2020.9.29课堂笔记(Sqoop介绍及数据迁移)

一.Sqoop概述

Sqoop是一个用于在Hadoop和关系数据库之间传输数据的工具：官网链接

• 将数据从RDBMS导入到HDFS、Hive、HBase- 从HDFS导出数据到RDBMS- 使用MapReduce导入和导出数据，提供并行操作和容错
  目标用户
• 系统管理员、数据库管理员- 大数据分析师、大数据开发工程师等

Sqoop环境搭建文档
一、Sqoop安装
安装Sqoop的前提是已经具备Java和Hadoop、Zookeeper、MySQL的环境，如何往Hive和HBase导入数据，应具备相关Hive、HBase环境。
1.1 下载并解压
1.上传安装包sqoop-1.4.6-cdh5.14.2.tar.gz到虚拟机中
2.解压sqoop安装包到指定目录，
[hadoop@hadoop102 software]$ tar -zxf sqoop-1.4.6-cdh5.14.2.tar.gz -C /opt/install/
3.创建软连接
[hadoop@hadoop102 ~]$ ln -s /opt/install/sqoop-1.4.6-cdh5.14.2/ /opt/install/sqoop
4.配置环境变量
[hadoop@hadoop102 ~]$ vi /etc/profile
添加如下内容：
export SQOOP_HOME=/opt/install/sqoop
export PATH=$SQOOP_HOME/bin:$PATH  
让配置文件生效。
[hadoop@hadoop102 ~]$ source /etc/profile
1.2 修改配置文件
Sqoop的配置文件与大多数大数据框架类似，在sqoop根目录下的conf目录中。
1.重命名配置文件
[hadoop@hadoop102 conf]$ mv sqoop-env-template.sh sqoop-env.sh
2.修改配置文件
sqoop-env.sh
export HADOOP_COMMON_HOME=/opt/install/hadoop
export HADOOP_MAPRED_HOME=/opt/install/hadoop
export HIVE_HOME=/opt/install/hive
export ZOOKEEPER_HOME=/opt/install/zookeeper
export ZOOCFGDIR=/opt/install/zookeeper
export HBASE_HOME=/opt/install/hbase
1.3 拷贝JDBC驱动
拷贝jdbc驱动到sqoop的lib目录下，驱动包见其他资料。
[hadoop@hadoop102 software] cp mysql-connector-java-5.1.27-bin.jar /opt/install/sqoop/lib/
1.4 验证Sqoop
我们可以通过某一个command来验证sqoop配置是否正确：
sqoop help
出现一些Warning警告（警告信息已省略），并伴随着帮助命令的输出：
Available commands:
  codegen            Generate code to interact with database records
  create-hive-table  Import a table definition into Hive
  eval               Evaluate a SQL statement and display the results
  export             Export an HDFS directory to a database table
  help               List available commands
  import             Import a table from a database to HDFS
  import-all-tables  Import tables from a database to HDFS
  import-mainframe   Import datasets from a mainframe server to HDFS
  job                Work with saved jobs
  list-databases     List available databases on a server
  list-tables        List available tables in a database
  merge              Merge results of incremental imports
  metastore          Run a standalone Sqoop metastore
  version            Display version information

See 'sqoop help COMMAND' for information on a specific command.
1.5 测试Sqoop是否能够成功连接数据库
[hadoop@hadoop102 conf]$ sqoop list-databases --connect jdbc:mysql://hadoop101:3306/ --username root --password 123456
这里需要配置自己mysql数据库的连接信息。
出现如下输出：
information_schema
hive
mysql
performance_schema

二.Sqoop操作：相关文档

1.从RDB导入数据到HDFS

sqoop import \
--connect jdbc:mysql://localhost:3306/retail_db \
--driver com.mysql.jdbc.Driver \
--table customers \
--username root \
--password ok \
--target-dir /data/retail_db/customers \
--m 3

sqoop-import是sqoop import的别名

• 通过Where语句过滤导入表

sqoop import \
 --connect jdbc:mysql://localhost:3306/retail_db \
 --driver com.mysql.jdbc.Driver \
 --table orders --where "order_id < 500" \
 --username root \
 --password ok \
 --delete-target-dir \
 --target-dir /data/retail_db/orders \
 --m 3

• 通过COLUMNS过滤导入表

sqoop import \
 --connect jdbc:mysql://localhost:3306/retail_db \
 --driver com.mysql.jdbc.Driver \
 --table customers \
 --columns "customer_id,customer_fname,customer_lname" \
 --username root \
 --password ok \
 --delete-target-dir \
 --target-dir /data/retail_db/customers \
 --m 3

• 使用query方式导入数据

sqoop import \
 --connect jdbc:mysql://localhost:3306/retail_db \
 --driver com.mysql.jdbc.Driver \
 --query "select * from orders where order_status!='CLOSED' and \$CONDITIONS"  \
 --username root \
 --password ok \
 --split-by order_id \
 --delete-target-dir \
 --target-dir /data/retail_db/orders \
 -m 3

• 使用Sqoop增量导入数据- Incremental指定增量导入的模式- 1)append：追加数据记录- 2)lastmodified：可追加更新的数据

 sqoop import \
 --connect jdbc:mysql://localhost:3306/retail_db \
 --table orders \
 --username root \
 --password ok \
 --incremental append \
 --check-column order_date \
 --last-value '2013-07-24 00:00:00' \
 --target-dir /data/retail_db/orders \
 --m 3 

2.导入数据到hive

Argument	Description
--hive-home	Override $HIVE_HOME
--hive-import	Import tables into Hive (Uses Hive’s default delimiters if none are set.)
--hive-overwrite	Overwrite existing data in the Hive table.
--create-hive-table	If set, then the job will fail if the target hive table exits. By default this property is false.
--hive-table	Sets the table name to use when importing to Hive.
--hive-drop-import-delims	Drops \n, \r, and \01 from string fields when importing to Hive.
--hive-delims-replacement	Replace \n, \r, and \01 from string fields with user defined string when importing to Hive.
--hive-partition-key	Name of a hive field to partition are sharded on
--hive-partition-value	String-value that serves as partition key for this imported into hive in this job.
--map-column-hive	Override default mapping from SQL type to Hive type for configured columns.

• 1.复制jar包

 #复制hive的jar包
 cp /opt/install/hive/lib/hive-common-1.1.0-cdh5.14.2.jar /opt/install/sqoop/lib/
 cp /opt/install/hive/lib/hive-shims* /opt/install/sqoop/lib/

• 2.导入

 sqoop import \
 --connect jdbc:mysql://localhost:3306/retail_db  \
 --table orders \
 --username root  \
 --password ok \
 --hive-import  \
 --create-hive-table \
--hive-database retail_db \
--hive-table orders \
--m 3

• 3.导入到hive分区表,注意分区字段不能当作普同字段导入表中

 sqoop import \
 --connect jdbc:mysql://localhost:3306/retail_db  \
 --driver com.mysql.jdbc.Driver \
 --query "select order_id,order_status from orders where order_date>='2013-11-03' and order_date<'2013-11-04' and \$CONDITIONS" \
 --username root  \
 --password ok \
 --delete-target-dir \
 --target-dir /data/retail_db/orders \
 --split-by order_status \
 --hive-import  \
 --create-hive-table \
 --hive-database retail_db \
 --hive-table orders \
 --hive-partition-key "order_date" \
 --hive-partition-value "2013-11-03" \
 --m 3

3.导入数据到HBase

must be in the form of a comma-separated list of composite key attributes

Argument	Description
--column-family	Sets the target column family for the import
--hbase-create-table	If specified, create missing HBase tables
--hbase-row-key
Specifies which input column to use as the row key In case, if input table contains composite key, then
--hbase-table	Specifies an HBase table to use as the target instead of HDFS
--hbase-bulkload	Enables bulk loading

#hbase创建表
create 'emp_hbase_import','details'
#导入到hbase
 sqoop import  \
 --connect jdbc:mysql://localhost:3306/sqoop \
 --username root  \
 --password ok \
 --table emp \
 --columns "EMPNO,ENAME,JOB,SAL,COMM"  \
 --hbase-table emp_hbase_import \
 --column-family details  \
 --hbase-row-key "EMPNO" \
 --m 1

4.hdfs导出到MySQL

#先在mysql创建一个空表
create table customers_demo as select * from customers where 1=2;
#创建目录,上传数据
hdfs dfs -mkdir /customerinput
hdfs dfs -put customers.csv /customerinput/
#导出到mysql
sqoop export \
--connect jdbc:mysql://localhost:3306/retail_db \
--username root \
--password ok \
--table customers_demo \
--export-dir /customerinput/ \
--fields-terminated-by ',' \
--m 1 

5.sqoop脚本

#sqoop脚本
#1编写脚本,内容如下
#############################
import
--connect
jdbc:mysql://hadoop01:3306/retail_db
--driver
com.mysql.jdbc.Driver
--table
customers
--username
root
--password
root
--target-dir
/data/retail_db/customers
--delete-target-dir
--m 3
##############################
#2执行脚本
sqoop --options-file job_RDBMS2HDFS.opt

6.sqoop的job任务

 #创建job 注意import前必须有空格
 sqoop job \
 --create mysqlToHdfs \
 -- import \
 --connect jdbc:mysql://localhost:3306/retail_db \
 --table orders \
 --username root \
 --password ok \
 --incremental append \
 --check-column order_date \
 --last-value '0' \
 --target-dir /data/retail_db/orders \
 --m 3 
 #查看job
 sqoop job --list
 #执行job,可设置crontab定时执行 用的比较多
 sqoop job --exec mysqlToHdfs