Flink demo wordCount词频统计及单机任务提交

本文相关内容出自Flink官方文档：http://flink.apache.org/

在完成本地Flink安装后，通过在安装目录下执行start脚本可启动单机Flink。

bindeMacBook-Air:libexec bin$ pwd
/usr/local/Cellar/apache-flink/1.10.1/libexec
bindeMacBook-Air:libexec bin$ ./bin/start-cluster.sh 

此时可通过 http://localhost:8081/ 查看Flink管理页面。

因为还没有提交任务，此时，Running Jobs为0。

此处Flink官方文档提供了Demo代码SocketWindowWordCount.java

public class SocketWindowWordCount {

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

        // the port to connect to
        final int port;
        try {
            final ParameterTool params = ParameterTool.fromArgs(args);
            port = params.getInt("port");
        } catch (Exception e) {
            System.err.println("No port specified. Please run 'SocketWindowWordCount --port <port>'");
            return;
        }

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

        // get input data by connecting to the socket
        DataStream<String> text = env.socketTextStream("localhost", 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;
        }
    }
}

因为这里是从本地socket端口获取输入数据，首先通过nc -lk 9000开放9000端口等待获取数据。

bindeMacBook-Air:~ bin$ nc -lk 9000
....

在Flink安装目录的examples下有对应的jar包，通过以下命令提交任务：

bindeMacBook-Air:libexec bin$ pwd
/usr/local/Cellar/apache-flink/1.10.1/libexec
bindeMacBook-Air:libexec bin$ ./bin/flink run examples/streaming/SocketWindowWordCount.jar --port 9000
Job has been submitted with JobID 34ab922549a5cac2ce2d4939dd348612

在UI界面可以看到当前存在一个正在运行的任务。
点击对应的Job Name可以进入任务详情页面，此时可以查看对应的JobGraph。

在socket端口进行输入：

bindeMacBook-Air:~ bin$ nc -lk 9000
a	c	b	d	a	a	d
c	c	a	b	b	c	a

如上图，可以查看到对应的输入输出的数据。
可以查看日志文件获取对应结果：

bindeMacBook-Air:libexec bin$ cat ./log/flink-bin-taskexecutor-3-bindeMacBook-Air.local.out 
a : 3
d : 2
b : 1
c : 1
c : 3
b : 2
a : 2