flink 1.11.2 学习笔记(5)-lambda表达式的使用问题

flink的api，提供了流畅的链式编程写法，写起来行云流水，感受一下：

SingleOutputStreamOperator<Tuple3<String, Integer, String>> counts = env
        //设置并行度1，方便观察输出
        .setParallelism(1)
        //添加kafka数据源
        .addSource(
                new FlinkKafkaConsumer011<>(
                        SOURCE_TOPIC,
                        new SimpleStringSchema(),
                        props))
        //转变成pojo对象
        .map((MapFunction<String, WordCountPojo>) value -> {
            WordCountPojo pojo = gson.fromJson(value, WordCountPojo.class);
            return pojo;
        })
        //设置watermark以及事件时间提取逻辑
        .assignTimestampsAndWatermarks(
                new BoundedOutOfOrdernessTimestampExtractor<WordCountPojo>(Time.milliseconds(200)) {
                    @Override
                    public long extractTimestamp(WordCountPojo element) {
                        return element.eventTimestamp;
                    }
                })
        //统计每个word的出现次数
        .flatMap(new FlatMapFunction<WordCountPojo, Tuple3<String, Integer, String>>() {
            @Override
            public void flatMap(WordCountPojo value, Collector<Tuple3<String, Integer, String>> out) throws Exception {
                String word = value.word;
                //获取每个统计窗口的时间（用于显示）
                String windowTime = sdf.format(new Date(TimeWindow.getWindowStartWithOffset(value.eventTimestamp, 0, 60 * 1000)));
                if (word != null && word.trim().length() > 0) {
                    //收集(类似:map-reduce思路)
                    out.collect(new Tuple3<>(word.trim(), 1, windowTime));
                }
            }
        })
        .keyBy(v -> v.f0)
        //按1分钟开窗（TumblingWindows）
        .timeWindow(Time.minutes(1))
        //允许数据延时10秒
        .allowedLateness(Time.seconds(10))
        //将word的count汇总
        .sum(1);

　　

如果idea环境，使用jdk1.8的话，可能会智能提示，让你把24行改与lambda表达式，看上去更清爽一些：

SingleOutputStreamOperator<Tuple3<String, Integer, String>> counts = env
    .setParallelism(1)
    .addSource(
            new FlinkKafkaConsumer011<>(
                    SOURCE_TOPIC,
                    new SimpleStringSchema(),
                    props))
    .map((MapFunction<String, WordCountPojo>) value -> {
        WordCountPojo pojo = gson.fromJson(value, WordCountPojo.class);
        return pojo;
    })
    .assignTimestampsAndWatermarks(
            new BoundedOutOfOrdernessTimestampExtractor<WordCountPojo>(Time.milliseconds(200)) {
                @Override
                public long extractTimestamp(WordCountPojo element) {
                    return element.eventTimestamp;
                }
            })
    .flatMap((FlatMapFunction<WordCountPojo, Tuple3<String, Integer, String>>) (value, out) -> {
        //改成lambda写法
        String word = value.word;
        String windowTime = sdf.format(new Date(TimeWindow.getWindowStartWithOffset(value.eventTimestamp, 0, 60 * 1000)));
        if (word != null && word.trim().length() > 0) {
            out.collect(new Tuple3<>(word.trim(), 1, windowTime));
        }
    })
    .keyBy(v -> v.f0)
    .timeWindow(Time.minutes(1))
    .allowedLateness(Time.seconds(10))
    .sum(1);

逻辑完全没变，但是运行后，会遇到一个报错：

Caused by: org.apache.flink.api.common.functions.InvalidTypesException: The generic type parameters of 'Collector' are missing. In many cases lambda methods don't provide enough information for automatic type extraction when Java generics are involved. An easy workaround is to use an (anonymous) class instead that implements the 'org.apache.flink.api.common.functions.FlatMapFunction' interface. Otherwise the type has to be specified explicitly using type information.

大致意思是，lambda写法无法提供足够的类型信息，无法推断出正确的类型，建议要么改成匿名类写法，要么用type information提供明细的类型信息。

 

解决方法：

SingleOutputStreamOperator<Tuple3<String, Integer, String>> counts = env
    .setParallelism(1)
    .addSource(
            new FlinkKafkaConsumer011<>(
                    SOURCE_TOPIC,
                    new SimpleStringSchema(),
                    props))
    .map((MapFunction<String, WordCountPojo>) value -> {
        WordCountPojo pojo = gson.fromJson(value, WordCountPojo.class);
        return pojo;
    })
    .assignTimestampsAndWatermarks(
            new BoundedOutOfOrdernessTimestampExtractor<WordCountPojo>(Time.milliseconds(200)) {
                @Override
                public long extractTimestamp(WordCountPojo element) {
                    return element.eventTimestamp;
                }
            })
    .flatMap((FlatMapFunction<WordCountPojo, Tuple3<String, Integer, String>>) (value, out) -> {
        String word = value.word;
        String windowTime = sdf.format(new Date(TimeWindow.getWindowStartWithOffset(value.eventTimestamp, 0, 60 * 1000)));
        if (word != null && word.trim().length() > 0) {
            out.collect(new Tuple3<>(word.trim(), 1, windowTime));
        }
    })
    //明细指定返回类型
    .returns(((TypeInformation) TupleTypeInfo.getBasicTupleTypeInfo(String.class, Integer.class, String.class)))
    .keyBy(0)
    .timeWindow(Time.minutes(1))
    .allowedLateness(Time.seconds(10))
    .sum(1);

27行这里，明细指定返回类型，同时keyBy的写法，略为调整下，就能正常运行了。