Storm拓扑在Execute或nextTuple方法中需要对共享的成员变量进行同步吗

这里是一个简单的Storm的Topology代码，它在单机模式下执行，可以用来观察Storm拓扑中各个模块的执行顺序。
使用Storm版本为storm-0.9.x都可以运行，用IDE，比如idea，创建一个Maven项目，然后在pom.xml中添加以下依赖：

       <dependency>
            <groupId>org.apache.storm</groupId>
            <artifactId>storm-core</artifactId>
            <version>0.9.3</version>
            <scope>compile</scope>
        </dependency>

import backtype.storm.Config;
import backtype.storm.LocalCluster;
import backtype.storm.StormSubmitter;
import backtype.storm.spout.SpoutOutputCollector;
import backtype.storm.task.OutputCollector;
import backtype.storm.task.TopologyContext;
import backtype.storm.topology.OutputFieldsDeclarer;
import backtype.storm.topology.TopologyBuilder;
import backtype.storm.topology.base.BaseRichBolt;
import backtype.storm.topology.base.BaseRichSpout;
import backtype.storm.tuple.Fields;
import backtype.storm.tuple.Tuple;
import backtype.storm.tuple.Values;
import backtype.storm.utils.Utils;

import java.util.Map;
import java.util.Random;

/**
 * This is a basic example of a Storm topology.
 *
 * 探究对象的变量在被多个线程共享时，Storm是否已经对它进行同步了？
 * 比如ExclamationBolt中的成员变量i,当设置其并行度为13时，其execute方法的打印结果
 * 是同一个i值会被打印13次。
 *
 * 结论：execute()方法中调用的公共方法或共享变量都会被Storm同步。
 */
public class TestCountTopology {
    
    

    public static class ExclamationBolt extends BaseRichBolt {
    
    
        private static final long serialVersionUID = 6618890434446310020L;

        OutputCollector _collector;
        TopologyContext _context;
        int i = 0; //这个变量被多个线程共享时，是否被Storm同步了?

        @Override
        public void prepare(Map conf, TopologyContext context,
                OutputCollector collector) {
            _collector = collector;
            System.out.println("prepare in BOLT is called!");
            _context = context;
        }

        @Override
        public void execute(Tuple tuple) {
            System.out.println("execute in BOLT is called! " + i);
            System.err.println("Worker_port: " + _context.getThisWorkerPort()
                    + " tasks: " + _context.getThisWorkerTasks()
                    + " task_id: " + _context.getThisTaskId() + " BOLT receive: "
                    + new Values(tuple.getString(0)) + " i: " + i);
            inc();
            _collector.emit(tuple, new Values(tuple.getString(0) + "!!!"));
            _collector.ack(tuple);
        }

        @Override
        public void declareOutputFields(OutputFieldsDeclarer declarer) {
            declarer.declare(new Fields("word"));
        }

        private void inc() {
            i++;
            System.err.println("task_id: " + _context.getThisTaskId() + " inc() was called");
        }
    }

    public static class WordSpout extends BaseRichSpout {
    
    
        private static final long serialVersionUID = 6962615351294880911L;

        SpoutOutputCollector _collector;
        int i = 0;

        @Override
        public void open(Map conf, TopologyContext context,
                SpoutOutputCollector collector) {
            _collector = collector;
            System.err.println("open in SPOUT is called!");
        }

        @Override
        public void nextTuple() {
            System.out.println("nextTuple in SPOUT is called! " + i);
            Utils.sleep(100);
            final String[] words = new String[] { "nathan", "mike", "jackson",
                    "golda", "bertels" };
            final Random rand = new Random();
            final String word = words[rand.nextInt(words.length)];
            _collector.emit(new Values(word));
            System.out.println("SPOUI emit: " + new Values(word) + " i: " + i);
            i++;
        }

        @Override
        public void declareOutputFields(OutputFieldsDeclarer declarer) {
            declarer.declare(new Fields("word"));
            System.out.println("declareOutputFields in SPOUT is called!");
        }

        public void ack(Object msgId) {
            System.out.println("ack in SPOUT is called!");
        }

        public void fail(Object msgId) {
            System.out.println("fail in SPOUT is called!");
        }

        public void close() {
            System.out.println("close in SPOUT is called!");
        }

    }

    public static void main(String[] args) throws Exception {
        TopologyBuilder builder = new TopologyBuilder();

        // builder.setSpout("word", new TestWordSpout(), 10);
        builder.setSpout("word", new WordSpout(), 1);
        builder.setBolt("exclaim1", new ExclamationBolt(), 13).shuffleGrouping("word");
        //builder.setBolt("exclaim2", new ExclamationBolt(), 3).shuffleGrouping("exclaim1");

        Config conf = new Config();
        conf.setDebug(false);// TOPOLOGY_DEBUG
        conf.setNumWorkers(2);

        if (args != null && args.length > 0) {
            conf.setNumWorkers(3);// TOPOLOGY_WORKERS

            StormSubmitter.submitTopologyWithProgressBar(args[0], conf,
                    builder.createTopology());
        } else {

            LocalCluster cluster = new LocalCluster();
            cluster.submitTopology("test", conf, builder.createTopology());
            Utils.sleep(10000);
            cluster.killTopology("test");
            cluster.shutdown();
            /*
             * comment out the cluster.killTopology() and cluster.shutdown()
             * methods, to avoid the file deletion error.
             */
        }
    }
}

这个拓扑由两个节点组成，WordSpout的方法nextTuple会反复被执行，每次随机从words这个字符数组中选出一个单词发送到流中；而ExclamationBolt会从流中读取这个单词，然后在它后面添加三个叹号”!!!”，然后再发送到流中。

比如WordSpout产生单词”nathan”，那么ExclamationBolt就会产生”nathan!!!”。

在上面的代码中，ExclamationBolt这个Bolt的并行度设置为13，这意味着Storm会产生13个线程，每个线程都会反复执行该Bolt的execute方法，在该方法中的变量i的值会被访问，然后自增1。

Q: 由于这个变量会被多个线程共享访问和修改，那么需要我们进行同步吗？

A: 不需要， 运行Topology发现，*ExclamationBolt的execute方法会将接收到的每个单词添加”!!!”后打印13次，变量i也会以相同值被打印13次后才自增1，这说明Storm帮我们完成了同步。