【Flink】DataStream入门：WordCount

1 项目依赖

我的Scala版本是2.12，这里Flink的依赖要和Scala版本相对应

    <properties>
        <java.version>1.8</java.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.12</artifactId>
            <version>1.12.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>1.12.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.12</artifactId>
            <version>1.12.0</version>
        </dependency>

    </dependencies>

2 本地运行

2.1 函数作为参数传递

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

public class WordCountSourceDataStream {
    
    
    public static void main(String[] args) throws Exception {
    
    
        // 1. 构建环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 2. 设置运行模式
        env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);

        // 3. 准备数据并接收
        DataStream<String> linesDS = env.fromElements("hadoop,hdfs,flink,spark", "hadoop,hdfs,flink", "hadoop,hdfs", "hadoop,spark");

        // 4. 处理数据
        // 4.1 每行按照逗号分割组成集合
        DataStream<String> wordsDS = linesDS.flatMap(new FlatMapFunction<String, String>() {
    
    
            @Override
            public void flatMap(String value, Collector<String> out) throws Exception {
    
    
                // value是一行单词
                String[] words = value.split(",");
                for (String word : words) {
    
    
                    // 收集每个word
                    out.collect(word);
                }
            }
        });

        // 4.2 将每个单词记为1，并组成元组返回
        DataStream<Tuple2<String, Integer>> word_1 = wordsDS.map(new MapFunction<String, Tuple2<String, Integer>>() {
    
    
            @Override
            public Tuple2<String, Integer> map(String value) throws Exception {
    
    
                return Tuple2.of(value, 1);
            }
        });

        // 4.3 对数据按照单词分组
        KeyedStream<Tuple2<String, Integer>, String> word_1_group = word_1.keyBy(tuple2 -> tuple2.f0);

        // 4.4 把分组的数据按照索引聚合
        DataStream<Tuple2<String, Integer>> result = word_1_group.sum(1);

        // 5. 输出结果
        result.print();

        // 6. 执行
        env.execute();
    }
}

2.2 Lambda匿名函数

对上面代码的4.1和4.2进行了修改

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.typeinfo.TypeHint;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

import java.util.Arrays;

public class WordCountSourceDataStreamL {
    
    
    public static void main(String[] args) throws Exception {
    
    
        // 1. 构建环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 2. 设置运行模式
        env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);

        // 3. 准备数据并接收
        DataStream<String> linesDS = env.fromElements("hadoop,hdfs,flink,spark", "hadoop,hdfs,flink", "hadoop,hdfs", "hadoop,spark");

        // 4. 处理数据
        // 4.1 每行按照逗号分割组成集合
        DataStream<String> wordsDS = linesDS.flatMap(
                (String value, Collector<String> out) -> Arrays.asList(value.split(",")).forEach(out::collect)
        ).returns(Types.STRING);

        // 4.2 将每个单词记为1，并组成元组返回
        DataStream<Tuple2<String, Integer>> word_1 = wordsDS.map(
                (String value) -> Tuple2.of(value, 1)
        ).returns(TypeInformation.of(new TypeHint<Tuple2<String, Integer>>(){
    
    }));

        // 4.3 对数据按照单词分组
        KeyedStream<Tuple2<String, Integer>, String> word_1_group = word_1.keyBy(tuple2 -> tuple2.f0);

        // 4.4 把分组的数据按照索引聚合
        DataStream<Tuple2<String, Integer>> result = word_1_group.sum(1);

        // 5. 输出结果
        result.print();

        // 6. 执行
        env.execute();
    }
}

控制台输出：

3 提交到Yarn上运行

对2.2代码的5进行了修改

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.typeinfo.TypeHint;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

import java.util.Arrays;

public class WordCountSourceDataStreamYarn {
    
    
    public static void main(String[] args) throws Exception {
    
    
        // 1. 构建环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 2. 设置运行模式
        env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);

        // 3. 准备数据并接收
        DataStream<String> linesDS = env.fromElements("hadoop,hdfs,flink,spark", "hadoop,hdfs,flink", "hadoop,hdfs", "hadoop,spark");

        // 4. 处理数据
        // 4.1 每行按照逗号分割组成集合
        DataStream<String> wordsDS = linesDS.flatMap(
                (String value, Collector<String> out) -> Arrays.asList(value.split(",")).forEach(out::collect)
        ).returns(Types.STRING);

        // 4.2 将每个单词记为1，并组成元组返回
        DataStream<Tuple2<String, Integer>> word_1 = wordsDS.map(
                (String value) -> Tuple2.of(value, 1)
        ).returns(TypeInformation.of(new TypeHint<Tuple2<String, Integer>>(){
    
    }));

        // 4.3 对数据按照单词分组
        KeyedStream<Tuple2<String, Integer>, String> word_1_group = word_1.keyBy(tuple2 -> tuple2.f0);

        // 4.4 把分组的数据按照索引聚合
        DataStream<Tuple2<String, Integer>> result = word_1_group.sum(1);

        // 5. 写入结果
        result.writeAsText("hdfs://localhost:9000/user/hadoop/flink/wordcount/output_"+System.currentTimeMillis());

        // 6. 执行
        env.execute();
    }
}

开启HDFS和cluster：

start-dfs.sh
start-cluster.sh

提交运行：

flink run -c WordCountSourceDataStreamYarn ~/IdeaProjects/FlinkStudy/target/FlinkStudy-1.0-SNAPSHOT.jar

运行结果：

4 Error和解决办法

Error：
Could not find a file system implementation for scheme ‘hdfs’
Solution：
https://blog.csdn.net/Tiezhu_Wang/article/details/114977487