Flink1.7本地安装教程

x33g5p2x  于2020-10-20 发布在 Flink  
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用几个简单的步骤启动并运行一个Flink示例程序。

Flink可以在Linux、Mac OS X和Windows上运行。要能够运行Flink,惟一的要求是有一个可以工作的Java 8.x安装。Windows用户,请看看Flink on Windows指南,它描述了如何在本地设置的Windows上运行Flink。

你可以发出以下命令,检查Java的正确安装:

java -version

如果您有Java 8,输出将是这样的:

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)
  1. downloads page下载一个二进制文件。您可以选择任何您喜欢的Hadoop/Scala组合。如果您计划只使用本地文件系统,那么任何Hadoop版本都可以很好地工作。
  2. 转到下载目录。
  3. 解压缩下载的归档文件。
$ cd ~/Downloads        # Go to download directory
$ tar xzf flink-*.tgz   # Unpack the downloaded archive
$ cd flink-1.7.1

MacOS X用户可以通过 Homebrew安装Flink。

$ brew install apache-flink
...
$ flink --version
Version: 1.2.0, Commit ID: 1c659cf

启动本地Flink集群

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

http://localhost:8081上检查Dispatcher的web前端,并确保一切正常运行。web前端应该报告一个可用的TaskManager实例。

Dispatcher: Overview

您还可以通过检查logs目录中的日志文件来验证系统是否在运行:

$ tail log/flink-*-standalonesession-*.log
INFO ... - Rest endpoint listening at localhost:8081
INFO ... - http://localhost:8081 was granted leadership ...
INFO ... - Web frontend listening at http://localhost:8081.
INFO ... - Starting RPC endpoint for StandaloneResourceManager at akka://flink/user/resourcemanager .
INFO ... - Starting RPC endpoint for StandaloneDispatcher at akka://flink/user/dispatcher .
INFO ... - ResourceManager akka.tcp://flink@localhost:6123/user/resourcemanager was granted leadership ...
INFO ... - Starting the SlotManager.
INFO ... - Dispatcher akka.tcp://flink@localhost:6123/user/dispatcher was granted leadership ...
INFO ... - Recovering all persisted jobs.
INFO ... - Registering TaskManager ... under ... at the SlotManager.

阅读代码

您可以在scalajava上找到这个SocketWindowWordCount示例的完整源代码。

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)
}
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;
        }
    }
}

运行示例

现在,我们要运行这个Flink应用程序。它将从套接字中读取文本,并且每5秒打印一次前5秒中每个不同单词出现的次数,即一个处理时间的滚动窗口,只要单词是浮动的。

  • 首先,我们使用netcat启动本地服务器
$ nc -l 9000
  • Submit the Flink program:
$ ./bin/flink run examples/streaming/SocketWindowWordCount.jar --port 9000
Starting execution of program

程序连接到套接字并等待输入。您可以检查web界面,以验证作业是否按预期运行:

Dispatcher: Overview

  • 单词以5秒的时间窗口(处理时间、滚动窗口)计数,并打印为stdout。监视任务管理器的输出文件,并在nc中写入一些文本(单击<return&gt后,一行一行地将输入发送给Flink):</return>
$ nc -l 9000
lorem ipsum
ipsum ipsum ipsum
bye

.outfile将在每次窗口结束时打印计数,只要有单词出现,例如:

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

stop Flink当你完成类型:

$ ./bin/stop-cluster.sh

转自https://github.com/apachecn/flink-doc-zh/

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