Sqoop1.4.4实现将Oracle10g中的增量数据导入Hive0.13.1，并更新Hive中的主表

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

> --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：