下面是在麒麟V10服务器上编译安装Storm的详细过程的完整攻略：

准备工作

在开始之前，需要做好以下准备：

安装Java Development Kit(JDK)：Storm是用Java编写的，需要JDK才能进行编译和执行。在麒麟V10服务器上，可以通过以下命令安装JDK:

sudo apt-get install default-jdk

安装Maven：Storm的编译需要使用Maven进行依赖管理。在麒麟V10服务器上，可以通过以下命令安装Maven：

sudo apt-get install maven

下载和编译Storm

下载Storm：可以从Storm官网（https://storm.apache.org/downloads.html）下载最新版本的Storm。下载完成后，解压缩到任意目录。
编译Storm：进入Storm源代码目录，执行以下命令进行编译：

mvn clean install -DskipTests=true

上述命令会将Storm的所有依赖包下载到本地，编译生成Storm的jar包，并安装到本地的Maven仓库中。

运行Storm

在编译完成后，可以通过以下命令来启动Storm：

bin/storm nimbus   # 启动Nimbus节点
bin/storm supervisor   # 启动Supervisor节点
bin/storm ui   # 启动Storm Web UI

此时，可以在浏览器中访问http://localhost:8080来访问Storm的Web UI。

示例：使用Storm进行单词计数

下面是使用Storm进行单词计数的示例，包含Topology定义和代码实现：

Topology定义

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

        builder.setSpout("spout", new RandomSentenceSpout(), 5);
        builder.setBolt("split", new SplitSentenceBolt(), 8).shuffleGrouping("spout");
        builder.setBolt("count", new CountBolt(), 12).fieldsGrouping("split", new Fields("word"));

        Config conf = new Config();
        conf.setDebug(true);

        LocalCluster cluster = new LocalCluster();
        cluster.submitTopology("word-count", conf, builder.createTopology());

        Utils.sleep(10000);

        cluster.killTopology("word-count");
        cluster.shutdown();
    }
}

上述代码定义了一个拓扑结构，包含三个组件：spout、split和count。其中，spout组件用来产生随机的句子；split组件将句子拆分为单词；count组件将每个单词的计数汇总。

代码实现

创建RandomSentenceSpout类，用来产生随机的句子：

```java
public class RandomSentenceSpout extends BaseRichSpout {
private SpoutOutputCollector collector;
private Random rand;

   public void open(Map conf, TopologyContext context, SpoutOutputCollector collector) {
       this.collector = collector;
       this.rand = new Random();
   }

   public void nextTuple() {
       Utils.sleep(100);
       String[] sentences = new String[]{
               "the cow jumped over the moon",
               "an apple a day keeps the doctor away",
               "four score and seven years ago",
               "snow white and the seven dwarfs",
               "i am at two with nature"
       };
       String sentence = sentences[rand.nextInt(sentences.length)];
       collector.emit(new Values(sentence));
   }

   public void declareOutputFields(OutputFieldsDeclarer declarer) {
       declarer.declare(new Fields("sentence"));
   }

}
```

创建SplitSentenceBolt类，用来将句子拆分为单词：

```java
public class SplitSentenceBolt extends BaseRichBolt {
private OutputCollector collector;

   public void prepare(Map conf, TopologyContext context, OutputCollector collector) {
       this.collector = collector;
   }

   public void execute(Tuple tuple) {
       String sentence = tuple.getStringByField("sentence");
       String[] words = sentence.split(" ");
       for (String word : words) {
           collector.emit(new Values(word));
       }
   }

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

}
```

创建CountBolt类，用来汇总每个单词的计数：

```java
public class CountBolt extends BaseRichBolt {
private OutputCollector collector;
private Map counts = new HashMap();

   public void prepare(Map conf, TopologyContext context, OutputCollector collector) {
       this.collector = collector;
   }

   public void execute(Tuple tuple) {
       String word = tuple.getStringByField("word");
       int count = 0;
       if (counts.containsKey(word)) {
           count = counts.get(word);
       }
       count++;
       counts.put(word, count);

       collector.emit(new Values(word, count));
   }

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

}
```

完成上述代码后，运行WordCountTopology类即可启动单词计数拓扑，可以在Storm的Web UI中查看拓扑的运行状态和结果。

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