Quickstart

Setup: Download and Start Flink

在Linux, Mac OS X或者 Windows上运行Flink 只需要 JAVA 7或者以上的版本，对于Windows用户来讲请参考Flink on Windows、

你可以使用下面的命令来查看当前安装的JAVA版本

java -version

如果你安装的是java8的版本，你看到的结果应该类似下面这样：

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)

下载与编译

从仓库克隆源代码

$ 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

启动一个Flink 本地集群

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

通过 http://localhost:8081 检查JobManager本地客户端保证所有的组件都运行成功

你也可以通过日志来检查系统是否已经运行

$ tail log/flink-*-jobmanager-*.log
INFO ... - Starting JobManager
INFO ... - Starting JobManager web frontend
INFO ... - Web frontend listening at 127.0.0.1:8081
INFO ... - Registered TaskManager at 127.0.0.1 (akka://flink/user/taskmanager)

代码实践

你可以通过GITHUB 来找到下面这段SocketWindowWordCount 的代码 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;
        }
    }
}

Run the Example

现在，我们要运行这个Flink应用程序。它将从一个套接字(socket )中读取文本，每5秒钟打印出在前5秒钟内每一个不同的单词的出现次数

首先我们使用netcat 来启动本地服务

$ nc -l 9000

提交Flink程序

$ ./bin/flink run examples/streaming/SocketWindowWordCount.jar --port 9000

Cluster configuration: Standalone cluster with JobManager at /127.0.0.1:6123
Using address 127.0.0.1:6123 to connect to JobManager.
JobManager web interface address http://127.0.0.1:8081
Starting execution of program
Submitting job with JobID: 574a10c8debda3dccd0c78a3bde55e1b. Waiting for job completion.
Connected to JobManager at Actor[akka.tcp://flink@127.0.0.1:6123/user/jobmanager#297388688]
11/04/2016 14:04:50     Job execution switched to status RUNNING.
11/04/2016 14:04:50     Source: Socket Stream -> Flat Map(1/1) switched to SCHEDULED
11/04/2016 14:04:50     Source: Socket Stream -> Flat Map(1/1) switched to DEPLOYING
11/04/2016 14:04:50     Fast TumblingProcessingTimeWindows(5000) of WindowedStream.main(SocketWindowWordCount.java:79) -> Sink: Unnamed(1/1) switched to SCHEDULED
11/04/2016 14:04:51     Fast TumblingProcessingTimeWindows(5000) of WindowedStream.main(SocketWindowWordCount.java:79) -> Sink: Unnamed(1/1) switched to DEPLOYING
11/04/2016 14:04:51     Fast TumblingProcessingTimeWindows(5000) of WindowedStream.main(SocketWindowWordCount.java:79) -> Sink: Unnamed(1/1) switched to RUNNING
11/04/2016 14:04:51     Source: Socket Stream -> Flat Map(1/1) switched to RUNNING

这个程序链接到socket上并等待输出，你可以通过web接口来验证运行结果是否符合预期。

单词数将在每5面统计一次并打印到stdout上，监控JobManager的输出文件并随机输入一些单词到nc

$ nc -l 9000
lorem ipsum
ipsum ipsum ipsum
bye

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

关闭Flink

$ ./bin/stop-local.sh

Apache Flink 快速实践(Quickstart)