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datax可以理解为sqoop的优化版，

速度比sqoop快

因为sqoop底层是map任务，而datax底层是基于内存

DataX 是一个异构数据源离线同步工具，致力于实现包括关系型数据库(MySQL、Oracle等)、HDFS、Hive、ODPS、HBase、FTP等各种异构数据源之间稳定高效的数据同步功能

datax 是让你编写 json

flume 是让你编写 conf

azkaban 是让你编写 flow

sqoop 是让你写命令

将mysql中的数据导入到hdfs上

{"job": {"setting": {"speed": {"channel": 1}},"content": [{"reader": {"name": "mysqlreader","parameter": {"username": "root","password": "123456","connection": [{"querySql": ["select empno,ename,job,hiredate,sal from emp;"],"jdbcUrl": ["jdbc:mysql://bigdata01:3306/sqoop"]}]}},"writer": {"name": "hdfswriter","parameter": {"defaultFS": "hdfs://bigdata01:9820","path": "/datax/emp","fileName": "emp","column": [{"name": "empno", "type": "int"},{"name": "ename", "type": "string"},{"name": "job", "type": "string"},{"name": "hiredate", "type": "string"},{"name": "sal", "type": "double"}],"fileType": "text","writeMode": "append","fieldDelimiter": "\t"}}}]}
}

将hdfs上的数据导入到mysql中

{"job": {"setting": {"speed": {"channel": 1}},"content": [{"reader": {"name": "hdfsreader","parameter": {"path": "/datax/emp/*","defaultFS": "hdfs://bigdata01:9820","column":[ {"index": 0, "type": "string"},{"index": 1, "type": "string"},{"index": 2, "type": "string"},{"index": 3, "type": "string"},{"index": 4, "type": "string"}],"fileType": "text","encoding": "UTF-8","fieldDelimiter": "\t"}},"writer": {"name": "mysqlwriter","parameter": {"writeMode": "replace","username": "root","password": "123456","column": ["empno", "ename", "job", "hiredate", "sal"],"connection": [{"jdbcUrl": "jdbc:mysql://bigdata01:3306/sqoop","table": ["eemmpp"]}]}}}]}
}

使用注意

注意点：

1）指定字段的类型时，datax中的类型只有下面几种，而不是像java一样的

2）默认的分隔符，即 "fieldDelimiter": "xxx" 不指定或者不写的时候，默认为 ' , ' 分割

3）

将mysql 中的数据导入hive（重要）*

说是把mysql中的数据导入hive，其实本质还是将mysql中的数据导入hdfs中

首先先创建一个hive表 指定到hdfs的路径上，再将mysql中的数据导入到这个路径即可

1）首先先创建一个hive表

reate external table if not exists ods_01_base_area ( id int COMMENT 'id标识', area_code string COMMENT '省份编码', province_name string COMMENT '省份名称', iso string COMMENT 'ISO编码' )row format delimited fields terminated by ',' stored as TextFile location '/data/nshop/ods/ods_01_base_area/'; -- 指定到hdfs的路径

2）将mysql的数据通过datax导入hdfs

注意指定路径和分隔符 ！ 一定要与创建hive表指定的路径一致 且 分隔符也保持一致

{"job": {"setting": {"speed": {"channel": 1}},"content": [{"reader": {"name": "mysqlreader","parameter": {"username": "root","password": "123456","column": ["id","area_code","province_name","iso"],"splitPk": "id","connection": [{"table": ["base_area"],"jdbcUrl": ["jdbc:mysql://bigdata01:3306/datax"]}]}},"writer": {"name": "hdfswriter","parameter": {"defaultFS": "hdfs://bigdata01:9820","path": "/data/nshop/ods/ods_01_base_area/","fileName": "base_area","column": [{"name": "id","type": "int"},{"name": "area_code","type": "string"},{"name": "province_name","type": "string"},{"name": "iso","type": "string"}],"fileType": "text","writeMode": "append","fieldDelimiter": ","}}}]}
}

3） 导入完成，此时hive表中就有了数据