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Flink中API使用详细范例--window-LMLPHP

------------------------正文开始---------------------------

Flink Window机制范例实录:

什么是Window?有哪些用途? 

1、window又可以分为基于时间(Time-based)的window

2、基于数量(Count-based)的window。

Flink DataStream API提供了Time和Count的window,同时增加了基于Session的window。

同时,由于某些特殊的需要,DataStream API也提供了定制化的window操作,供用户自定义window。

下面,主要介绍Time-Based window以及Count-Based window,以及自定义的window操作,Session-Based Window操作将会在后续的文章中讲到。

1、Time-Based Window 

细分:基于时间的window又分为:

增量聚合;全量聚合。

--------------------------------增量聚合-------------------------------:

Flink中API使用详细范例--window-LMLPHP

类似于 Flink Sql中的 group window,计算结果不断的更新;

------------------------------------------------------------------------------

代码示例:

1.1、Tumbling window(翻滚) 
此处的window要在keyed Stream上应用window操作,当输入1个参数时,代表Tumbling window操作,每分钟统计一次,此处用scala语言实现:

增量聚合代码---- 求和操作:

//todo 获得数据源后进行算子操作
        DataStream<StartAppCount> windowedData = startupInfoData.keyBy("appId")     //以设备id进行分组
                        .timeWindow(Time.minutes(60))                      //指定时间窗口大小为5分钟,指定时间间隔为5分钟
                        .aggregate(new CountAgg(), new WindowResultFunction());

        windowedData.print();

CountAgg自定义的函数,需要实现 AggregateFunction函数

public class CountAgg implements AggregateFunction<StartupInfoData, Long, Long> {

    @Override
    public Long createAccumulator() {                            //初始化算子
        return 0L;
    }

    @Override
    public Long add(StartupInfoData startupInfoData, Long acc) {    //传入一个入参后,做累加操作,将算子加1
        return acc + 1;
    }

    @Override
    public Long getResult(Long acc) {                              //最输出merge产生的结果
        return acc;
    }

    @Override
    public Long merge(Long acc1, Long acc2) {                     //对算子进行每一个的累和
        return acc1 + acc2;
    }
}

输出函数格式:

public class WindowResultFunction implements WindowFunction<Long, StartAppCount, Tuple, TimeWindow>
{
    @Override
    public void apply(
            Tuple key,                              // 窗口的主键,即 appId
            TimeWindow window,                      // 窗口
            Iterable<Long> aggregateResult,         // 聚合函数的结果,即 count 值
            Collector<StartAppCount> collector      // 输出类型为 StartAppCount
    ) throws Exception
    {
        String appId = ((Tuple1<String>) key).f0;
        Long count = aggregateResult.iterator().next();
        collector.collect(StartAppCount.of(appId, window.getEnd(), count));
    }

自定义输出类的类格式:

public class StartAppCount {

    public String appId;     // 商品ID
    public long windowEnd;  // 窗口结束时间戳
    public long count;  // 商品的点击量

    public static StartAppCount of (String appId, long windowEnd, long count) {
        StartAppCount result = new StartAppCount();
        result.appId = appId;
        result.windowEnd = windowEnd;
        result.count = count;
        return result;
    }

    @Override
    public String toString() {
        return "WordWithCount{" +
                "appId='" + appId + '\'' +
                ", count=" + count +
                '}';
    }

}

增量聚合代码---- 求平均值操作:

public class AverageAggregate implements AggregateFunction<Tuple2<String,Long>, Tuple2<Long, Long>, Double> {
    @Override
    public Tuple2<Long, Long> createAccumulator() {
        return new Tuple2<>(0L, 0L);
    }

    @Override
    public Tuple2<Long, Long> add(Tuple2<String, Long> value, Tuple2<Long, Long> acc) {   //可以理解为缓存的中间值
        return new Tuple2<>(acc.f0 + value.f1, acc.f1 + 1L);   //传入的值加到acc的第一个值得到传入值, 第二个值为个数
    }

    @Override
    public Double getResult(Tuple2<Long, Long> acc) {
        return (double)acc.f0 / acc.f1;
    }

    @Override
    public Tuple2<Long, Long> merge(Tuple2<Long, Long> acc1, Tuple2<Long, Long> acc2) {    //进行累和合并
        return new Tuple2<>(acc1.f0+acc2.f0, acc1.f1+acc2.f1);
    }
}

使用sum进行求和的代码:

DataStream<WordWithCount> windowCounts = text.flatMap(new FlatMapFunction<String, WordWithCount>() {
            public void flatMap(String value, Collector<WordWithCount> out) throws Exception {
                String[] splits = value.split("\\s");
                for (String word : splits) {
                    out.collect(new WordWithCount(word, 1L));
                }
            }
        }).keyBy("word")
                .timeWindow(Time.seconds(2), Time.seconds(1))//指定时间窗口大小为2秒,指定时间间隔为1秒
                .sum("count");//在这里使用sum或者reduce都可以
                /*.reduce(new ReduceFunction<WordWithCount>() {
                                    public WordWithCount reduce(WordWithCount a, WordWithCount b) throws Exception {

                                        return new WordWithCount(a.word,a.count+b.count);
                                    }
                                })*/
        //把数据打印到控制台并且设置并行度
        windowCounts.print().setParallelism(1);

使用reduce进行求和的方法:

DataStream<WordWithCount> windowCounts = text.flatMap(new FlatMapFunction<String, WordWithCount>() {
            public void flatMap(String value, Collector<WordWithCount> out) throws Exception {
                String[] splits = value.split("\\s");
                for (String word : splits) {
                    out.collect(new WordWithCount(word, 1L));
                }
            }
        }).keyBy("word")
                .timeWindow(Time.seconds(2), Time.seconds(1))//指定时间窗口大小为2秒,指定时间间隔为1秒
                //.sum("count");//在这里使用sum或者reduce都可以
                .reduce(new ReduceFunction<WordWithCount>() {
                                    public WordWithCount reduce(WordWithCount a, WordWithCount b) throws Exception {

                                        return new WordWithCount(a.word,a.count+b.count);
                                    }
                                });

--------------------------------全量的时间窗口操作-------------------------------:

Flink中API使用详细范例--window-LMLPHP

 代码示例:

public class MyprocessWindowFunction extends ProcessWindowFunction<Tuple2<String, Long>, String, String, TimeWindow> {

    @Override
    public void process(String s, Context context, Iterable<Tuple2<String, Long>> iterable, Collector<String> out) throws Exception {
        long count = 0;
        for(Tuple2<String,Long> in : iterable)
        {
            count++;
        }

        out.collect("Window: " + context.window() + "count: " + count);
    }
}

1.2、Sliding window(滑动) 

//todo 获得数据源后进行算子操作
        DataStream<StartAppCount> windowedData = startupInfoData.keyBy("appId")     //以设备id进行分组
                        .timeWindow(Time.minutes(60), Time.seconds(5))                      //指定时间窗口大小为5分钟,指定时间间隔为5分钟
                        .aggregate(new CountAgg(), new WindowResultFunction());

        windowedData.print();

2、Count-Based Window 


2.1、Tumbling Window (滚动计数窗口)

和Time-Based一样,Count-based window同样支持翻滚与滑动窗口,即在Keyed Stream上,统计每100个元素的数量之和

public class FlinkCountWindowDemo {

 public static void main(String[] args) throws Exception
{ final ParameterTool
params = ParameterTool.fromArgs(args); final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); env.getConfig().setGlobalJobParameters(params); env.setParallelism(1); final int windowSize = params.getInt("window", 100); // read source data DataStreamSource<Tuple2<String, String>> inStream = env.addSource(new StreamDataSource()); // calculate DataStream<Tuple2<String, String>> outStream = inStream .keyBy(0) .countWindow(windowSize) .reduce( new ReduceFunction<Tuple2<String, String>>() { @Override public Tuple2<String, String> reduce(Tuple2<String, String> value1, Tuple2<String, String> value2) throws Exception { return Tuple2.of(value1.f0, value1.f1 + "" + value2.f1); } } ); outStream.print(); env.execute("WindowWordCount"); } }

2.2、Sliding Window 

盗用 Flink 原理与实现:Window 机制 中的一张图,假设有一个滑动计数窗口,每2个元素计算一次最近4个元素的总和,那么窗口工作示意图如下所示:

Flink中API使用详细范例--window-LMLPHP

 代码示例:

public class FlinkCountWindowDemo {

    public static void main(String[] args) throws Exception {
        final ParameterTool params = ParameterTool.fromArgs(args);
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.getConfig().setGlobalJobParameters(params);
        env.setParallelism(1);
        final int windowSize = params.getInt("window", 3);
        final int slideSize = params.getInt("slide", 2);

        // read source data
        DataStreamSource<Tuple2<String, String>> inStream = env.addSource(new StreamDataSource());

        // calculate
        DataStream<Tuple2<String, String>> outStream = inStream
                                                           .keyBy(0)
                                                           .countWindow(windowSize, slideSize)
                                                           .reduce(
                                                               new ReduceFunction<Tuple2<String, String>>() {
                                                                   @Override
                                                                   public Tuple2<String, String> reduce(Tuple2<String, String> value1, Tuple2<String, String> value2) throws Exception {
                                                                       return Tuple2.of(value1.f0, value1.f1 + "" + value2.f1);
                                                                   }
                                                               }
                                                           );
        outStream.print();
        env.execute("WindowWordCount");
    }
}

3、Advanced Window(自定义window) 

自定义的Window需要指定3个function。 
3.1、Window Assigner:负责将元素分配到不同的window。

WindowAPI提供了自定义的WindowAssigner接口,我们可以实现WindowAssigner的public abstract Collection<W> assignWindows(T element, long timestamp)方法。同时,对于基于Count的window而言,默认采用了GlobalWindow的window assigner,例如:keyValue.window(GlobalWindows.create())

04-09 00:01