1.3 Apache Flink本地安装

Apache Flink 定义

Apache Flink是一个框架和分布式处理引擎，用于对无界和有界数据流进行状态计算。Flink设计为在全部常见的集群环境中运行，之内存速度和任何规模执行计算。

设置：下载并启动Flink

Flink可在Linux，Mac OS X和Windows上运行。为了可以运行Flink，惟一的要求是安装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。

您能够经过发出如下命令来检查Java的正确安装：

java -version

若是你有Java 8，输出将以下所示：

java version "1.8.0_201"
Java(TM) SE Runtime Environment (build 1.8.0_201-b09)
Java HotSpot(TM) 64-Bit Server VM (build 25.201-b09, mixed mode)
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下载和解压缩

1. 从下载页面下载二进制文件。您能够选择任何您喜欢的Hadoop / Scala组合。若是您打算只使用本地文件系统，任何Hadoop版本均可以正常工做。
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-1.8.0
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对于MacOS X用户，能够经过Homebrew安装Flink 。

$ brew install apache-flink ... 
$ flink --version Version: 1.8.0, Commit ID: 4caec0d
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启动本地Flink群集

$ ./bin/start-cluster.sh # Start Flink
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检查分派器的web前端在HTTP：//本地主机：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。

您还能够经过检查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.
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阅读代码

您能够在Scala中找到此SocketWindowWordCount示例的完整源代码，并在GitHub上找到Java。

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)
}
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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;
        }
    }
}
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运行示例

如今，咱们将运行此Flink应用程序。它将从套接字读取文本，而且每5秒打印一次前5秒内每一个不一样单词的出现次数，即处理时间的翻滚窗口，只要文字漂浮在其中。

• 首先，咱们使用netcat来启动本地服务器

$ nc -l 9000
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• 提交Flink计划：

$ ./bin/flink run examples/streaming/SocketWindowWordCount.jar --port 9000 Starting execution of program
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程序链接到套接字并等待输入。您能够检查Web界面以验证做业是否按预期运行：

• 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
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该.out文件将在每一个时间窗口结束时，只要打印算做字浮在，例如：

$ tail -f log/flink-*-taskexecutor-*.out
lorem : 1
bye : 1
ipsum : 4
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要中止Flink 所要作的操做：

$ ./bin/stop-cluster.sh
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