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();
    }
}

由于定时器中的时间为timestamp+10000，当输入分别输入1000，spark；11000,spark（该条数据触发定时器，调用onTimer()方法），输出如下结果

 同时其还会产生一个新的定时器：21000触发的定时器

注意

1.processElement()方法处理数据时一条一条进行处理的

2. 该案例实现了滚动窗口的功能，而滚动窗口的底层实现原理与此相似：processElement()方法+onTimer()方法

案例二：使用定时器实现类似滚动窗口的功能

ProcessFunctionWithTimerDemo2

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;

/**
 * 只有keyedStream在使用ProcessFunction时可以使用State和Timer定时器
 */
public class ProcessFunctionWithTimerDemo2 {

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

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
        //1000,hello
        DataStreamSource<String> lines = env.socketTextStream("localhost", 8888);

        SingleOutputStreamOperator<String> linesWithWaterMark = lines.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<String>(Time.seconds(0)) {
            @Override
            public long extractTimestamp(String element) {
                return Long.parseLong(element.split(",")[0]);
            }
        });

        SingleOutputStreamOperator<Tuple2<String, Integer>> wordAndOne = linesWithWaterMark.map(new MapFunction<String, Tuple2<String, Integer>>() {
            @Override
            public Tuple2<String, Integer> map(String line) throws Exception {
                String word = line.split(",")[1];
                return Tuple2.of(word, 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;

            @Override
            public void open(Configuration parameters) throws Exception {
                ListStateDescriptor<Tuple2<String, Integer>> listStateDescriptor = new ListStateDescriptor<Tuple2<String, Integer>>(
                        "list-state",
                        TypeInformation.of(new TypeHint<Tuple2<String, Integer>>(){})
                );

                bufferState = getRuntimeContext().getListState(listStateDescriptor);
            }

            @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();

                //10:14:13   ->   10:15:00
                //输入的时间 [10:14:00, 10:14:59) 注册的定时器都是 10:15:00
                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时间，就会触发onTimer方法
                long timer = timestamp - timestamp % 60000 + 60000;
                System.out.println("next timer is: " + timer);
                ctx.timerService().registerEventTimeTimer(timer);
            }

            @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);
                }

                //请求当前ListState中的数据
                bufferState.clear();
            }
        }).print();

        env.execute();


    }
}

注意的代码

//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时间，就会触发onTimer方法
                long timer = timestamp - timestamp % 60000 + 60000;
                System.out.println("next timer is: " + timer);
                ctx.timerService().registerEventTimeTimer(timer);

改变：使用Process Time

ProcessFunctionWithTimerDemo3

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;

/**
 * 只有keyedStream在使用ProcessFunction时可以使用State和Timer定时器
 *
 * Processing Time类型的定时器
 *
 */
public class ProcessFunctionWithTimerDemo3 {

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

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        //hello
        DataStreamSource<String> lines = env.socketTextStream("localhost", 8888);

        SingleOutputStreamOperator<Tuple2<String, Integer>> wordAndOne = lines.map(new MapFunction<String, Tuple2<String, Integer>>() {
            @Override
            public Tuple2<String, Integer> map(String word) throws Exception {
                return Tuple2.of(word, 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;

            @Override
            public void open(Configuration parameters) throws Exception {
                ListStateDescriptor<Tuple2<String, Integer>> listStateDescriptor = new ListStateDescriptor<Tuple2<String, Integer>>(
                        "list-state",
                        TypeInformation.of(new TypeHint<Tuple2<String, Integer>>(){})
                );

                bufferState = getRuntimeContext().getListState(listStateDescriptor);
            }

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

                bufferState.add(value);
                //获取当前的processing time
                long currentProcessingTime = ctx.timerService().currentProcessingTime();

                //10:14:13   ->   10:15:00
                //输入的时间 [10:14:00, 10:14:59) 注册的定时器都是 10:15:00
                System.out.println("current processing time is : " + currentProcessingTime);

                //注册定时器，如果注册的是ProcessingTime类型的定时器，当SubTask所在机器的ProcessingTime大于等于注册定时器的时间，就会触发onTimer方法
                long timer = currentProcessingTime - currentProcessingTime % 60000 + 60000;
                System.out.println("next timer is: " + timer);
                //注册ProcessingTime的定时器
                ctx.timerService().registerProcessingTimeTimer(timer);
            }

            @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);
                }

                //请求当前ListState中的数据
                bufferState.clear();
            }
        }).print();

        env.execute();


    }
}

2. apply方法对窗口进行全量聚合

　　　窗口每触发一次时，会调用一次apply方法，相当于是对窗口中的全量数据进行计算

package cn._51doit.flink.day09;

import com.alibaba.fastjson.JSON;
import org.apache.flink.api.java.tuple.Tuple;
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.datastream.WindowedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;
import org.apache.flink.streaming.api.functions.windowing.WindowFunction;
import org.apache.flink.streaming.api.windowing.assigners.SlidingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.util.Collector;


/**
 * apply是在窗口内进行全量的聚合，浪费资源
 */
public class HotGoodsTopN {

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

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
        env.enableCheckpointing(60000);
        env.setParallelism(1);
        //json字符串
        DataStreamSource<String> lines = env.socketTextStream("localhost", 8888);

        SingleOutputStreamOperator<MyBehavior> behaviorDataStream = lines.process(new ProcessFunction<String, MyBehavior>() {
            @Override
            public void processElement(String value, Context ctx, Collector<MyBehavior> out) throws Exception {
                try {
                    MyBehavior behavior = JSON.parseObject(value, MyBehavior.class);
                    //输出
                    out.collect(behavior);
                } catch (Exception e) {
                    //e.printStackTrace();
                    //TODO 记录出现异常的数据
                }
            }
        });

        //提取EventTime生成WaterMark
        SingleOutputStreamOperator<MyBehavior> behaviorDataStreamWithWaterMark = behaviorDataStream.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<MyBehavior>(Time.seconds(0)) {
            @Override
            public long extractTimestamp(MyBehavior element) {
                return element.timestamp;
            }
        });
        
        //按照指定的字段进行分组
        KeyedStream<MyBehavior, Tuple> keyed = behaviorDataStreamWithWaterMark.keyBy("itemId", "type");

        //窗口长度为10分组，一分钟滑动一次
        WindowedStream<MyBehavior, Tuple, TimeWindow> window = keyed.window(SlidingEventTimeWindows.of(Time.minutes(10), Time.minutes(1)));

        //SingleOutputStreamOperator<MyBehavior> sum = window.sum("counts");
        SingleOutputStreamOperator<ItemViewCount> sum = window.apply(new WindowFunction<MyBehavior, ItemViewCount, Tuple, TimeWindow>() {

            //当窗口触发是，会调用一次apply方法，相当于是对窗口中的全量数据进行计算
            @Override
            public void apply(Tuple tuple, TimeWindow window, Iterable<MyBehavior> input, Collector<ItemViewCount> out) throws Exception {
                //窗口的起始时间
                long start = window.getStart();
                //窗口的结束时间
                long end = window.getEnd();
                //获取分组的key
                String itemId = tuple.getField(0);
                String type = tuple.getField(1);

                int count = 0;
                for (MyBehavior myBehavior : input) {
                    count++;
                }
                //输出结果
                out.collect(ItemViewCount.of(itemId, type, start, end, count++));
            }
        });

        sum.print();

        env.execute();

    }
}

此处的计算是全量计算，效率不高，因为其要等到窗口数据攒足了才触发定时器，执行apply方法，这个apply方法相当于对窗口中的全量数据进行计算。假设窗口一直不触发，其会将数据缓存至窗口内存中，其实就是state中，窗口内部会有state，无需自己定义。窗口若是很长的话，缓存在内存中的数据就会很多。，解决办法是，窗口来一条数据就进行一次累加计算，即增量计算（效率更高，内存中存的知识次数）

3. 使用aggregate方法实现增量聚合

HotGoodsTopNAdv　　

package cn._51doit.flink.day09;

import com.alibaba.fastjson.JSON;
import org.apache.flink.api.common.functions.AggregateFunction;
import org.apache.flink.api.common.state.ValueState;
import org.apache.flink.api.common.state.ValueStateDescriptor;
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.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.datastream.WindowedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;
import org.apache.flink.streaming.api.functions.windowing.WindowFunction;
import org.apache.flink.streaming.api.windowing.assigners.SlidingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.util.Collector;

import java.util.ArrayList;
import java.util.Comparator;
import java.util.List;

/**
 * 在窗口内增量聚合，效率更高
 */
public class HotGoodsTopNAdv {

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

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
        env.enableCheckpointing(60000);
        env.setParallelism(1);
        //json字符串
        DataStreamSource<String> lines = env.socketTextStream("localhost", 8888);

        SingleOutputStreamOperator<MyBehavior> behaviorDataStream = lines.process(new ProcessFunction<String, MyBehavior>() {
            @Override
            public void processElement(String value, Context ctx, Collector<MyBehavior> out) throws Exception {
                try {
                    MyBehavior behavior = JSON.parseObject(value, MyBehavior.class);
                    //输出
                    out.collect(behavior);
                } catch (Exception e) {
                    //e.printStackTrace();
                    //TODO 记录出现异常的数据
                }
            }
        });

        //提取EventTime生成WaterMark
        SingleOutputStreamOperator<MyBehavior> behaviorDataStreamWithWaterMark = behaviorDataStream.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<MyBehavior>(Time.seconds(0)) {
            @Override
            public long extractTimestamp(MyBehavior element) {
                return element.timestamp;
            }
        });
        
        //按照指定的字段进行分组
        KeyedStream<MyBehavior, Tuple> keyed = behaviorDataStreamWithWaterMark.keyBy("itemId", "type");

        //窗口长度为10分组，一分钟滑动一次
        WindowedStream<MyBehavior, Tuple, TimeWindow> window = keyed.window(SlidingEventTimeWindows.of(Time.minutes(10), Time.minutes(1)));


        //SingleOutputStreamOperator<MyBehavior> counts = window.sum("counts");
        //自定义窗口聚合函数
        SingleOutputStreamOperator<ItemViewCount> aggDataStream = window.aggregate(new MyWindowAggFunction(), new MyWindowFunction());

        //按照窗口的start、end进行分组，将窗口相同的数据进行排序
        aggDataStream.keyBy("type", "windowStart", "windowEnd")
                .process(new KeyedProcessFunction<Tuple, ItemViewCount, List<ItemViewCount>>() {

                    private transient ValueState<List<ItemViewCount>> valueState;

                    @Override
                    public void open(Configuration parameters) throws Exception {
                        ValueStateDescriptor<List<ItemViewCount>> stateDescriptor = new ValueStateDescriptor<List<ItemViewCount>>(
                                "list-state",
                                TypeInformation.of(new TypeHint<List<ItemViewCount>>() {})
                        );

                        valueState = getRuntimeContext().getState(stateDescriptor);
                    }

                    @Override
                    public void processElement(ItemViewCount value, Context ctx, Collector<List<ItemViewCount>> out) throws Exception {

                        //将数据添加到State中缓存
                        List<ItemViewCount> buffer = valueState.value();
                        if(buffer == null) {
                            buffer = new ArrayList<>();
                        }
                        buffer.add(value);
                        valueState.update(buffer);
                        //注册定时器
                        ctx.timerService().registerEventTimeTimer(value.windowEnd + 1);
                    }

                    @Override
                    public void onTimer(long timestamp, OnTimerContext ctx, Collector<List<ItemViewCount>> out) throws Exception {

                        //将ValueState中的数据取出来
                        List<ItemViewCount> buffer = valueState.value();
                        //按照次数降序排序
                        buffer.sort(new Comparator<ItemViewCount>() {
                            @Override
                            public int compare(ItemViewCount o1, ItemViewCount o2) {
                                return -(int)(o1.viewCount - o2.viewCount);
                            }
                        });
                        //清空State
                        valueState.update(null);
                        out.collect(buffer);
                    }
                }).print(); //打印结果


        env.execute();

    }


    //三个泛型：
    //第一个：输入的数据类型
    //第二个：计数/累加器的类型
    //第三个：输出的数据类型
    public static class MyWindowAggFunction implements AggregateFunction<MyBehavior, Long, Long> {

        //初始化一个计数器
        @Override
        public Long createAccumulator() {
            return 0L;
        }

        //每输入一条数据就调用一次add方法
        @Override
        public Long add(MyBehavior value, Long accumulator) {
            return accumulator + value.counts;
        }

        @Override
        public Long getResult(Long accumulator) {
            return accumulator;
        }

        //只针对SessionWindow有效，对应滚动窗口、滑动窗口不会调用此方法
        @Override
        public Long merge(Long a, Long b) {
            return null;
        }
    }

    //传入4个泛型
    //第一个：输入的数据类型（Long类型的次数）
    //第二个：输出的数据类型（ItemViewCount）
    //第三个：分组的key(分组的字段)
    //第四个：窗口对象（起始时间、结束时间）
    public static class MyWindowFunction implements WindowFunction<Long, ItemViewCount, Tuple, TimeWindow> {
        @Override
        public void apply(Tuple tuple, TimeWindow window, Iterable<Long> input, Collector<ItemViewCount> out) throws Exception {
            //输入的Key
            String itemId = tuple.getField(0);
            String type = tuple.getField(1);
            //窗口的起始时间
            long start = window.getStart();
            //窗口结束时间
            long end = window.getEnd();
            //窗口集合的结果
            Long count = input.iterator().next();
            //输出数据
            out.collect(ItemViewCount.of(itemId, type, start, end, count));
        }
    }
}

涉及的重要知识点：

• 自定义聚合函数：

    //三个泛型：
    //第一个：输入的数据类型
    //第二个：计数/累加器的类型
    //第三个：输出的数据类型
    public static class MyWindowAggFunction implements AggregateFunction<MyBehavior, Long, Long> {
        //初始化一个计数器
        @Override
        public Long createAccumulator() {
            return 0L;
        }
        //每输入一条数据就调用一次add方法
        @Override
        public Long add(MyBehavior value, Long accumulator) {
            return accumulator + value.counts;
        }
        @Override
        public Long getResult(Long accumulator) {
            return accumulator;
        }
        //只针对SessionWindow有效，对应滚动窗口、滑动窗口不会调用此方法
        @Override
        public Long merge(Long a, Long b) {
            return null;
        }
    }

• 自定义WindowFunction

    //传入4个泛型
    //第一个：输入的数据类型（Long类型的次数）
    //第二个：输出的数据类型（ItemViewCount）
    //第三个：分组的key(分组的字段)
    //第四个：窗口对象（起始时间、结束时间）
    public static class MyWindowFunction implements WindowFunction<Long, ItemViewCount, Tuple, TimeWindow> {
        @Override
        public void apply(Tuple tuple, TimeWindow window, Iterable<Long> input, Collector<ItemViewCount> out) throws Exception {
            //输入的Key
            String itemId = tuple.getField(0);
            String type = tuple.getField(1);
            //窗口的起始时间
            long start = window.getStart();
            //窗口结束时间
            long end = window.getEnd();
            //窗口集合的结果
            Long count = input.iterator().next();
            //输出数据
            out.collect(ItemViewCount.of(itemId, type, start, end, count));
        }
    }

  4.使用ProcessFunction结合定时器实现排序

package cn._51doit.flink.day09;

import com.alibaba.fastjson.JSON;
import org.apache.flink.api.common.functions.AggregateFunction;
import org.apache.flink.api.common.state.ValueState;
import org.apache.flink.api.common.state.ValueStateDescriptor;
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.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.datastream.WindowedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;
import org.apache.flink.streaming.api.functions.windowing.WindowFunction;
import org.apache.flink.streaming.api.windowing.assigners.SlidingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.util.Collector;

import java.util.ArrayList;
import java.util.Comparator;
import java.util.List;

/**
 * 在窗口内增量聚合，效率更高
 */
public class HotGoodsTopNAdv {

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

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
        env.enableCheckpointing(60000);
        env.setParallelism(1);
        //json字符串
        DataStreamSource<String> lines = env.socketTextStream("localhost", 8888);

        SingleOutputStreamOperator<MyBehavior> behaviorDataStream = lines.process(new ProcessFunction<String, MyBehavior>() {
            @Override
            public void processElement(String value, Context ctx, Collector<MyBehavior> out) throws Exception {
                try {
                    MyBehavior behavior = JSON.parseObject(value, MyBehavior.class);
                    //输出
                    out.collect(behavior);
                } catch (Exception e) {
                    //e.printStackTrace();
                    //TODO 记录出现异常的数据
                }
            }
        });

        //提取EventTime生成WaterMark
        SingleOutputStreamOperator<MyBehavior> behaviorDataStreamWithWaterMark = behaviorDataStream.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<MyBehavior>(Time.seconds(0)) {
            @Override
            public long extractTimestamp(MyBehavior element) {
                return element.timestamp;
            }
        });
        
        //按照指定的字段进行分组
        KeyedStream<MyBehavior, Tuple> keyed = behaviorDataStreamWithWaterMark.keyBy("itemId", "type");

        //窗口长度为10分组，一分钟滑动一次
        WindowedStream<MyBehavior, Tuple, TimeWindow> window = keyed.window(SlidingEventTimeWindows.of(Time.minutes(10), Time.minutes(1)));


        //SingleOutputStreamOperator<MyBehavior> counts = window.sum("counts");
        //自定义窗口聚合函数
        SingleOutputStreamOperator<ItemViewCount> aggDataStream = window.aggregate(new MyWindowAggFunction(), new MyWindowFunction());

        //按照窗口的start、end进行分组，将窗口相同的数据进行排序
        aggDataStream.keyBy("type", "windowStart", "windowEnd")
                .process(new KeyedProcessFunction<Tuple, ItemViewCount, List<ItemViewCount>>() {

                    private transient ValueState<List<ItemViewCount>> valueState;

                    @Override
                    public void open(Configuration parameters) throws Exception {
                        ValueStateDescriptor<List<ItemViewCount>> stateDescriptor = new ValueStateDescriptor<List<ItemViewCount>>(
                                "list-state",
                                TypeInformation.of(new TypeHint<List<ItemViewCount>>() {})
                        );

                        valueState = getRuntimeContext().getState(stateDescriptor);
                    }

                    @Override
                    public void processElement(ItemViewCount value, Context ctx, Collector<List<ItemViewCount>> out) throws Exception {

                        //将数据添加到State中缓存
                        List<ItemViewCount> buffer = valueState.value();
                        if(buffer == null) {
                            buffer = new ArrayList<>();
                        }
                        buffer.add(value);
                        valueState.update(buffer);
                        //注册定时器
                        ctx.timerService().registerEventTimeTimer(value.windowEnd + 1);
                    }

                    @Override
                    public void onTimer(long timestamp, OnTimerContext ctx, Collector<List<ItemViewCount>> out) throws Exception {

                        //将ValueState中的数据取出来
                        List<ItemViewCount> buffer = valueState.value();
                        //按照次数降序排序
                        buffer.sort(new Comparator<ItemViewCount>() {
                            @Override
                            public int compare(ItemViewCount o1, ItemViewCount o2) {
                                return -(int)(o1.viewCount - o2.viewCount);
                            }
                        });
                        //清空State
                        valueState.update(null);
                        out.collect(buffer);
                    }
                }).print(); //打印结果


        env.execute();

    }


    //三个泛型：
    //第一个：输入的数据类型
    //第二个：计数/累加器的类型
    //第三个：输出的数据类型
    public static class MyWindowAggFunction implements AggregateFunction<MyBehavior, Long, Long> {
        //初始化一个计数器
        @Override
        public Long createAccumulator() {
            return 0L;
        }
        //每输入一条数据就调用一次add方法
        @Override
        public Long add(MyBehavior value, Long accumulator) {
            return accumulator + value.counts;
        }
        @Override
        public Long getResult(Long accumulator) {
            return accumulator;
        }
        //只针对SessionWindow有效，对应滚动窗口、滑动窗口不会调用此方法
        @Override
        public Long merge(Long a, Long b) {
            return null;
        }
    }

    //传入4个泛型
    //第一个：输入的数据类型（Long类型的次数）
    //第二个：输出的数据类型（ItemViewCount）
    //第三个：分组的key(分组的字段)
    //第四个：窗口对象（起始时间、结束时间）
    public static class MyWindowFunction implements WindowFunction<Long, ItemViewCount, Tuple, TimeWindow> {
        @Override
        public void apply(Tuple tuple, TimeWindow window, Iterable<Long> input, Collector<ItemViewCount> out) throws Exception {
            //输入的Key
            String itemId = tuple.getField(0);
            String type = tuple.getField(1);
            //窗口的起始时间
            long start = window.getStart();
            //窗口结束时间
            long end = window.getEnd();
            //窗口集合的结果
            Long count = input.iterator().next();
            //输出数据
            out.collect(ItemViewCount.of(itemId, type, start, end, count));
        }
    }
}