企业推广软件有哪些关键词优化快速

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>>() {@Overridepublic 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>() {@Overridepublic 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>>() {@Overridepublic 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>() {@Overridepublic 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>() {@Overridepublic 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();}
}