
前段时间,项目中使用到了流式处理方面的技术,学习了一下storm,编写了一个小实例。
1.引入jar包
4.0.0 org.springframework.boot spring-boot-starter-parent2.3.12.RELEASE com.example control0.0.1-SNAPSHOT control Demo project for Spring Boot 1.8 org.springframework.boot spring-boot-starterlog4j-to-slf4j org.apache.logging.log4j jul-to-slf4j org.slf4j logback-classic ch.qos.logback org.springframework.boot spring-boot-starter-weborg.apache.storm storm-core2.2.1 com.codahale.metrics metrics-core3.0.2 slf4j-api org.slf4j org.springframework.boot spring-boot-maven-pluginorg.projectlombok lombokorg.apache.maven.plugins maven-shade-pluginpackage shade false commons-logging:commons-logging javax.servlet:servlet-api javax.mail:javax.mail-api
2.编写程序:
(1)编写输入流类
package test.storm;
import java.util.Map;
import java.util.Random;
import org.apache.storm.spout.SpoutOutputCollector;
import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.topology.base.baseRichSpout;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.Values;
public class WordSpout extends baseRichSpout {
private SpoutOutputCollector collector;
private static String[] words = {"星期一","星期二","星期三","星期四","星期五","星期六","星期日"};
public WordSpout() {
System.out.println("--====================WordSpout===---------------");
}
public void nextTuple() {
//随机取 words 字符串中一个词。
String word = words[new Random().nextInt(words.length)];
//发射元组到输出收集器
collector.emit(new Values(word));
}
public void open(Map arg0, TopologyContext arg1, SpoutOutputCollector arg2) {
this.collector=arg2;
//定义数据源输出收集器
}
public void declareOutputFields(OutputFieldsDeclarer arg0) {
// TODO Auto-generated method stub
//声明输出字段的名称为为 word
arg0.declare(new Fields("word"));
}
}
(2)整理数据流并输出
package test.storm;
import java.util.Map;
import org.apache.storm.task.OutputCollector;
import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.topology.base.baseRichBolt;
import org.apache.storm.tuple.Tuple;
public class ProcessBolt extends baseRichBolt {
public ProcessBolt() {
// TODO Auto-generated constructor stub
System.out.println("-----------ProcessBolt==============");
}
@Override
public void execute(Tuple arg0) {
// TODO Auto-generated method stub
//此处直接对接受到的元组进行处理,然后输出到控制台,这里没有将处理后的数据再送到输出收集器中。
//取得元组的数据
String word = (String) arg0.getValue(0);
String out = "Hello :" + word + "!";
//输出到控制台,使用 err.println 会显示红色,所以这里使用 err
System.err.println(out);
}
@Override
public void prepare(Map arg0, TopologyContext arg1, OutputCollector arg2) {
// TODO Auto-generated method stub
System.out.println("-----------prepare==============");
}
@Override
public void declareOutputFields(OutputFieldsDeclarer arg0) {
// TODO Auto-generated method stub
System.out.println("-----------declareOutputFields==============");
}
}
(3)编写启动类:
package test.storm;
import org.apache.storm.Config;
import org.apache.storm.LocalCluster;
import org.apache.storm.StormSubmitter;
import org.apache.storm.topology.TopologyBuilder;
public class WordStorm {
public WordStorm() {
// TODO Auto-generated constructor stub
}
public static void main(String[] args) throws Exception {
// TODO Auto-generated method stub
//定义 TopologyBuilder
TopologyBuilder builder=new TopologyBuilder();
//定义 Spout
builder.setSpout("Spout_ID", new WordSpout());
//定义 Bolt
builder.setBolt("Bolt_ID", new ProcessBolt()).localOrShuffleGrouping("Spout_ID");
//下面开始定义运行模式
final Config config=new Config();
config.setDebug(true);
//设置workers
config.setNumWorkers(1);
config.setMaxSpoutPending(1);
if (args != null && args.length > 0) {
//集群运行模式
config.setNumWorkers(3);
StormSubmitter.submitTopologyWithProgressBar(args[0], config, builder.createTopology());
}else {
//使用本地模式运行
final LocalCluster localCluster=new LocalCluster();
localCluster.submitTopology(WordStorm.class.getSimpleName(), config, builder.createTopology());
org.apache.storm.utils.Utils.sleep(20000);
localCluster.killTopology(WordStorm.class.getSimpleName());
localCluster.shutdown();
}
}
}
运行结果:
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