初识Flink

Apache Flink是一个用于分布式流和批处理数据处理的开源平台。Flink的核心是流数据流引擎，为数据流上的分布式计算提供数据分发，通信和容错。Flink在流引擎之上构建批处理，覆盖本机迭代支持，托管内存和程序优化。

一、Flink 的下载安装启动

设置：下载并启动Flink

Flink可在Linux，Mac OS X和Windows上运行。为了能够运行Flink，唯一的要求是安装一个有效的Java 8.x. Windows用户，请查看Windows上的Flink指南，该指南介绍了如何在Windows上运行Flink以进行本地设置。

您可以通过发出以下命令来检查Java的正确安装：

java -version
 

如果你有Java 8，输出将如下所示：

       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)
   
  

• 下载和解压缩
• MacOS X.

1. 从下载页面下载二进制文件。您可以选择任何您喜欢的Hadoop / Scala组合。如果您打算只使用本地文件系统，任何Hadoop版本都可以正常工作。
2. 转到下载目录。
3. 解压缩下载的存档。

        $ cd ~/Downloads # Go to download directory
        $ tar xzf flink-*.tgz # Unpack the downloaded archive
        $ cd flink-1.7.0
    
   

二、启动本地Flink群集

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

检查web前端ui页面在http://localhost:8081，并确保一切都正常运行。Web前端应报告单个可用的TaskManager实例。

您还可以通过检查logs目录中的日志文件来验证系统是否正在运行：

       $ 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.
   
  

三、阅读代码

您可以在Scala中找到此SocketWindowWordCount示例的完整源代码，并在GitHub上找到Java。

• Scala的

      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的

      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;
       }
       }
      }
  
 

四、运行示例

现在，我们将运行此Flink应用程序。它将从套接字读取文本，并且每5秒打印一次前5秒内每个不同单词的出现次数，即处理时间的翻滚窗口，只要文字漂浮在其中。

• 首先，我们使用netcat来启动本地服务器

$ nc -l 9000
  

• 提交Flink计划：

       $ ./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：

• 单词在5秒的时间窗口（处理时间，翻滚窗口）中计算并打印到stdout。监视TaskManager的输出文件并写入一些文本nc（输入在点击后逐行发送到Flink）：

       $ nc -l 9000
       lorem ipsum
       ipsum ipsum ipsum
       bye
   
  

该.out文件将在每个时间窗口结束时，只要打印算作字浮在，例如：

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

要停止Flink你可以执行如下命令：

$ ./bin/stop-cluster.sh
  

文章来源: blog.csdn.net，作者：血煞风雨城2018，版权归原作者所有，如需转载，请联系作者。

原文链接：blog.csdn.net/qq_31905135/article/details/86649409