【电商日志项目之六】数据分析-Hive方式

环境
　　hadoop-2.6.5
　　hive-1.2.1

一、Hive和Hbase整合
如果使用Hive进行分析，Hive要从Hbase取数据（当然可以直接将数据存到Hive），那么就需要将Hive和HBase做整合，将hbase的列映射到hive即可。

步骤1:把hive-hbase-handler-1.2.1.jar cp到hbase/lib 下，同时把hbase中的所有的jar，cp到hive/lib

[root@node101 lib]# scp /usr/local/hive-1.2./lib/hive-hbase-handler-1.2..jar node104:/usr/local/hbase-0.98.12.1-hadoop2/lib

hive-hbase-handler-1.2..jar % 113KB .2KB/s :


[root@node104 lib]# scp /usr/local/hbase-0.98.12.1-hadoop2/lib/* node101:/usr/local/hive-1.2.1/lib/

步骤2:在hive的配置文件hive-site.xml 增加属性：hbase连接的zookeeper节点

<property>

<name>hbase.zookeeper.quorum</name>

<value>node101,node102,node103</value>

</property>

步骤3:在Hive创建映射到HBase的表
举例如下：
(1)内部表

CREATE TABLE hbasetbl(key int, value string)

STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'

WITH SERDEPROPERTIES ("hbase.columns.mapping" = ":key,cf1:val")

TBLPROPERTIES ("hbase.table.name" = "xyz", "hbase.mapred.output.outputtable" = "xyz");

hbase.columns.mapping是映射规则==》:key是row_key，cf1是列族，val是列值；

hbase.table.name是映射到HBase的表名，
hbase.mapred.output.outputtable是HBase在HDFS中存储文件名。

如果Hbase不存在表xyx，那么hive创建表xyz之后，在HBase也会创建xyz表,在Hive里向表xyz插入数据数据会存到HBase里而不是Hive。

(2)外部表

CREATE EXTERNAL TABLE tmp_order

(key string, id string, user_id string)

STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'

WITH SERDEPROPERTIES ("hbase.columns.mapping" = ":key,order:order_id,order:user_id")

TBLPROPERTIES ("hbase.table.name" = "t_order");

创建外部表，需要HBase要事先有这个表：create 't_order','order'，否则会报错

hive和hbase同步整合

二、Hive+Sqoop

1. 在hive中创建hbase的event_log对应表

CREATE EXTERNAL TABLE event_logs(

key string, pl string, en string, s_time bigint, p_url string, u_ud string, u_sd string

) ROW FORMAT SERDE 'org.apache.hadoop.hive.hbase.HBaseSerDe'

STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'

with serdeproperties('hbase.columns.mapping'=':key,log:pl,log:en,log:s_time,log:p_url,log:u_ud,log:u_sd')

tblproperties('hbase.table.name'='eventlog');

-- 2. 在hive中创建mysql的对应表，执行HQL之后分析的结果保存该表，然后通过sqoop工具导出到mysql

CREATE TABLE `stats_view_depth` (

`platform_dimension_id` bigint ,

`data_dimension_id` bigint ,

`kpi_dimension_id` bigint ,

`pv1` bigint ,

`pv2` bigint ,

`pv3` bigint ,

`pv4` bigint ,

`pv5_10` bigint ,

`pv10_30` bigint ,

`pv30_60` bigint ,

`pv60_plus` bigint ,

`created` string

) row format delimited fields terminated by '\t';

3. hive创建临时表:把hql分析之后的中间结果存放到当前的临时表。

CREATE TABLE `stats_view_depth_tmp`(`pl` string, `date` string, `col` string, `ct` bigint);

4. 编写UDF(platformdimension & datedimension)<需要注意，要删除DimensionConvertClient类中所有FileSystem关闭的操作>

-- 5. 上传transformer-0.0.1.jar到hdfs的/sxt/transformer文件夹中
-- 6. 创建hive的function

create function platform_convert as 'com.sxt.transformer.hive.PlatformDimensionUDF' using jar 'hdfs://sxt/sxt/transformer/transformer-0.0.1.jar';

create function date_convert as 'com.sxt.transformer.hive.DateDimensionUDF' using jar 'hdfs://sxt/sxt/transformer/transformer-0.0.1.jar';

以上为准备工作

------------------------------------------------------------------------
7. hql编写(统计用户角度的浏览深度)<注意：时间为外部给定>

from (

select

pl, from_unixtime(cast(s_time/ as bigint),'yyyy-MM-dd') as day, u_ud,

(case when count(p_url) =  then "pv1"

when count(p_url) =  then "pv2"

when count(p_url) =  then "pv3"

when count(p_url) =  then "pv4"

when count(p_url) >=  and count(p_url) < then "pv5_10"

when count(p_url) >=  and count(p_url) < then "pv10_30"

when count(p_url) >= and count(p_url) < then "pv30_60"

else 'pv60_plus' end) as pv

from event_logs

where

en='e_pv'

and p_url is not null

and pl is not null

and s_time >= unix_timestamp('2019-07-08','yyyy-MM-dd')*

and s_time < unix_timestamp('2019-07-09','yyyy-MM-dd')*

group by

pl, from_unixtime(cast(s_time/ as bigint),'yyyy-MM-dd'), u_ud

) as tmp

insert overwrite table stats_view_depth_tmp

select pl,day,pv,count(distinct u_ud) as ct where u_ud is not null group by pl,day,pv;

--把临时表的多行数据，转换一行

with tmp as

(

select pl,`date` as date1,ct as pv1, as pv2, as pv3, as pv4, as pv5_10, as pv10_30, as pv30_60, as pv60_plus from stats_view_depth_tmp where col='pv1' union all

select pl,`date` as date1, as pv1,ct as pv2, as pv3, as pv4, as pv5_10, as pv10_30, as pv30_60, as pv60_plus from stats_view_depth_tmp where col='pv2' union all

select pl,`date` as date1, as pv1, as pv2,ct as pv3, as pv4, as pv5_10, as pv10_30, as pv30_60, as pv60_plus from stats_view_depth_tmp where col='pv3' union all

select pl,`date` as date1, as pv1, as pv2, as pv3,ct as pv4, as pv5_10, as pv10_30, as pv30_60, as pv60_plus from stats_view_depth_tmp where col='pv4' union all

select pl,`date` as date1, as pv1, as pv2, as pv3, as pv4,ct as pv5_10, as pv10_30, as pv30_60, as pv60_plus from stats_view_depth_tmp where col='pv5_10' union all

select pl,`date` as date1, as pv1, as pv2, as pv3, as pv4, as pv5_10,ct as pv10_30, as pv30_60, as pv60_plus from stats_view_depth_tmp where col='pv10_30' union all

select pl,`date` as date1, as pv1, as pv2, as pv3, as pv4, as pv5_10, as pv10_30,ct as pv30_60, as pv60_plus from stats_view_depth_tmp where col='pv30_60' union all

select pl,`date` as date1, as pv1, as pv2, as pv3, as pv4, as pv5_10, as pv10_30, as pv30_60,ct as pv60_plus from stats_view_depth_tmp where col='pv60_plus' union all

select 'all' as pl,`date` as date1,ct as pv1, as pv2, as pv3, as pv4, as pv5_10, as pv10_30, as pv30_60, as pv60_plus from stats_view_depth_tmp where col='pv1' union all

select 'all' as pl,`date` as date1, as pv1,ct as pv2, as pv3, as pv4, as pv5_10, as pv10_30, as pv30_60, as pv60_plus from stats_view_depth_tmp where col='pv2' union all

select 'all' as pl,`date` as date1, as pv1, as pv2,ct as pv3, as pv4, as pv5_10, as pv10_30, as pv30_60, as pv60_plus from stats_view_depth_tmp where col='pv3' union all

select 'all' as pl,`date` as date1, as pv1, as pv2, as pv3,ct as pv4, as pv5_10, as pv10_30, as pv30_60, as pv60_plus from stats_view_depth_tmp where col='pv4' union all

select 'all' as pl,`date` as date1, as pv1, as pv2, as pv3, as pv4,ct as pv5_10, as pv10_30, as pv30_60, as pv60_plus from stats_view_depth_tmp where col='pv5_10' union all

select 'all' as pl,`date` as date1, as pv1, as pv2, as pv3, as pv4, as pv5_10,ct as pv10_30, as pv30_60, as pv60_plus from stats_view_depth_tmp where col='pv10_30' union all

select 'all' as pl,`date` as date1, as pv1, as pv2, as pv3, as pv4, as pv5_10, as pv10_30,ct as pv30_60, as pv60_plus from stats_view_depth_tmp where col='pv30_60' union all

select 'all' as pl,`date` as date1, as pv1, as pv2, as pv3, as pv4, as pv5_10, as pv10_30, as pv30_60,ct as pv60_plus from stats_view_depth_tmp where col='pv60_plus'

)

from tmp

insert overwrite table stats_view_depth

select ,,,sum(pv1),sum(pv2),sum(pv3),sum(pv4),sum(pv5_10),sum(pv10_30),sum(pv30_60),sum(pv60_plus),'2019-07-08' group by pl,date1;

7. sqoop脚步编写(统计用户角度)

sqoop export --connect jdbc:mysql://hh:3306/report --username hive --password hive --table stats_view_depth --export-dir /hive/bigdater.db/stats_view_depth/* --input-fields-terminated-by "\\t" --update-mode allowinsert --update-key platform_dimension_id,data_dimension_id,kpi_dimension_id

8. shell脚步编写通过脚本自动化操作

view_depth_run.sh

#!/bin/bash

startDate=''

endDate=''

until [ $# -eq  ]

do

    if [ $'x' = '-sdx' ]; then

        shift

        startDate=$

    elif [ $'x' = '-edx' ]; then

        shift

        endDate=$

    fi

    shift

done

if [ -n "$startDate" ] && [ -n "$endDate" ]; then

    echo "use the arguments of the date"

else

    echo "use the default date"

    startDate=$(date -d last-day +%Y-%m-%d)

    endDate=$(date +%Y-%m-%d)

fi

echo "run of arguments. start date is:$startDate, end date is:$endDate"

echo "start run of view depth job "

## 用户角度浏览深度分析

echo "start insert user data to hive tmp table"

hive  -e "from (select pl, from_unixtime(cast(s_time/1000 as bigint),'yyyy-MM-dd') as day, u_ud, (case when count(p_url) = 1 then 'pv1' when count(p_url) = 2 then 'pv2' when count(p_url) = 3 then 'pv3' when count(p_url) = 4 then 'pv4' when count(p_url) >= 5 and count(p_url) <10 then 'pv5_10' when count(p_url) >= 10 and count(p_url) <30 then 'pv10_30' when count(p_url) >=30 and count(p_url) <60 then 'pv30_60'  else 'pv60_plus' end) as pv from event_logs where en='e_pv' and p_url is not null and pl is not null and s_time >= unix_timestamp('$startDate','yyyy-MM-dd')*1000 and s_time < unix_timestamp('$endDate','yyyy-MM-dd')*1000 group by pl, from_unixtime(cast(s_time/1000 as bigint),'yyyy-MM-dd'), u_ud) as tmp insert overwrite table stats_view_depth_tmp select pl,day,pv,count(distinct u_ud) as ct where u_ud is not null group by pl,day,pv"

echo "start insert user data to hive table"

hive  -e "with tmp as (select pl,date,ct as pv1,0 as pv2,0 as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv1' union all select pl,date,0 as pv1,ct as pv2,0 as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv2' union all select pl,date,0 as pv1,0 as pv2,ct as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv3' union all select pl,date,0 as pv1,0 as pv2,0 as pv3,ct as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv4' union all select pl,date,0 as pv1,0 as pv2,0 as pv3,0 as pv4,ct as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv5_10' union all select pl,date,0 as pv1,0 as pv2,0 as pv3,0 as pv4,0 as pv5_10,ct as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv10_30' union all select pl,date,0 as pv1,0 as pv2,0 as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,ct as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv30_60' union all select pl,date,0 as pv1,0 as pv2,0 as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,ct as pv60_plus from stats_view_depth_tmp where col='pv60_plus' union all select 'all' as pl,date,ct as pv1,0 as pv2,0 as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv1' union all select 'all' as pl,date,0 as pv1,ct as pv2,0 as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv2' union all select 'all' as pl,date,0 as pv1,0 as pv2,ct as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv3' union all select 'all' as pl,date,0 as pv1,0 as pv2,0 as pv3,ct as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv4' union all select 'all' as pl,date,0 as pv1,0 as pv2,0 as pv3,0 as pv4,ct as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv5_10' union all select 'all' as pl,date,0 as pv1,0 as pv2,0 as pv3,0 as pv4,0 as pv5_10,ct as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv10_30' union all select 'all' as pl,date,0 as pv1,0 as pv2,0 as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,ct as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv30_60' union all select 'all' as pl,date,0 as pv1,0 as pv2,0 as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,ct as pv60_plus from stats_view_depth_tmp where col='pv60_plus' ) from tmp insert overwrite table stats_view_depth select platform_convert(pl),date_convert(date),5,sum(pv1),sum(pv2),sum(pv3),sum(pv4),sum(pv5_10),sum(pv10_30),sum(pv30_60),sum(pv60_plus),date group by pl,date"

#会话角度浏览深度分析

echo "start insert session data to hive tmp table"

hive  -e "from (select pl, from_unixtime(cast(s_time/1000 as bigint),'yyyy-MM-dd') as day, u_sd, (case when count(p_url) = 1 then 'pv1' when count(p_url) = 2 then 'pv2' when count(p_url) = 3 then 'pv3' when count(p_url) = 4 then 'pv4' when count(p_url) >= 5 and count(p_url) <10 then 'pv5_10' when count(p_url) >= 10 and count(p_url) <30 then 'pv10_30' when count(p_url) >=30 and count(p_url) <60 then 'pv30_60'  else 'pv60_plus' end) as pv from event_logs where en='e_pv' and p_url is not null and pl is not null and s_time >= unix_timestamp('$startDate','yyyy-MM-dd')*1000 and s_time < unix_timestamp('$endDate','yyyy-MM-dd')*1000 group by pl, from_unixtime(cast(s_time/1000 as bigint),'yyyy-MM-dd'), u_sd ) as tmp insert overwrite table stats_view_depth_tmp select pl,day,pv,count(distinct u_sd) as ct where u_sd is not null group by pl,day,pv"

## insert into

echo "start insert session data to hive table"

hive --database bigdater -e "with tmp as (select pl,date,ct as pv1,0 as pv2,0 as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv1' union all select pl,date,0 as pv1,ct as pv2,0 as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv2' union all select pl,date,0 as pv1,0 as pv2,ct as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv3' union all select pl,date,0 as pv1,0 as pv2,0 as pv3,ct as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv4' union all select pl,date,0 as pv1,0 as pv2,0 as pv3,0 as pv4,ct as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv5_10' union all select pl,date,0 as pv1,0 as pv2,0 as pv3,0 as pv4,0 as pv5_10,ct as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv10_30' union all select pl,date,0 as pv1,0 as pv2,0 as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,ct as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv30_60' union all select pl,date,0 as pv1,0 as pv2,0 as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,ct as pv60_plus from stats_view_depth_tmp where col='pv60_plus' union all select 'all' as pl,date,ct as pv1,0 as pv2,0 as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv1' union all select 'all' as pl,date,0 as pv1,ct as pv2,0 as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv2' union all select 'all' as pl,date,0 as pv1,0 as pv2,ct as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv3' union all select 'all' as pl,date,0 as pv1,0 as pv2,0 as pv3,ct as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv4' union all select 'all' as pl,date,0 as pv1,0 as pv2,0 as pv3,0 as pv4,ct as pv5_10,0 as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv5_10' union all select 'all' as pl,date,0 as pv1,0 as pv2,0 as pv3,0 as pv4,0 as pv5_10,ct as pv10_30,0 as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv10_30' union all select 'all' as pl,date,0 as pv1,0 as pv2,0 as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,ct as pv30_60,0 as pv60_plus from stats_view_depth_tmp where col='pv30_60' union all select 'all' as pl,date,0 as pv1,0 as pv2,0 as pv3,0 as pv4,0 as pv5_10,0 as pv10_30,0 as pv30_60,ct as pv60_plus from stats_view_depth_tmp where col='pv60_plus' ) from tmp insert into table stats_view_depth select platform_convert(pl),date_convert(date),6,sum(pv1),sum(pv2),sum(pv3),sum(pv4),sum(pv5_10),sum(pv10_30),sum(pv30_60),sum(pv60_plus),'2015-12-13' group by pl,date"

## sqoop

echo "run the sqoop script,insert hive data to mysql table"

sqoop export --connect jdbc:mysql://hh:3306/report --username hive --password hive --table stats_view_depth --export-dir /hive/bigdater.db/stats_view_depth/* --input-fields-terminated-by \\t --update-mode allowinsert --update-key platform_dimension_id,data_dimension_id,kpi_dimension_id

echo "complete run the view depth job"

巴特西

【电商日志项目之六】数据分析-Hive方式

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