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
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。
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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=$1 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 777 ods_log.sh 脚本使用 [kris@hadoop101 module]$ ods_log.sh 2019-02-11 查看导入数据 hive (gmall)> select * from ods_start_log where dt='2019-02-11' limit 2; select * from ods_event_log where dt='2019-02-11' limit 2; 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')[0] 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')[1] 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')[2] 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')[3] 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')[4] as lang, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[5] as source, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[6] as os, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[7] as area, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[8] as model, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[9] as brand, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[10] 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')[11] as gmail, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[12] 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')[13] 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')[14] as network, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[15] as lng, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[16] as lat, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[17] as ops, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[18] 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":"1549683362200","en":"start","kv":{"entry":"5","loading_time":"4","action":"1","open_ad_type":"1","detail":""}} | 1549728087940 | +-------------+-------------------------------------------------------------------------------------------------------------------------+----------------+--+
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')[0] 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')[1] 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')[2] 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')[3] 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')[4] as lang, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[5] as source, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[6] as os, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[7] as area, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[8] as model, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[9] as brand, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[10] 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')[11] as gmail, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[12] 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')[13] 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')[14] as network, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[15] as lng, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[16] as lat, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[17] as ops, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[18] 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 2;
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=$1 else do_date=`date -d "-1 day" +%F` fi sql=" add jar /opt/module/hive/hivefunction-1.0-SNAPSHOT.jar; create temporary function base_analizer as 'com.atguigu.udf.BaseFieldUDF'; create temporary function flat_analizer as 'com.atguigu.udtf.EventJsonUDTF'; set hive.exec.dynamic.partition.mode=nonstrict; insert overwrite table "$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')[0] 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')[1] 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')[2] 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')[3] 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')[4] as lang, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[5] as source, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[6] as os, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[7] as area, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[8] as model, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[9] as brand, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[10] 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')[11] as gmail, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[12] 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')[13] 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')[14] as network, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[15] as lng, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[16] as lat, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[17] as ops, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[18] 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')[0] 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')[1] 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')[2] 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')[3] 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')[4] as lang, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[5] as source, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[6] as os, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[7] as area, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[8] as model, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[9] as brand, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[10] 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')[11] as gmail, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[12] 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')[13] 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')[14] as network, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[15] as lng, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[16] as lat, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[17] as ops, split(base_analizer(line,'mid,uid,vc,vn,l,sr,os,ar,md,ba,sv,g,hw,t,nw,ln,la'),'\t')[18] 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 2019-02-11 查询导入结果 hive (gmall)> select * from dwd_start_log where dt='2019-02-11' limit 2; select * from dwd_comment_log where dt='2019-02-11' limit 2; 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,'$.kv.category') category, server_time, dt from dwd_base_event_log where dt='2019-02-10' and event_name='display'; 测试 hive (gmall)> select * from dwd_display_log limit 2;
2 ）商品详情页表
建表语句 hive (gmall)> drop table if exists dwd_newsdetail_log; CREATE EXTERNAL TABLE `dwd_newsdetail_log`( `mid_id` string, `user_id` string, `version_code` string, `version_name` string, `lang` string, `source` string, `os` string, `area` string, `model` string, `brand` string, `sdk_version` string, `gmail` string, `height_width` string, `app_time` string, `network` string, `lng` string, `lat` string, `entry` string, `action` string, `newsid` string, `showtype` string, `news_staytime` string, `loading_time` string, `type1` string, `category` string, `server_time` string) PARTITIONED BY (dt string) location '/warehouse/gmall/dwd/dwd_newsdetail_log/'; 导入数据 hive (gmall)> set hive.exec.dynamic.partition.mode=nonstrict; insert overwrite table dwd_newsdetail_log PARTITION (dt) select mid_id, user_id, version_code, version_name, lang, source, os, area, model, brand, sdk_version, gmail, height_width, app_time, network, lng, lat, get_json_object(event_json,'$.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,'$.kv.category') category, server_time, dt from dwd_base_event_log where dt='2019-02-10' and event_name='newsdetail'; 测试 hive (gmall)> select * from dwd_newsdetail_log limit 2;
3 ）商品列表页表
建表语句 hive (gmall)> drop table if exists dwd_loading_log; CREATE EXTERNAL TABLE `dwd_loading_log`( `mid_id` string, `user_id` string, `version_code` string, `version_name` string, `lang` string, `source` string, `os` string, `area` string, `model` string, `brand` string, `sdk_version` string, `gmail` string, `height_width` string, `app_time` string, `network` string, `lng` string, `lat` string, `action` string, `loading_time` string, `loading_way` string, `extend1` string, `extend2` string, `type` string, `type1` string, `server_time` string) PARTITIONED BY (dt string) location '/warehouse/gmall/dwd/dwd_loading_log/'; 导入数据 hive (gmall)> set hive.exec.dynamic.partition.mode=nonstrict; insert overwrite table dwd_loading_log PARTITION (dt) select mid_id, user_id, version_code, version_name, lang, source, os, area, model, brand, sdk_version, gmail, height_width, app_time, network, lng, lat, get_json_object(event_json,'$.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 2;
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 2;
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 2;
6 用户前台活跃表
1）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/'; 2）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,'$.kv.active_source') active_source, server_time, dt from dwd_base_event_log where dt='2019-02-10' and event_name='active_foreground'; 3）测试 hive (gmall)> select * from dwd_active_foreground_log limit 2;
7 用户后台活跃表
1）建表语句 hive (gmall)> drop table if exists dwd_active_background_log; CREATE EXTERNAL TABLE `dwd_active_background_log`( `mid_id` string, `user_id` string, `version_code` string, `version_name` string, `lang` string, `source` string, `os` string, `area` string, `model` string, `brand` string, `sdk_version` string, `gmail` string, `height_width` string, `app_time` string, `network` string, `lng` string, `lat` string, `active_source` string, `server_time` string ) PARTITIONED BY (dt string) location '/warehouse/gmall/dwd/dwd_background_log/'; 2）导入数据 hive (gmall)> set hive.exec.dynamic.partition.mode=nonstrict; insert overwrite table dwd_active_background_log PARTITION (dt) select mid_id, user_id, version_code, version_name, lang, source, os, area, model, brand, sdk_version, gmail, height_width, app_time, network, lng, lat, get_json_object(event_json,'$.kv.active_source') active_source, server_time, dt from dwd_base_event_log where dt='2019-02-10' and event_name='active_background'; 3）测试 hive (gmall)> select * from dwd_active_background_log limit 2;
8 评论表
1）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/'; 2）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'; 3）测试 hive (gmall)> select * from dwd_comment_log limit 2;
9 收藏表
1）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/'; 2）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'; 3）测试 hive (gmall)> select * from dwd_favorites_log limit 2;
10 点赞表
1）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/'; 2）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,'$.kv.add_time') add_time, server_time, dt from dwd_base_event_log where dt='2019-02-10' and event_name='praise'; 3）测试 hive (gmall)> select * from dwd_praise_log limit 2;
11 启动日志表
1）建表语句 hive (gmall)> drop table if exists dwd_start_log; CREATE EXTERNAL TABLE `dwd_start_log`( `mid_id` string, `user_id` string, `version_code` string, `version_name` string, `lang` string, `source` string, `os` string, `area` string, `model` string, `brand` string, `sdk_version` string, `gmail` string, `height_width` string, `app_time` string, `network` string, `lng` string, `lat` string, `entry` string, `open_ad_type` string, `action` string, `loading_time` string, `detail` string, `extend1` string, `server_time` string ) PARTITIONED BY (dt string) location '/warehouse/gmall/dwd/dwd_start_log/'; 2）导入数据 hive (gmall)> set hive.exec.dynamic.partition.mode=nonstrict; insert overwrite table dwd_start_log PARTITION (dt) select mid_id, user_id, version_code, version_name, lang, source, os, area, model, brand, sdk_version, gmail, height_width, app_time, network, lng, lat, get_json_object(event_json,'$.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,'$.kv.extend1') extend1, server_time, dt from dwd_base_start_log where dt='2019-02-10' and event_name='start'; 3）测试 hive (gmall)> select * from dwd_start_log limit 2;
12 错误日志表
1）建表语句 hive (gmall)> drop table if exists dwd_error_log; CREATE EXTERNAL TABLE `dwd_error_log`( `mid_id` string, `user_id` string, `version_code` string, `version_name` string, `lang` string, `source` string, `os` string, `area` string, `model` string, `brand` string, `sdk_version` string, `gmail` string, `height_width` string, `app_time` string, `network` string, `lng` string, `lat` string, `errorBrief` string, `errorDetail` string, `server_time` string) PARTITIONED BY (dt string) location '/warehouse/gmall/dwd/dwd_error_log/'; 2）导入数据 hive (gmall)> set hive.exec.dynamic.partition.mode=nonstrict; insert overwrite table dwd_error_log PARTITION (dt) select mid_id, user_id, version_code, version_name, lang, source, os, area, model, brand, sdk_version, gmail, height_width, app_time, network, lng, lat, get_json_object(event_json,'$.kv.errorBrief') errorBrief, get_json_object(event_json,'$.kv.errorDetail') errorDetail, server_time, dt from dwd_base_event_log where dt='2019-02-10' and event_name='error'; 3）测试 hive (gmall)> select * from dwd_error_log limit 2;
DWD层加载数据脚本
1）在hadoop101的/home/kris/bin目录下创建脚本
[kris@hadoop101 bin]$ 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=$1 else do_date=`date -d "-1 day" +%F` fi sql=" set hive.exec.dynamic.partition.mode=nonstrict; insert overwrite table "$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" 2）增加脚本执行权限 [kris@hadoop101 bin]$ chmod 777 dwd.sh 3）脚本使用 [kris@hadoop101 module]$ dwd.sh 2019-02-11 4）查询导入结果 hive (gmall)> select * from dwd_start_log where dt='2019-02-11' limit 2; select * from dwd_comment_log where dt='2019-02-11' limit 2; 5）脚本执行时间 企业开发中一般在每日凌晨30分~1点