hive学习笔记之八：Sqoop

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关于Sqoop

Sqoop是Apache开源项目，用于在Hadoop和关系型数据库之间高效传输大量数据，本文将与您一起实践以下内容：

部署Sqoop
用Sqoop将hive表数据导出至MySQL
用Sqoop将MySQL数据导入到hive表

部署

在hadoop账号的家目录下载Sqoop的1.4.7版本：

wget https://mirror.bit.edu.cn/apache/sqoop/1.4.7/sqoop-1.4.7.bin__hadoop-2.6.0.tar.gz

解压：

tar -zxvf sqoop-1.4.7.bin__hadoop-2.6.0.tar.gz

解压后得到文件夹sqoop-1.4.7.bin__hadoop-2.6.0，将mysql-connector-java-5.1.47.jar复制到sqoop-1.4.7.bin__hadoop-2.6.0/lib目录下
进入目录sqoop-1.4.7.bin__hadoop-2.6.0/conf，将sqoop-env-template.sh改名为sqoop-env.sh：

mv sqoop-env-template.sh sqoop-env.sh

用编辑器打开sqoop-env.sh，增加下面三个配置，HADOOP_COMMON_HOME和HADOOP_MAPRED_HOME是完整的hadoop路径，HIVE_HOME是完整的hive路径：

export HADOOP_COMMON_HOME=/home/hadoop/hadoop-2.7.7
export HADOOP_MAPRED_HOME=/home/hadoop/hadoop-2.7.7
export HIVE_HOME=/home/hadoop/apache-hive-1.2.2-bin

安装和配置完成了，进入sqoop-1.4.7.bin__hadoop-2.6.0/bin，执行./sqoop version查看sqoop版本，如下所示，可见是1.4.7版本（有些环境变量没配置会输出告警，在此先忽略）：

[hadoop@node0 bin]$ ./sqoop version
Warning: /home/hadoop/sqoop-1.4.7.bin__hadoop-2.6.0/bin/../../hbase does not exist! HBase imports will fail.
Please set $HBASE_HOME to the root of your HBase installation.
Warning: /home/hadoop/sqoop-1.4.7.bin__hadoop-2.6.0/bin/../../hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /home/hadoop/sqoop-1.4.7.bin__hadoop-2.6.0/bin/../../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
Warning: /home/hadoop/sqoop-1.4.7.bin__hadoop-2.6.0/bin/../../zookeeper does not exist! Accumulo imports will fail.
Please set $ZOOKEEPER_HOME to the root of your Zookeeper installation.
20/11/02 12:02:58 INFO sqoop.Sqoop: Running Sqoop version: 1.4.7
Sqoop 1.4.7
git commit id 2328971411f57f0cb683dfb79d19d4d19d185dd8
Compiled by maugli on Thu Dec 21 15:59:58 STD 2017

sqoop装好之后，接下来体验其功能

MySQL准备

为了接下来的实战，需要把MySQL准备好，这里给出的MySQL的配置供您参考：

MySQL版本：5.7.29
MySQL服务器IP：192.168.50.43
MySQL服务端口：3306
账号：root
密码：123456
数据库名：sqoop

关于MySQL部署，我这为了省事儿，是用docker部署的，参考《群晖DS218+部署mysql》

从hive导入MySQL（export）

执行以下命令，将hive的数据导入到MySQL：

./sqoop export 
--connect jdbc:mysql://192.168.50.43:3306/sqoop 
--table address 
--username root 
--password 123456 
--export-dir "/user/hive/warehouse/address" 
--fields-terminated-by ","

查看address表，数据已经导入：

在这里插入图片描述

从MySQL导入hive（import）

在hive的命令行模式执行以下语句，新建名为address2的表结构和address一模一样：

create table address2 (addressid int, province string, city string) 
row format delimited 
fields terminated by ",";

执行以下命令，将MySQL的address表的数据导入到hive的address2表，-m 2表示启动2个map任务：

./sqoop import 
--connect jdbc:mysql://192.168.50.43:3306/sqoop 
--table address 
--username root 
--password 123456 
--target-dir "/user/hive/warehouse/address2" 
-m 2

执行完毕后，控制台输入类似以下内容：

		Virtual memory (bytes) snapshot=4169867264
		Total committed heap usage (bytes)=121765888
	File Input Format Counters 
		Bytes Read=0
	File Output Format Counters 
		Bytes Written=94
20/11/02 16:09:22 INFO mapreduce.ImportJobBase: Transferred 94 bytes in 16.8683 seconds (5.5726 bytes/sec)
20/11/02 16:09:22 INFO mapreduce.ImportJobBase: Retrieved 5 records.

去查看hive的address2表，可见数据已经成功导入：

hive> select * from address2;
OK
1	guangdong	guangzhou
2	guangdong	shenzhen
3	shanxi	xian
4	shanxi	hanzhong
6	jiangshu	nanjing
Time taken: 0.049 seconds, Fetched: 5 row(s)