Quick Start

注意：本节内容仅仅演示启动运行一个Streaming处理Word Count的Flink程序，不包括代码编写部分，使用Flink已经打包好的示例代码，位置在 examples/streaming/SocketWindowWordCount.jar

安装：下载并启动Flink

Flink在Linux，Mac OS X和Windows上运行。为了能够运行Flink，唯一的要求是安装Java 7.x（或更高版本）。 Windows用户请查看Flink on Windows指南，介绍如何在Windows上运行Flink进行本地设置。

检查Java版本：

java -version

如果你安装了Java 8，将会有如下显示：

java version "1.8.0_121"
Java(TM) SE Runtime Environment (build 1.8.0_121-b13)
Java HotSpot(TM) 64-Bit Server VM (build 25.121-b13, mixed mode)

Flink下载地址：http://flink.apache.org/downloads.html

启动一个本地节点

$ ./bin/start-local.sh

浏览器访问http://localhost:8081，可以监控Flink相关信息：

源码阅读

你可以在Github上查看SocketWindowWordCount example的源码。

Java代码：

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;
        }
    }
}

Scala代码：

object SocketWindowWordCount {

    def main(args: Array[String]) : Unit = {

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

        // get the execution environment
        val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment

        // get input data by connecting to the socket
        val text = env.socketTextStream("localhost", port, '\n')

        // parse the data, group it, window it, and aggregate the counts
        val windowCounts = text
            .flatMap { w => w.split("\\s") }
            .map { w => WordWithCount(w, 1) }
            .keyBy("word")
            .timeWindow(Time.seconds(5), Time.seconds(1))
            .sum("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
    case class WordWithCount(word: String, count: Long)
}

运行示例代码

首先，本地开启一个netcat：

$ nc -l 9000

提交Flink程序：

$ ./bin/flink run examples/streaming/SocketWindowWordCount.jar --port 9000

Cluster configuration: Standalone cluster with JobManager at /127.0.0.1:6123
Using address 127.0.0.1:6123 to connect to JobManager.
JobManager web interface address http://127.0.0.1:8081
Starting execution of program
Submitting job with JobID: 574a10c8debda3dccd0c78a3bde55e1b. Waiting for job completion.
Connected to JobManager at Actor[akka.tcp://[email protected]:6123/user/jobmanager#297388688]
11/04/2016 14:04:50     Job execution switched to status RUNNING.
11/04/2016 14:04:50     Source: Socket Stream -> Flat Map(1/1) switched to SCHEDULED
11/04/2016 14:04:50     Source: Socket Stream -> Flat Map(1/1) switched to DEPLOYING
11/04/2016 14:04:50     Fast TumblingProcessingTimeWindows(5000) of WindowedStream.main(SocketWindowWordCount.java:79) -> Sink: Unnamed(1/1) switched to SCHEDULED
11/04/2016 14:04:51     Fast TumblingProcessingTimeWindows(5000) of WindowedStream.main(SocketWindowWordCount.java:79) -> Sink: Unnamed(1/1) switched to DEPLOYING
11/04/2016 14:04:51     Fast TumblingProcessingTimeWindows(5000) of WindowedStream.main(SocketWindowWordCount.java:79) -> Sink: Unnamed(1/1) switched to RUNNING
11/04/2016 14:04:51     Source: Socket Stream -> Flat Map(1/1) switched to RUNNING

运行界面如下图：

该程序对5秒内的输入值进行WordCount统计：

$ nc -l 9000
lorem ipsum
ipsum ipsum ipsum
bye

tail动态读取统计结果：

$ tail -f log/flink-*-jobmanager-*.out
lorem : 1
bye : 1
ipsum : 4

停止Flink，执行以下命令：

$ ./bin/stop-local.sh

Quickstart指南