前面我们已经安装了flink，与storm一样，flink也有两种模式，一是本地模式，主要用于学习和测试，另一个是集群模式，实际生产中使用这种模式。本节将阐述如何使用本地模式的flink进行词频统计。

1 系统、软件以及前提约束

• CentOS 7 64 工作站 作者的机子ip是192.168.100.200，请读者根据自己实际情况设置
• idea 2018.1
• 在Win10中安装nc

  https://www.jianshu.com/p/4f6fb8834ad9

2 操作

• 1 在idea中创建一个maven项目
• 2 修改该maven项目的pom.xml中的依赖

   <dependencies>
        <dependency>
            <!--spark依赖-->
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>2.2.0</version>
        </dependency>
        <!--scala依赖-->
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>2.11.8</version>
        </dependency>
        <!--hadoop依赖-->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.6.0-cdh5.7.0</version>
        </dependency>
        <!--hbase依赖-->
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-client</artifactId>
            <version>2.0.0-cdh6.0.1</version>
        </dependency>
        <!--storm依赖-->
        <dependency>
            <groupId>org.apache.storm</groupId>
            <artifactId>storm-core</artifactId>
            <exclusions>
                <exclusion>
                    <groupId>org.slf4j</groupId>
                    <artifactId>log4j-over-slf4j</artifactId>
                </exclusion>
            </exclusions>
            <version>1.2.1</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-core -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-core</artifactId>
            <version>1.5.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>1.5.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.11</artifactId>
            <version>1.5.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.11</artifactId>
            <version>1.5.1</version>
        </dependency>
    </dependencies>

• 3 在src/main/java中添加SocketWindowWordCountWithFlink.java

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.ReduceFunction;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;

public class SocketWindowWordCountWithFlink {
    public static void main(String[] args) throws Exception {
        // final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        // local模式
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironment();
        DataStream<String> text = env.socketTextStream("localhost", 9999, "\n");
        @SuppressWarnings("serial")
        DataStream<WordWithCount> windowCounts = text
                .flatMap(new FlatMapFunction<String, WordWithCount>() {
                    public void flatMap(String value, Collector<WordWithCount> out) throws Exception {
                        for (String word : value.split("\\s")) {
                            out.collect(new WordWithCount(word, 1L));
                        }
                    }
                })
                .keyBy("word").timeWindow(Time.seconds(5), Time.seconds(1))
                .reduce(new ReduceFunction<WordWithCount>() {
                    public WordWithCount reduce(WordWithCount a, WordWithCount b) throws Exception {
                        return new WordWithCount(a.word, a.count + b.count);
                    }
                });
        windowCounts.print().setParallelism(1);
        env.execute("Socket Window WordCount(zyl_test)");
    }

    public static class WordWithCount {
        public String word;
        public long count;

        public WordWithCount() {
        }

        public WordWithCount(String word, long count) {
            this.word = word;
            this.count = count;
        }

        @Override
        public String toString() {
            return word + " : " + count;
        }
    }
}

• 4 测试

  （1）打开windows命令行，执行以下命令：

nc -l -p 9999

（2）在idea中执行SocketWindowWordCountWithFlink.java

（3）在nc窗口输入字符串，观察idea中的控制台，会有统计结果打印。

以上就是使用Flink的本地模式进行的词频统计过程，在本实验中，我们通过人输入字符串来模拟源源不断到来的数据流。

原文地址：https://www.cnblogs.com/alichengxuyuan/p/12576766.html