Flink——进行数据转换时，报：Recovery is suppressed by NoRestartBackoffTimeStrategy

热词统计案例：

用flink中的窗口函数（apply）读取kafka中数据，并对热词进行统计。

apply:全量聚合函数，指在窗口触发的时候才会对窗口内的所有数据进行一次计算（等窗口的数据到齐，才开始进行聚合计算，可实现对窗口内的数据进行排序等需求）。

代码演示：

kafka发送消息端：

package com.bigdata.Day04;

import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;

import java.util.Properties;
import java.util.Random;

public class Demo01_windows_kafka发消息 {

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

        // Properties 它是map的一种
        Properties properties = new Properties();
        // 设置连接kafka集群的ip和端口
        properties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG,"bigdata01:9092");
        properties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,
                "org.apache.kafka.common.serialization.StringSerializer");
        properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,
                "org.apache.kafka.common.serialization.StringSerializer");
        // 创建了一个消息生产者对象
        KafkaProducer kafkaProducer = new KafkaProducer<>(properties);
        String[] arr = {"联通换猫","遥遥领先","恒大歌舞团","恒大足球队","郑州烂尾楼"};
        Random random = new Random();
        for (int i = 0; i < 500; i++) {
            ProducerRecord record = new ProducerRecord<>("topic1",arr[random.nextInt(arr.length)]);
            // 调用这个里面的send方法
            kafkaProducer.send(record);
            Thread.sleep(50);
        }

        kafkaProducer.close();
    }
}

kafka接受消息端：

package com.bigdata.Day04;

import org.apache.commons.lang3.time.DateFormatUtils;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;

import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.windowing.WindowFunction;
import org.apache.flink.streaming.api.windowing.assigners.TumblingProcessingTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.util.Collector;
import org.apache.kafka.clients.consumer.KafkaConsumer;

import java.util.Properties;

public class Demo02_kafka收消息 {

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

        //1. env-准备环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);

        //2. source-加载数据
        Properties properties = new Properties();
        properties.setProperty("bootstrap.servers","bigdata01:9092");
        properties.setProperty("group.id", "g2");
        FlinkKafkaConsumer<String> kafkaSource = new FlinkKafkaConsumer<String>("topic1",new SimpleStringSchema(),properties);
        DataStreamSource<String> dataStreamSource = env.addSource(kafkaSource);
        // transformation-数据处理转换
        DataStream<Tuple2<String,Integer>> mapStream = dataStreamSource.map(new MapFunction<String, Tuple2<String,Integer>>() {
            @Override
            public Tuple2<String,Integer> map(String word) throws Exception {

                return Tuple2.of(word,1);
            }
        });
        KeyedStream<Tuple2<String, Integer>, String> keyedStream = mapStream.keyBy(tuple2 -> tuple2.f0);
        keyedStream.window(TumblingProcessingTimeWindows.of(Time.seconds(5)))
                // 第一个泛型是输入数据的类型，第二个泛型是返回值类型   第三个是key 的类型， 第四个是窗口对象
                .apply(new WindowFunction<Tuple2<String, Integer>, String, String, TimeWindow>() {
            @Override
            public void apply(
                    String key,  // 分组key    {"俄乌战争",[1,1,1,1,1]}
                    TimeWindow window, // 窗口对象
                    Iterable<Tuple2<String, Integer>> input, // 分组key在窗口的所有数据
                    Collector<String> out  // 用于输出
            ) throws Exception {
                long start = window.getStart();
                long end = window.getEnd();

                // lang3 包下的工具类
                String startStr = DateFormatUtils.format(start,"yyyy-MM-dd HH:mm:ss");
                String endStr = DateFormatUtils.format(end,"yyyy-MM-dd HH:mm:ss");
                int sum = 0;

                for(Tuple2<String,Integer> tuple2: input){
                    sum += tuple2.f1;
                }
                out.collect(key +"," + startStr +","+endStr +",sum="+sum);
            }
        }).print();



        //5. execute-执行
        env.execute();
    }
}

当执行kafka接收消息端时，会报如下错误：

错误原因：在对kafka中数据进行KeyBy分组处理时，使用了lambda表达式

解决方法：

在使用KeyBy时，将函数的各种参数类型都写清楚，修改后的代码如下：

package com.bigdata.Day04;

import org.apache.commons.lang3.time.DateFormatUtils;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;

import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.windowing.WindowFunction;
import org.apache.flink.streaming.api.windowing.assigners.TumblingProcessingTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.util.Collector;
import org.apache.kafka.clients.consumer.KafkaConsumer;

import java.util.Properties;

public class Demo02_kafka收消息 {

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

        //1. env-准备环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);

        //2. source-加载数据
        Properties properties = new Properties();
        properties.setProperty("bootstrap.servers","bigdata01:9092");
        properties.setProperty("group.id", "g2");
        FlinkKafkaConsumer<String> kafkaSource = new FlinkKafkaConsumer<String>("topic1",new SimpleStringSchema(),properties);
        DataStreamSource<String> dataStreamSource = env.addSource(kafkaSource);
        // transformation-数据处理转换
        DataStream<Tuple2<String,Integer>> mapStream = dataStreamSource.map(new MapFunction<String, Tuple2<String,Integer>>() {
            @Override
            public Tuple2<String,Integer> map(String word) throws Exception {

                return Tuple2.of(word,1);
            }
        });
        KeyedStream<Tuple2<String, Integer>, String> keyedStream = mapStream.keyBy(new KeySelector<Tuple2<String, Integer>, String>() {
            @Override
            public String getKey(Tuple2<String, Integer> tuple2) throws Exception {
                return tuple2.f0;
            }
        });
        keyedStream.window(TumblingProcessingTimeWindows.of(Time.seconds(5)))
                // 第一个泛型是输入数据的类型，第二个泛型是返回值类型   第三个是key 的类型， 第四个是窗口对象
                .apply(new WindowFunction<Tuple2<String, Integer>, String, String, TimeWindow>() {
            @Override
            public void apply(
                    String key,  // 分组key    {"俄乌战争",[1,1,1,1,1]}
                    TimeWindow window, // 窗口对象
                    Iterable<Tuple2<String, Integer>> input, // 分组key在窗口的所有数据
                    Collector<String> out  // 用于输出
            ) throws Exception {
                long start = window.getStart();
                long end = window.getEnd();

                // lang3 包下的工具类
                String startStr = DateFormatUtils.format(start,"yyyy-MM-dd HH:mm:ss");
                String endStr = DateFormatUtils.format(end,"yyyy-MM-dd HH:mm:ss");
                int sum = 0;

                for(Tuple2<String,Integer> tuple2: input){
                    sum += tuple2.f1;
                }
                out.collect(key +"," + startStr +","+endStr +",sum="+sum);
            }
        }).print();



        //5. execute-执行
        env.execute();
    }
}