Storm入门（七）可靠性机制代码示例

一、关联代码

使用maven，代码如下。

pom.xml 参考 http://www.cnblogs.com/hd3013779515/p/6970551.html

MessageTopology.java

package cn.ljh.storm.reliability;

import org.apache.storm.Config;
import org.apache.storm.LocalCluster; import org.apache.storm.topology.TopologyBuilder; import org.apache.storm.utils.Utils; public class MessageTopology { public static void main(String[] args) throws Exception { TopologyBuilder builder = new TopologyBuilder(); builder.setSpout("MessageSpout", new MessageSpout(), 1); builder.setBolt("SpilterBolt", new SpliterBolt(), 5).shuffleGrouping("MessageSpout"); builder.setBolt("WriterBolt", new WriterBolt(), 1).shuffleGrouping("SpilterBolt"); Config conf = new Config(); conf.setDebug(false); LocalCluster cluster = new LocalCluster(); cluster.submitTopology("messagetest", conf, builder.createTopology()); Utils.sleep(20000); cluster.killTopology("messagetest"); cluster.shutdown(); } }

MessageSpou.java

package cn.ljh.storm.reliability;

import org.apache.storm.topology.OutputFieldsDeclarer;

import java.util.Map; import org.apache.storm.spout.SpoutOutputCollector; import org.apache.storm.task.TopologyContext; import org.apache.storm.topology.base.BaseRichSpout; import org.apache.storm.tuple.Fields; import org.apache.storm.tuple.Values; import org.slf4j.Logger; import org.slf4j.LoggerFactory; public class MessageSpout extends BaseRichSpout { public static Logger LOG = LoggerFactory.getLogger(MessageSpout.class); private SpoutOutputCollector _collector; private int index = 0; private String[] subjects = new String[]{ "Java,Python", "Storm,Kafka", "Spring,Solr", "Zookeeper,FastDFS", "Dubbox,Redis" }; public void open(Map conf, TopologyContext context, SpoutOutputCollector collector) { _collector = collector; } public void nextTuple() { if(index < subjects.length){ String sub = subjects[index]; //使用messageid参数，使可靠性机制生效 _collector.emit(new Values(sub), index); index++; } } public void declareOutputFields(OutputFieldsDeclarer declarer) { declarer.declare(new Fields("subjects")); } @Override public void ack(Object msgId) { LOG.info("【消息发送成功！】(msgId = " + msgId + ")"); } @Override public void fail(Object msgId) { LOG.info("【消息发送失败！】(msgId = " + msgId + ")"); LOG.info("【重发进行中。。。】"); _collector.emit(new Values(subjects[(Integer)msgId]), msgId); LOG.info("【重发成功！】"); } }

SpliterBolt.java

package cn.ljh.storm.reliability;

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.Fields; import org.apache.storm.tuple.Tuple; import org.apache.storm.tuple.Values; public class SpliterBolt extends BaseRichBolt { OutputCollector _collector; private boolean flag = false; public void prepare(Map conf, TopologyContext context, OutputCollector collector) { _collector = collector; } public void execute(Tuple tuple) { try{ String subjects = tuple.getStringByField("subjects"); // if(!flag && subjects.equals("Spring,Solr")){ // flag = true; // int a = 1/0; // }  String[] words = subjects.split(","); for(String word : words){ //注意：要携带tuple对象，用于处理异常时重发策略。 _collector.emit(tuple, new Values(word)); } //对tuple进行ack  _collector.ack(tuple); }catch(Exception ex){ ex.printStackTrace(); //对tuple进行fail，使重发。  _collector.fail(tuple); } } public void declareOutputFields(OutputFieldsDeclarer declarer) { declarer.declare(new Fields("word")); } }

WriterBolt.java

package cn.ljh.storm.reliability;

import java.io.FileWriter;
import java.io.IOException; 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; import org.slf4j.Logger; import org.slf4j.LoggerFactory; public class WriterBolt extends BaseRichBolt { private static Logger LOG = LoggerFactory.getLogger(WriterBolt.class); OutputCollector _collector; private FileWriter fileWriter; private boolean flag = false; public void prepare(Map conf, TopologyContext context, OutputCollector collector) { _collector = collector; if(fileWriter == null){ try { fileWriter = new FileWriter("D:\\test\\"+"words.txt"); } catch (IOException e) { e.printStackTrace(); } } } public void execute(Tuple tuple) { try { String word = tuple.getStringByField("word"); // if(!flag && word.equals("Kafka")){ // flag = true; // int a = 1/0; // } fileWriter.write(word + "\r\n"); fileWriter.flush(); } catch (Exception e) { e.printStackTrace(); //对tuple进行fail，使重发。  _collector.fail(tuple); } //对tuple进行ack  _collector.ack(tuple); } public void declareOutputFields(OutputFieldsDeclarer declarer) { } }

二、执行效果

1、代码要点说明

MessageSpout.java

（1）发射tuple时要设置messageId来使可靠性机制生效

_collector.emit(new Values(sub), index);

（2）重写ack和fail方法

@Override
   public void ack(Object msgId) {
       LOG.info("【消息发送成功！】(msgId = " + msgId + ")");
   }

   @Override
   public void fail(Object msgId) { LOG.info("【消息发送失败！】(msgId = " + msgId + ")"); LOG.info("【重发进行中。。。】"); _collector.emit(new Values(subjects[(Integer)msgId]), msgId); LOG.info("【重发成功！】"); }

SpliterBolt.java

（1）发射新tuple时设置输入tuple参数，以使新tuple和输入tuple为一个整体

_collector.emit(tuple, new Values(word));

（2）完成处理后进行ack，失败时进行fail

_collector.ack(tuple);

_collector.fail(tuple);

WriterBolt.java

（1）完成处理后进行ack，失败时进行fail

_collector.ack(tuple);

_collector.fail(tuple);

2、正常处理结果

3、放开SpliterBolt 的错误代码

结果显示能够正确的重发。

4、放开SpliterBolt 的错误代码

能够正确进行重发，但是文件中storm字符串出现了两次。

5、总结

通过以上测试，如果在第一个bolt处理时出现异常，可以让整个数据进行重发，如果第二个bolt处理时出现异常，也可以让整个数据进行重发，但是同时出现了重复处理的事务性问题，需要进行特殊的处理。

（1）如果数据入库的话，可以把messageId也进行入库保存。此messageId可以用来判断是否重复处理。

（2）事务性tuple尽量不要拆分。

（3）使用storm的Trident框架。