本地安装教程

译者：flink.sojb.cn

只需几个简单的步骤即可启动并运行Flink示例程序。

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)

下载并编译

从我们的某个存储库克隆源代码，例如：

$ git clone https://github.com/apache/flink.git
$ cd flink
$ mvn clean package -DskipTests # this will take up to 10 minutes
$ cd build-target               # this is where Flink is installed to

启动本地Flink群集

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

检查分派器的web前端在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://[[email protected]](/cdn-cgi/l/email-protection):6123/user/resourcemanager was granted leadership ...
INFO ... - Starting the SlotManager.
INFO ... - Dispatcher akka.tcp://[[email protected]](/cdn-cgi/l/email-protection):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上的GitHub上找到此SocketWindowWordCount示例的完整源代码。

• Scala
• Java

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 <return>）：</return>

$ 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当你做类型：

$ ./bin/stop-cluster.sh

下一步

查看更多示例以更好地了解Flink的编程API。完成后，请继续阅读流处理指南。