Flink AggregatingState 实例

Flink AggregatingState 实例

AggregatingState介绍

• 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

需求：计算每个设备10秒内的平均温度

import org.apache.flink.api.common.eventtime.SerializableTimestampAssigner;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.AggregateFunction;
import org.apache.flink.api.common.state.AggregatingState;
import org.apache.flink.api.common.state.AggregatingStateDescriptor;
import org.apache.flink.api.common.typeinfo.TypeHint;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
import org.apache.flink.util.Collector;
 
import java.time.Duration;
import java.util.Random;
 
public class AggregatingStateTest {
    public static void main(String[] args) throws Exception {
        // 计算每个设备10s内温度的平均值
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
        env.getConfig().setAutoWatermarkInterval(100l);
 
        DataStreamSource<Tuple3<String, Integer, Long>> tuple3DataStreamSource = env.addSource(new SourceFunction<Tuple3<String, Integer, Long>>() {
            boolean flag = true;
 
            @Override
            public void run(SourceContext<Tuple3<String, Integer, Long>> ctx) throws Exception {
                String[] str = {"水阀1", "水阀2", "水阀3"};
                while (flag) {
                    int i = new Random().nextInt(3);
                    // 温度
                    int temperature = new Random().nextInt(100);
                    Thread.sleep(1000l);
                    // 设备号、温度、事件时间
                    ctx.collect(new Tuple3<String, Integer, Long>(str[i], temperature, System.currentTimeMillis()));
                }
            }
 
            @Override
            public void cancel() {
                flag = false;
            }
        });
 
        tuple3DataStreamSource.assignTimestampsAndWatermarks(WatermarkStrategy.<Tuple3<String, Integer, Long>>forBoundedOutOfOrderness(Duration.ofSeconds(2))
                .withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, Integer, Long>>() {
                    @Override
                    public long extractTimestamp(Tuple3<String, Integer, Long> stringIntegerLongTuple3, long l) {
                        return stringIntegerLongTuple3.f2;
                    }
                })).keyBy(new KeySelector<Tuple3<String, Integer, Long>, String>() {
            @Override
            public String getKey(Tuple3<String, Integer, Long> stringIntegerLongTuple3) throws Exception {
                return stringIntegerLongTuple3.f0;
            }
        }).process(new KeyedProcessFunction<String, Tuple3<String, Integer, Long>, String>() {
            Long interval = 10 * 1000l;
            // <Integer, Double>这个类型是aggregatingState中的输入和输出类型
            AggregatingState<Integer, Double> aggregatingState = null;
            @Override
            public void open(Configuration parameters) throws Exception {
@Override
            public void open(Configuration parameters) throws Exception {
                super.open(parameters);
                // <Integer, Tuple2<Integer,Integer>, Double>这是输入，中间状态，输出类型。TypeInformation.of(new TypeHint<Tuple2<Integer,Integer>>(){})这个是aggregatingState存储的数据的类型
                AggregatingStateDescriptor<Integer, Tuple2<Integer,Integer>, Double> aggregatingStateDescriptor =
                        new AggregatingStateDescriptor<Integer, Tuple2<Integer,Integer>, Double>("aggregatingState", new MyAggregate(), TypeInformation.of(new TypeHint<Tuple2<Integer,Integer>>(){}));
                aggregatingState = getRuntimeContext().getAggregatingState(aggregatingStateDescriptor);
            }
 
            @Override
            public void processElement(Tuple3<String, Integer, Long> value, Context ctx, Collector<String> out) throws Exception {
                // 10s的起始的时间
                Long start = ctx.timestamp() - (ctx.timestamp() % interval);
                Long timerTimestamp = start + interval;
                ctx.timerService().registerEventTimeTimer(timerTimestamp);
                aggregatingState.add(value.f1);
            }
 
            @Override
            public void onTimer(long timestamp, OnTimerContext ctx, Collector<String> out) throws Exception {
                super.onTimer(timestamp, ctx, out);
                Double aDouble = aggregatingState.get();
                String str = "[" + ctx.getCurrentKey() + "] " + "十秒内的平均温度为：" + aDouble;
                out.collect(str);
            }
        }).print();
 
        env.execute("aggregatingState");
    }
 
    private static class MyAggregate implements AggregateFunction<Integer, Tuple2<Integer,Integer>, Double> {
 
        @Override
        public Tuple2<Integer, Integer> createAccumulator() {
            // 初始化温度和次数
            return new Tuple2<Integer, Integer>(0,0);
        }
 
        @Override
        public Tuple2<Integer, Integer> add(Integer integer, Tuple2<Integer, Integer> integerIntegerTuple2) {
            // 历史温度加上本次温度，次数加1
            return new Tuple2<Integer, Integer>(integerIntegerTuple2.f0 + integer, integerIntegerTuple2.f1 +1);
        }
 
        @Override
        public Double getResult(Tuple2<Integer, Integer> integerIntegerTuple2) {
            return Double.valueOf(integerIntegerTuple2.f0 / integerIntegerTuple2.f1);
        }
 
        @Override
        public Tuple2<Integer, Integer> merge(Tuple2<Integer, Integer> integerIntegerTuple2, Tuple2<Integer, Integer> acc1) {
            return new Tuple2<Integer, Integer>(integerIntegerTuple2.f0 + acc1.f0, integerIntegerTuple2.f1 + acc1.f1);
        }
    }
}

原文链接：https://blog.csdn.net/qq_35514685/article/details/124351482