Storm中Trident流合并的例子demo

      流的合并操作，是指根据两个流的关联条件将两个流合并成一个流，然后在进行后面的处理操作，如果使用Spout和Bolt这种编程模型的话写起来会比较困难和繁琐，因为要设置缓冲区来保存第一次过来的数据，后续还要进行两者的比较，使用Trident应用起来比较方便，对原来的编程模型进行了一定的抽象。代码实例：

     需求：

             两个spout： spout1:里面的数据是 name ,id 和tel,

            spout2是sex 和id；

             首先对spout1进行过滤，过滤掉不是186的电话号码，然后显示

             然后根据将过滤后的stream和spout2进行合并

代码如下：

过滤 ：

public class FilelterTest  extends BaseFilter{

	@Override
	public boolean isKeep(TridentTuple tuple) {
		String get =tuple.getString(2);
		if(get.startsWith("186")){
			return true;
		}
		return false;
	}
}

过滤  显示方法

public class FunctionTest extends BaseFunction {

	@Override
	public void execute(TridentTuple tuple, TridentCollector collector) {
		String getName = tuple.getString(0);
		String getid = tuple.getString(1);
		String getTel = tuple.getString(2);
		System.out.println("过滤后数据：============"+tuple.getValues());
		collector.emit(new Values(getName,getid,getTel));
	}

}

最终显示方法：
public class FunctionTest3 extends BaseFunction {

	@Override
	public void execute(TridentTuple tuple, TridentCollector collector) {
		
		System.out.println(tuple.getValues());
	}

}

TopoMain

public class TestTrident {
	static FixedBatchSpout spout = new FixedBatchSpout(new Fields("name","idName","tel"),
			3, new Values("Jack","1","186107"), new Values("Tome","2","1514697"), new Values(
					"Lay","3","186745"), new Values("Lucy","4","1396478"));
	
	static FixedBatchSpout spout2 = new FixedBatchSpout(new Fields("sex","idSex"),
			3, new Values("Boy","1"), new Values("Boy","2"), new Values(
					"Gril","3"), new Values("Gril","4"));
	
	public static void main(String[] args) {
		//设置是否循环
		spout.setCycle(false);
		//构建Trident的Topo
		TridentTopology topology = new TridentTopology();
		//定义过滤器： 电话号码不是 186开头的过滤
		FilelterTest ft =new FilelterTest();
		//定义方法 用来显示过滤后的数据
		FunctionTest function = new FunctionTest();
		//根据spout构建第一个Stream
		Stream st = topology.newStream("sp1", spout);
		//对第一个Stream数据显示。
	         Stream st_1=st.each(new Fields("name","idName","tel"), function, new Fields("out_name","out_idName","out_tel"));
		//根据第二个Spout构建Stream，为了测试join用
		Stream st2 = topology.newStream("sp2", spout2);
		/**
		 * 开始Join  st和st2这两个流，类比sql中join的话是：
		 * st join st2 on  joinFields1 =  joinFields2
		 * 需要注意的是以st为数据基础
		 * topology.join(st, new Fields("idName"), st2, new Fields("idSex"), new Fields("id","name","tel","sex"))
		 * 那么结果将是以spout为数据基础，结果会将上面的4个数据信息全部打出
		 */
	    Stream st3=	topology.join(st, new Fields("idName"), st2, new Fields("idSex"), new Fields("Res_id","Res_name","Res_tel","Res_sex"));
	    //创建一个方法，为了显示合并和过滤后的结果
	    FunctionTest3 t3 =new FunctionTest3();
	    st3.each(new Fields("Res_id","Res_name","Res_tel","Res_sex"), ft).each( new Fields("Res_id","Res_name","Res_tel","Res_sex"),t3, new Fields("out1_id","out1_name","ou1t_tel","out1_sex"));
		
	    
	    Config cf = new Config();
		cf.setNumWorkers(2);
		cf.setNumAckers(0);
		cf.setDebug(false);
		
		LocalCluster lc = new LocalCluster();
		lc.submitTopology("TestTopo", cf, topology.build());
	}
}

结果：

Spout1的数据：[Jack, 1, 186107]
Spout1的数据：[Tome, 2, 1514697]
Spout1的数据：[Lay, 3, 186745]
Spout1的数据：[Lucy, 4, 1396478]
[1, Jack, 186107, Boy]
[3, Lay, 186745, Gril]