flink对文件top打包执行代码

以下是使用Flink对文件top打包执行的代码：

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.ReduceFunction;
import org.apache.flink.api.common.functions.RuntimeContext;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.core.fs.FileSystem;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

public class FileTop {

    public static void main(String[] args) throws Exception {
        final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        // 读取文件
        DataSource<String> input = env.readTextFile("path/to/file");

        // 进行单词计数
        input.flatMap(new LineSplitter())
             .groupBy(0)
             .reduce(new WordCountReducer())
             .writeAsCsv("path/to/output", FileSystem.WriteMode.OVERWRITE);

        env.execute("File Top");
    }

    private static class LineSplitter implements FlatMapFunction<String, Tuple2<String, Integer>> {

        @Override
        public void flatMap(String line, Collector<Tuple2<String, Integer>> out) {
            // 按空格拆分单词
            for (String word : line.split(" ")) {
                out.collect(new Tuple2<>(word, 1));
            }
        }
    }

    private static class WordCountReducer implements ReduceFunction<Tuple2<String, Integer>> {

        @Override
        public Tuple2<String, Integer> reduce(Tuple2<String, Integer> t1, Tuple2<String, Integer> t2) {
            // 对相同单词进行计数
            return new Tuple2<>(t1.f0, t1.f1 + t2.f1);
        }
    }
}

这个程序首先读取一个文件，然后对文件中的每一行进行单词计数。最后将计数结果写出到文件中。你可以将这个程序打包成一个可执行的 jar 文件，在命令行中执行：

$ flink run -c FileTop path/to/jar

其中 `FileTop` 是程序的入口类，`path/to/jar` 是打包后的 jar 文件路径。