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import os
import sys
import random
import string
sys.path.append(os.path.dirname(sys.path[0]))
from utils.db_util import DBUtil
from utils.ssh_util import SSHUtil
from utils.common_util import CommonUtil,DateTypes
from utils.spark_util import SparkUtil
from datetime import datetime
from utils.templates import Templates
class JudgeCount(Templates):
def __init__(self, site_name='us', date_type="month", date_info='2024-01'):
super().__init__()
self.site_name = site_name
self.date_type = date_type
self.date_info = date_info
self.db_save = 'judge_count'
# 初始化self.spark对
self.spark = self.create_spark_object(
app_name=f"{self.db_save}: {self.site_name}, {self.date_type}, {self.date_info}")
def judge_count(self, count=1000):
sql = f"select * from dwt_st_asin_reverse where site_name='{self.site_name}' and date_type='{self.date_type}' and date_info='{self.date_info}' limit {count};"
print(f"sql: {sql}")
df = self.spark.sql(sql).cache()
df.show(10)
df_count = df.count()
if df_count < count:
raise Exception(f"{self.site_name}站点: 反查数量不对, 抛出异常, {df_count}")
else:
print(f"{self.site_name}站点: 反查数据量正常, {df_count}")
def run(self):
# self.judge_count()
pass
if __name__ == '__main__':
site_name = CommonUtil.get_sys_arg(1, None)
date_type = CommonUtil.get_sys_arg(2, None)
date_info = CommonUtil.get_sys_arg(3, None)
# 获取最后一个参数
last_flag = CommonUtil.get_sys_arg(len(sys.argv) - 1, None)
cur_date = datetime.now().date()
handle_obj = JudgeCount(site_name=site_name, date_type=date_type, date_info=date_info)
handle_obj.run() # 检验反查数量
sql_date_type = date_type
print(f"执行参数为{sys.argv}")
db_type = 'postgresql_cluster'
CommonUtil.judge_is_work_hours(site_name=site_name, date_type=date_type, date_info=date_info, principal='fangxingjun',
priority=3, export_tools_type=1, belonging_to_process='反查搜索词')
# 获取数据库连接
engine = DBUtil.get_db_engine(db_type, site_name)
suffix = str(date_info).replace("-", "_")
export_cols = [
"st_key",
"search_term",
"st_ao_val",
"st_type",
"st_rank",
"st_search_num",
"st_search_rate",
"st_search_sum",
"st_adv_counts",
"st_quantity_being_sold",
"asin",
"asin_st_zr_orders",
"asin_st_zr_orders_sum",
"asin_st_zr_flow",
"asin_st_sp_orders",
"asin_st_sp_orders_sum",
"asin_st_sp_flow",
"st_asin_zr_page",
"st_asin_zr_page_row",
"st_asin_zr_page_rank",
"st_asin_zr_updated_at",
"st_asin_sp_page",
"st_asin_sp_page_rank",
"st_asin_sp_page_row",
"st_asin_sp_updated_at",
"st_asin_sb1_page",
"st_asin_sb1_updated_at",
"st_asin_sb2_page",
"st_asin_sb2_updated_at",
"st_asin_sb3_page",
"st_asin_sb3_updated_at",
"st_asin_ac_page",
"st_asin_ac_updated_at",
"st_asin_bs_page",
"st_asin_bs_updated_at",
"st_asin_er_page",
"st_asin_er_updated_at",
"st_asin_tr_page",
"st_asin_tr_updated_at",
"st_zr_current_page_asin_counts",
"st_sp_current_page_asin_counts",
"st_brand_label"
]
# 4_week逻辑
if date_type == "4_week" or date_type == DateTypes.month_week.name:
export_tb_target = f"{site_name}_last_4_week_st"
export_tb_copy = f"{site_name}_last_4_week_st_copy1"
export_table = export_tb_copy
sql = f"""
drop table if exists {export_tb_copy};
create table if not exists {export_tb_copy}
(
like {export_tb_target} including comments including defaults
);
truncate table {export_tb_copy};
SELECT create_distributed_table('{export_tb_copy}', 'asin');
"""
# 执行SQL语句
DBUtil.engine_exec_sql(engine, sql)
print("导出的字段：", export_cols)
if date_type == DateTypes.month.name:
# 处理导出表
export_table = f"{site_name}_st_month_{suffix}"
year_month_before = CommonUtil.get_month_offset(date_info, -1).replace("-", "_")
export_master_table = export_table
# export_tb_before = f"us_st_month_{year_month_before}"
# 基础数据结构表
export_tb_before = f"us_st_month_2023_base"
sql = f"""
drop table if exists {export_master_table};
create table if not exists public.{export_master_table}
(
like public.{export_tb_before} including comments including defaults
);
truncate table public.{export_master_table};
SELECT create_distributed_table('{export_master_table}', 'asin');
"""
# 执行SQL语句
DBUtil.engine_exec_sql(engine, sql)
print("导出的字段：", export_cols)
# 关闭与pg的链接
engine.dispose()
if last_flag == "month_append":
partition_dict = {
"site_name": site_name,
"date_type": "month",
"date_info": date_info
}
else:
partition_dict = {
"site_name": site_name,
"date_type": date_type,
"date_info": date_info
}
# 导出执行sqoop的sh编写
sh = CommonUtil.build_export_sh(
site_name=site_name,
db_type=db_type,
hive_tb="dwt_st_asin_reverse",
export_tb=export_table,
col=export_cols,
partition_dict=partition_dict
)
client = SSHUtil.get_ssh_client()
SSHUtil.exec_command_async(client, sh, ignore_err=False)
client.close()
# 重新获取数据库连接
engine = DBUtil.get_db_engine(db_type, site_name)
# 导出后执行（构建索引、切换表名）
salt_id = ''.join(random.sample(string.ascii_letters + string.digits, 8))
sql_index_asin = f"""
CREATE INDEX {site_name}_st_asin_{salt_id}_key ON public.{export_table} USING hash (asin);
"""
# 执行SQL语句
DBUtil.engine_exec_sql(engine, sql_index_asin)
sql_index_zr_flow = f"""
CREATE INDEX {site_name}_st_zr_{salt_id}_key ON public.{export_table} USING btree (asin_st_zr_flow);
"""
DBUtil.engine_exec_sql(engine, sql_index_zr_flow)
sql_index_search_term = f"""
CREATE INDEX {site_name}_search_term_{salt_id}_key ON public.{export_table} USING btree (search_term);
"""
DBUtil.engine_exec_sql(engine, sql_index_search_term)
if date_type == '4_week' or date_type == DateTypes.month_week.name:
# 交换表名
#DBUtil.exchange_tb(engine, export_tb_copy, export_tb_target, cp_index_flag=False)
sql_1 = f"""alter table {export_tb_target} rename to {export_tb_target}_back; """
DBUtil.engine_exec_sql(engine, sql_1)
sql_2 = f"""alter table {export_tb_copy} rename to {export_tb_target};"""
DBUtil.engine_exec_sql(engine, sql_2)
sql_3 = f"""alter table {export_tb_target}_back rename to {export_tb_copy};"""
DBUtil.engine_exec_sql(engine, sql_3)
# 往导出流程表插入导出完成数据，方便监听导出脚本是否全部完成
if date_type == '4_week' or date_type == DateTypes.month.name:
# us站点可以直接插入数据执行
update_workflow_sql = f"""
REPLACE INTO selection.workflow_progress (site_name, page, table_name, date_type, date_info, status, status_val, is_end, over_date, calculate_responsible, exhibition_responsible, remark)
VALUES('{site_name}', '反查搜索词', '{export_table}', '{date_type}', '{date_info}', '导出pg集群完成', 6, '是', CURRENT_TIME,'fangxingjun@yswg.com.cn', 'wangfeng@yswg.com.cn', '反查搜索词')
"""
if date_type == DateTypes.month_week.name or date_type == '4_week':
sql_date_type = '30_day'
if date_type == '4_week':
cur_date = CommonUtil.get_calDay_by_dateInfo(SparkUtil.get_spark_session("get_4_week_date"), date_type, date_info)
update_workflow_sql = f"""
REPLACE INTO selection.workflow_progress (site_name, page, table_name, date_type, date_info, status, status_val, is_end, over_date, calculate_responsible, exhibition_responsible, remark)
VALUES('{site_name}', '反查搜索词', 'us_last_4_week_st', '{sql_date_type}', '{cur_date}', '导出pg集群完成', 6, '是', CURRENT_TIME,'fangxingjun@yswg.com.cn', 'wangfeng@yswg.com.cn', '反查搜索词')
"""
print(update_workflow_sql)
mysql_engine = DBUtil.get_db_engine('mysql', 'us')
DBUtil.engine_exec_sql(mysql_engine, update_workflow_sql)
# 关闭链接
mysql_engine.dispose()
engine.dispose()
CommonUtil.modify_export_workflow_status(update_workflow_sql, site_name, date_type, date_info)