（二） kafka-jstorm集群实时日志分析 之 ---------jstorm集成spring

    　后面为了引入Dubbo RPC框架（用spring配置），先把spring 引入jstorm中，请先了解一下jsorm多线程方面的文档：http://storm.apache.org/documentation/Understanding-the-parallelism-of-a-Storm-topology.html　．

　　

A worker process executes a subset of a topology. A worker process belongs to a specific topology and may run one or more executors for one or more components (spouts or bolts) of this topology. A running topology consists of many such processes running on many machines within a Storm cluster.

An executor is a thread that is spawned by a worker process. It may run one or more tasks for the same component (spout or bolt).

A task performs the actual data processing — each spout or bolt that you implement in your code executes as many tasks across the cluster. The number of tasks for a component is always the same throughout the lifetime of a topology, but the number of executors (threads) for a component can change over time. This means that the following condition holds true: #threads ≤ #tasks. By default, the number of tasks is set to be the same as the number of executors, i.e. Storm will run one task per thread.

　　不管是spout还是bolt，都会被每个线程初始化，（你可以打印日志，或断点观察）．Bolt的prepare方法，或Spout方法的open方法都会随着实例化而调用，你可以认为是特殊的构造函数吧．

　　多线程环境下每个ｂｏｌｔ的每个实例都可能被不同机器执行．每个ｂｏｌｔ所需要的服务可能不同，这就需要在每个Bolt类中加载ｓｐｒｉｎｇ，即初始化．比如

public static class WordCounterBoltCh03 extends BaseRichBolt {
		private static final Logger log = LoggerFactory.getLogger(WordCounterBoltCh03.class);

		private static final long serialVersionUID = 1L;
		private Map countMap;
		private OutputCollector collector;
		private String name;
		private int id;
		
		private static final ApplicationContext applicationContext;
		private static final LogManager logManager;
		
		static{
			applicationContext = SpringUtil.of("learningJstormConfig/spring-kafkabolt-context.xml");／／ｓｐｒｉｎｇ初始化
			logManager = applicationContext.getBean(LogManager.class);
			log.info("--------------ApplicationContext initialized from learningJstormConfig/spring-kafkabolt-context.xml ");
		}

		@Override
		public void prepare(Map stormConf, TopologyContext context, OutputCollector collector) {
			this.countMap = new HashMap<>();
			this.collector = collector;
			this.name = context.getThisComponentId();
			this.id = context.getThisTaskId();
			log.info("-----------------WordCounterBoltCh03 prepare");
		}

		@Override
		public void execute(Tuple input) {
			String word = null;
			try {
				word = input.getStringByField("word");
			} catch (Throwable e) {
			}
			if (null != word) {
				if (!countMap.containsKey(word)) {
					countMap.put(word, 1);
				} else {
					Integer count = countMap.get(word);
					count++;
					countMap.put(word, count);
					
					logManager.write(word + ":" + countMap.get(word));
				}
			} else {
				if ("signals.".equals(input.getSourceStreamId()) && "refreshCache".equals(input.getStringByField("action"))) {
					cleanup();
					countMap.clear();
				}
			}

			this.collector.ack(input);

		}

		@Override
		public void declareOutputFields(OutputFieldsDeclarer declarer) {

		}

		@Override
		public void cleanup() {
			log.info("{cleanup................}");
			countMap.forEach((k, v) -> {
				log.info("{clean up.................}");
				log.info("k : {} , v : {}", k, v);
			});
		}

	}

　spring配置文件如下：

　最好，每个Spout,每个Bolt都定制Spring配置文件．即每个Spout,每个Bolt都加载并初始化Spring环境．