Hive整合HBase
hive和hbase整合:
在整合後, hive相當於hbase的客戶端
在整合後, 實現二者數據的同步插入
官網介紹
在整合時需要注意
- Hive 0.90整合Hbase至少要求Hbase版本爲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
安裝了hive和hbase
# 環境回顧
hive數據庫 node1 數據源 node3 客戶端node4
hive --service metastore
hive
hbase 主node1,備node4
hbase shell
步驟
1、在node3中的hive的配置文件hive-site.xml增加自己的zookeeper集羣屬性(僅此一步)
node3是用於hive集羣中用於啓動元數據存儲的節點hive --service metastore
<property>
<name>hbase.zookeeper.quorum</name>
<value>node2,node3,node4</value>
</property>
創建內部表
1.在hive(node4)中建表
在hive中出創建表, 同時在hbase中也創建這張表(圖2), 只不過名字不同,hive中:hbasetbl,hive中 xyz
## hive中的建表語句
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 ,如果同步則測試在hbase中插入數據是否會同步到hive?會
需要注意的是列族格式cf1:val必須一致,數據纔有效,圖1數據雖然插入成功,但是格式不正確 ,只有圖2纔會同步到hive中, 如圖2
圖1
圖2
3.不僅在hbase中插入數據會同步到hive; 在hive中插入數據也會同步到hbase中
設置hive本地運行,提升速度
set hive.exec.mode.local.auto=true;
# hive中插入數據,並查看數據是否同步到hbase( 圖1,圖2 )
insert into hbasetbl values(233,'blibli');
圖1
圖2
4.查看錶存放的位置
我們可以通過可視化界面查看到hive和hbase中存放表的地方都沒有數據, 但是通過使用hbase的溢寫命名flush 'xyz'便可將數據持久化到hdfs中, 如下圖
創建外部表
1.Hive建表語句
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.如果直接按照內部表創建的方式會出現下面的異常
message:HBase table t_order doesn't exist while the table is declared as an external table.
3.異常信息提示我們hbase中不存在關聯的表,因此我們需要在hbase中創建該表
4.在hbase建完表以後便可以在hive中創建外部關聯表( 即可執行內部表的關聯操作—對應的是內部表步驟3,4,5)
5.測試Hbase數據同步到hive
注意: set hive.cli.print.header=true;開啓hive中列名稱的顯示,默認關閉
hbase中數據顯示
hive中數據顯示
6.測試hive數據同步到hbase(hive中插入數據,hbase查看是否同步)
在項目中的使用
-
在hive中創建hbase的event_log對應表
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'); -
創建mysql在hive中的對應表，hive中的表，執行HQL之後分析的結果保存該表，然後通過sqoop工具導出到
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'; -
hive創建臨時表:把hql分析之後的中間結果存放到當前的臨時表。
CREATE TABLE `stats_view_depth_tmp`(`pl` string, `date` string, `col` string, `ct` bigint); -
編寫UDF(platformdimension & datedimension)
-
上傳transformer-0.0.1.jar到hdfs的/myCluster/usr文件夾中
-
創建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'; -
hql編寫(統計用戶角度的瀏覽深度)<注意：時間爲外部給定>
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;把臨時表的多行數據，轉換一行,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; -
sqoop腳本編寫(統計用戶角度, 將hive中的數據導出到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 -
hql編寫(統計會話角度的瀏覽深度)<注意：時間爲外部給定>
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;把臨時表的多行數據，轉換一行,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; -
sqoop腳本編寫(統計會話角度, 將hive中的數據導出到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 -
編寫腳本, 實現項目自動化部署
#!/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"