【Flink】Mac设置本地运行环境

下载并启动flink

运行flink，唯一的前置要求是就是安装了java8。
可以本地查看命令：

java -version

如果是安装了java8，则输出的命令则类似于：

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)

对于mac系统，可以通过Homebrew来安装

$ brew install apache-flink
...
$ flink --version
Version: 1.2.0, Commit ID: 1c659cf

启动本地的flink集群

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

你可以检查 http://localhost:8081，是否有页面正常可以访问。
你也可以校验log文件路径的数据：

$ 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 ... at ResourceManager

阅读代码

你可以编译并运行SocketWindowWordCount 代码，github地址为 SocketWindowWordCount

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, "
");

        // 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

运行一个flink程序，它将从套接字读取文本，并且每5秒打印一次在前5秒内每个不同单词的出现次数。

首先，我们使用netcat启动本地服务器

$ nc -l 9000

提交flink程序

$ ./bin/flink run examples/streaming/SocketWindowWordCount.jar --port 9000
Starting execution of program

这个程序通过连接套接字接受等待输入。

$ nc -l 9000
lorem ipsum
ipsum ipsum ipsum
bye

那么就可以在log中看出print的输出

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

最后停止flink集群的命令为：

$ ./bin/stop-cluster.sh