,}，预期 new_count={new_count:,}")
         if written_count != new_count:
-            raise RuntimeError(
-                f"写入数量校验失败！实际写入 {written_count} != 预期 {new_count}，终止删除历史分区，请人工检查。"
+            print(f"❌ 写入校验失败 written_count={written_count} != new_count={new_count}，保留历史分区不删！")
+            self.df_history.unpersist()
+            return
+
+        # 安全控制：当日整合分区行数必须 >= 历史总和才允许删历史（防止整合丢失数据）
+        if new_count < old_count:
+            print(f"⚠️ 校验未通过 new_count={new_count} < old_count={old_count}，跳过历史分区删除，请人工排查！")
+            self.df_history.unpersist()
+            return
+
+        print(f"校验通过，删除所有 date_info < {self.date_info} 的历史分区")
+        partitions = self.spark.sql(f"SHOW PARTITIONS {self.hive_table}").collect()
+        for row in partitions:
+            # row[0] 形如 "site_name=us/date_info=2026-05-10"
+            parts = dict(p.split('=') for p in row[0].split('/'))
+            p_site = parts.get('site_name')
+            p_date = parts.get('date_info')
+            if p_site == self.site_name and p_date < self.date_info:
+                print(f"  删除分区: site_name={p_site}, date_info={p_date}")
+                self.spark.sql(
+                    f"ALTER TABLE {self.hive_table} DROP IF EXISTS "
+                    f"PARTITION (site_name='{p_site}', date_info='{p_date}')"
                 )
-        print("写入校验通过！")
-
-        print(f"正在删除历史分区数据")
-        self.spark.sql(f"""
-        ALTER TABLE {hive_tb} DROP IF EXISTS PARTITION (site_name='{self.site_name}', date_info='{self.last_date_info}')
-        """)
                 HdfsUtils.delete_hdfs_file(
-            CommonUtil.build_hdfs_path(hive_tb, partition_dict={"site_name": self.site_name, "date_info": self.last_date_info})
+                    CommonUtil.build_hdfs_path(
+                        self.hive_table,
+                        partition_dict={"site_name": p_site, "date_info": p_date},
+                    )
+                )
+        print("历史分区清理完毕!")
+        self.df_history.unpersist()
+
+    def write_to_doris(self):
+        """整合后的当日数据写 Doris dwd_asin_profit_rate_latest
+        Doris UNIQUE KEY(site_name, asin, price) + sequence_col=asin_crawl_date 自动按抓取时间取最新
+        """
+        df_to_doris = self.df_save.select(
+            F.lit(self.site_name).alias('site_name'),
+            F.col('asin'),
+            F.round(F.col('price'), 2).cast('decimal(20,2)').alias('price'),
+            F.col('ocean_profit'),
+            F.col('air_profit'),
+            F.to_timestamp(F.col('asin_crawl_date')).alias('asin_crawl_date'),
+            F.to_timestamp(F.col('updated_time')).alias('update_time'),
+        ).cache()
+        count = df_to_doris.count()
+        print(f"写入 Doris 数据量：{count:,}")
+        df_to_doris.show(10, truncate=False)
+
+        table_columns = "site_name, asin, price, ocean_profit, air_profit, asin_crawl_date, update_time"
+        DorisHelper.spark_export_with_columns(
+            df_save=df_to_doris,
+            db_name=self.doris_db,
+            table_name=self.doris_table,
+            table_columns=table_columns,
         )
+        df_to_doris.unpersist()
+        self.df_save.unpersist()
         print("success!")
 
 
 if __name__ == "__main__":
     site_name = sys.argv[1]
     date_info = sys.argv[2]
-    handle_obj = DimAsinProfitRateInfo(site_name, date_info)
-    handle_obj.run()
+    DimAsinProfitRateInfo(site_name, date_info).run()

Pyspark_job/script/export_need_profit_rate.py

+"""
+author: CT
+description: 导出待计算利润率的 ASIN
+             1) Hive dwt_flow_asin 月维度读取 date_info >= '2025-05' 的所有 ASIN：
+                asin / price / category_first_id / asin_crawl_date
+             2) Doris dwt.{site}_flow_asin_30day 读取所有相关 ASIN：
+                asin / price / category_first_id / asin_crawl_date
+             3) union 后按 (asin, price) 去重保留 asin_crawl_date 最新
+             4) LEFT JOIN 分类、INNER JOIN 当日 keepa 增量
+             5) keepa 关联到的 ASIN 全部导出 PG {site}_asin_profit_rate_calc 重新计算利润率
+执行示例: spark-submit export_need_profit_rate.py us 2026-05-15
+"""
+import os
+import sys
+
+sys.path.append(os.path.dirname(sys.path[0]))
+
+from pyspark.sql import functions as F, Window
+
+from utils.spark_util import SparkUtil
+from utils.db_util import DBUtil
+from utils.DorisHelper import DorisHelper
+
+START_MONTH = '2025-05'
+
+
+class ExportNeedProfitRate(object):
+
+    def __init__(self, site_name, date_info):
+        self.site_name = site_name
+        self.date_info = date_info  # 计算时间 yyyy-MM-dd（用作当日 keepa 增量分区）
+        self.spark = SparkUtil.get_spark_session(
+            f"{self.__class__.__name__}: {self.site_name} {self.date_info}"
+        )
+
+    def run(self):
+        df_export = self.build_export_df()
+        self.write_to_pg(df_export)
+
+    def build_export_df(self):
+        # 1. Hive dwt_flow_asin 月维度，date_info >= 2025-05 所有月份
+        sql_dwt = f"""
+            SELECT asin,
+                   asin_price       AS price,
+                   category_first_id,
+                   asin_crawl_date
+            FROM dwt_flow_asin
+            WHERE site_name = '{self.site_name}'
+              AND date_type = 'month'
+              AND date_info >= '{START_MONTH}'
+              AND asin_price > 0
+        """
+        print(f"sql_dwt =\n{sql_dwt}")
+        df_dwt = self.spark.sql(sqlQuery=sql_dwt) \
+            .withColumn('price', F.round(F.col('price'), 2).cast('decimal(20,2)')) \
+            .withColumn('asin_crawl_date', F.to_timestamp(F.col('asin_crawl_date')))
+
+        # 2. Doris dwt.{site}_flow_asin_30day 所有相关 ASIN
+        doris_sql = f"""
+            SELECT asin, price, category_first_id, asin_crawl_date
+            FROM dwt.{self.site_name}_flow_asin_30day
+            WHERE price > 0
+        """
+        print(f"doris_sql =\n{doris_sql}")
+        df_doris = DorisHelper.spark_import_with_sql(self.spark, doris_sql) \
+            .withColumn('price', F.round(F.col('price'), 2).cast('decimal(20,2)')) \
+            .withColumn('asin_crawl_date', F.col('asin_crawl_date').cast('timestamp')) \
+            .select('asin', 'price', 'category_first_id', 'asin_crawl_date')
+
+        # 3. union + 按 (asin, price) 去重保留 asin_crawl_date 最新
+        df_flow = df_dwt.unionByName(df_doris).repartition(40, 'asin', 'price')
+        window = Window.partitionBy('asin', 'price').orderBy(F.col('asin_crawl_date').desc_nulls_last())
+        df_flow = df_flow.withColumn('rk', F.row_number().over(window)) \
+                         .filter('rk = 1') \
+                         .drop('rk') \
+                         .cache()
+
+        # 4. 分类名 LEFT JOIN
+        sql_cate = f"""
+            SELECT category_first_id, en_name AS category
+            FROM dim_bsr_category_tree
+            WHERE site_name = '{self.site_name}' AND nodes_num = 2
+        """
+        df_cate = self.spark.sql(sqlQuery=sql_cate)
+
+        # 5. keepa 当日增量 INNER JOIN
+        sql_keepa = f"""
+            SELECT asin, package_length, package_width, package_height, item_weight AS weight
+            FROM dim_keepa_asin_info
+            WHERE site_name = '{self.site_name}' AND date_info = '{self.date_info}'
+        """
+        df_keepa = self.spark.sql(sqlQuery=sql_keepa) \
+            .filter((F.col('package_length') > 0) &
+                    (F.col('package_width') > 0) &
+                    (F.col('package_height') > 0) &
+                    (F.col('weight') > 0)) \
+            .repartition(40, 'asin')
+
+        df_result = df_flow \
+            .join(df_cate, on='category_first_id', how='left') \
+            .join(df_keepa, on='asin', how='inner') \
+            .withColumn('source_month', F.date_format(F.col('asin_crawl_date'), 'yyyy-MM')) \
+            .withColumn('part_key', F.ntile(50).over(Window.orderBy(F.rand()))) \
+            .select(
+                'asin', 'price', 'category',
+                'package_length', 'package_width', 'package_height', 'weight',
+                'part_key', 'source_month', 'asin_crawl_date',
+            )
+
+        count = df_result.count()
+        print(f"待计算利润率数据量：{count:,}")
+        df_result.show(10, truncate=False)
+        df_flow.unpersist()
+        return df_result
+
+    def write_to_pg(self, df_export):
+        con_info   = DBUtil.get_connection_info(db_type='postgresql_cluster', site_name=self.site_name)
+        table_name = f"{self.site_name}_asin_profit_rate_calc"
+        print(f"导出到 PG {table_name}")
+        df_export.write.format("jdbc") \
+            .option("url",      con_info["url"]) \
+            .option("dbtable",  table_name) \
+            .option("user",     con_info["username"]) \
+            .option("password", con_info["pwd"]) \
+            .mode("append") \
+            .save()
+        print("success")
+
+
+if __name__ == "__main__":
+    site_name = sys.argv[1]
+    date_info = sys.argv[2]
+    ExportNeedProfitRate(site_name, date_info).run()