《Flink官方文档》Quick Start

安装: 下载并开始使用Flink

Flink 可以运行在 Linux, Mac OS X和Windows上。为了运行Flink, 唯一的要求是必须在Java 7.x (或者更高版本)上安装。Windows 用户, 请查看 Flink在Windows上的安装指南。

你可以使用以下命令检查Java当前运行的版本：

java -version

如果你有安装Java 8，命令行有如下回显

java version "1.8.0_111"

Java(TM) SE Runtime Environment (build 1.8.0_111-b14)

Java HotSpot(TM) 64-Bit Server VM (build 25.111-b14, mixed mode)

** 下载和解压 **

从下载页下载一个二进制的包，你可以选择任何你喜欢的Hadoop/Scala组合包。如果你计划使用文件系统，那么可以使用任何Hadoop版本。
进入下载目录
解压下载的压缩包

$ cd ~/Downloads        # Go to download directory
$ tar xzf flink-*.tgz   # Unpack the downloaded archive
$ cd flink-1.2.0
Start a Local Flink Cluster

MacOS X

对于 MacOS X 用户, Flink 可以通过Homebrew 进行安装。

 ~~~bash 
 $ brew install apache-flink … 
 $ flink –version 
 Version: 1.2.0, Commit ID: 1c659cf ~~~

启动一个本地的Flink集群

使用如下命令启动Flink：

$ ./bin/start-local.sh  # Start Flink

通过访问http://localhost:8081检查JobManager网页,确保所有组件都已运行。网页会显示一个有效的TaskManager实例。

译注：本地需要有localhost 127.0.0.1的域名映射

你也可以通过检查日志目录里的日志文件来验证系统是否已经运行：


$ tail log/flink-*-jobmanager-*.log
INFO ... - Starting JobManager
INFO ... - Starting JobManager web frontend
INFO ... - Web frontend listening at 127.0.0.1:8081
INFO ... - Registered TaskManager at 127.0.0.1 (akka://flink/user/taskmanager)

阅读源码

你可以在GitHub中找到SocketWindowWordCount完整的代码，有JAVA和SCALA两个版本。

Scala


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)
}

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;
        }
    }
}

运行例子

现在, 我们可以运行Flink 应用程序。这个例子将会从一个socket中读一段文本，并且每隔5秒打印每个单词出现的数量。例如 a tumbling window of processing time, as long as words are floating in.

第一步, 我们可以通过netcat命令来启动本地服务。

$ 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://flink@127.0.0.1:6123/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

译者注：你也可以提交一个简单的任务examples/batch/WordCount.jar任务，也可以界面提交任务，提交前需要选择一下Entry Class。

程序连接socket并等待输入，你可以通过web界面来验证任务期望的运行结果：

单词的数量在5秒的时间窗口中进行累加（使用处理时间和tumbling窗口），并打印在stdout。监控JobManager的输出文件，并在nc写一些文本(回车一行就发送一行输入给Flink) :

$ nc -l 9000
lorem ipsum
ipsum ipsum ipsum
bye

译者注：mac下使用命令nc -l -p 9000来启动监听端口，如果有问题可以telnet localhost 9000看下监听端口是否已经启动，如果启动有问题可以重装netcat ，使用命令brew install netcat。

.out文件将被打印每个时间窗口单词的总数：

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

使用以下命令来停止Flink:

$ ./bin/stop-local.sh

下一步

Check out更多的例子来熟悉Flink的编程API。当你完成这些，可以继续阅读streaming指南。

原创文章，转载请注明： 转载自并发编程网 – ifeve.com本文链接地址: 《Flink官方文档》Quick Start

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