数仓1.1 分层| ODS& DWD层

2021-08-05 09:44:49

数仓分层

ODS：Operation Data Store
原始数据

DWD(数据清洗/DWI) data warehouse detail
数据明细详情，去除空值，脏数据，超过极限范围的
明细解析
具体表

DWS(宽表-用户行为，轻度聚合) data warehouse service ----->有多少个宽表？多少个字段
服务层--留存-转化-GMV-复购率-日活
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轻度聚合对DWD

ADS(APP/DAL/DF)-出报表结果 Application Data Store
做分析处理同步到RDS数据库里边

数据集市：狭义ADS层； 广义上指DWD DWS ADS 从hadoop同步到RDS的数据

数仓搭建之ODS & DWD

1）创建gmall数据库

hive (default)> create database gmall;

说明：如果数据库存在且有数据，需要强制删除时执行：drop database gmall cascade;

2）使用gmall数据库

hive (default)> use gmall;

1. ODS层

原始数据层，存放原始数据，直接加载原始日志、数据，数据保持原貌不做处理。

① 创建启动日志表ods_start_log

1）创建输入数据是lzo输出是text，支持json解析的分区表

hive (gmall)>

drop table if exists ods_start_log;

CREATE EXTERNAL TABLE ods_start_log (`line` string)

PARTITIONED BY (`dt` string)

STORED AS

INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'

OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'

LOCATION '/warehouse/gmall/ods/ods_start_log';

Hive的LZO压缩：https://cwiki.apache.org/confluence/display/Hive/LanguageManual+LZO

加载数据；

时间格式都配置成YYYY-MM-DD格式，这是Hive默认支持的时间格式

hive (gmall)> load data inpath '/origin_data/gmall/log/topic_start/2019-02-10' into table gmall.ods_start_log partition(dt="2019-02-10");
hive (gmall)> select * from ods_start_log limit 2;

② 创建事件日志表ods_event_log

创建输入数据是lzo输出是text，支持json解析的分区表

drop table if exists ods_event_log;

create external table ods_event_log

(`line` string)

partitioned by (`dt` string)

stored as

INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'

OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'

location '/warehouse/gmall/ods/ods_event_log';

hive (gmall)> load data inpath '/origin_data/gmall/log/topic_event/2019-02-10' into table gmall.ods_event_log partition(dt="2019-02-10");

ODS层加载数据的脚本

1）在hadoop101的/home/kris/bin目录下创建脚本

[kris@hadoop101 bin]$ vim ods_log.sh

#!/bin/bash

# 定义变量方便修改

APP=gmall

hive=/opt/module/hive/bin/hive

# 如果是输入的日期按照取输入日期；如果没输入日期取当前时间的前一天

if [ -n "$1" ] ;then

 do_date=$

else

 do_date=`date -d "-1 day" +%F`

fi 

echo "===日志日期为 $do_date==="

sql="

load data inpath '/origin_data/gmall/log/topic_start/$do_date' into table "$APP".ods_start_log partition(dt='$do_date');

load data inpath '/origin_data/gmall/log/topic_event/$do_date' into table "$APP".ods_event_log partition(dt='$do_date');

"

$hive -e "$sql"

[ -n 变量值 ] 判断变量的值，是否为空

-- 变量的值，非空，返回true

-- 变量的值，为空，返回false

查看date命令的使用，[kris@hadoop101  ~]$ date --help

增加脚本执行权限

[kris@hadoop101 bin]$ chmod  ods_log.sh

脚本使用

[kris@hadoop101 module]$ ods_log.sh --

查看导入数据

hive (gmall)>

select * from ods_start_log where dt='2019-02-11' limit ;

select * from ods_event_log where dt='2019-02-11' limit ;

脚本执行时间

企业开发中一般在每日凌晨30分~1点

2. DWD层数据解析

对ODS层数据进行清洗（去除空值，脏数据，超过极限范围的数据，行式存储改为列存储，改压缩格式）

DWD解析过程，临时过程，两个临时表： dwd_base_event_log、dwd_base_start_log

建12张表外部表: 以日期分区,dwd_base_event_log在这张表中根据event_name将event_json中的字段通过get_json_object函数一个个解析开来;

DWD层创建基础明细表

明细表用于存储ODS层原始表转换过来的明细数据。

1） 创建启动日志基础明细表：

drop table if exists dwd_base_start_log;

create external table dwd_base_start_log(

`mid_id` string,

`user_id` string,

`version_code` string,

`version_name` string,

`lang` string,

`source` string,

`os` string,

`area` string,

`model` string,

`brand` string,

`sdk_version` string,

`gmail` string,

`height_width` string,

`app_time` string,

`network` string,

`lng` string,

`lat` string,

`event_name` string,

`event_json` string,

`server_time` string)

partitioned by(`dt` string)

stored as parquet

location "/warehouse/gmall/dwd/dwd_base_start_log"

其中event_name和event_json用来对应事件名和整个事件。这个地方将原始日志1对多的形式拆分出来了。操作的时候我们需要将原始日志展平，需要用到UDF和UDTF。

2）创建事件日志基础明细表

drop table if exists dwd_base_event_log;

create external table dwd_base_event_log(

`mid_id` string,

`user_id` string,

`version_code` string,

`version_name` string,

`lang` string,

`source` string,

`os` string,

`area` string,

`model` string,

`brand` string,

`sdk_version` string,

`gmail` string,

`height_width` string,

`app_time` string,

`network` string,

`lng` string,

`lat` string,

`event_name` string,

`event_json` string,

`server_time` string)

partitioned by(`dt` string)

stored as parquet

location "/warehouse/gmall/dwd/dwd_base_event_log"

自定义UDF函数（解析公共字段）

UDF:解析公共字段 + 事件et(json数组)+ 时间戳

自定义UDTF函数（解析具体事件字段） process 1进多出(可支持多进多出)

UDTF:对传入的事件et（json数组）-->返回event_name| event_json(取出事件et里边的每个具体事件--json_Array)

解析启动日志基础明细表

将jar包添加到Hive的classpath

创建临时函数与开发好的java class关联

hive (gmall)> add jar /opt/module/hive/hivefunction-1.0-SNAPSHOT.jar;

hive (gmall)> create temporary function base_analizer as "com.atguigu.udf.BaseFieldUDF";

hive (gmall)> create temporary function flat_analizer as "com.atguigu.udtf.EventJsonUDTF";

hive (gmall)> set hive.exec.dynamic.partition.mode=nonstrict;

1）解析启动日志基础明细表

insert overwrite table dwd_base_start_log

partition(dt)

select mid_id,user_id,version_code,version_name,lang,source,os,area,model,brand,sdk_version,gmail,height_width,app_time,network,

lng,lat,event_name, event_json,server_time,dt from(

select split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[] as mid_id,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as user_id,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as version_code,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as version_name,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as lang,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as source,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as os,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as area,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as model,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as brand,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as sdk_version,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as gmail,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as height_width,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as app_time,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as network,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as lng,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as lat,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as ops,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as server_time,

dt

from ods_start_log where dt='2019-02-10' and base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la')<>''

) sdk_log lateral view flat_analizer(ops) tmp_k as event_name, event_json;
将ops lateral view 成event_name和event_json;

+-------------+-------------------------------------------------------------------------------------------------------------------------+----------------+--+

| event_name  |                                                       event_json                                                        |  server_time   |

+-------------+-------------------------------------------------------------------------------------------------------------------------+----------------+--+

| start       | {"ett":"","en":"start","kv":{"entry":"","loading_time":"","action":"","open_ad_type":"","detail":""}}  |   |

+-------------+-------------------------------------------------------------------------------------------------------------------------+----------------+--+

解析事件日志基础明细表

1）解析事件日志基础明细表

insert overwrite table dwd_base_event_log

partition(dt='2019-02-10')

select mid_id,user_id,version_code,version_name,lang,source,os,area,model,brand,sdk_version,gmail,height_width,app_time,network,

lng,lat,event_name, event_json,server_time from(

select split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[] as mid_id,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as user_id,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as version_code,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as version_name,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as lang,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as source,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as os,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as area,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as model,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as brand,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as sdk_version,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as gmail,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as height_width,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as app_time,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as network,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as lng,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as lat,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as ops,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as server_time

from ods_event_log where dt='2019-02-10' and base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la')<>''

) sdk_log lateral view flat_analizer(ops) tmp_k as event_name, event_json;
测试

hive (gmall)> select * from dwd_base_event_log limit ;

DWD层加载数据脚本

1）在hadoop101的/home/kris/bin目录下创建脚本

[kris@hadoop101 bin]$ vim dwd.sh

#编写脚本：

#!/bin/bash

APP=gmall

hive=/opt/module/hive/bin/hive

if [ -n "$1" ] ;then

    do_date=$

else

    do_date=`date -d "-1 day" +%F`

fi

sql="

add jar /opt/module/hive/hivefunction-1.0-SNAPSHOT.jar;

create temporary function base_analizer as 'com.atguigu.udf.BaseFieldUDF';

create temporary function flat_analizer as 'com.atguigu.udtf.EventJsonUDTF';

set hive.exec.dynamic.partition.mode=nonstrict;

insert overwrite table "$APP".dwd_base_start_log

partition(dt)

select mid_id,user_id,version_code,version_name,lang,source,os,area,model,brand,sdk_version,gmail,height_width,app_time,network,

lng,lat,event_name, event_json,server_time,dt from(

select split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[] as mid_id,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as user_id,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as version_code,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as version_name,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as lang,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as source,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as os,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as area,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as model,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as brand,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as sdk_version,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as gmail,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as height_width,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as app_time,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as network,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as lng,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as lat,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as ops,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as server_time,

dt

from "$APP".ods_start_log where dt='$do_date' and base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la')<>''

) sdk_log lateral view flat_analizer(ops) tmp_k as event_name, event_json;

insert overwrite table "$APP".dwd_base_event_log

partition(dt='$do_date')

select mid_id,user_id,version_code,version_name,lang,source,os,area,model,brand,sdk_version,gmail,height_width,app_time,network,

lng,lat,event_name, event_json,server_time from(

select split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[] as mid_id,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as user_id,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as version_code,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as version_name,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as lang,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as source,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as os,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as area,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as model,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as brand,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]   as sdk_version,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as gmail,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as height_width,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as app_time,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as network,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as lng,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as lat,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as ops,

split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[]  as server_time

from "$APP".ods_event_log where dt='$do_date' and base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la')<>''

) sdk_log lateral view flat_analizer(ops) tmp_k as event_name, event_json;

"

$hive -e "$sql"
[kris@hadoop101 bin]$ chmod +x dwd_base.sh

[kris@hadoop101 bin]$ dwd_base.sh --

查询导入结果

hive (gmall)>

select * from dwd_start_log where dt='2019-02-11' limit ;

select * from dwd_comment_log where dt='2019-02-11' limit ;

脚本执行时间

企业开发中一般在每日凌晨30分~1点

3. DWD层

1） 商品点击表

建表

hive (gmall)>

drop table if exists dwd_display_log;

CREATE EXTERNAL TABLE dwd_display_log(

`mid_id` string,

`user_id` string,

`version_code` string,

`version_name` string,

`lang` string,

`source` string,

`os` string,

`area` string,

`model` string,

`brand` string,

`sdk_version` string,

`gmail` string,

`height_width` string,

`app_time` string,

`network` string,

`lng` string,

`lat` string,

`action` string,

`newsid` string,

`place` string,

`extend1` string,

`category` string,

`server_time` string

)

PARTITIONED BY (dt string)

location '/warehouse/gmall/dwd/dwd_display_log/';

导入数据

hive (gmall)>

set hive.exec.dynamic.partition.mode=nonstrict;

insert overwrite table dwd_display_log

PARTITION (dt)

select

mid_id,

user_id,

version_code,

version_name,

lang,

source,

os,

area,

model,

brand,

sdk_version,

gmail,

height_width,

app_time,

network,

lng,

lat,

get_json_object(event_json,'$.kv.action') action,

get_json_object(event_json,'$.kv.newsid') newsid,

get_json_object(event_json,'$.kv.place') place,

get_json_object(event_json,'$.kv.extend1') extend1,

get_json_object(event_json,'$.kv.category') category,

server_time,

dt

from dwd_base_event_log

where dt='2019-02-10' and event_name='display';

测试

hive (gmall)> select * from dwd_display_log limit ;

2 ）商品详情页表

建表语句

hive (gmall)>

drop table if exists dwd_newsdetail_log;

CREATE EXTERNAL TABLE `dwd_newsdetail_log`(

`mid_id` string,

`user_id` string,

`version_code` string,

`version_name` string,

`lang` string,

`source` string,

`os` string,

`area` string,

`model` string,

`brand` string,

`sdk_version` string,

`gmail` string,

`height_width` string,

`app_time` string,

`network` string,

`lng` string,

`lat` string,

`entry` string,

`action` string,

`newsid` string,

`showtype` string,

`news_staytime` string,

`loading_time` string,

`type1` string,

`category` string,

`server_time` string)

PARTITIONED BY (dt string)

location '/warehouse/gmall/dwd/dwd_newsdetail_log/';

导入数据

hive (gmall)>

set hive.exec.dynamic.partition.mode=nonstrict;

insert overwrite table dwd_newsdetail_log

PARTITION (dt)

select

mid_id,

user_id,

version_code,

version_name,

lang,

source,

os,

area,

model,

brand,

sdk_version,

gmail,

height_width,

app_time,

network,

lng,

lat,

get_json_object(event_json,'$.kv.entry') entry,

get_json_object(event_json,'$.kv.action') action,

get_json_object(event_json,'$.kv.newsid') newsid,

get_json_object(event_json,'$.kv.showtype') showtype,

get_json_object(event_json,'$.kv.news_staytime') news_staytime,

get_json_object(event_json,'$.kv.loading_time') loading_time,

get_json_object(event_json,'$.kv.type1') type1,

get_json_object(event_json,'$.kv.category') category,

server_time,

dt

from dwd_base_event_log

where dt='2019-02-10' and event_name='newsdetail';

测试

hive (gmall)> select * from dwd_newsdetail_log limit ;

3 ）商品列表页表

建表语句

hive (gmall)>

drop table if exists dwd_loading_log;

CREATE EXTERNAL TABLE `dwd_loading_log`(

`mid_id` string,

`user_id` string,

`version_code` string,

`version_name` string,

`lang` string,

`source` string,

`os` string,

`area` string,

`model` string,

`brand` string,

`sdk_version` string,

`gmail` string,

`height_width` string,

`app_time` string,

`network` string,

`lng` string,

`lat` string,

`action` string,

`loading_time` string,

`loading_way` string,

`extend1` string,

`extend2` string,

`type` string,

`type1` string,

`server_time` string)

PARTITIONED BY (dt string)

location '/warehouse/gmall/dwd/dwd_loading_log/';

导入数据

hive (gmall)>

set hive.exec.dynamic.partition.mode=nonstrict;

insert overwrite table dwd_loading_log

PARTITION (dt)

select

mid_id,

user_id,

version_code,

version_name,

lang,

source,

os,

area,

model,

brand,

sdk_version,

gmail,

height_width,

app_time,

network,

lng,

lat,

get_json_object(event_json,'$.kv.action') action,

get_json_object(event_json,'$.kv.loading_time') loading_time,

get_json_object(event_json,'$.kv.loading_way') loading_way,

get_json_object(event_json,'$.kv.extend1') extend1,

get_json_object(event_json,'$.kv.extend2') extend2,

get_json_object(event_json,'$.kv.type') type,

get_json_object(event_json,'$.kv.type1') type1,

server_time,

dt

from dwd_base_event_log

where dt='2019-02-10' and event_name='loading';

测试

hive (gmall)> select * from dwd_loading_log limit ;

4 广告表

建表语句

hive (gmall)>

drop table if exists dwd_ad_log;

CREATE EXTERNAL TABLE `dwd_ad_log`(

`mid_id` string,

`user_id` string,

`version_code` string,

`version_name` string,

`lang` string,

`source` string,

`os` string,

`area` string,

`model` string,

`brand` string,

`sdk_version` string,

`gmail` string,

`height_width` string,

`app_time` string,

`network` string,

`lng` string,

`lat` string,

`entry` string,

`action` string,

`content` string,

`detail` string,

`ad_source` string,

`behavior` string,

`newstype` string,

`show_style` string,

`server_time` string)

PARTITIONED BY (dt string)

location '/warehouse/gmall/dwd/dwd_ad_log/';

导入数据

hive (gmall)>

set hive.exec.dynamic.partition.mode=nonstrict;

insert overwrite table dwd_ad_log

PARTITION (dt)

select

mid_id,

user_id,

version_code,

version_name,

lang,

source,

os,

area,

model,

brand,

sdk_version,

gmail,

height_width,

app_time,

network,

lng,

lat,

get_json_object(event_json,'$.kv.entry') entry,

get_json_object(event_json,'$.kv.action') action,

get_json_object(event_json,'$.kv.content') content,

get_json_object(event_json,'$.kv.detail') detail,

get_json_object(event_json,'$.kv.source') ad_source,

get_json_object(event_json,'$.kv.behavior') behavior,

get_json_object(event_json,'$.kv.newstype') newstype,

get_json_object(event_json,'$.kv.show_style') show_style,

server_time,

dt

from dwd_base_event_log

where dt='2019-02-10' and event_name='ad';

测试

hive (gmall)> select * from dwd_ad_log limit ;

5 消息通知表

建表语句

hive (gmall)>

drop table if exists dwd_notification_log;

CREATE EXTERNAL TABLE `dwd_notification_log`(

`mid_id` string,

`user_id` string,

`version_code` string,

`version_name` string,

`lang` string,

`source` string,

`os` string,

`area` string,

`model` string,

`brand` string,

`sdk_version` string,

`gmail` string,

`height_width` string,

`app_time` string,

`network` string,

`lng` string,

`lat` string,

`action` string,

`noti_type` string,

`ap_time` string,

`content` string,

`server_time` string

)

PARTITIONED BY (dt string)

location '/warehouse/gmall/dwd/dwd_notification_log/';

导入数据

hive (gmall)>

set hive.exec.dynamic.partition.mode=nonstrict;

insert overwrite table dwd_notification_log

PARTITION (dt)

select

mid_id,

user_id,

version_code,

version_name,

lang,

source,

os,

area,

model,

brand,

sdk_version,

gmail,

height_width,

app_time,

network,

lng,

lat,

get_json_object(event_json,'$.kv.action') action,

get_json_object(event_json,'$.kv.noti_type') noti_type,

get_json_object(event_json,'$.kv.ap_time') ap_time,

get_json_object(event_json,'$.kv.content') content,

server_time,

dt

from dwd_base_event_log

where dt='2019-02-10' and event_name='notification';

测试

hive (gmall)> select * from dwd_notification_log limit ;

6 用户前台活跃表

）建表语句

hive (gmall)>

drop table if exists dwd_active_foreground_log;

CREATE EXTERNAL TABLE `dwd_active_foreground_log`(

`mid_id` string,

`user_id` string,

`version_code` string,

`version_name` string,

`lang` string,

`source` string,

`os` string,

`area` string,

`model` string,

`brand` string,

`sdk_version` string,

`gmail` string,

`height_width` string,

`app_time` string,

`network` string,

`lng` string,

`lat` string,

`active_source` string,

`server_time` string)

PARTITIONED BY (dt string)

location '/warehouse/gmall/dwd/dwd_foreground_log/';

）导入数据

hive (gmall)>

set hive.exec.dynamic.partition.mode=nonstrict;

insert overwrite table dwd_active_foreground_log

PARTITION (dt)

select

mid_id,

user_id,

version_code,

version_name,

lang,

source,

os,

area,

model,

brand,

sdk_version,

gmail,

height_width,

app_time,

network,

lng,

lat,

get_json_object(event_json,'$.kv.active_source') active_source,

server_time,

dt

from dwd_base_event_log

where dt='2019-02-10' and event_name='active_foreground';

）测试

hive (gmall)> select * from dwd_active_foreground_log limit ;

7 用户后台活跃表

）建表语句

hive (gmall)>

drop table if exists dwd_active_background_log;

CREATE EXTERNAL TABLE `dwd_active_background_log`(

`mid_id` string,

`user_id` string,

`version_code` string,

`version_name` string,

`lang` string,

`source` string,

`os` string,

`area` string,

`model` string,

`brand` string,

`sdk_version` string,

`gmail` string,

 `height_width` string,

`app_time` string,

`network` string,

`lng` string,

`lat` string,

`active_source` string,

`server_time` string

)

PARTITIONED BY (dt string)

location '/warehouse/gmall/dwd/dwd_background_log/';

）导入数据

hive (gmall)>

set hive.exec.dynamic.partition.mode=nonstrict;

insert overwrite table dwd_active_background_log

PARTITION (dt)

select

mid_id,

user_id,

version_code,

version_name,

lang,

source,

os,

area,

model,

brand,

sdk_version,

gmail,

height_width,

app_time,

network,

lng,

lat,

get_json_object(event_json,'$.kv.active_source') active_source,

server_time,

dt

from dwd_base_event_log

where dt='2019-02-10' and event_name='active_background';

）测试

hive (gmall)> select * from dwd_active_background_log limit ;

8 评论表

）建表语句

hive (gmall)>

drop table if exists dwd_comment_log;

CREATE EXTERNAL TABLE `dwd_comment_log`(

`mid_id` string,

`user_id` string,

`version_code` string,

`version_name` string,

`lang` string,

`source` string,

`os` string,

`area` string,

`model` string,

`brand` string,

`sdk_version` string,

`gmail` string,

`height_width` string,

`app_time` string,

`network` string,

`lng` string,

`lat` string,

`comment_id` int,

`userid` int,

`p_comment_id` int,

`content` string,

`addtime` string,

`other_id` int,

`praise_count` int,

`reply_count` int,

`server_time` string

)

PARTITIONED BY (dt string)

location '/warehouse/gmall/dwd/dwd_comment_log/';

）导入数据

hive (gmall)>

set hive.exec.dynamic.partition.mode=nonstrict;

insert overwrite table dwd_comment_log

PARTITION (dt)

select

mid_id,

user_id,

version_code,

version_name,

lang,

source,

os,

area,

model,

brand,

sdk_version,

gmail,

height_width,

app_time,

network,

lng,

lat,

get_json_object(event_json,'$.kv.comment_id') comment_id,

get_json_object(event_json,'$.kv.userid') userid,

get_json_object(event_json,'$.kv.p_comment_id') p_comment_id,

get_json_object(event_json,'$.kv.content') content,

get_json_object(event_json,'$.kv.addtime') addtime,

get_json_object(event_json,'$.kv.other_id') other_id,

get_json_object(event_json,'$.kv.praise_count') praise_count,

get_json_object(event_json,'$.kv.reply_count') reply_count,

server_time,

dt

from dwd_base_event_log

where dt='2019-02-10' and event_name='comment';

）测试

hive (gmall)> select * from dwd_comment_log limit ;

9 收藏表

）建表语句

hive (gmall)>

drop table if exists dwd_favorites_log;

CREATE EXTERNAL TABLE `dwd_favorites_log`(

`mid_id` string,

`user_id` string,

`version_code` string,

`version_name` string,

`lang` string,

`source` string,

`os` string,

`area` string,

`model` string,

`brand` string,

`sdk_version` string,

`gmail` string,

`height_width` string,

`app_time` string,

`network` string,

`lng` string,

`lat` string,

`id` int,

`course_id` int,

`userid` int,

`add_time` string,

`server_time` string

)

PARTITIONED BY (dt string)

location '/warehouse/gmall/dwd/dwd_favorites_log/';

）导入数据

hive (gmall)>

set hive.exec.dynamic.partition.mode=nonstrict;

insert overwrite table dwd_favorites_log

PARTITION (dt)

select

mid_id,

user_id,

version_code,

version_name,

lang,

source,

os,

area,

model,

brand,

sdk_version,

gmail,

height_width,

app_time,

network,

lng,

lat,

get_json_object(event_json,'$.kv.id') id,

get_json_object(event_json,'$.kv.course_id') course_id,

get_json_object(event_json,'$.kv.userid') userid,

get_json_object(event_json,'$.kv.add_time') add_time,

server_time,

dt

from dwd_base_event_log

where dt='2019-02-10' and event_name='favorites';

）测试

hive (gmall)> select * from dwd_favorites_log limit ;

10 点赞表

）建表语句

hive (gmall)>

drop table if exists dwd_praise_log;

CREATE EXTERNAL TABLE `dwd_praise_log`(

`mid_id` string,

`user_id` string,

`version_code` string,

`version_name` string,

`lang` string,

`source` string,

`os` string,

`area` string,

`model` string,

`brand` string,

`sdk_version` string,

`gmail` string,

`height_width` string,

`app_time` string,

`network` string,

`lng` string,

`lat` string,

`id` string,

`userid` string,

`target_id` string,

`type` string,

`add_time` string,

`server_time` string

)

PARTITIONED BY (dt string)

location '/warehouse/gmall/dwd/dwd_praise_log/';

）导入数据

hive (gmall)>

set hive.exec.dynamic.partition.mode=nonstrict;

insert overwrite table dwd_praise_log

PARTITION (dt)

select

mid_id,

user_id,

version_code,

version_name,

lang,

source,

os,

area,

model,

brand,

sdk_version,

gmail,

height_width,

app_time,

network,

lng,

lat,

get_json_object(event_json,'$.kv.id') id,

get_json_object(event_json,'$.kv.userid') userid,

get_json_object(event_json,'$.kv.target_id') target_id,

get_json_object(event_json,'$.kv.type') type,

get_json_object(event_json,'$.kv.add_time') add_time,

server_time,

dt

from dwd_base_event_log

where dt='2019-02-10' and event_name='praise';

）测试

hive (gmall)> select * from dwd_praise_log limit ;

11 启动日志表

）建表语句

hive (gmall)>

drop table if exists dwd_start_log;

CREATE EXTERNAL TABLE `dwd_start_log`(

`mid_id` string,

`user_id` string,

`version_code` string,

`version_name` string,

`lang` string,

`source` string,

`os` string,

`area` string,

`model` string,

`brand` string,

`sdk_version` string,

`gmail` string,

`height_width` string,

`app_time` string,

`network` string,

`lng` string,

`lat` string,

`entry` string,

`open_ad_type` string,

`action` string,

`loading_time` string,

`detail` string,

`extend1` string,

`server_time` string

)

PARTITIONED BY (dt string)

location '/warehouse/gmall/dwd/dwd_start_log/';

）导入数据

hive (gmall)>

set hive.exec.dynamic.partition.mode=nonstrict;

insert overwrite table dwd_start_log

PARTITION (dt)

select

mid_id,

user_id,

version_code,

version_name,

lang,

source,

os,

area,

model,

brand,

sdk_version,

gmail,

height_width,

app_time,

network,

lng,

lat,

get_json_object(event_json,'$.kv.entry') entry,

get_json_object(event_json,'$.kv.open_ad_type') open_ad_type,

get_json_object(event_json,'$.kv.action') action,

get_json_object(event_json,'$.kv.loading_time') loading_time,

get_json_object(event_json,'$.kv.detail') detail,

get_json_object(event_json,'$.kv.extend1') extend1,

server_time,

dt

from dwd_base_start_log

where dt='2019-02-10' and event_name='start';

）测试

hive (gmall)> select * from dwd_start_log limit ;

12 错误日志表

）建表语句

hive (gmall)>

drop table if exists dwd_error_log;

CREATE EXTERNAL TABLE `dwd_error_log`(

`mid_id` string,

`user_id` string,

`version_code` string,

`version_name` string,

`lang` string,

`source` string,

`os` string,

`area` string,

`model` string,

`brand` string,

`sdk_version` string,

`gmail` string,

`height_width` string,

`app_time` string,

`network` string,

`lng` string,

`lat` string,

`errorBrief` string,

`errorDetail` string,

`server_time` string)

PARTITIONED BY (dt string)

location '/warehouse/gmall/dwd/dwd_error_log/';

）导入数据

hive (gmall)>

set hive.exec.dynamic.partition.mode=nonstrict;

insert overwrite table dwd_error_log

PARTITION (dt)

select

mid_id,

user_id,

version_code,

version_name,

lang,

source,

os,

area,

model,

brand,

sdk_version,

gmail,

height_width,

app_time,

network,

lng,

lat,

get_json_object(event_json,'$.kv.errorBrief') errorBrief,

get_json_object(event_json,'$.kv.errorDetail') errorDetail,

server_time,

dt

from dwd_base_event_log

where dt='2019-02-10' and event_name='error';

）测试

hive (gmall)> select * from dwd_error_log limit ;

DWD层加载数据脚本

1）在hadoop101的/home/kris/bin目录下创建脚本

[kris@hadoop101 bin]$ vim dwd.sh

#!/bin/bash

# 定义变量方便修改

APP=gmall

hive=/opt/module/hive/bin/hive

# 如果是输入的日期按照取输入日期；如果没输入日期取当前时间的前一天

if [ -n "$1" ] ;then

    do_date=$

else

    do_date=`date  -d "-1 day"  +%F`

fi

sql="

set hive.exec.dynamic.partition.mode=nonstrict;

insert overwrite table "$APP".dwd_display_log

PARTITION (dt)

select

    mid_id,

    user_id,

    version_code,

    version_name,

    lang,

    source,

    os,

    area,

    model,

    brand,

    sdk_version,

    gmail,

    height_width,

    app_time,

    network,

    lng,

    lat,

    get_json_object(event_json,'$.kv.action') action,

    get_json_object(event_json,'$.kv.newsid') newsid,

    get_json_object(event_json,'$.kv.place') place,

    get_json_object(event_json,'$.kv.extend1') extend1,

    get_json_object(event_json,'$.kv.category') category,

    server_time,

    dt

from "$APP".dwd_base_event_log

where dt='$do_date' and event_name='display';

insert overwrite table "$APP".dwd_newsdetail_log

PARTITION (dt)

select

    mid_id,

    user_id,

    version_code,

    version_name,

    lang,

    source,

    os,

    area,

    model,

    brand,

    sdk_version,

    gmail,

    height_width,

    app_time,

    network,

    lng,

    lat,

    get_json_object(event_json,'$.kv.entry') entry,

    get_json_object(event_json,'$.kv.action') action,

    get_json_object(event_json,'$.kv.newsid') newsid,

    get_json_object(event_json,'$.kv.showtype') showtype,

    get_json_object(event_json,'$.kv.news_staytime') news_staytime,

    get_json_object(event_json,'$.kv.loading_time') loading_time,

    get_json_object(event_json,'$.kv.type1') type1,

    get_json_object(event_json,'$.kv.category') category,

    server_time,

    dt

from "$APP".dwd_base_event_log

where dt='$do_date' and event_name='newsdetail';

insert overwrite table "$APP".dwd_loading_log

PARTITION (dt)

select

    mid_id,

    user_id,

    version_code,

    version_name,

    lang,

    source,

    os,

    area,

    model,

    brand,

    sdk_version,

    gmail,

    height_width,

    app_time,

    network,

    lng,

    lat,

    get_json_object(event_json,'$.kv.action') action,

    get_json_object(event_json,'$.kv.loading_time') loading_time,

    get_json_object(event_json,'$.kv.loading_way') loading_way,

    get_json_object(event_json,'$.kv.extend1') extend1,

    get_json_object(event_json,'$.kv.extend2') extend2,

    get_json_object(event_json,'$.kv.type') type,

    get_json_object(event_json,'$.kv.type1') type1,

    server_time,

    dt

from "$APP".dwd_base_event_log

where dt='$do_date' and event_name='loading';

insert overwrite table "$APP".dwd_ad_log

PARTITION (dt)

select

    mid_id,

    user_id,

    version_code,

    version_name,

    lang,

    source,

    os,

    area,

    model,

    brand,

    sdk_version,

    gmail,

    height_width,

    app_time,

    network,

    lng,

    lat,

    get_json_object(event_json,'$.kv.entry') entry,

    get_json_object(event_json,'$.kv.action') action,

    get_json_object(event_json,'$.kv.content') content,

    get_json_object(event_json,'$.kv.detail') detail,

    get_json_object(event_json,'$.kv.source') ad_source,

    get_json_object(event_json,'$.kv.behavior') behavior,

    get_json_object(event_json,'$.kv.newstype') newstype,

    get_json_object(event_json,'$.kv.show_style') show_style,

    server_time,

    dt

from "$APP".dwd_base_event_log

where dt='$do_date' and event_name='ad';

insert overwrite table "$APP".dwd_notification_log

PARTITION (dt)

select

    mid_id,

    user_id,

    version_code,

    version_name,

    lang,

    source,

    os,

    area,

    model,

    brand,

    sdk_version,

    gmail,

    height_width,

    app_time,

    network,

    lng,

    lat,

    get_json_object(event_json,'$.kv.action') action,

    get_json_object(event_json,'$.kv.noti_type') noti_type,

    get_json_object(event_json,'$.kv.ap_time') ap_time,

    get_json_object(event_json,'$.kv.content') content,

    server_time,

    dt

from "$APP".dwd_base_event_log

where dt='$do_date' and event_name='notification';

insert overwrite table "$APP".dwd_active_foreground_log

PARTITION (dt)

select

    mid_id,

    user_id,

    version_code,

    version_name,

    lang,

    source,

    os,

    area,

    model,

    brand,

    sdk_version,

    gmail,

    height_width,

    app_time,

    network,

    lng,

    lat,

    get_json_object(event_json,'$.kv.active_source') active_source,

    server_time,

    dt

from "$APP".dwd_base_event_log

where dt='$do_date' and event_name='active_background';

insert overwrite table "$APP".dwd_active_background_log

PARTITION (dt)

select

    mid_id,

    user_id,

    version_code,

    version_name,

    lang,

    source,

    os,

    area,

    model,

    brand,

    sdk_version,

    gmail,

    height_width,

    app_time,

    network,

    lng,

    lat,

    get_json_object(event_json,'$.kv.active_source') active_source,

    server_time,

    dt

from "$APP".dwd_base_event_log

where dt='$do_date' and event_name='active_background';

insert overwrite table "$APP".dwd_comment_log

PARTITION (dt)

select

    mid_id,

    user_id,

    version_code,

    version_name,

    lang,

    source,

    os,

    area,

    model,

    brand,

    sdk_version,

    gmail,

    height_width,

    app_time,

    network,

    lng,

    lat,

    get_json_object(event_json,'$.kv.comment_id') comment_id,

    get_json_object(event_json,'$.kv.userid') userid,

    get_json_object(event_json,'$.kv.p_comment_id') p_comment_id,

    get_json_object(event_json,'$.kv.content') content,

    get_json_object(event_json,'$.kv.addtime') addtime,

    get_json_object(event_json,'$.kv.other_id') other_id,

    get_json_object(event_json,'$.kv.praise_count') praise_count,

    get_json_object(event_json,'$.kv.reply_count') reply_count,

    server_time,

    dt

from "$APP".dwd_base_event_log

where dt='$do_date' and event_name='comment';

insert overwrite table "$APP".dwd_favorites_log

PARTITION (dt)

select

    mid_id,

    user_id,

    version_code,

    version_name,

    lang,

    source,

    os,

    area,

    model,

    brand,

    sdk_version,

    gmail,

    height_width,

    app_time,

    network,

    lng,

    lat,

    get_json_object(event_json,'$.kv.id') id,

    get_json_object(event_json,'$.kv.course_id') course_id,

    get_json_object(event_json,'$.kv.userid') userid,

    get_json_object(event_json,'$.kv.add_time') add_time,

    server_time,

    dt

from "$APP".dwd_base_event_log

where dt='$do_date' and event_name='favorites';

insert overwrite table "$APP".dwd_praise_log

PARTITION (dt)

select

    mid_id,

    user_id,

    version_code,

    version_name,

    lang,

    source,

    os,

    area,

    model,

    brand,

    sdk_version,

    gmail,

    height_width,

    app_time,

    network,

    lng,

    lat,

    get_json_object(event_json,'$.kv.id') id,

    get_json_object(event_json,'$.kv.userid') userid,

    get_json_object(event_json,'$.kv.target_id') target_id,

    get_json_object(event_json,'$.kv.type') type,

    get_json_object(event_json,'$.kv.add_time') add_time,

    server_time,

    dt

from "$APP".dwd_base_event_log

where dt='$do_date' and event_name='praise';

insert overwrite table "$APP".dwd_start_log

PARTITION (dt)

select

    mid_id,

    user_id,

    version_code,

    version_name,

    lang,

    source,

    os,

    area,

    model,

    brand,

    sdk_version,

    gmail,

    height_width,

    app_time,

    network,

    lng,

    lat,

    get_json_object(event_json,'$.kv.entry') entry,

    get_json_object(event_json,'$.kv.open_ad_type') open_ad_type,

    get_json_object(event_json,'$.kv.action') action,

    get_json_object(event_json,'$.kv.loading_time') loading_time,

    get_json_object(event_json,'$.kv.detail') detail,

    get_json_object(event_json,'$.kv.extend1') extend1,

    server_time,

    dt

from "$APP".dwd_base_start_log

where dt='$do_date' and event_name='start';

insert overwrite table "$APP".dwd_error_log

PARTITION (dt)

select

    mid_id,

    user_id,

    version_code,

    version_name,

    lang,

    source,

    os,

    area,

    model,

    brand,

    sdk_version,

    gmail,

    height_width,

    app_time,

    network,

    lng,

    lat,

    get_json_object(event_json,'$.kv.errorBrief') errorBrief,

    get_json_object(event_json,'$.kv.errorDetail') errorDetail,

    server_time,

    dt

from "$APP".dwd_base_event_log

where dt='$do_date' and event_name='error';

"

$hive -e "$sql"

）增加脚本执行权限

[kris@hadoop101 bin]$ chmod  dwd.sh

）脚本使用

[kris@hadoop101 module]$ dwd.sh --

）查询导入结果

hive (gmall)>

select * from dwd_start_log where dt='2019-02-11' limit ;

select * from dwd_comment_log where dt='2019-02-11' limit ;

）脚本执行时间

企业开发中一般在每日凌晨30分~1点

数据在hdfs上保存时间，半或1年清理下，可下载压缩保存下来

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