大致架构
* 每个应用实例部署一个日志agent
* agent实时将日志发送到kafka
* storm实时计算日志
* storm计算结果保存到hbase
storm消费kafka
- 创建实时计算项目并引入storm和kafka相关的依赖
<dependency> <groupId>org.apache.storm</groupId> <artifactId>storm-core</artifactId> <version>1.0.2</version> <scope>provided</scope></dependency><dependency> <groupId>org.apache.storm</groupId> <artifactId>storm-kafka</artifactId> <version>1.0.2</version></dependency><dependency> <groupId>org.apache.kafka</groupId> <artifactId>kafka_2.10</artifactId> <version>0.8.2.0</version></dependency>- 创建消费kafka的spout，直接用storm提供的KafkaSpout即可。
- 创建处理从kafka读取数据的Bolt，JsonBolt负责解析kafka读取到的json并发送到下个Bolt进一步处理（下一步处理的Bolt不再写，只要继承BaseRichBolt就可以对tuple处理）。
public class JsonBolt extends BaseRichBolt { private static final Logger LOG = LoggerFactory .getLogger(JsonBolt.class); private Fields fields; private OutputCollector collector; public JsonBolt() { this.fields = new Fields("hostIp", "instanceName", "className", "methodName", "createTime", "callTime", "errorCode"); } @Override public void prepare(Map stormConf, TopologyContext context, OutputCollector collector) { this.collector = collector; } @Override public void execute(Tuple tuple) { String spanDataJson = tuple.getString(0); LOG.info("source data:{}", spanDataJson); Map<String, Object> map = (Map<String, Object>) JSONValue .parse(spanDataJson); Values values = new Values(); for (int i = 0, size = this.fields.size(); i < size; i++) { values.add(map.get(this.fields.get(i))); } this.collector.emit(tuple, values); this.collector.ack(tuple); } @Override public void declareOutputFields(OutputFieldsDeclarer declarer) { declarer.declare(this.fields); }}- 创建拓扑MyTopology，先配置好KafkaSpout的配置SpoutConfig，其中zk的地址端口和根节点，将id为KAFKA_SPOUT_ID的spout通过shuffleGrouping关联到jsonBolt对象。
public class MyTopology { private static final String TOPOLOGY_NAME = "SPAN-DATA-TOPOLOGY"; private static final String KAFKA_SPOUT_ID = "kafka-stream"; private static final String JsonProject_BOLT_ID = "jsonProject-bolt"; public static void main(String[] args) throws Exception { String zks = "132.122.252.51:2181"; String topic = "span-data-topic"; String zkRoot = "/kafka-storm"; BrokerHosts brokerHosts = new ZkHosts(zks); SpoutConfig spoutConf = new SpoutConfig(brokerHosts, topic, zkRoot, KAFKA_SPOUT_ID); spoutConf.scheme = new SchemeAsMultiScheme(new StringScheme()); spoutConf.zkServers = Arrays.asList(new String[] { "132.122.252.51" }); spoutConf.zkPort = 2181; JsonBolt jsonBolt = new JsonBolt(); TopologyBuilder builder = new TopologyBuilder(); builder.setSpout(KAFKA_SPOUT_ID, new KafkaSpout(spoutConf)); builder.setBolt(JsonProject_BOLT_ID, jsonBolt).shuffleGrouping( KAFKA_SPOUT_ID); Config config = new Config(); config.setNumWorkers(1); if (args.length == 0) { LocalCluster cluster = new LocalCluster(); cluster.submitTopology(TOPOLOGY_NAME, config, builder.createTopology()); Utils.waitForSeconds(100); cluster.killTopology(TOPOLOGY_NAME); cluster.shutdown(); } else { StormSubmitter.submitTopology(args[0], config, builder.createTopology()); } }}- 本地测试时直接不带运行参数运行即可，放到集群是需带拓扑名称作为参数。
- 另外需要注意的是：KafkaSpout默认从上次运行停止时的位置开始继续消费，即不会从头开始消费一遍，因为KafkaSpout默认每2秒钟会提交一次kafka的offset位置到zk上，如果要每次运行都从头开始消费可以通过配置实现。
