hadoop配置中设置布尔参数的问题

我有以下代码：

这是司机： 我在这里设置布尔参数区分大小写

package stubs;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
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 AvgWordLength extends Configured implements Tool{

  public static void main(String[] args) throws Exception {
      int exitCode = ToolRunner.run(new Configuration(), new AvgWordLength(), args);
      System.exit(exitCode);
  }

@Override
public int run(String[] args) throws Exception {
    // TODO Auto-generated method stub
    /*
     * Validate that two arguments were passed from the command line.
     */
    if (args.length != 2) {
      System.out.printf("Usage: AvgWordLength <input dir> <output dir>\n");
      return -1;
    }

    /*
     * Instantiate a Job object for your job's configuration. 
     */
    Job job = new Job(getConf());
    getConf().setBoolean("caseSensitive",true);
    /*
     * Specify the jar file that contains your driver, mapper, and reducer.
     * Hadoop will transfer this jar file to nodes in your cluster running 
     * mapper and reducer tasks.
     */
    job.setJarByClass(AvgWordLength.class);

    /*
     * Specify an easily-decipherable name for the job.
     * This job name will appear in reports and logs.
     */
    job.setJobName("Average Word Length");

    FileInputFormat.setInputPaths(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));

    job.setMapperClass(LetterMapper.class);
    job.setReducerClass(AverageReducer.class);
    job.setMapOutputKeyClass(Text.class);
    job.setMapOutputValueClass(IntWritable.class);

    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(DoubleWritable.class);

    /*
     * TODO implement
     */

    /*
     * Start the MapReduce job and wait for it to finish.
     * If it finishes successfully, return 0. If not, return 1.
     */
    boolean success = job.waitForCompletion(true);
    return success ? 0 : 1;
}
}

不起作用

我找到了答案

以下是驱动程序的更新代码：

package stubs;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
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 AvgWordLength extends Configured implements Tool{

  public static void main(String[] args) throws Exception {
      Configuration conf = new Configuration();
      conf.setBoolean("caseSensitive",true);
      int exitCode = ToolRunner.run(conf, new AvgWordLength(), args);
      System.exit(exitCode);
  }

@Override
public int run(String[] args) throws Exception {
    // TODO Auto-generated method stub
    /*
     * Validate that two arguments were passed from the command line.
     */
    if (args.length != 2) {
      System.out.printf("Usage: AvgWordLength <input dir> <output dir>\n");
      return -1;
    }

    /*
     * Instantiate a Job object for your job's configuration. 
     */
    Job job = new Job(getConf());

    /*
     * Specify the jar file that contains your driver, mapper, and reducer.
     * Hadoop will transfer this jar file to nodes in your cluster running 
     * mapper and reducer tasks.
     */
    job.setJarByClass(AvgWordLength.class);

    /*
     * Specify an easily-decipherable name for the job.
     * This job name will appear in reports and logs.
     */
    job.setJobName("Average Word Length");

    FileInputFormat.setInputPaths(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));

    job.setMapperClass(LetterMapper.class);
    job.setReducerClass(AverageReducer.class);
    job.setMapOutputKeyClass(Text.class);
    job.setMapOutputValueClass(IntWritable.class);

    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(DoubleWritable.class);

    /*
     * TODO implement
     */

    /*
     * Start the MapReduce job and wait for it to finish.
     * If it finishes successfully, return 0. If not, return 1.
     */
    boolean success = job.waitForCompletion(true);
    return success ? 0 : 1;
}
}

包存根；
导入org.apache.hadoop.conf.Configuration；
导入org.apache.hadoop.conf.Configured；
导入org.apache.hadoop.fs.Path；
导入org.apache.hadoop.io.DoubleWritable；
导入org.apache.hadoop.io.IntWritable；
导入org.apache.hadoop.io.Text；
导入org.apache.hadoop.mapreduce.Job；
导入org.apache.hadoop.mapreduce.lib.input.FileInputFormat；
导入org.apache.hadoop.mapreduce.lib.output.FileOutputFormat；
导入org.apache.hadoop.util.Tool；
导入org.apache.hadoop.util.ToolRunner；
公共类AvgWordLength扩展配置的实现工具{
公共静态void main（字符串[]args）引发异常{
Configuration conf=新配置（）；
conf.setBoolean（“区分大小写”，真）；
int exitCode=ToolRunner.run（conf，new AvgWordLength（），args）；
系统退出（退出代码）；
}
@凌驾
公共int运行（字符串[]args）引发异常{
//TODO自动生成的方法存根
/*
*验证是否从命令行传递了两个参数。
*/
如果（参数长度！=2）{
System.out.printf（“用法：AvgWordLength\n”）；
返回-1；
}
/*
*为作业的配置实例化作业对象。
*/
Job Job=新作业（getConf（））；
/*
*指定包含驱动程序、映射程序和减速器的jar文件。
*Hadoop将把这个jar文件传输到集群中运行的节点
*mapper和reducer任务。
*/
job.setJarByClass（AvgWordLength.class）；
/*
*为作业指定易于识别的名称。
*此作业名称将出现在报告和日志中。
*/
setJobName（“平均字长”）；
setInputPaths（作业，新路径（args[0]）；
setOutputPath（作业，新路径（args[1]）；
job.setMapperClass（lettmapper.class）；
job.setReducerClass（AverageEducer.class）；
job.setMapOutputKeyClass（Text.class）；
setMapOutputValueClass（IntWritable.class）；
job.setOutputKeyClass（Text.class）；
job.setOutputValueClass（DoubleWritable.class）；
/*
*待办事项实施
*/
/*
*启动MapReduce作业并等待其完成。
*如果成功完成，则返回0。如果不成功，则返回1。
*/
布尔值success=job.waitForCompletion（true）；
返回成功？0:1；
}
}

作业的配置必须在传递给作业的构造函数之前完成

之后的每一个变化都不被承认

getConf().setBoolean("caseSensitive",true);

package stubs;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
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 AvgWordLength extends Configured implements Tool{

  public static void main(String[] args) throws Exception {
      Configuration conf = new Configuration();
      conf.setBoolean("caseSensitive",true);
      int exitCode = ToolRunner.run(conf, new AvgWordLength(), args);
      System.exit(exitCode);
  }

@Override
public int run(String[] args) throws Exception {
    // TODO Auto-generated method stub
    /*
     * Validate that two arguments were passed from the command line.
     */
    if (args.length != 2) {
      System.out.printf("Usage: AvgWordLength <input dir> <output dir>\n");
      return -1;
    }

    /*
     * Instantiate a Job object for your job's configuration. 
     */
    Job job = new Job(getConf());

    /*
     * Specify the jar file that contains your driver, mapper, and reducer.
     * Hadoop will transfer this jar file to nodes in your cluster running 
     * mapper and reducer tasks.
     */
    job.setJarByClass(AvgWordLength.class);

    /*
     * Specify an easily-decipherable name for the job.
     * This job name will appear in reports and logs.
     */
    job.setJobName("Average Word Length");

    FileInputFormat.setInputPaths(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));

    job.setMapperClass(LetterMapper.class);
    job.setReducerClass(AverageReducer.class);
    job.setMapOutputKeyClass(Text.class);
    job.setMapOutputValueClass(IntWritable.class);

    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(DoubleWritable.class);

    /*
     * TODO implement
     */

    /*
     * Start the MapReduce job and wait for it to finish.
     * If it finishes successfully, return 0. If not, return 1.
     */
    boolean success = job.waitForCompletion(true);
    return success ? 0 : 1;
}
}