Flink02-学习-套接字分词

flatmap()

AMapFunction仅适用于执行一对一转换的情况：对于每个进入的流元素，map()都会发出一个转换后的元素。否则，您需要使用 flatmap()

DataStream<TaxiRide> rides = env.addSource(new TaxiRideSource(...));DataStream<EnrichedRide> enrichedNYCRides = rides.flatMap(new NYCEnrichment());enrichedNYCRides.print();

连同FlatMapFunction：

DataStream<TaxiRide> rides = env.addSource(new TaxiRideSource(...));DataStream<EnrichedRide> enrichedNYCRides = rides.flatMap(new NYCEnrichment());enrichedNYCRides.print();

通过Collector此接口提供的功能，该flatmap()方法可以发出任意数量的流元素，包括不发出任何元素。

实践

Flink 的 DataStream API 允许你流式传输任何可以序列化的数据。Flink 自己的序列化器用于

基本类型，即 String、Long、Integer、Boolean、Array
复合类型：Tuples、POJO
对于其他类型，Flink 会回退到 Kryo。Flink 也可以使用其他序列化器。

pom内容如上个内容，此处不再赘述。
定义本机变量
连接的 IP 地址为 0.0.0.0（监听所有网络接口）

// 本机String ip = "0.0.0.0";//开启的端口号int port = 8886;

获取flink环境

StreamExecutionEnvironment executionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment();

使用套接字-socket流

  DataStreamSource<String> stringDataStreamSource = executionEnvironment.socketTextStream(ip, port, "\n");

FlatMap-分词
数据转换 - 分词和计数。
使用 flatMap 操作对每行文本进行分词处理；
将每行文本按空白字符分割成单词数组；
为每个单词生成一个 (单词, 1) 的元组（Tuple2）；
结果是一个包含 (单词, 计数) 对的流

SingleOutputStreamOperator<Tuple2<String, Long>> tuple2SingleOutputStreamOperator = stringDataStreamSource.flatMap(new FlatMapFunction<String, Tuple2<String, Long>>() {@Overridepublic void flatMap(String s, Collector<Tuple2<String, Long>> collector) throws Exception {String[] splits = s.split("\\s");for (String word : splits) {collector.collect(Tuple2.of(word, 1L));}}});

分组和窗口计算
keyBy: 按照元组的第一个字段（单词）进行分组；
window: 定义滑动窗口：窗口大小：5秒，滑动间隔：1秒；
sum(1): 对每个窗口内相同单词的计数（元组的第二个字段）求和；

SingleOutputStreamOperator<Tuple2<String, Long>> word = tuple2SingleOutputStreamOperator.keyBy(new KeySelector<Tuple2<String, Long>, Object>() {@Overridepublic Object getKey(Tuple2<String, Long> stringLongTuple2) throws Exception {return stringLongTuple2.f0;}}).window(SlidingProcessingTimeWindows.of(Time.seconds(5), Time.seconds(1))).sum(1);

完整代码

package org.example.snow.demo2;import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.assigners.SlidingProcessingTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;public class startDemo {public static void main(String[] args) throws Exception {String ip = "0.0.0.0";int port = 8886;StreamExecutionEnvironment executionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment();// 使用socket流创建一个从 socket 读取文本的数据流，以换行符 \n 作为分隔符DataStreamSource<String> stringDataStreamSource = executionEnvironment.socketTextStream(ip, port, "\n");SingleOutputStreamOperator<Tuple2<String, Long>> tuple2SingleOutputStreamOperator = stringDataStreamSource.flatMap(new FlatMapFunction<String, Tuple2<String, Long>>() {@Overridepublic void flatMap(String s, Collector<Tuple2<String, Long>> collector) throws Exception {String[] splits = s.split("\\s");for (String word : splits) {collector.collect(Tuple2.of(word, 1L));}}});SingleOutputStreamOperator<Tuple2<String, Long>> word = tuple2SingleOutputStreamOperator.keyBy(new KeySelector<Tuple2<String, Long>, Object>() {@Overridepublic Object getKey(Tuple2<String, Long> stringLongTuple2) throws Exception {return stringLongTuple2.f0;}}).window(SlidingProcessingTimeWindows.of(Time.seconds(5), Time.seconds(1))).sum(1);word.print();executionEnvironment.execute("stream!");}
}