时间：2021-07-01 10:21:17 帮助过：5人阅读
将Oracle中的业务基础表增量数据导入Hive中，与当前的全量表合并为最新的全量表。通过Sqoop将Oracle中表的导入Hive，模拟全量表和
需求
将Oracle中的业务基础表增量数据导入Hive中，与当前的全量表合并为最新的全量表。
设计
涉及的三张表：
步骤：
步骤1：通过Sqoop将Oracle中表的导入Hive，模拟全量表和增量表
为了模拟场景，需要一张全量表，和一张增量表，由于数据源有限，所以两个表都来自Oracle中的OMP_SERVICE，全量表包含所有数据，，在Hive中名称叫service_all，增量表包含部分时间段数据，在Hive中名称叫service_tmp。
（1）全量表导入：导出所有数据，只要部分字段，导入到Hive指定表里
为实现导入Hive功能，需要先配置HCatalog（HCatalog是Hive子模块）的环境变量，/etc/profile中新增：
export HCAT_HOME=/home/fulong/Hive/apache-hive-0.13.1-bin/hcatalog
执行以下命令导入数据：
fulong@FBI006:~/Sqoop/sqoop-1.4.4/bin$ ./sqoop import \
> --connect jdbc:oracle:thin:@192.168.0.147:1521:ORCLGBK --username SP --password fulong \
> --table OMP_SERVICE \
> --columns "SERVICE_CODE,SERVICE_NAME,SERVICE_PROCESS,CREATE_TIME,ENABLE_ORG,ENABLE_PLATFORM,IF_DEL" \
> --hive-import --hive-table SERVICE_ALL
注意：用户名必须大写
（2）增量表导入：只导出所需时间范围内的数据，只要部分字段，导入到Hive指定表里
使用以下命令导入数据：
fulong@FBI006:~/Sqoop/sqoop-1.4.4/bin$ ./sqoop import \
> --connect jdbc:oracle:thin:@192.168.0.147:1521:ORCLGBK --username SP --password fulong \
> --table OMP_SERVICE \
> --columns "SERVICE_CODE,SERVICE_NAME,SERVICE_PROCESS,CREATE_TIME,ENABLE_ORG,ENABLE_PLATFORM,IF_DEL" \
> --where "CREATE_TIME > to_date('2012/12/4 17:00:00','yyyy-mm-dd hh24:mi:ss') and CREATE_TIME < to_date('2012/12/4 18:00:00','yyyy-mm-dd hh24:mi:ss')" \
> --hive-import --hive-overwrite --hive-table SERVICE_TMP
注意：
（3）验证导入结果：列出所有表，统计行数，查看表结构
hive> show tables;
OK
searchlog
searchlog_tmp
service_all
service_tmp
Time taken: 0.04 seconds, Fetched: 4 row(s)
hive> select count(*) from service_all;
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=
In order to set a constant number of reducers:
set mapreduce.job.reduces=
Starting Job = job_1407233914535_0013, Tracking URL = :8088/proxy/application_1407233914535_0013/
Kill Command = /home/fulong/Hadoop/hadoop-2.2.0/bin/hadoop job -kill job_1407233914535_0013
Hadoop job information for Stage-1: number of mappers: 3; number of reducers: 1
2014-08-21 16:51:47,389 Stage-1 map = 0%, reduce = 0%
2014-08-21 16:51:59,816 Stage-1 map = 33%, reduce = 0%, Cumulative CPU 1.36 sec
2014-08-21 16:52:01,996 Stage-1 map = 67%, reduce = 0%, Cumulative CPU 2.45 sec
2014-08-21 16:52:07,877 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 3.96 sec
2014-08-21 16:52:17,639 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 5.29 sec
MapReduce Total cumulative CPU time: 5 seconds 290 msec
Ended Job = job_1407233914535_0013
MapReduce Jobs Launched:
Job 0: Map: 3 Reduce: 1 Cumulative CPU: 5.46 sec HDFS Read: 687141 HDFS Write: 5 SUCCESS
Total MapReduce CPU Time Spent: 5 seconds 460 msec
OK
6803
Time taken: 59.386 seconds, Fetched: 1 row(s)
hive> select count(*) from service_tmp;
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=
In order to set a constant number of reducers:
set mapreduce.job.reduces=
Starting Job = job_1407233914535_0014, Tracking URL = :8088/proxy/application_1407233914535_0014/
Kill Command = /home/fulong/Hadoop/hadoop-2.2.0/bin/hadoop job -kill job_1407233914535_0014
Hadoop job information for Stage-1: number of mappers: 3; number of reducers: 1
2014-08-21 16:53:03,951 Stage-1 map = 0%, reduce = 0%
2014-08-21 16:53:15,189 Stage-1 map = 67%, reduce = 0%, Cumulative CPU 2.17 sec
2014-08-21 16:53:16,236 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 3.38 sec
2014-08-21 16:53:57,935 Stage-1 map = 100%, reduce = 22%, Cumulative CPU 3.78 sec
2014-08-21 16:54:01,811 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 5.34 sec
MapReduce Total cumulative CPU time: 5 seconds 340 msec
Ended Job = job_1407233914535_0014
MapReduce Jobs Launched:
Job 0: Map: 3 Reduce: 1 Cumulative CPU: 5.66 sec HDFS Read: 4720 HDFS Write: 3 SUCCESS
Total MapReduce CPU Time Spent: 5 seconds 660 msec
OK
13
Time taken: 75.856 seconds, Fetched: 1 row(s)
hive> describe service_all;
OK
service_code string
service_name string
service_process string
create_time string
enable_org string
enable_platform string
if_del string
Time taken: 0.169 seconds, Fetched: 7 row(s)
hive> describe service_tmp;
OK
service_code string
service_name string
service_process string
create_time string
enable_org string
enable_platform string
if_del string
Time taken: 0.117 seconds, Fetched: 7 row(s)
合并新表的逻辑如下：
执行以下sql语句可以合并得到更新后的全量表：