Flink入门使用

完全参考：Flink1.3QuickStart

启动本地运行

首先找一台安装了hadoop的linux。
将安装包解压，到bin目录启动local模式的脚本。

tar -zxvf flink-1.3.1-bin-hadoop26-scala_2.11.tgz
./start-local.sh

运行wordCount例子

这个例子从sokect端口中每隔5秒读取其中的输入并进行记数。

//执行完nc输入单词，程序会开始记数。
nc -l 9001
//开另一个xshell，执行运行程序的命令
./bin/flink run examples/streaming/SocketWindowWordCount.jar --port 9001
//到log目录下可以看到输出了记数的文件

运行的jar中的源码如下：

package org.apache.flink.streaming.examples.socket;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.ReduceFunction;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;

@SuppressWarnings("serial")
public class SocketWindowWordCount {

    public static void main(String[] args) throws Exception {

        // the host and the port to connect to
        final String hostname;
        final int port;
        try {
            final ParameterTool params = ParameterTool.fromArgs(args);
            hostname = params.has("hostname") ? params.get("hostname") : "localhost";
            port = params.getInt("port");
        } catch (Exception e) {
            System.err.println("No port specified. Please run 'SocketWindowWordCount " +
                "--hostname <hostname> --port <port>', where hostname (localhost by default) " +
                "and port is the address of the text server");
            System.err.println("To start a simple text server, run 'netcat -l <port>' and " +
                "type the input text into the command line");
            return;
        }

        // get the execution environment
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // get input data by connecting to the socket
        DataStream<String> text = env.socketTextStream(hostname, 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))

                .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;
        }
    }
}

创建flink项目

window的命令行执行以下命令即可下载一个模板项目，导入IDE中就可以愉快地撸了。

mvn archetype:generate -DarchetypeGroupId=org.apache.flink -DarchetypeArtifactId=flink-quickstart-java -DarchetypeVersion=1.3.0