DataX （原理， 增量全量同步数据实践）
目录理解mysql -> mysql (增量，全量)增量导入全量导入mysql -> hive （增量，全量）增量导入全量导入理解datax每张表都需要对应的配置文件。Reader：数据采集模块，负责采集数据源的数据，将数据发送给Framework。Writer：数据写入模块，负责不断向Framework取数据，并将数据写入到目的端。Framework：用于连接reader和write
目录
理解
datax每张表都需要对应的配置文件。
Reader：数据采集模块，负责采集数据源的数据，将数据发送给Framework。
Writer：数据写入模块，负责不断向Framework取数据，并将数据写入到目的端。
Framework：用于连接reader和writer，作为两者的数据传输通道，并处理缓冲，流控，并发，数据转换等核心技术问题。
datax底层是以双缓冲阻塞队列为整个数据交换的媒介,读进程负责读取并向队列中添加读到的记录，写进程负责接收数据并从队列中取出写入记录。
mysql -> mysql (增量，全量)
1.先创建2张mysql数据表数据源
1.CREATE DATABASE /*!32312 IF NOT EXISTS*/`userdb` /*!40100 DEFAULT CHARACTER SET utf8 */;
2.USE `userdb`;
3.CREATE TABLE `emp` (
`id` int(11) DEFAULT NULL,
`name` varchar(100) DEFAULT NULL,
`deg` varchar(100) DEFAULT NULL,
`salary` int(11) DEFAULT NULL,
`dept` varchar(10) DEFAULT NULL,
`create_time` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
`update_time` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
`is_delete` bigint(20) DEFAULT '1'
) ENGINE=InnoDB DEFAULT CHARSET=latin1;4.insert into `emp`(`id`,`name`,`deg`,`salary`,`dept`,`create_time`,`update_time`,`is_delete`) values (1201,'gopal','manager',50000,'TP','2018-06-17 18:54:32','2019-01-17 11:19:32',1),(1202,'manishahello','Proof reader',50000,'TPP','2018-06-15 18:54:32','2018-06-17 18:54:32',0),(1203,'khalillskjds','php dev',30000,'AC','2018-06-17 18:54:32','2019-03-14 09:18:27',1),(1204,'prasanth_xxx','php dev',30000,'AC','2018-06-17 18:54:32','2019-04-07 09:09:24',1),(1205,'kranthixxx','admin',20000,'TP','2018-06-17 18:54:32','2018-12-08 11:50:33',0),(1206,'garry','manager',50000,'TPC','2018-12-10 21:41:09','2018-12-10 21:41:09',1),(1207,'oliver','php dev',2000,'AC','2018-12-15 13:49:13','2018-12-15 13:49:13',1),(1208,'hello','phpDev',200,'TP','2018-12-16 09:41:48','2018-12-16 09:41:48',1),(1209,'ABC','HELLO',300,NULL,'2018-12-16 09:42:04','2018-12-16 09:42:24',1),(1210,'HELLO','HELLO',5800,'TP','2019-01-24 09:02:43','2019-01-24 09:02:43',1),(1211,'WORLD','TEST',8800,'AC','2019-01-24 09:03:15','2019-01-24 09:03:15',1),(1212,'sdfs','sdfsdf',8500,'AC','2019-03-13 22:01:38','2019-03-13 22:01:38',1),(1213,NULL,'sdfsdf',9800,'sdfsdf','2019-03-14 09:08:31','2019-03-14 09:08:54',1),(1214,'xxx','sdfsdf',9500,NULL,'2019-03-14 09:13:32','2019-03-14 09:13:44',0),(1215,'sdfsf','sdfsdfsdf',9870,'TP','2019-04-07 09:10:39','2019-04-07 09:11:18',0),(1216,'hello','HELLO',5600,'AC','2019-04-07 09:37:05','2019-04-07 09:37:05',1),(1217,'HELLO2','hello2',7800,'TP','2019-04-07 09:37:40','2019-04-07 09:38:17',1);
目标表：
1.USE userdb;
2.CREATE TABLE `emp3` (
`id` INT(11) DEFAULT NULL,
`name` VARCHAR(100) DEFAULT NULL,
`deg` VARCHAR(100) DEFAULT NULL,
`salary` INT(11) DEFAULT NULL,
`create_time` timestamp default CURRENT_TIMESTAMP,
`update_time` timestamp default CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP
) ENGINE=INNODB DEFAULT CHARSET=latin1;
增量导入
2.编写dataX配置文件：vim mysql2mysql.json
{
"job": {
"setting": {
"speed": {
"channel":1
}
},
"content": [
{
"reader": {
"name": "mysqlreader",
"parameter": {
"username": "root",
"password": "root%123",
"connection": [
{
"querySql": [
"select id,name,deg,salary,create_time,update_time from emp where create_time > '${start_time}' and create_time < '${end_time}';"
],
"jdbcUrl": [
"jdbc:mysql://192.168.72.112:3306/userdb"
]
}
]
}
},
"writer": {
"name": "mysqlwriter",
"parameter": {
"writeMode": "insert",
"username": "root",
"password": "root%123",
"column": [
"id",
"name",
"deg",
"salary","create_time","update_time"
],
"session": [
"set session sql_mode='ANSI'"
],
"preSql": [
"delete from emp3"
],
"connection": [
{
"jdbcUrl": "jdbc:mysql://192.168.72.112:3306/userdb?useUnicode=true&characterEncoding=utf-8",
"table": [
"emp3"
]
}
]
}
}
}
]
}
}3.执行dataX同步任务
python ../bin/datax.py ./mysql2mysql.json -p "-Dstart_time='2018-06-17 00:00:00' -Dend_time='2018-06-18 23:59:59'"结果查看：增量同步17-18日的数据成功。
全量导入
只需将上面配置文件中的querySql 改为： select id,name,deg,salary,create_time,update_time from emp 即可。
mysql -> hive （增量，全量）
增量导入
1.创建hive表create external table emp(
id int,name string,
deg string,salary double,
dept string,
create_time timestamp,
update_time timestamp ,
isdeleted string) row format delimited fields terminated by '\001';
2.编辑dataX配置文件{
"job": {
"setting": {
"speed": {
"channel":1
}
},
"content": [
{
"reader": {
"name": "mysqlreader",
"parameter": {
"username": "root",
"password": "root%123",
"connection": [
{
"querySql": [
"select * from emp where create_time > '${start_time}' and create_time < '${end_time}'"
],
"jdbcUrl": [
"jdbc:mysql://192.168.72.112:3306/userdb"
]
}
]
}
},
"writer": {
"name": "hdfswriter",
"parameter": {
"defaultFS": "hdfs://node01:8020",
"fileType": "text",
"path": "/warehouse/tablespace/external/hive/datax.db/emp",
"fileName": "emp",
"column": [
{
"name": "id",
"type": "INT"
},
{
"name": "name",
"type": "STRING"
},
{
"name": "deg",
"type": "STRING"
},
{
"name": "salary",
"type": "DOUBLE"
},
{
"name": "dept",
"type": "STRING"
},
{
"name": "create_time",
"type": "TIMESTAMP"
},
{
"name": "update_time",
"type": "TIMESTAMP"
},
{
"name": "isdeleted",
"type": "STRING"
}
],
"writeMode": "append",
"fieldDelimiter": "\u0001"
}
}
}
]
}
}
注意：writeMode 仅支持append, nonConflict两种模式
3.执行脚本
python ../bin/datax.py ./mysql2hive.json -p "-Dstart_time='2018-06-17 00:00:00' -Dend_time='2018-06-18 23:59:59'"
查看增量导入结果
全量导入
全量只需修改配置文件querysql即可。
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