hadoop - 如何为Hadoop的Map-reduce作业设置配置？

假设我要为MR作业设置以下配置:

mapred.map.tasks
mapred.reduce.tasks
mapred.tasktracker.map.tasks.maximum
mapred.tasktracker.reduce.tasks.maximum
mapred.reduce.slowstart.completed.maps

我有什么可能的设置方式？

我可以在mapred-site.xml中进行设置。但这将适用于我执行的所有作业。

如果我要为各个作业专门设置这些设置，这是否有效:

conf.set("mapred.tasktracker.map.tasks.maximum", 10)

(我在任何地方都没有看到这种类型)

或仅通过命令行参数

如-D mapred.tasktracker.map.tasks.maximum=10
(这似乎是更常用的用法)

最佳答案

解决方案1:
创建一个BaseJob类:

public abstract class BaseJob extends Configured implements Tool {

// method to set the configuration for the job and the mapper and the reducer classes
protected Job setupJob(Transformation transformation, final Configuration conf) throws Exception {

    //Get the job object from the global configuration
    Job job = new Job(conf);

    //Set the transformation specific details
    if(transformation.getMapperClass() != null)
    job.setMapperClass(transformation.getMapperClass());

    if(transformation.getReducerClass() != null)
    job.setReducerClass(transformation.getReducerClass());

    if(transformation.getMapOutputKeyClass() != null)
    job.setMapOutputKeyClass(transformation.getMapOutputKeyClass());

    if(transformation.getMapOutputValueClass() != null)
    job.setMapOutputValueClass(transformation.getMapOutputValueClass());

    if(transformation.getPartitionerClass() != null)
    job.setPartitionerClass(transformation.getPartitionerClass());

    if(transformation.getSortComparatorClass() != null)
    job.setSortComparatorClass(transformation.getSortComparatorClass());

    if(transformation.getGroupingComparator() != null)
    job.setGroupingComparatorClass(transformation.getGroupingComparator());

    if(transformation.getInputFormatClass() != null)
    job.setInputFormatClass(transformation.getInputFormatClass());

    if(transformation.getOutputKeyClass() != null)
    job.setOutputKeyClass(transformation.getOutputKeyClass());

    if(transformation.getOutputValueClass() != null)
    job.setOutputValueClass(transformation.getOutputValueClass());

    if(transformation.getJarByClass() != null)
    job.setJarByClass(transformation.getJarByClass());

    return job;
}

protected abstract class Transformation {
    public abstract Class<?> getJarByClass();
    public abstract Class<? extends Mapper> getMapperClass();
    public abstract Class<? extends Reducer> getCombinerClass();
    public abstract Class<? extends Reducer> getReducerClass();
    public abstract Class<?> getOutputKeyClass();
    public abstract Class<?> getOutputValueClass();
    public abstract Class<?> getMapOutputKeyClass();
    public abstract Class<?> getMapOutputValueClass();
    public abstract Class<? extends Partitioner> getPartitionerClass();
    public abstract Class<? extends WritableComparator> getSortComparatorClass();
    public abstract Class<? extends WritableComparator> getGroupingComparator();
    public abstract Class<? extends InputFormat<?,?>> getInputFormatClass();
    public abstract Class<? extends OutputFormat<?,?>> getOutputFormatClass();
}

}

然后编写MyTransformationJob类并设置配置

 public class MyTransformationJob extends BaseJob {

           private Job getJobConf(final Configuration conf) throws Exception {


        Transformation tranformation = new Transformation() {
            @Override
            public Class<? extends Reducer> getCombinerClass() {
                return null;
            }

            @Override
            public Class<?> getJarByClass() {
                return MyTransformationJob .class;
            }

            @Override
            public Class<? extends Mapper> getMapperClass() {
                return MyMapper.class;
            }

            @Override
            public Class<?> getOutputKeyClass() {
                return Text.class;
            }

            @Override
            public Class<?> getOutputValueClass() {
                return NullWritable.class;
            }

            @Override
            public Class<? extends Reducer> getReducerClass() {

                if(StringUtils.equals(jobParams[3], "header")){
                    return HeaderReducer.class;
                }
                return ValuesReducer.class;

            }

            @Override
            public Class<?> getMapOutputKeyClass() {
                return Text.class;
            }

            @Override
            public Class<?> getMapOutputValueClass() {
                return LinkedMapWritable.class;
            }

            @Override
            public Class<? extends Partitioner> getPartitionerClass() {
                return StationKeyPartitioner.class;
            }

            @Override
            public Class<? extends WritableComparator> getSortComparatorClass() {
                return StationKeySortComparator.class;
            }

            @Override
            public Class<? extends WritableComparator> getGroupingComparator() {
                return UniqueIdGroupingComparator.class;
            }

            @Override
            public Class<? extends InputFormat<?,?>> getInputFormatClass() {
                return KeyValueTextInputFormat.class;
            }

            @Override
            public Class<? extends OutputFormat<?,?>> getOutputFormatClass() {
                return null;
            }

        };

        return setupJob(tranformation,conf);

    }

 }

这样，您可以指定具有不同配置和类的多个作业。

解决方案2:

您可以创建本地配置并指定您提到的值

示例测试类:

public class ConfigurationTest extends TestCase {

  @Test
  public void test() throws IOException {

    Configuration conf = new Configuration();
    conf.addResource("hadoop-local.xml");

    assertThat(conf.get("mapred.reduce.tasks"), is("2"));
}

}

关于hadoop - 如何为Hadoop的Map-reduce作业设置配置？，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/24881606/