概述
Apache Sqoop™是一种在Apache Hadoop和结构化数据存储(例如关系数据库)之间高效传输批量数据的工具。通过内嵌的MapReduce程序实现关系型数据库和HDFS、Hbase、Hive等数据的导入导出。
安装
1.访问sqoop的网址http://sqoop.apache.org/，选择相应的sqoop版本下载，本案例选择下载的是1.4.7下载地址：https://mirrors.tuna.tsinghua.edu.cn/apache/sqoop/1.4.7/sqoop-1.4.7.bin__hadoop-2.6.0.tar.gz，下载完相应的工具包后，解压Sqoop.
[root@CentOS ~]# tar -zxf sqoop-1.4.7.bin__hadoop-2.6.0.tar.gz -C /usr/
[root@CentOS ~]# cd /usr/
[root@CentOS usr]# mv sqoop-1.4.7.bin__hadoop-2.6.0 sqoop-1.4.7
[root@CentOS ~]# cd /usr/sqoop-1.4.7/
2.配置SQOOP_HOME环境变量
[root@CentOS sqoop-1.4.7]# vi ~/.bashrc
JAVA_HOME=/usr/java/latest
HADOOP_HOME=/usr/hadoop-2.9.2
HBASE_HOME=/usr/hbase-1.2.4
HIVE_HOME=/usr/apache-hive-1.2.2-bin
SQOOP_HOME=/usr/sqoop-1.4.7
ZOOKEEPER_HOME=/usr/zookeeper-3.4.6
PATH=$PATH:$JAVA_HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$HBASE_HOME/bin:$HIVE_HOME/bin:$SQOOP_HOME/bin:$ZOOKEEPER_HOME/bin
CLASSPATH=.
export JAVA_HOME
export HADOOP_HOME
export HBASE_HOME
export HIVE_HOME
export PATH
export CLASSPATH
export SQOOP_HOME
export ZOOKEEPER_HOME
[root@CentOS sqoop-1.4.7]# source ~/.bashrc
3.修改conf下的sqoop-env.sh.template配置文件
[root@CentOS sqoop-1.4.7]# mv conf/sqoop-env-template.sh conf/sqoop-env.sh
[root@CentOS sqoop-1.4.7]# vi conf/sqoop-env.sh
#Set path to where bin/hadoop is available
export HADOOP_COMMON_HOME=/usr/hadoop-2.9.2
#Set path to where hadoop-*-core.jar is available
export HADOOP_MAPRED_HOME=/usr/hadoop-2.9.2
#set the path to where bin/hbase is available
export HBASE_HOME=/usr/hbase-1.2.4
#Set the path to where bin/hive is available
export HIVE_HOME=/usr/apache-hive-1.2.2-bin
#Set the path for where zookeper config dir is
export ZOOCFGDIR=/usr/zookeeper-3.4.6/conf
4.将MySQL驱动jar拷贝到sqoop的lib目录下
[root@CentOS ~]# cp /usr/apache-hive-1.2.2-bin/lib/mysql-connector-java-5.1.48.jar /usr/sqoop-1.4.7/lib/
5.验证Sqoop是否安装成功
[root@CentOS sqoop-1.4.7]# sqoop version
Warning: /usr/sqoop-1.4.7/../hbase does not exist! HBase imports will fail.
Please set $HBASE_HOME to the root of your HBase installation.
Warning: /usr/sqoop-1.4.7/../hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /usr/sqoop-1.4.7/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
Warning: /usr/sqoop-1.4.7/../zookeeper does not exist! Accumulo imports will fail.
Please set $ZOOKEEPER_HOME to the root of your Zookeeper installation.
19/12/22 08:40:12 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
[root@CentOS sqoop-1.4.7]# sqoop list-tables --connect jdbc:mysql://192.168.52.1:3306/mysql --username root --password root
导入&导出
参考：
Import工具将单个表从RDBMS导入到HDFS。表中的每一行在HDFS中均表示为单独的记录。记录可以存储为文本文件(每行一个记录)，也可以二进制表示形式存储为Avro或SequenceFiles。
RDBMS–>HDFS
如果出现以下错误
需要加入-Dorg.apache.sqoop.splitter.allow_text_splitter=true参数
全表导入
sqoop import \
-Dorg.apache.sqoop.splitter.allow_text_splitter=true \
--driver com.mysql.jdbc.Driver \
--connect jdbc:mysql://train:3306/file?characterEncoding=UTF-8 \
--username root \
--password root \
--table file \
--num-mappers 4 \
--fields-terminated-by '\t' \
--target-dir /mysql/test/t_file \
--delete-target-dir
字段导入
sqoop import \
-Dorg.apache.sqoop.splitter.allow_text_splitter=true \
--driver com.mysql.jdbc.Driver \
--connect jdbc:mysql://train:3306/file?characterEncoding=UTF-8 \
--username root \
--password root \
--table file \
--columns id,oldname,newname \
--target-dir /mysql/test/t_file02 \
--delete-target-dir \
--num-mappers 4 \
--fields-terminated-by '\t'
导入查询
sqoop import \
-Dorg.apache.sqoop.splitter.allow_text_splitter=true \
--driver com.mysql.jdbc.Driver \
--connect jdbc:mysql://train:3306/file \
--username root \
--password root \
--num-mappers 3 \
--fields-terminated-by '\t' \
--query 'select id, oldname, newname from file where $CONDITIONS LIMIT 100 ' \
--split-by id \
--target-dir /mysql/test/t_file03 \
--delete-target-dir
如果要并行导入查询结果，则每个Map任务将需要执行查询的副本，其结果由sqoop推断的边界条件进行分区。您的查询必须包含令牌$CONDITIONS，每个sqoop进程将用唯一条件表达式替换该令牌。还必须使用–split-by选择拆分列。
RDBMS导入
全量导入
sqoop import \
-Dorg.apache.sqoop.splitter.allow_text_splitter=true \
--connect jdbc:mysql://train:3306/file \
--username root \
--password root \
--table user \
--num-mappers 3 \
--hive-import \
--fields-terminated-by "\t" \
--hive-overwrite \
--hive-table baizhi.t_user
导入分区
sqoop import \
-Dorg.apache.sqoop.splitter.allow_text_splitter=true \
--connect jdbc:mysql://train:3306/test \
--username root \
--password root \
--table t_user \
--num-mappers 3 \
--hive-import \
--fields-terminated-by "\t" \
--hive-overwrite \
--hive-table baizhi.t_user \
--hive-partition-key city \
--hive-partition-value 'bj'
RDBMS–>Hbase
sqoop import \
-Dorg.apache.sqoop.splitter.allow_text_splitter=true \
--connect jdbc:mysql://train:3306/test \
--username root \
--password root \
--table t_user \
--num-mappers 3 \
--hbase-table baizhi:t_user1 \
--column-family cf1 \
--hbase-create-table \
--hbase-row-key id \
--hbase-bulkload
sqoop-export
Export工具将一组文件从HDFS导出回RDBMS。目标表必须已经存在于数据库中。根据用户指定的定界符，读取输入文件并将其解析为一组记录。
HDFS–>MySQL
测试数据
0 zhangsan true 20 2020-01-11
1 lisi false 25 2020-01-10
3 wangwu true 30 2020-01-17
4 zhaoliu false 50 1990-02-08
5 win7 true 20 1991-02-88
Mysql中需存在数据表
create table t_user(
id int primary key auto_increment,
name VARCHAR(32),
sex boolean,
age int,
birthDay date
) CHARACTER SET=utf8;
sqoop-export \
--connect jdbc:mysql://train:3306/test \
--username root \
--password root \
--table t_user \
--update-key id \
--update-mode allowinsert \
--export-dir /demo/src \
--input-fields-terminated-by '\t'
HBASE–>RDBMS
HBASE–>HIVE
HIVE-RDBMS 等价 HDFS–>RDBMS
1.准备测试数据 t_employee
7369,SMITH,CLERK,7902,1980-12-17 00:00:00,800,\N,20
7499,ALLEN,SALESMAN,7698,1981-02-20 00:00:00,1600,300,30
7521,WARD,SALESMAN,7698,1981-02-22 00:00:00,1250,500,30
7566,JONES,MANAGER,7839,1981-04-02 00:00:00,2975,\N,20
7654,MARTIN,SALESMAN,7698,1981-09-28 00:00:00,1250,1400,30
7698,BLAKE,MANAGER,7839,1981-05-01 00:00:00,2850,\N,30
7782,CLARK,MANAGER,7839,1981-06-09 00:00:00,2450,\N,10
7788,SCOTT,ANALYST,7566,1987-04-19 00:00:00,1500,\N,20
7839,KING,PRESIDENT,\N,1981-11-17 00:00:00,5000,\N,10
7844,TURNER,SALESMAN,7698,1981-09-08 00:00:00,1500,0,30
7876,ADAMS,CLERK,7788,1987-05-23 00:00:00,1100,\N,20
7900,JAMES,CLERK,7698,1981-12-03 00:00:00,950,\N,30
7902,FORD,ANALYST,7566,1981-12-03 00:00:00,3000,\N,20
7934,MILLER,CLERK,7782,1982-01-23 00:00:00,1300,\N,10
create database if not exists baizhi;
use baizhi;
drop table if exists t_employee;
CREATE TABLE t_employee(
empno INT,
ename STRING,
job STRING,
mgr INT,
hiredate TIMESTAMP,
sal DECIMAL(7,2),
comm DECIMAL(7,2),
deptno INT)
row format delimited
fields terminated by ','
collection items terminated by '|'
map keys terminated by '>'
lines terminated by '\n'
stored as textfile;
load data local inpath '/root/test/t_employee' overwrite into table t_employee;
drop table if exists t_employee_hbase;
create external table t_employee_hbase(empno INT,
ename STRING,
job STRING,
mgr INT,
hiredate TIMESTAMP,
sal DECIMAL(7,2),
comm DECIMAL(7,2),
deptno INT)
STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
WITH SERDEPROPERTIES("hbase.columns.mapping" = ":key,cf1:name,cf1:job,cf1:mgr,cf1:hiredate,cf1:sal,cf1:comm,cf1:deptno")
TBLPROPERTIES("hbase.table.name" = "baizhi:t_employee");
insert overwrite table t_employee_hbase select empno,ename,job,mgr,hiredate,sal,comm,deptno from t_employee;
2.将HBase的数据导出到HDFS
use baizhi;
INSERT OVERWRITE DIRECTORY '/demo/src/employee' ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' STORED AS TEXTFILE select * from t_employee_hbase;
3.将HDFS中的数据导出到RDBMS中
sqoop-export \
--connect jdbc:mysql://train:3306/test \
--username root \
--password root \
--table t_employee \
--update-key id \
--update-mode allowinsert \
--export-dir /demo/src/employee \
--input-fields-terminated-by ',' \
--input-null-string '\\N' \
--input-null-non-string '\\N';
来源：CSDN
作者：丿沐染烟忱丶
链接：https://blog.csdn.net/weixin_45607513/article/details/104209690