Flink教程(17) Keyed State状态管理之AggregatingState使用案例 求平均值

系列文章

Flink教程(13) Keyed State状态管理之ValueState的使用 温差报警
Flink教程(14) Keyed State状态管理之MapState使用案例
Flink教程(15) Keyed State状态管理之ListState使用 ValueState实现
Flink教程(16) Keyed State状态管理之ReducingState使用案例 求最大值
Flink教程(17) Keyed State状态管理之AggregatingState使用案例 求平均值

一、AggregatingState的方法

• AggregatingState需要和AggregateFunction配合使用
• add()方法添加一个元素，触发AggregateFunction计算
• get()获取State的值
  

二、AggregatingState描述器

在定义描述器时，第二个参数需要AggregateFunction类

//定义描述器
AggregatingStateDescriptor aggregatingStateDescriptor = new AggregatingStateDescriptor(
         "avg-temp",
         new SensorRecordUtils.MyAvgTemp(),
         TypeInformation.of(new TypeHint<Tuple2<Double, Integer>>(){
    
    })
 );

//获取ReducingState
aggregatingState = getRuntimeContext().getAggregatingState(aggregatingStateDescriptor);

三、自定义的AggregateFunction类

Flink求平均值，Tuple2的第一个参数时当前温度总和，第二个参数是数据的个数。

getResult里accumulator.f0 / accumulator.f1就求得了平均值。

public static class MyAvgTemp implements AggregateFunction<SensorRecord, Tuple2<Double, Integer>, Double> {
    
    

    @Override
    public Tuple2<Double, Integer> createAccumulator() {
    
    
        return Tuple2.of(0.0, 0);
    }

    @Override
    public Tuple2<Double, Integer> add(SensorRecord value, Tuple2<Double, Integer> accumulator) {
    
    
        Integer currentCount = accumulator.f1;
        currentCount += 1;
        accumulator.f1 = currentCount;
        return new Tuple2<>(accumulator.f0 + value.getRecord(), accumulator.f1);
    }

    @Override
    public Double getResult(Tuple2<Double, Integer> accumulator) {
    
    
        return accumulator.f0 / accumulator.f1;
    }

    @Override
    public Tuple2<Double, Integer> merge(Tuple2<Double, Integer> a, Tuple2<Double, Integer> b) {
    
    
        return new Tuple2<>(a.f0 + b.f0, a.f1 + b.f1);
    }
}

四、程序主体

public class Test06_AggregatingState {
    
    

    public static void main(String[] args) throws Exception {
    
    

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        //方便测试，设置为1
        env.setParallelism(1);

        DataStreamSource<String> source = env.socketTextStream(BaseConstant.URL, BaseConstant.PORT);

        /*
        设置watermark和指定时间属性
         */
        SingleOutputStreamOperator<SensorRecord> dataStream = source
                .map(new SensorRecordUtils.BeanMap());

        dataStream
                .keyBy(SensorRecord::getId)
                .process(new MyKeyedProcessFunction())
                .print();

        env.execute();
    }
}

五、KeyedProcessFunction处理类

public static class MyKeyedProcessFunction extends KeyedProcessFunction<String, SensorRecord, Tuple2<String, Double>> {
    
    

    private transient AggregatingState aggregatingState;

    @Override
    public void open(Configuration parameters) throws Exception {
    
    
        super.open(parameters);

        //定义描述器
        AggregatingStateDescriptor aggregatingStateDescriptor = new AggregatingStateDescriptor(
                "avg-temp",
                new SensorRecordUtils.MyAvgTemp(),
                TypeInformation.of(new TypeHint<Tuple2<Double, Integer>>(){
    
    })
        );

        //获取ReducingState
        aggregatingState = getRuntimeContext().getAggregatingState(aggregatingStateDescriptor);
    }

    @Override
    public void processElement(SensorRecord value, Context ctx, Collector<Tuple2<String, Double>> out) throws Exception {
    
    

        aggregatingState.add(value);
        out.collect(Tuple2.of(value.getId(), (Double) aggregatingState.get()) );
    }
}