storm的trident编程模型

storm的基本概念别人总结的，

https://blog.csdn.net/pickinfo/article/details/50488226 

编程模型最关键最难就是实现局部聚合的业务逻辑聚合类实现Aggregator接口重写方法aggregate，聚合使用存储中间聚合过程状态的类，本地hashmap的去重逻辑还有加入redis后进行的一些去重操作，数据的持久（判断三天内的带播控量）

public class SaleSum implements Aggregator<SaleSumState> {
    private Logger logger  = org.slf4j.LoggerFactory.getLogger(SaleSum.class);

    /**
     *
     */
    private static final long serialVersionUID = -6879728480425771684L;

    private int partitionIndex ;
    @Override
    public SaleSumState init(Object batchId, TridentCollector collector) {
        return new SaleSumState();

    }

    @Override
    public void aggregate(SaleSumState val, TridentTuple tuple, TridentCollector collector) {
      double oldSum=val.saleSum;
      double price=tuple.getDoubleByField("price");
      double newSum=oldSum+price;
      val.saleSum=newSum;
      }

    @Override
    public void complete(SaleSumState val, TridentCollector collector) {
        collector.emit(new Values(val.saleSum));
    }

    @Override
    public void prepare(Map conf, TridentOperationContext context) {

    }

    @Override
    public void cleanup() {

    }
}

public class TridentDemo {

public static final String SPOUT_ID = "kafak_spout";

public static void main(String[] args) {

1、创建一个strom此程序的topology 为TridentTopology

TridentTopology topology = new TridentTopology();2、连接kafka的三要素：zk地址:port  topic        //1.从kafak读取数据，        //只会被成功处理 一次 ，有且只有此一次 提供容错机制  处理失败会在后续的批次进行提交        BrokerHosts zkHost = new ZkHosts("hadoop01:2181,hadoop02:2181,hadoop03:2181");        TridentKafkaConfig kafkaConfig = new TridentKafkaConfig(zkHost, "test");//两种构造器         定义从哪消费相当于spark中earliest与largest        kafkaConfig.startOffsetTime = kafka.api.OffsetRequest.LatestTime();        kafkaConfig.scheme = new SchemeAsMultiScheme(new StringScheme());        //透明事务kafka的spout        OpaqueTridentKafkaSpout kafkaSpout = new OpaqueTridentKafkaSpout(kafkaConfig);        //严格模式的事务级别        TransactionalTridentKafkaSpout kafkaSpout1 = new TransactionalTridentKafkaSpout(kafkaConfig);        //普通的kafak级别 {"str","msg"}        //严格的kafak级别 {"str","msg"，上一批次的值}        Stream stream = topology.newStream(SPOUT_ID, kafkaSpout);        // stream.each(new Fields("str"),new PrintTestFilter2());3.进行日志数据的解析，自定义解析类实现了Funtion接口，重写execute方法进行字段解析，在发送出来collector.emit(new Values(timestamp,yyyyMMddStr,yyyyMMddHHStr,yyyyMMddHHmmStr,consumer,productName,price，country,province,city));

   进去的字段名定义为"str",出来的解析字段分别定义了字段名 ，后续做打印测试Stream hasPraseSteam = stream.each(new Fields("str"), new ParseFunction(), new Fields("timeStamp", "yyyyMMddStr", "yyyyMMddHHStr", "yyyyMMddHHmmStr", "consumer", "productNmae", "price", "country", "provence", "city"));        //  .each(new Fields("str", "timeStamp", "yyyyMMddStr", "yyyyMMddHHStr", "yyyyMMddHHmmStr", "consumer", "productNmae", "price", "country", "provence", "city"), new PrintTestFilter2());

4.进行一个同时进行次数与求和统计的例子，storm是一个服务器节点多个work（jvm），一个work中的task执行自己spout，bolt任务trident中最重要的地方就是自定义聚合的实现（SaleSum类）,常常是实现业务逻辑的地方，规定如何进行数据的聚合，  进行的是各个分区的局部聚合

        //1. 对每天电商的销售额        //去掉用不到的自地段 保留需要用到的字段        //分区统计的流        Stream partitionStatStream = hasPraseSteam.project(new Fields("yyyyMMddStr", "price"))                .shuffle()                .groupBy(new Fields("yyyyMMddStr"))                .chainedAgg()                .partitionAggregate(new Fields("price"), new SaleSum(), new Fields("saleTotalpartByDay")) //进行同一批次各个分区的局部销售额统计                .partitionAggregate(new Fields("price"), new Count(), new Fields("oderNumOfpartDay"))//同一批次中各个分区的订单数                .chainEnd()                .toStream()                .parallelismHint(2);

5. //全局统计 每天的总销售额进行 进行分组全局聚合一般的 顺序=============先进行分区统计，在进行全局统计（相当于hadoop的combine与spark中reducebykey）        TridentState saleGlobalState = partitionStatStream.groupBy(new Fields("yyyyMMddStr"))                .persistentAggregate(new MemoryMapState.Factory(), new Fields("saleTotalpartByDay"), new Sum(), new Fields("saleGlobalAmtDay"));        //测试        saleGlobalState.newValuesStream().each(new Fields("yyyyMMddStr", "saleGlobalAmtDay"), new PrintTestFilter2());        //全局统计 每天的订单总数        TridentState oderGlobalState = partitionStatStream.groupBy(new Fields("yyyyMMddStr"))                .persistentAggregate(new MemoryMapState.Factory(), new Fields("oderNumOfpartDay"), new Sum(), new Fields("oderGlobalAmtDay"));        oderGlobalState.newValuesStream().each(new Fields("yyyyMMddStr", "oderGlobalAmtDay"), new PrintTestFilter2());

//2.给与地域时段  维度 统计

//    "timeStamp","yyyyMMddStr","yyyyMMddHHStr","yyyyMMddHHmmStr","consumer","productNmae","price","country","provence","city"

TridentState state = hasPraseSteam.project(new Fields("yyyyMMddHHStr", "price", "country", "provence", "city"))                .each(new Fields("yyyyMMddHHStr", "country", "provence", "city"), new ContactKey(), new Fields("addrAndHour"))                //  .project()                .groupBy(new Fields("addrAndHour"))                .persistentAggregate(new MemoryMapState.Factory(), new Fields("price"), new Sum(), new Fields("saleAmtOfAddrAndHour"));

//测试        state.newValuesStream().each(new Fields("addrAndHour"), new PrintTestFilter2());

//3.使用hbase存入 结果状态        /**rowkey         * value         * 非实物 ：就简单存储一个value         * 严格的事实控制： 存储: batchId和统计值         * 透明事务控制 ： batchId和统计值和上个批次的统计值

*/        HBaseMapState.Options<OpaqueValue> opts=new HBaseMapState.Options<OpaqueValue>();        opts.tableName="test";        opts.columnFamily="info";        //1.1以后设置列名使用下面类        TridentHBaseMapMapper mapMapper= new SimpleTridentHBaseMapMapper("saleAmtOfAddrAndHour");        opts.mapMapper = mapMapper;        StateFactory Hbasefactory=HBaseMapState.opaque(opts);

6.进行hbase存储，storm在给apache后，（1.0版本？后）已经实现与hbase的集成接口，事物类型要与topoloy一致

persistentAggregate为最终的持久化函数，存储可以为内存/hbase,返回值为tridentState

//        HBaseMapState.Options<Object> opts=new HBaseMapState.Options<Object>();//        opts.tableName="test";//        opts.columnFamily="info";//        //1.1以后设置列名使用下面类，存入hbase的列名//        TridentHBaseMapMapper mapMapper= new SimpleTridentHBaseMapMapper("saleAmtOfAddrAndHour");//        opts.mapMapper = mapMapper;//       StateFactory Hbasefactory1=HBaseMapState.nonTransactional(opts);

TridentState HbaseState = hasPraseSteam.project(new Fields("yyyyMMddHHStr", "price", "country", "provence", "city"))                .each(new Fields("yyyyMMddHHStr", "country", "provence", "city"), new ContactKey(), new Fields("addrAndHour"))                //  .project()                .groupBy(new Fields("addrAndHour"))                .persistentAggregate(Hbasefactory, new Fields("price"), new Sum(), new Fields("saleAmtOfAddrAndHour"));

//进行drpc查询        LocalDRPC localDRPC = new LocalDRPC();        topology.newDRPCStream("saleAmtOfDay", localDRPC)                .each(new Fields("args"), new SplitFunction1(), new Fields("requestDate"))                .stateQuery(saleGlobalState, new Fields("requestDate"), new MapGet(),                        new Fields("saleGlobalAmtOfDay1"))                .project(new Fields("requestDate", "saleGlobalAmtOfDay1"))                .each(new Fields("saleGlobalAmtOfDay1"), new FilterNull())        //  .each(new Fields("requestDate", "saleGlobalAmtOfDay1"), new PrintTestFilter2())        ;

topology.newDRPCStream("numOrderOfDay", localDRPC)                .each(new Fields("args"), new SplitFunction1(), new Fields("requestDate"))                .stateQuery(oderGlobalState, new Fields("requestDate"), new MapGet(),                        new Fields("numOrderGlobalOfDay1"))                .project(new Fields("requestDate", "numOrderGlobalOfDay1"))                .each(new Fields("numOrderGlobalOfDay1"), new FilterNull())        ;

topology.newDRPCStream("saleTotalAmtOfAddrAndHour", localDRPC)                .each(new Fields("args"), new SplitFunction1(), new Fields("requestAddrAndHour"))                .stateQuery(HbaseState, new Fields("requestAddrAndHour"),                        new MapGet(), new Fields("saleTotalAmtOfAddrAndHour"))                .project(new Fields("requestAddrAndHour", "saleTotalAmtOfAddrAndHour"))                .each(new Fields("saleTotalAmtOfAddrAndHour"), new FilterNull())        ;

7.提交本地还是集群运行，drpc可以进行对持久化后的state进行数据查询        Config conf = new Config();        if (args == null || args.length <= 0) {            // 本地测试            LocalCluster localCluster = new LocalCluster();            // topology名称唯一            localCluster.submitTopology("odeR", conf, topology.build());            while (true) {

try {                    Thread.sleep(10000);                } catch (InterruptedException e) {                    e.printStackTrace();                }                String saleAmtResult =                        localDRPC.execute("saleAmtOfDay", "20160828 20160827");

System.err.println("saleAmtResult=" + saleAmtResult);

String numberOrderResult =                        localDRPC.execute("numOrderOfDay", "20160828 20160827");                System.err.println("numberOrderResult=" + numberOrderResult);

String saleTotalAmtOfAddrAndHourRessult =                        localDRPC.execute("saleTotalAmtOfAddrAndHour", "苏州_江苏_中国_2016082815");

System.err.println(saleTotalAmtOfAddrAndHourRessult);            }        } else {            try {                StormSubmitter.submitTopology(args[0], conf, topology.build());            } catch (AlreadyAliveException e) {                e.printStackTrace();            } catch (InvalidTopologyException e) {                e.printStackTrace();            } catch (AuthorizationException e) {                e.printStackTrace();            }        }    }}

原文地址：https://www.cnblogs.com/hejunhong/p/10384492.html