数据开发（三）
一、Hive概述
Hive是基于Hadoop的一个数据仓库工具，可以将结构化的数据文件映射为一张数据表，并提供类似于SQL(HiveSQL)的操作功能。在Hive中，本质上是将SQL转换成为MapReduce程序。
二、
1、建表语句
CREATE TABLE `app.app_test`( `data_dt` string COMMENT '时间', `start_city_id` string COMMENT '城市ID', `start_city_name` string COMMENT '城市名称', `predict_list` string COMMENT '序列', `order_num` string COMMENT '单量') COMMENT '分拣中心预测表--预处理' PARTITIONED BY ( `dt` string) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' STORED AS INPUTFORMAT 'org.apache.hadoop.mapred.TextInputFormat' OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat' LOCATION 'hdfs://ns14/user/mart_coo/app.db/app_test' TBLPROPERTIES ( 'transient_lastDdlTime'='1527746321')
（1）STORED AS TEXTFILE：默认的是文本格式，当然也可以通过设置hive.default.fileformat修改默认设置；使用DELIMITED语句解析分割文件，通过ESCAPED BY语句设置分隔符的转义符号，当数据中含有分隔符时，就必须转义；通过NULL DEFINED AS语句可以设置一个自定义的空处理格式，默认情况下NULL='\N'
②STORED AS SEQUENCEFILE：压缩序列文件格式
③STORED AS ORC：ORC文件格式
④STORED AS PARQUET
⑤STORED AS AVRO
⑥STORED AS RCFILE：Record Columnar File
⑦STORED BY：non-native table format
⑧INPUTFORMAT and OUTPUTFORMAT
（2）PARTITIONED BY
在创建表时，使用PARTITIONED BY语句，则所创建表就是分区表；分区表可以有一个或多个分区列，给唯一的分区列值创建一个目录。表或分区可以继续通过CLUSTERED BY细分桶，使用SORT BY语句使桶内的数据按升序或降序排序，在某些情况下，可以提升某种查询的性能。
注意点：①分区列式虚拟的，不属于数据本身 ②分区列不能和表结构字段重名，如果需要直接用表结构字段中的名字作为分区列，那必须将表结构中字段重新命名，否则会报错
（3）外部表
创建外部表时，通过指定location语句的路径来覆盖配置文件中指定的文件夹，外部表的特点：当外部表被删除时，其对应的数据并非也从系统中删除。
2、查询表
三、注意编码格式
1、报错
出现这种情况时是格式问题。
重新建个文本，复制粘贴下试试。
四、
1、建表
drop table app.app_basic_dashboard_goods_sale_predict_daily; CREATE TABLE app.app_basic_dashboard_goods_sale_predict_daily ( seller_id bigint COMMENT '商家id', seller_name string COMMENT '商家名称', dept_id bigint COMMENT '事业部门id', dept_no string COMMENT '事业部门编号', dept_name string COMMENT '事业部名字', goods_id bigint COMMENT '商品id', goods_no string COMMENT '商品序号', goods_name string COMMENT '商品名称', warehouse_id bigint COMMENT '入仓id', warehouse_no string COMMENT '入仓编号', warehouse_name string COMMENT '入仓名称', in_warehouse_city string COMMENT '入仓城市', satisfy_alpha string COMMENT '入仓服务水平C', safe_stock_days string COMMENT '安全库存天数', alt string COMMENT '出仓ALT', vlt string COMMENT '出仓VLT', in_stock_safety_num int COMMENT '安全库存', target_stock_days string COMMENT '目标库存天数', bp string COMMENT 'BP', out_warehouse_no string COMMENT '出仓编号', out_warehouse_name string COMMENT '出仓名称', out_warehouse_city string COMMENT '出仓城市', in_stock_max_num int COMMENT '目标库存', bef_sales_1d string COMMENT 'T-1日销量', bef_sales_2d string COMMENT 'T-2日销量', bef_sales_3d string COMMENT 'T-3日销量', bef_sales_4d string COMMENT 'T-4日销量', bef_sales_5d string COMMENT 'T-5日销量', bef_sales_6d string COMMENT 'T-6日销量', bef_sales_7d string COMMENT 'T-7日销量', in_stock_sales_14d string COMMENT '历史14日销量和', in_stock_sales_28d string COMMENT '历史28日销量和', predict_sales_1d string COMMENT 'T+1日预测销量', predict_sales_2d string COMMENT 'T+2日预测销量', predict_sales_3d string COMMENT 'T+3日预测销量', predict_sales_4d string COMMENT 'T+4日预测销量', predict_sales_5d string COMMENT 'T+5日预测销量', predict_sales_6d string COMMENT 'T+6日预测销量', predict_sales_7d string COMMENT 'T+7日预测销量', in_stock_predict_sales_14d string COMMENT '14日预测销量和', in_stock_predict_sales_28d string COMMENT '28日预测销量和', yn int COMMENT '删除标识 1为没删，0为删除', create_pin string COMMENT '创建人', update_pin string COMMENT '更新人', create_time timestamp COMMENT '创建时间', update_time timestamp COMMENT '更新时间', ts timestamp COMMENT '时间戳') COMMENT 'KA商品销量预测接口表' PARTITIONED BY ( dt string) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' STORED AS INPUTFORMAT 'org.apache.hadoop.mapred.TextInputFormat' OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
2、
2.1只删除表中数据，不删除表
truncate table app.app_basic_dashboard_goods_sale_predict_daily_tem
2.2删除分区
ALTER TABLE app_basic_dashboard_goods_sale_predict_daily_tem DROP partition(dt = '2018-08-16')
2.3 按条件删除
2.3删除表及其数据
drop table app.app_basic_dashboard_goods_sale_predict_daily_tem
显示建表语句
show create app.app_XXXXXXX
3、配置任务
#!/usr/bin/env python3
################################################################
# AUTHOR: wn
# CREATED TIME: 2018-08-09
# MODIFIED BY:
# MODIFTED TIME:
# REVIEWED BY:
# REVIEWED TIME:
# COMMENTS: goods
################################################################
#===============================================================================
# FILE: exe_app_basic_goods_stock_num_daily_da_d.py
# USAGE: ./exe_app_basic_goods_stock_num_daily_da_d.py
# SRC_TABLE:
# TGT_TABLE: app_basic_goods_stock_num_daily_da
#===============================================================================
import sys
import os
import time
import datetime
import logging
import calendar
sys.path.append(os.getenv('HIVE_TASK'))
from HiveTask import HiveTask
ht = HiveTask()
today = ht.oneday(1)[0:10]
yesterday = ht.oneday(0)[0:10]
sql1 = """
use app;
insert overwrite table app.app_basic_dashboard_goods_sale_predict_daily_tem partition
(
dt = '"""+yesterday+"""'
)
select
c.seller_id, --商家编号
c.seller_name, --商家名称
c.dept_id, --事业部id
c.dept_no, --事业部编号
c.dept_name, --事业部名称
--c.seller_no, --供应商编号
--c.seller_name, --供应商名称
c.goods_id, --商品id
c.goods_no, --商品编号
c.goods_name, --商品名称
c.warehouse_id, --入仓id
c.warehouse_no, --入仓编号
c.warehouse_name, --入仓名称
h.loc_city_name as in_warehouse_city, --入仓城市
case
when d.satisfy_alpha is null
then '0.8'
else d.satisfy_alpha
end as satisfy_alpha, -- 出仓服务水平c (basis)
case
when d.safe_stock_days is null
then '5'
else d.safe_stock_days
end as safe_stock_days, -- 安全库存天数(basis)
case
when d.alt is null
then '10'
else d.alt
end as alt, -- 出仓alt(小时)(basis)
case
when j.totaltime is null
then '36'
else j.totaltime
end as vlt, --运输时间
f.in_stock_safety_num as in_stock_safety_num, --安全库存（计划调拨表取数）
case
when d.target_stock_days is null
then '10'
else d.target_stock_days
end as target_stock_days, -- 目标库存天数(basis)
case
when d.bp is null
then '20'
else d.bp
end as BP, -- BP
f.out_warehouse_no as out_warehouse_no, --配出仓编码
f.out_warehouse_name as out_warehouse_name, --配出仓名称
i.loc_city_name as out_warehouse_city, --入仓城市
f.in_stock_max_num as in_stock_max_num, --目标库存（计划调拨表取数）
case
when split(g.sales_week_detail, ',') [6] is NULL
then '0'
else split(g.sales_week_detail, ',') [6]
end as bef_sales_1d, --T-1日销量(逆向)
case
when split(g.sales_week_detail, ',') [5] is NULL
then '0'
else split(g.sales_week_detail, ',') [5]
end as bef_sales_2d, --T-2日销量
case
when split(g.sales_week_detail, ',') [4] is NULL
then '0'
else split(g.sales_week_detail, ',') [4]
end as bef_sales_3d, --T-3日销量
case
when split(g.sales_week_detail, ',') [3] is NULL
then '0'
else split(g.sales_week_detail, ',') [3]
end as bef_sales_4d, --T-4日销量
case
when split(g.sales_week_detail, ',') [2] is NULL
then '0'
else split(g.sales_week_detail, ',') [2]
end as bef_sales_5d, --T-5日销量
case
when split(g.sales_week_detail, ',') [1] is NULL
then '0'
else split(g.sales_week_detail, ',') [1]
end as bef_sales_6d, --T-6日销量
case
when split(g.sales_week_detail, ',') [0] is NULL
then '0'
else split(g.sales_week_detail, ',') [0]
end as bef_sales_7d, --T-7日销量
case
when g.sales_14d is NULL
then '0'
else g.sales_14d
end as in_stock_sales_14d, --14日销量
case
when g.sales_28d is NULL
then '0'
else g.sales_28d
end as in_stock_sales_28d, --28日销量
case
when split(g.predict_sales_week_detail, ',')[0] is NULL and m.avg_year_sellnum is NULL --都空为0
then '0'
when split(g.predict_sales_week_detail, ',')[0] is NULL and m.avg_year_sellnum is not NULL --没有预测值，按销量平均值计
then round(m.avg_year_sellnum)
else split(g.predict_sales_week_detail, ',') [0] --预测值计
end as predict_sales_1d, --T+1日预测销量(正向)
case
when split(g.predict_sales_week_detail, ',')[1] is NULL and m.avg_year_sellnum is NULL
then '0'
when split(g.predict_sales_week_detail, ',')[1] is NULL and m.avg_year_sellnum is not NULL
then round(m.avg_year_sellnum)
else split(g.predict_sales_week_detail, ',') [1]
end as predict_sales_2d, --T+2日预测销量
case
when split(g.predict_sales_week_detail, ',')[2] is NULL and m.avg_year_sellnum is NULL
then '0'
when split(g.predict_sales_week_detail, ',')[2] is NULL and m.avg_year_sellnum is not NULL
then round(m.avg_year_sellnum)
else split(g.predict_sales_week_detail, ',') [2]
end as predict_sales_3d, --T+3日预测销量
case
when split(g.predict_sales_week_detail, ',')[3] is NULL and m.avg_year_sellnum is NULL
then '0'
when split(g.predict_sales_week_detail, ',')[3] is NULL and m.avg_year_sellnum is not NULL
then round(m.avg_year_sellnum)
else split(g.predict_sales_week_detail, ',') [3]
end as predict_sales_4d, --T+4日预测销量
case
when split(g.predict_sales_week_detail, ',')[4] is NULL and m.avg_year_sellnum is NULL
then '0'
when split(g.predict_sales_week_detail, ',')[4] is NULL and m.avg_year_sellnum is not NULL
then round(m.avg_year_sellnum)
else split(g.predict_sales_week_detail, ',') [4]
end as predict_sales_5d, --T+5日预测销量
case
when split(g.predict_sales_week_detail, ',')[5] is NULL and m.avg_year_sellnum is NULL
then '0'
when split(g.predict_sales_week_detail, ',')[5] is NULL and m.avg_year_sellnum is not NULL
then round(m.avg_year_sellnum)
else split(g.predict_sales_week_detail, ',') [5]
end as predict_sales_6d, --T+6日预测销量
case
when split(g.predict_sales_week_detail, ',')[6] is NULL and m.avg_year_sellnum is NULL
then '0'
when split(g.predict_sales_week_detail, ',')[6] is NULL and m.avg_year_sellnum is not NULL
then round(m.avg_year_sellnum)
else split(g.predict_sales_week_detail, ',') [6]
end as predict_sales_7d, --T+7日预测销量
case
when g.predict_sales_14d is NULL and m.avg_year_sellnum is NULL
then '0'
when g.predict_sales_14d is NULL and m.avg_year_sellnum is not NULL
then round(m.avg_year_sellnum*14)
else g.predict_sales_14d
end as in_stock_predict_sales_14d, --14日预测销量
case
when g.predict_sales_28d is NULL and m.avg_year_sellnum is NULL
then '0'
when g.predict_sales_28d is NULL and m.avg_year_sellnum is not NULL
then round(m.avg_year_sellnum*28)
else g.predict_sales_28d
end as in_stock_predict_sales_28d, --28日预测销量
1 AS yn, --删除标识 1为没删，0为删除
'plumber' AS create_pin, --创建人
'plumber' AS update_pin , --更新人
current_timestamp AS create_time, --创建时间
current_timestamp AS update_time, --更新时间
current_timestamp AS ts --时间戳
from
fdm.fdm_eclp_stock1_saleable_warehouse_stock_chain c --可销售库存表
join
(
select
a.dept_no,
a.goods_id,
a.warehouse_no,
max(a.start_date) as start_date
from
fdm.fdm_eclp_stock1_saleable_warehouse_stock_chain a
where
a.dept_no in
(
select dept_no from app.app_basic_dashboard_goods_seller --商户名称表
)
and a.dp = 'ACTIVE'
and a.yn = 1
group by
a.dept_no,
a.warehouse_no,
a.goods_id
)
b
on
c.dept_no = b.dept_no
and c.warehouse_no = b.warehouse_no
and c.goods_id = b.goods_id
and c.start_date = b.start_date
left join fdm.fdm_log_scm_ka_allot_sys_pre_allot d --参数表
on
c.dept_no = d.dept_no
and c.warehouse_no = d.warehouse_no
and c.goods_no = d.goods_id
and d.yn = 1
and d.dt = sysdate( - 1)
left join
(
select
*
from
fdm.fdm_log_scm_ka_allot_allot_plan --调拨计划表
where
id in
(
select
max(e.id)
from
fdm.fdm_log_scm_ka_allot_allot_plan e
where
e.dt = sysdate( - 1)
group by
e.dept_name,
e.in_warehouse_no,
e.goods_no
)
and dt = sysdate( - 1)
)
f
on
f.dept_no = c.dept_no
and f.in_warehouse_no = c.warehouse_no
and f.goods_no = c.goods_no
left join app.app_ka_predict_sales_da g ---线下门店销量预测表
on
c.dept_no = g.dept_no
and c.goods_no = g.goods_no
and c.warehouse_no = g.warehouse_no
and g.dt = sysdate( - 1)
left join dim.dim_wms_store h --库房维表，取出入仓所属城市
on
c.warehouse_name = h.dim_store_name
left join dim.dim_wms_store i --库房维表，取出仓所属城市
on
f.out_warehouse_name = i.dim_store_name
left join
(
select
startcityid,
startcityname,
endcityid,
endcityname,
totalaging,
totaltime,
yn
from
(
select
case
when
(
startprovinceid in(1, 2, 3, 4)
)
then startprovinceid
else startcityid
end as startcityid,
case
when
(
startprovinceid in(1, 2, 3, 4)
)
then concat(startprovincename, '市')
else startcityname
end as startcityname,
case
when
(
endprovinceid in(1, 2, 3, 4)
)
then endprovinceid
else endcityid
end as endcityid,
case
when
(
endprovinceid in(1, 2, 3, 4)
)
then concat(endprovincename, '市')
else endcityname
end as endcityname,
ceil(avg(totalaging)) as totalaging,
ceil(avg(totaltime)) as totaltime,
max(1) as yn
from
fdm.fdm_staticroutebatchgenerate_staticroutebatchgenerate_chain
where
dp = 'ACTIVE'
and yn = 1
group by
case
when
(
startprovinceid in(1, 2, 3, 4)
)
then startprovinceid
else startcityid
end,
case
when
(
startprovinceid in(1, 2, 3, 4)
)
then concat(startprovincename, '市')
else startcityname
end,
case
when
(
endprovinceid in(1, 2, 3, 4)
)
then endprovinceid
else endcityid
end,
case
when
(
endprovinceid in(1, 2, 3, 4)
)
then concat(endprovincename, '市')
else endcityname
end
)
route
)
j --vlt
on
h.loc_city_name = j.startcityname
and i.loc_city_name = j.endcityname
left join app.app_basic_goods_stock_num_daily_da m
on
c.seller_id=m.seller_id
and c.warehouse_id=m.warehouse_id
and c.goods_id=m.goods_id
and m.dt = sysdate( - 1)
"""
ht.exec_sql(schema_name = 'app', table_name = 'app_basic_dashboard_goods_sale_predict_daily_tem', sql = sql1, merge_flag = True)
4、
#!/usr/bin/env python3
################################################################
# AUTHOR: wn
# CREATED TIME: 2018-08-09
# MODIFIED BY:
# MODIFTED TIME:
# REVIEWED BY:
# REVIEWED TIME:
# COMMENTS: goods
################################################################
#===============================================================================
# FILE: exe_app_basic_dashboard_goods_sale_predict_daily.py
# USAGE: ./exe_app_basic_dashboard_goods_sale_predict_daily.py
# SRC_TABLE:
# TGT_TABLE: app.app_basic_dashboard_goods_sale_predict_daily
#===============================================================================
import sys
import os
import time
import datetime
import logging
import calendar
sys.path.append(os.getenv('HIVE_TASK'))
from HiveTask import HiveTask
ht = HiveTask()
today = ht.oneday(1)[0:10]
yesterday = ht.oneday(0)[0:10]
sql1 = """
use app;
insert overwrite table app.app_basic_dashboard_goods_sale_predict_daily partition
(
dt = '"""+yesterday+"""'
)
select
k.seller_id, --商家编号
k.seller_name, --商家名称
k.dept_id, --事业部id
k.dept_no, --事业部编号
k.dept_name, --事业部名称
k.goods_id, --商品id
k.goods_no, --商品编号
k.goods_name, --商品名称
k.warehouse_id, --入仓id
k.warehouse_no, --入仓编号
k.warehouse_name, --入仓名称
k.in_warehouse_city, --入仓城市
k.satisfy_alpha, -- 出仓服务水平c (basis)
k.safe_stock_days, -- 安全库存天数(basis)
k.alt, -- 出仓alt(小时)(basis)
k.vlt, --运输时间
case
when k.in_stock_safety_num is null
and
(
m.safe_stock_days + m.alt / 24 + m.vlt / 24
)
>= 0
and
(
m.safe_stock_days + m.alt / 24 + m.vlt / 24
)
<= 1
then round(m.satisfy_alpha *(m.safe_stock_days + m.alt / 24 + m.vlt / 24) * m.predict_sales_1d)
when k.in_stock_safety_num is null
and
(
m.safe_stock_days + m.alt / 24 + m.vlt / 24
)
> 1
and
(
m.safe_stock_days + m.alt / 24 + m.vlt / 24
)
<= 2
then round(m.satisfy_alpha *(m.safe_stock_days + m.alt / 24 + m.vlt / 24) *(m.predict_sales_1d + m.predict_sales_2d) / 2)
when k.in_stock_safety_num is null
and
(
m.safe_stock_days + m.alt / 24 + m.vlt / 24
)
> 2
and
(
m.safe_stock_days + m.alt / 24 + m.vlt / 24
)
<= 3
then round(m.satisfy_alpha *(m.safe_stock_days + m.alt / 24 + m.vlt / 24) *(m.predict_sales_1d + m.predict_sales_2d + m.predict_sales_3d) / 3)
when k.in_stock_safety_num is null
and
(
m.safe_stock_days + m.alt / 24 + m.vlt / 24
)
> 3
and
(
m.safe_stock_days + m.alt / 24 + m.vlt / 24
)
<= 4
then round(m.satisfy_alpha *(m.safe_stock_days + m.alt / 24 + m.vlt / 24) *(m.predict_sales_1d + m.predict_sales_2d + m.predict_sales_3d + m.predict_sales_4d) / 4)
when k.in_stock_safety_num is null
and
(
m.safe_stock_days + m.alt / 24 + m.vlt / 24
)
> 4
and
(
m.safe_stock_days + m.alt / 24 + m.vlt / 24
)
<= 5
then round(m.satisfy_alpha *(m.safe_stock_days + m.alt / 24 + m.vlt / 24) *(m.predict_sales_1d + m.predict_sales_2d + m.predict_sales_3d + m.predict_sales_4d + m.predict_sales_5d) / 5)
when k.in_stock_safety_num is null
and
(
m.safe_stock_days + m.alt / 24 + m.vlt / 24
)
> 5
and
(
m.safe_stock_days + m.alt / 24 + m.vlt / 24
)
<= 6
then round(m.satisfy_alpha *(m.safe_stock_days + m.alt / 24 + m.vlt / 24) *(m.predict_sales_1d + m.predict_sales_2d + m.predict_sales_3d + m.predict_sales_4d + m.predict_sales_5d + m.predict_sales_6d) / 6)
when k.in_stock_safety_num is null
and
(
m.safe_stock_days + m.alt / 24 + m.vlt / 24
)
> 6
and
(
m.safe_stock_days + m.alt / 24 + m.vlt / 24
)
<= 7
then round(m.satisfy_alpha *(m.safe_stock_days + m.alt / 24 + m.vlt / 24) *(m.predict_sales_1d + m.predict_sales_2d + m.predict_sales_3d + m.predict_sales_4d + m.predict_sales_5d + m.predict_sales_6d + m.predict_sales_7d) / 7)
when k.in_stock_safety_num is null
and
(
m.safe_stock_days + m.alt / 24 + m.vlt / 24
)
> 7
and
(
m.safe_stock_days + m.alt / 24 + m.vlt / 24
)
<= 14
then round(m.satisfy_alpha *(m.safe_stock_days + m.alt / 24 + m.vlt / 24) *(m.in_stock_predict_sales_14d) / 14)
when k.in_stock_safety_num is null
and
(
m.safe_stock_days + m.alt / 24 + m.vlt / 24
)
> 14
then round(m.satisfy_alpha *(m.safe_stock_days + m.alt / 24 + m.vlt / 24) *(m.in_stock_predict_sales_28d) / 28)
else round(k.in_stock_safety_num)
end as in_stock_safety_num, --安全库存
k.target_stock_days, -- 目标库存天数(basis)
k.BP, -- BP
k.out_warehouse_no, --配出仓编码
k.out_warehouse_name, --配出仓名称
k.out_warehouse_city, --出仓城市
case
when k.in_stock_max_num is null
then round(m.satisfy_alpha * m.target_stock_days * m.in_stock_predict_sales_14d / 14 + m.BP)
else round(k.in_stock_max_num)
end as in_stock_max_num, --目标库存
k.bef_sales_1d, --T-1日销量(逆向)
k.bef_sales_2d, --T-2日销量
k.bef_sales_3d, --T-3日销量
k.bef_sales_4d, --T-4日销量
k.bef_sales_5d, --T-5日销量
k.bef_sales_6d, --T-6日销量
k.bef_sales_7d, --T-7日销量
k.in_stock_sales_14d, --14日销量
k.in_stock_sales_28d, --28日销量
k.predict_sales_1d, --T+1日预测销量(正向)
k.predict_sales_2d, --T+2日预测销量
k.predict_sales_3d, --T+3日预测销量
k.predict_sales_4d, --T+4日预测销量
k.predict_sales_5d, --T+5日预测销量
k.predict_sales_6d, --T+6日预测销量
k.predict_sales_7d, --T+7日预测销量
k.in_stock_predict_sales_14d, --14日预测销量
k.in_stock_predict_sales_28d, --28日预测销量
1 AS yn, --删除标识 1为没删，0为删除
'plumber' AS create_pin, --创建人
'plumber' AS update_pin, --更新人
current_timestamp AS create_time, --创建时间
current_timestamp AS update_time, --更新时间
current_timestamp AS ts --时间戳
from
(
select
*
from
app.app_basic_dashboard_goods_sale_predict_daily_tem
where
dt = sysdate( - 1)
)
k
join
(
select
*
from
app.app_basic_dashboard_goods_sale_predict_daily_tem
where
dt = sysdate( - 1)
)
m
on
k.dept_no = m.dept_no
and k.goods_no = m.goods_no
and k.warehouse_no = m.warehouse_no
"""
ht.exec_sql(schema_name = 'app', table_name = 'app_basic_dashboard_goods_sale_predict_daily', sql = sql1, merge_flag = True)
