Apache Sqoop （最新版本）

概述

Apache Sqoop（TM）是一种旨在在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等数据的倒入导出。

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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 
SQOOP_HOME=/usr/sqoop-1.4.7
HADOOP_HOME=/usr/hadoop-2.9.2
HIVE_HOME=/usr/apache-hive-1.2.2-bin
JAVA_HOME=/usr/java/latest
PATH=$PATH:$JAVA_HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$HIVE_HOME/bin:$SQOOP_HOME/bin
CLASSPATH=.
export JAVA_HOME
export PATH
export HADOOP_HOME
export CLASSPATH
export HIVE_HOME
export SQOOP_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=

#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：http://sqoop.apache.org/docs/1.4.7/SqoopUserGuide.html

sqoop-import

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。

$ sqoop import (generic-args) (import-args)
$ sqoop-import (generic-args) (import-args)

全表导入

sqoop import \
--driver com.mysql.jdbc.Driver \
--connect jdbc:mysql://CentOS:3306/test?characterEncoding=UTF-8 \
--username root \
--password root \
--table t_user \
--num-mappers 4 \
--fields-terminated-by '\t' \
--target-dir /mysql/test/t_user \
--delete-target-dir 

参数	含义
–-connect	连接的数据库地址
-–username	连接的数据库的用户名
–-password	连接的数据库的密码
–-table	想要导出数据的表
–target-dir	要导入到hdfs中的目录（如果不指定，默认存储在“/user/用户名/导入的表名” 目录下）
-–delete-target-dir	表示如果在hdfs中有该目录，则先删除，然后再导入数据到该目录下
–num-mappers	表示设置的maptask个数，默认为4个，决定最终在hdfs中生成的文件个数（将table中的数据分成几个文件分别存储）
–fields-terminated-by	指定字段的分割符号

字段导入

sqoop import \
--driver com.mysql.jdbc.Driver \
--connect jdbc:mysql://CentOS:3306/test?characterEncoding=UTF-8 \
--username root \
--password root \
--table t_user \
--columns "id,name,age" \
--where "id > 2 or name like '%z%'" \
--target-dir /mysql/test/t_user1 \
--delete-target-dir \
--num-mappers 4 \
--fields-terminated-by '\t'

字段	含义
–columns	指定要查询的字段
–where	指定过滤条件

导入查询

sqoop import \
--driver com.mysql.jdbc.Driver \
--connect jdbc:mysql://CentOS:3306/test \
--username root \
--password root \
--num-mappers 3 \
--fields-terminated-by '\t' \
--query 'select id, name,sex, age ,birthDay from t_user where $CONDITIONS LIMIT 100' \
--split-by id \
--target-dir /mysql/test/t_user2 \
--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。您还必须使用--split-by选择拆分列。

注意：split-by必须是数字类型，如果不是数字类型系统会报错！

Import failed: java.io.IOException: Generating splits for a textual index column allowed only in case of "-Dorg.apache.sqoop.splitter.allow_text_splitter=true" property passed asa parameter

需要指定 --split-by 主键 并指定 "-Dorg.apache.sqoop.splitter.allow_text_splitter=true"参数即可。
参考：https://www.cnblogs.com/youchi/p/10342875.html

RDBMS 导入Hive

全量导入

sqoop import \
--connect jdbc:mysql://CentOS: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

[root@CentOS ~]# cp /usr/apache-hive-1.2.2-bin/lib/hive-common-1.2.2.jar /usr/sqoop-1.4.7/lib/
[root@CentOS ~]# cp /usr/apache-hive-1.2.2-bin/lib/hive-exec-1.2.2.jar /usr/sqoop-1.4.7/lib/

参数	含义
–hive-import	将数据导入到hive中
–hive-overwrite	如果表已经存在，将原有数据覆盖
–hive-table	指定导入hive中那张表里

导入分区

sqoop import \
--connect jdbc:mysql://CentOS: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'

参数	含义
–hive-partition-key	指定分区表的字段
–hive-partition-value	指定分区值

RDBMS-> Hbase

sqoop import \
--connect jdbc:mysql://CentOS:3306/test \
--username root \
--password root \
--table t_user \
--num-mappers 3 \
--hbase-table baizhi:t_user \
--column-family cf1 \
--hbase-create-table \
--hbase-row-key id \
--hbase-bulkload 

参数	含义
–hbase-table	写入hbase中的表
–column-family	导入的列簇
–hbase-create-table	创建表
–hbase-row-key	指定字段作为rowkey
–hbase-bulkload	启动Hbase 批量写入

启动Hbase服务，创建baizhi 数据库，t_user由系统自动创建！

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

0       zhangsan        true    20      2020-01-11
1       lisi    false   25      2020-01-10
3       wangwu  true    36      2020-01-17
4       zhaoliu        false   50      1990-02-08
5       win7    true    20      1991-02-08

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://CentOS:3306/test \
--username root \
--password root \
--table t_user  \
--update-key id  \
--update-mode allowinsert \
--export-dir /demo/src \
--input-fields-terminated-by '\t'

参数	说明
–export-dir	导出的数据
–input-fields-terminated-by	字段分割符号

导入模式可选值可以是updateonly或者allowinsert，updateonly仅仅会更新已经存在的记录。

HBASE -> MySQL

HBASE -> HIVE

HIVE-RDBMS 等价 HDFS => RDBMS

①准备测试数据

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/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先尝试将HBase的数据导出到HDFS

INSERT OVERWRITE  DIRECTORY '/demo/src/employee' ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' STORED AS TEXTFILE select empno,ename,job,mgr,hiredate,sal,comm,deptno from t_employee_hbase;

③将HDFS中数据导出RDBMS

sqoop export \
--connect jdbc:mysql://CentOS: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';