java - 不使用 JobConf 运行 Hadoop 作业

Question

我找不到提交不使用已弃用JobConf类的 Hadoop 作业的单个示例。 JobClient，尚未被弃用，仍然只支持带JobConf参数的方法。

Configuration有人可以指出一个 Java 代码示例，该示例仅使用类（不是）提交 Hadoop map/reduce 作业JobConf，并使用mapreduce.lib.input包而不是包mapred.input？

Answer 1

希望这有帮助

import java.io.File;

import org.apache.commons.io.FileUtils;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class MapReduceExample extends Configured implements Tool {

    static class MyMapper extends Mapper<LongWritable, Text, LongWritable, Text> {
        public MyMapper(){

        }

        protected void map(
                LongWritable key,
                Text value,
                org.apache.hadoop.mapreduce.Mapper<LongWritable, Text, LongWritable, Text>.Context context)
                throws java.io.IOException, InterruptedException {
            context.getCounter("mygroup", "jeff").increment(1);
            context.write(key, value);
        };
    }

    @Override
    public int run(String[] args) throws Exception {
        Job job = new Job();
        job.setMapperClass(MyMapper.class);
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        job.waitForCompletion(true);
        return 0;
    }

    public static void main(String[] args) throws Exception {
        FileUtils.deleteDirectory(new File("data/output"));
        args = new String[] { "data/input", "data/output" };
        ToolRunner.run(new MapReduceExample(), args);
    }
}

Answer 2

我相信本教程说明了使用 Hadoop 0.20.1 删除已弃用的 JobConf 类。

Answer 3

这是一个很好的示例，带有可下载的代码：http ://sonerbalkir.blogspot.com/2010/01/new-hadoop-api-020x.html 它也有两年多的历史了，并且没有讨论新 API 的官方文档。伤心。

Answer 4

在之前的 API 中，有三种提交作业的方法，其中一种是提交作业并获取 RunningJob 的引用并获取 RunningJob 的 id。

submitJob(JobConf) : only submits the job, then poll the returned handle to the RunningJob to query status and make scheduling decisions.

如何使用新的 Api 并获取 RunningJob 的引用并获取 runningJob 的 id，因为没有一个 api 返回对 RunningJob 的引用

http://hadoop.apache.org/docs/current/api/org/apache/hadoop/mapreduce/Job.html

谢谢

Answer 5

尝试使用Configuration和Job。这是一个例子：

（替换你的Mapper, Combiner, Reducerclasses 和其他配置）

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

public class WordCount {
  public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
    Configuration conf = new Configuration();
    if(args.length != 2) {
      System.err.println("Usage: <in> <out>");
      System.exit(2);
    }
    Job job = Job.getInstance(conf, "Word Count");

    // set jar
    job.setJarByClass(WordCount.class);

    // set Mapper, Combiner, Reducer
    job.setMapperClass(TokenizerMapper.class);
    job.setCombinerClass(IntSumReducer.class);
    job.setReducerClass(IntSumReducer.class);

    /* Optional, set customer defined Partioner:
     * job.setPartitionerClass(MyPartioner.class);
     */

    // set output key
    job.setMapOutputKeyClass(Text.class);
    job.setMapOutputValueClass(IntWritable.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);

    // set input and output path
    FileInputFormat.addInputPath(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));

    // by default, Hadoop use TextInputFormat and TextOutputFormat
    // any customer defined input and output class must implement InputFormat/OutputFormat interface
    job.setInputFormatClass(TextInputFormat.class);
    job.setOutputFormatClass(TextOutputFormat.class);

    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}