本地安装教程 - 《Flink 中文文档 - 帮助手册 - 教程》

安装:下载并启动Flink
- 启动本地Flink集群
阅读代码
运行示例
下一个步骤

用几个简单的步骤启动并运行一个Flink示例程序。

安装:下载并启动Flink

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)

从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。
转到下载目录。
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

在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。

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scala 和java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:

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

$ 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

要stop Flink当你完成类型:

$ ./bin/stop-cluster.sh

下一个步骤

查看更多的examples，以更好地了解Flink的编程api。完成后，请继续阅读streaming guide。