全量mysqltomysql
进入目录编写json
cd /usr/local/datax/job
vi zabbixmysql2mysql.json
写入的表结构要和reader的表结构一样，先建立好
编写json文件
{
"job": {
"content": [
{
"reader": {
"name": "mysqlreader",
"parameter": {
"username": "test",
"password": "123",
"column": [
"itemid",
"clock",
"timestamp",
"source",
"severity",
"value",
"logeventid",
"ns"
],
"splitPk": "itemid",
"connection": [
{
"table": [
"history_log"
],
"jdbcUrl": [
"jdbc:mysql://172.16.3.89:3306/zabbix"
]
}
]
}
},
"writer": {
"name": "mysqlwriter",
"parameter": {
"writeMode": "insert",
"username": "test",
"password": "123",
"column": [
"itemid",
"clock",
"timestamp",
"source",
"severity",
"value",
"logeventid",
"ns"
],
"preSql": [
"truncate history_log_copy1"
],
"connection": [
{
"jdbcUrl": "jdbc:mysql://172.16.3.89:3306/chenzhenhua2?useUnicode=true&characterEncoding=utf8",
"table": [
"history_log_copy1"
]
}
]
}
}
}
],
"setting": {
"speed": {
"channel": 6
}
}
}
}
注意：“writeMode”: “insert”,也可以为update，update更加稳妥一点
“preSql”: [ “truncate history_log_copy1” ], 在写入前提前清空表清空表
如果写入的数据库为mysql8以上版本，必须修改mysql-connector-java的插件
cd /usr/local/datax/plugin/writer/mysqlwriter/libs
mv mysql-connector-java-5.1.34.jar mysql-connector-java-5.1.34.jar-bak
我这边上传的为mysql-connector-java-8.0.16.jar，下载地址https://static.runoob.com/download/mysql-connector-java-8.0.16.jar
增量同步
Datax需要解决的另一个难题在于增量更新。
首先需要说明， Datax本身在大部分reader插件中提供了where配置项，用于做增量更新。例如mysqlerader md文件说明如下：
* **where**
* 描述：筛选条件，MysqlReader根据指定的column、table、where条件拼接SQL，并根据这个SQL进行数据抽取。在实际业务场景中，往往会选择当天的数据进行同步，可以将where条件指定为gmt_create > $bizdate 。注意：不可以将where条件指定为limit 10，limit不是SQL的合法where子句。<br />
where条件可以有效地进行业务增量同步。如果不填写where语句，包括不提供where的key或者value，DataX均视作同步全量数据。
* 必选：否 <br />
* 默认值：无 <br />
* **querySql**
* 描述：在有些业务场景下，where这一配置项不足以描述所筛选的条件，用户可以通过该配置型来自定义筛选SQL。当用户配置了这一项之后，DataX系统就会忽略table，column这些配置型，直接使用这个配置项的内容对数据进行筛选，例如需要进行多表join后同步数据，使用select a,b from table_a join table_b on table_a.id = table_b.id <br />
`当用户配置querySql时，MysqlReader直接忽略table、column、where条件的配置`，querySql优先级大于table、column、where选项。
* 必选：否 <br />
* 默认值：无 <br />
示例：
新建json
vi new.json
{
"job": {
"content": [
{
"reader": {
"name": "mysqlreader",
"parameter": {
"username": "root",
"password": "123",
"where": "created_at > FROM_UNIXTIME(${create_time}) and created_at < FROM_UNIXTIME(${end_time})",
"column": [
"id",
"rpt_date",
"rpt_hour",
"unit_id",
"build_id",
"num",
"run_state",
"created_at"
],
"splitPk": "id",
"connection": [
{
"table": [
"rpt_warning_hour"
],
"jdbcUrl": [
"jdbc:mysql://172.16.5.11:3306/smart_fire"
]
}
]
}
},
"writer": {
"name": "mysqlwriter",
"parameter": {
"writeMode": "update",
"username": "test",
"password": "123",
"column": [
"id",
"rpt_date",
"rpt_hour",
"unit_id",
"build_id",
"num",
"run_state",
"created_at"
],
"connection": [
{
"jdbcUrl": "jdbc:mysql://172.16.3.89:3306/chenzhenhua2?useUnicode=true&characterEncoding=utf8",
"table": [
"rpt_warning_hour"
]
}
]
}
}
}
],
"setting": {
"speed": {
"channel": 6
}
}
}
}
上面需要注意的事情为FROM_UNIXTIME将表里面的时间格式转换为时间戳格式，如果表里默认为时间戳不需要转换。
${…}就是将变量传入，上次更新{create_time}上次更新时间，{end_time}为现在本地时间。
然后再编写一个python脚本可以将参数传入json即可，vi dataxScheduler.py
import time,os,sys
print "going to execute"
configFilePath = sys.argv[1]
logFilePath = sys.argv[2]
lastTimeExecuteRecord = sys.argv[3]
lastExecuteTime=""
try:
fo = open(lastTimeExecuteRecord, "r")
lastExecuteTime = fo.read()
print lastExecuteTime
except IOError:
lastExecuteTime = int(1)
lastExecuteTime = int(lastExecuteTime)
print("last time execute time: " + str(lastExecuteTime))
currentTime = int(time.time())
print("currentTime is :"+ str(currentTime))
#os.system("python /usr/local/datax/bin/datax.py " + configFilePath + " --lastTime" + lastExecuteTime + " --currentTime" + currentTime + " >> " + logFilePath)
script2execute = "python /usr/local/datax/bin/datax.py %s -p \"-Dcreate_time=%s -Dend_time=%s\" >> %s"%(configFilePath,lastExecuteTime,currentTime,logFilePath)
print("to be excute script:"+script2execute)
os.system(script2execute)
print("script execute ending")
# update timestamp to file
fo = open(lastTimeExecuteRecord, "w+")
fo.write(str(currentTime))
fo.close()
print("ending---",lastTimeExecuteRecord)
运行
python /usr/local/datax/job/dataxScheduler.py '/usr/local/datax/job/new.json' '/usr/local/datax/job/test_job.log' '/usr/local/datax/job/test_job.record'
测试，增加数据后再次运行，数据对应增加了，加入到定时任务执行即可完成增量同步。
但这个写脚本的方式还是非常笨拙的，下一篇介绍的datax-web会更好的去解决增量同步的问题。
