flink-----实时项目---day05-------1. ProcessFunction 2. apply对窗口进行全量聚合 3使用aggregate方法实现增量聚合 4.使用ProcessFunction结合定时器实现排序

1. ProcessFunction

　　ProcessFunction是一个低级的流处理操作，可以访问所有(非循环)流应用程序的基本构建块:

• event(流元素)
• state(容错，一致性，只能在Keyed流中使用)
• timers(事件时间和处理时间，只能在keyed流中使用)

　　ProcessFunction可以被认为是增加了keyed state和timers功能的FlatMapFunction。ProcesseFunction可以通过RuntimeContext访问Flink中的Keyed State，通过processElement方法中的Context实例访问流元素的时间戳，以及timerServer(注册定时器)，如果watermark大于等于注册定时器的时间，就会调用onTimer方法（此处相当于一个回调函数）,在调用期间，所有state的范围再次限定在创建定时器的key上，从而允许定时器操作keyed state。

注意：如果我们想要使用keyed state和timers（定时器），我们必须在一个keyed stream上应用ProcessFunction，如下所示

stream.keyBy(...).process(new MyProcessFunction())

 

案例1：使用ProcessFunction注册定时器

　　此处要实现的功能就是使用定时器定时输出一些数据，不能使用窗口函数，数据的类型为：时间戳，单词（123422，hello）

ProcessFunctionWithTimerDemo

package cn._51doit.flink.day09;

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.state.ListState;
import org.apache.flink.api.common.state.ListStateDescriptor;
import org.apache.flink.api.common.typeinfo.TypeHint;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;

public class ProcessFunctionWithTimerDemo {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStreamSource<String> lines = env.socketTextStream("feng05", 8888);
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
        // 得到watermark，并没有对原始数据进行处理
        SingleOutputStreamOperator<String> lineWithWaterMark = lines.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<String>(Time.seconds(0)) {
            @Override
            public long extractTimestamp(String element) {
                return Long.parseLong(element.split(",")[0]);
            }
        });
        // 处理数据，获取指定字段
        SingleOutputStreamOperator<Tuple2<String, Integer>> wordAndOne = lineWithWaterMark.map(new MapFunction<String, Tuple2<String, Integer>>() {
            @Override
            public Tuple2<String, Integer> map(String value) throws Exception {
                String[] fields = value.split(",");
                return Tuple2.of(fields[1], 1);
            }
        });
        //调用keyBy进行分组
        KeyedStream<Tuple2<String, Integer>, Tuple> keyed = wordAndOne.keyBy(0);
        // 没有划分窗口，直接调用底层的process方法
        keyed.process(new KeyedProcessFunction<Tuple, Tuple2<String, Integer>, Tuple2<String,Integer>>() {
            private transient ListState<Tuple2<String, Integer>> bufferState;
            // 定义状态描述器
            @Override
            public void open(Configuration parameters) throws Exception {
                ListStateDescriptor<Tuple2<String, Integer>> listStateDescriptor = new ListStateDescriptor<>(
                        "list-state",
                        TypeInformation.of(new TypeHint<Tuple2<String, Integer>>() {})
                );
                bufferState = getRuntimeContext().getListState(listStateDescriptor);
            }
            // 不划分窗口的话，该方法是来一条数据处理一条数据，这样输出端的压力会很大
            @Override
            public void processElement(Tuple2<String, Integer> value, Context ctx, Collector<Tuple2<String, Integer>> out) throws Exception {
                //out.collect(value);
                bufferState.add(value);
                //获取当前的event time
                Long timestamp = ctx.timestamp();
                System.out.println("current event time is : " + timestamp);

                //注册定时器，如果注册的是EventTime类型的定时器，当WaterMark大于等于注册定时器的实际，就会触发onTimer方法
                ctx.timerService().registerEventTimeTimer(timestamp+10000);
            }

            @Override
            public void onTimer(long timestamp, OnTimerContext ctx, Collector<Tuple2<String, Integer>> out) throws Exception {
                Iterable<Tuple2<String, Integer>> iterable = bufferState.get();
                for (Tuple2<String, Integer> tp : iterable) {
                    out.collect(tp);

                }
            }
        }).print();

        env.execute();
    }
}

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