Hive整合HBase实现数据同步

2020-07-26 20:02:17 浏览数 (1)

Hive整合HBase

  • hive和hbase整合:
  • 前提
  • 步骤
    • 创建内部表
      • 1.在hive(node4)中建表
      • 2.在hbase端查看是否同步了表xyz ,如果同步则测试在hbase中插入数据是否会同步到hive?会
      • 3.不仅在hbase中插入数据会同步到hive; 在hive中插入数据也会同步到hbase中
      • 4.查看表存放的位置
    • 创建外部表
      • 1.Hive建表语句
      • 2.如果直接按照内部表创建的方式会出现下面的异常
      • 3.异常信息提示我们hbase中不存在关联的表,因此我们需要在**hbase中**创建该表
      • 4.在hbase建完表以后便可以在hive中创建外部关联表( 即可执行内部表的关联操作---对应的是内部表步骤3,4,5)
      • 5.测试Hbase数据同步到hive
      • 6.测试hive数据同步到hbase(hive中插入数据,hbase查看是否同步)
  • 在项目中的使用

hive和hbase整合:

在整合后, hive相当于hbase的客户端 在整合后, 实现二者数据的同步插入

官网介绍 在整合时需要注意

  • Hive 0.90整合Hbase至少要求Hbase版本为0.92 ,更早版本的Hive要工作在 Hbase 0.89/0.90
  • Hive 1.x 整合Hbase要求版本在 0.98版本以下 ,Hive的2.x版本要求HBase在1.x以上版本

前提

安装了hive和hbase

代码语言：javascript复制
# 环境回顾
hive数据库 node1  数据源 node3 客户端node4
hive --service metastore
hive

hbase 主node1,备node4 
hbase shell

步骤

1、在node3中的hive的配置文件hive-site.xml增加自己的zookeeper集群属性(仅此一步) node3是用于hive集群中用于启动元数据存储的节点hive --service metastore

代码语言：javascript复制
  <property>
    <name>hbase.zookeeper.quorum</name>
    <value>node2,node3,node4</value>
  </property>

创建内部表

1.在hive(node4)中建表

在hive中出创建表, 同时在hbase中也创建这张表(图2), 只不过名字不同,hive中:hbasetbl,hive中 xyz

代码语言：javascript复制
## hive中的建表语句
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");

2.在hbase端查看是否同步了表xyz ,如果同步则测试在hbase中插入数据是否会同步到hive?会

需要注意的是列族格式cf1:val必须一致,数据才有效,图1数据虽然插入成功,但是格式不正确 ,只有图2才会同步到hive中, 如图2

图1

图2

3.不仅在hbase中插入数据会同步到hive; 在hive中插入数据也会同步到hbase中

代码语言：javascript复制
设置hive本地运行,提升速度
set hive.exec.mode.local.auto=true;

# hive中插入数据,并查看数据是否同步到hbase( 图1,图2 )
insert into hbasetbl values(233,'blibli');

图1

图2

4.查看表存放的位置

我们可以通过可视化界面查看到hive和hbase中存放表的地方都没有数据, 但是通过使用hbase的溢写命名flush 'xyz'便可将数据持久化到hdfs中, 如下图

创建外部表

1.Hive建表语句

代码语言：javascript复制
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");

2.如果直接按照内部表创建的方式会出现下面的异常

代码语言：javascript复制
message:HBase table t_order doesn't exist while the table is declared as an external table.

3.异常信息提示我们hbase中不存在关联的表,因此我们需要在hbase中创建该表

4.在hbase建完表以后便可以在hive中创建外部关联表( 即可执行内部表的关联操作—对应的是内部表步骤3,4,5)

5.测试Hbase数据同步到hive

注意: set hive.cli.print.header=true;开启hive中列名称的显示,默认关闭

hbase中数据显示

hive中数据显示

6.测试hive数据同步到hbase(hive中插入数据,hbase查看是否同步)

在项目中的使用

  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. 创建mysql在hive中的对应表，hive中的表，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)
  5. 上传transformer-0.0.1.jar到hdfs的/myCluster/usr文件夹中
  6. 创建hive的function create function platform_convert as 'com.sxt.transformer.hive.PlatformDimensionUDF' using jar 'hdfs://myCluster/usr/transformer/transformer-0.0.1.jar'; create function date_convert as 'com.sxt.transformer.hive.DateDimensionUDF' using jar 'hdfs://hdfs://myCluster/usr/transformer/transformer-0.0.1.jar';
  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/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('2019-11-17','yyyy-MM-dd')*1000 and s_time < unix_timestamp('2019-11-19','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; 把临时表的多行数据，转换一行,生成新表stats_view_depth_tmp with tmp as ( select pl,`date` as date1,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` as date1,0 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` as date1,0 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` as date1,0 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` as date1,0 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` as date1,0 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` as date1,0 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` as date1,0 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` as date1,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` as date1,0 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` as date1,0 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` as date1,0 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` as date1,0 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` as date1,0 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` as date1,0 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` as date1,0 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 2,3,6,sum(pv1),sum(pv2),sum(pv3),sum(pv4),sum(pv5_10),sum(pv10_30),sum(pv30_60),sum(pv60_plus),'2017-01-10' group by pl,date1;
  8. sqoop脚本编写(统计用户角度, 将hive中的数据导出到mysql) 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
  9. hql编写(统计会话角度的浏览深度)<注意：时间为外部给定> 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('2015-12-13','yyyy-MM-dd')*1000 and s_time < unix_timestamp('2015-12-14','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; 把临时表的多行数据，转换一行,生成新表stats_view_depth_tmp 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),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;
  10. sqoop脚本编写(统计会话角度, 将hive中的数据导出到mysql) 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 "\01" --update-mode allowinsert --update-key platform_dimension_id,data_dimension_id,kpi_dimension_id
  11. 编写脚本, 实现项目自动化部署 #!/bin/bash startDate='' endDate='' until [ $# -eq 0 ] do if [ $1'x' = '-sdx' ]; then shift startDate=$1 elif [ $1'x' = '-edx' ]; then shift endDate=$1 fi shift done if [ -n "$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:$endDate" echo "start run of view depth job " ## insert overwrite echo "start insert user data to hive tmp table" hive -e "from (select pl, from_unixtime(cast(s_time/1000 as bigint),'yyyy-MM-dd') as day, u_ud, (case when count(p_url) = 1 then 'pv1' when count(p_url) = 2 then 'pv2' when count(p_url) = 3 then 'pv3' when count(p_url) = 4 then 'pv4' when count(p_url) >= 5 and count(p_url) <10 then 'pv5_10' when count(p_url) >= 10 and count(p_url) <30 then 'pv10_30' when count(p_url) >=30 and count(p_url) <60 then 'pv30_60' else 'pv60_plus' end) as pv from event_logs where en='e_pv' and p_url is not null and pl is not null and s_time >= unix_timestamp('$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" echo "start insert session date 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 "\01" --update-mode allowinsert --update-key platform_dimension_id,data_dimension_id,kpi_dimension_id echo "complete run the view depth job"
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