Storm Trident示例CombinerAggregator

CombinerAggregator首先在每个分区上运行partitionAggregate，在每个partition内先聚合，然后运行全局重新分区(global)操作以合并同一批次的所有分区到一个单独的分区，即把前面每个partition聚合的结果，再放到一个单独的partition进行聚合。 这里的网络传输与其他两个聚合器相比较少。 因此，CombinerAggregator的总体性能比Aggregator和ReduceAggregator好。

省略部分代码，省略部分可参考：https://blog.csdn.net/nickta/article/details/79666918

FixedBatchSpout spout = new FixedBatchSpout(new Fields("user", "score"), 3,    
                new Values("nickt1", 4),   
                new Values("nickt2", 7),    
                new Values("nickt3", 8),   
                new Values("nickt4", 9),    
                new Values("nickt5", 7),   
                new Values("nickt6", 11),   
                new Values("nickt7", 5)   
                );   
        spout.setCycle(false);   
        TridentTopology topology = new TridentTopology();   
        topology.newStream("spout1", spout)   
                .shuffle()   
                .each(new Fields("user", "score"),new Debug("shuffle print:"))  
                .parallelismHint(5)  
                .aggregate(new Fields("score"), new CombinerAggregator<Integer>() {  
  
                    //partition当中的每个tuple调用 1次  
                    public Integer init(TridentTuple tuple) {  
                        return tuple.getInteger(0);  
                    }  
  
                    //聚合结果  
                    //第1次调用时，val1值为zero返回的值,之后的调用为上次调用 combine的返回值  
                    //val2为每次init返回的值  
                    public Integer combine(Integer val1, Integer val2) {  
                        return val1+val2;  
                    }  
  
                    //如果partition如此没有tuple，也会调用   
                    public Integer zero() {  
                        return 0;  
                    }  
                      
                }, new Fields("sum"))  
                .each(new Fields("sum"),new Debug("sum print:"))  
                .parallelismHint(5);  

输出：

[partition0-Thread-58-b-0-executor[33 33]]> DEBUG(shuffle print:): [nickt3, 8]
[partition1-Thread-126-b-0-executor[34 34]]> DEBUG(shuffle print:): [nickt2, 7]
[partition2-Thread-60-b-0-executor[35 35]]> DEBUG(shuffle print:): [nickt1, 4]
[partition1-Thread-70-b-1-executor[39 39]]> DEBUG(sum print:): [19]
[partition4-Thread-146-b-0-executor[37 37]]> DEBUG(shuffle print:): [nickt4, 9]
[partition4-Thread-146-b-0-executor[37 37]]> DEBUG(shuffle print:): [nickt6, 11]
[partition0-Thread-58-b-0-executor[33 33]]> DEBUG(shuffle print:): [nickt5, 7]
[partition2-Thread-62-b-1-executor[40 40]]> DEBUG(sum print:): [27]
[partition0-Thread-58-b-0-executor[33 33]]> DEBUG(shuffle print:): [nickt7, 5]
[partition3-Thread-39-b-1-executor[41 41]]> DEBUG(sum print:): [5]