环境

• Ubuntu 18
• jdk8
• flink-1.8.1

安装步骤

1. 安装jdk（略）

2. 下载flink-1.8.1-bin-scala_2.12.tgz，解压到指定目录

   wget http://mirror.bit.edu.cn/apache/flink/flink-1.8.1/flink-1.8.1-bin-scala_2.12.tgz

   sudo mkdir /opt/flink

   sudo chown test flink

   sudo chgrp test flink

   tar -zxvf flink-1.8.1-bin-scala_2.12.tgz -C /opt/flink

3. 单机资源有限，修改配置文件flink-conf.yaml

   The heap size for the JobManager JVM

   jobmanager.heap.size: 256m

   The heap size for the TaskManager JVM

   taskmanager.heap.size: 256m

启动

bin目录下执行./start-cluster.sh

jps进程查看

3857 TaskManagerRunner
3411 StandaloneSessionClusterEntrypoint
3914 Jps

查看web页面

运行example

查看结果文件

在idea中会启动一个本地的flink，适合作为开发环境

maven中添加依赖

<dependencies>
    <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-streaming-java -->
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-streaming-java_2.12</artifactId>
        <version>1.8.1</version>
    </dependency>
    <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-java -->
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-java</artifactId>
        <version>1.8.1</version>
    </dependency>
    <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-clients -->
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-clients_2.12</artifactId>
        <version>1.8.1</version>
    </dependency>
</dependencies>

example代码

package test;
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 StreamingWindowWordCountJava {

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

    // the port to connect to
    final int port = 9000;

    // get the execution environment
    final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

    // get input data by connecting to the socket
    DataStream<String> text = env.socketTextStream("192.168.29.129", port, "\n");

    // parse the data, group it, window it, and aggregate the counts
    DataStream<WordWithCount> windowCounts = text
            .flatMap(new FlatMapFunction<String, WordWithCount>() {
                //@Override
                public void flatMap(String value, Collector<WordWithCount> out) {
                    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>() {
                //@Override
                public WordWithCount reduce(WordWithCount a, WordWithCount b) {
                    return new WordWithCount(a.word, a.count + b.count);
                }
            });

    // print the results with a single thread, rather than in parallel
    windowCounts.print().setParallelism(1);

    env.execute("Socket Window WordCount");
}

// Data type for words with count
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;
    }
}
}

IDEA中运行结果

代码打包运行

上述代码，打包成simple-flink-code.jar

在flink的bin目录下执行：

./flink run -c test.StreamingWindowWordCountJava /home/test/Desktop/simple-flink-code.jar（注意运行类前面写上package名，-c参数顺序在jar包前面，否则报错）

FLINK实例-WORDCOUNT详细步骤