spring-boot-starter-elastic-job-lite
spring boot starter for Elastic-Job
1、add pom
<dependency>
<groupId>cn.itshaw</groupId>
<artifactId>spring-boot-starter-elastic-job-lite</artifactId>
<version>${spring-boot-starter-elastic-job-lite.version}</version>
</dependency>
2、regsiter center config
elaticjob:
zookeeper:
server-lists: ip1:2181,ip2:2181,ip3:2181
namespace: elastic-job-lite-demo
baseSleepTimeMilliseconds: ##The initial value of the interval of time waiting for retrial (milliseconds)
maxSleepTimeMilliseconds: ##The maximum of the interval of time waiting for retrial (milliseconds)
maxRetries: ##max retries
sessionTimeoutMilliseconds: ##Session timeout time (milliseconds)
connectionTimeoutMilliseconds: ##Connection timeout time (milliseconds)
digest: ##Permission token to connect to Zookeeper. Default is not required to verify permission
event:
rdb:
url: jdbc:mysql://ip:port/db_name
username: username
password: password
driver-class-name: com.mysql.jdbc.Driver
3、cread job classes
SimpleJob Demo：
@ElasticJobLite(cron="0 * * * * ?",shardingTotalCount = 3,shardingItemParameters = "0=Beijing,1=Shanghai,2=Guangzhou")
@Component
public class MySimpleJob implements SimpleJob {
@Override
public void execute(ShardingContext context) {
switch (context.getShardingItem()) {
case 0:
// do something by sharding item 0
break;
case 1:
// do something by sharding item 1
break;
case 2:
// do something by sharding item 2
break;
// case n: ...
}
}
}
DataflowJob Demo：
@ElasticJobLite(cron="0 * * * * ?",shardingTotalCount = 3,shardingItemParameters = "0=Beijing,1=Shanghai,2=Guangzhou")
@Component
public class MyDataflowJob implements DataflowJob {
@Override
public List fetchData(ShardingContext shardingContext) {
System.out.println(String.format("Item: %s | Time: %s | Thread: %s | %s",
shardingContext.getShardingItem(), new SimpleDateFormat("HH:mm:ss").format(new Date()), Thread.currentThread().getId(), "DATAFLOW FETCH"));
return null;
}
@Override
public void processData(ShardingContext shardingContext, List data) {
System.out.println(String.format("Item: %s | Time: %s | Thread: %s | %s",
shardingContext.getShardingItem(), new SimpleDateFormat("HH:mm:ss").format(new Date()), Thread.currentThread().getId(), "DATAFLOW PROCESS"));
}
}
4、@ElasticJobLite config lists
value 作业启动时间的cron表达式(优先使用cron配置);
cron 作业启动时间的cron表达式;
shardingTotalCount 作业分片总数(默认为1);
shardingItemParameters 分片序列号和个性化参数对照表;
dataSource 作业数据源;
disabled 设置作业是否启动时禁止;
failover 是否开启失效转移,只有对monitorExecution的情况下才可以开启失效转移（默认为false）;
monitorExecution 设置监控作业执行时状态（默认true）; 每次作业执行时间和间隔时间均非常短的情况, 建议不监控作业运行时状态以提升效率, 因为是瞬时 状态, 所以无必要监控. 请用户自行增加数据堆积监控. 并且不能保证数据重复选取, 应在作业中实现幂等性. 也无法实现作业失效转移.
每次作业执行时间和间隔时间均较长短的情况, 建议监控作业运行时状态, 可保证数据不会重复选取.
overwritedefault 设置本地配置是否可覆盖注册中心配置（默认为false）;如果可覆盖, 每次启动作业都以本地配置为准.
jobName 作业名称（默认为任务的全类名）;
streamingProcess 作业是否流式处理（只对Dataflowjob类型有效）（默认为true
maxTimeDiffSeconds 设置最大容忍的本机与注册中心的时间误差秒数（默认为-1);如果时间误差超过配置秒数则作业启动时将抛异常.配置为-1表示不检查时间 误差.
monitorPort 设置作业辅助监控端口(默认为-1）;配置为-1表示不配置监控端口.
reconcileIntervalMinutes 设置修复作业服务器不一致状态服务执行间隔分钟数(默认为10分钟), 每隔一段时间监视作业服务器的状态，如果不正确则重新分片.
jobShardingStrategyClass 设置作业分片策略实现类全路径（默认为com.dangdang.ddframe.job.plugin.sharding.strategy.AverageAllocationJobShardingStrategy）
Ref:
http://www.infoq.com/cn/articles/dangdang-distributed-work-framework-elastic-job