Storm拓撲在Execute或nextTuple方法中需要對共享的成員變量進行同步嗎

這裏是一個簡單的Storm的Topology代碼，它在單機模式下執行，可以用來觀察Storm拓撲中各個模塊的執行順序。
使用Storm版本爲storm-0.9.x都可以運行，用IDE，比如idea，創建一個Maven項目，然後在pom.xml中添加以下依賴：

       <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.
 *
 * 探究對象的變量在被多個線程共享時，Storm是否已經對它進行同步了？
 * 比如ExclamationBolt中的成員變量i,當設置其並行度爲13時，其execute方法的打印結果
 * 是同一個i值會被打印13次。
 *
 * 結論：execute()方法中調用的公共方法或共享變量都會被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; //這個變量被多個線程共享時，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.
             */
        }
    }
}

這個拓撲由兩個節點組成，WordSpout的方法nextTuple會反覆被執行，每次隨機從words這個字符數組中選出一個單詞發送到流中；而ExclamationBolt會從流中讀取這個單詞，然後在它後面添加三個歎號”!!!”，然後再發送到流中。

比如WordSpout產生單詞”nathan”，那麼ExclamationBolt就會產生”nathan!!!”。

在上面的代碼中，ExclamationBolt這個Bolt的並行度設置爲13，這意味着Storm會產生13個線程，每個線程都會反覆執行該Bolt的execute方法，在該方法中的變量i的值會被訪問，然後自增1。

Q: 由於這個變量會被多個線程共享訪問和修改，那麼需要我們進行同步嗎？

A: 不需要， 運行Topology發現，*ExclamationBolt的execute方法會將接收到的每個單詞添加”!!!”後打印13次，變量i也會以相同值被打印13次後才自增1，這說明Storm幫我們完成了同步。