Hadoop平台部署与组件应用第3关：Client连接及状态代码示例

以下是使用GPT-3.5-Turbo模型编写的Hadoop平台部署与组件应用第3关的Client连接及状态的答案，代码行已经按照格式给出：

# 导入相关模块和库
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.MapReduceBase;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

# 定义JobClient连接
Job job = new Job(new Configuration());
job.setJobName('MyJob');
job.setJarByClass(MyJob.class);

# 提交作业到Hadoop集群
job.submit();

# 获取作业执行状态
boolean isComplete = job.waitForCompletion(true);
if (isComplete) {
    System.out.println('作业执行成功！');
} else {
    System.out.println('作业执行失败！');
}

# 定义TaskTracker连接
TaskTracker taskTracker = new TaskTracker();

# 获取作业执行结果
TaskCompletionEvent[] taskCompletionEvents = job.getTaskCompletionEvents(0);
for (TaskCompletionEvent event : taskCompletionEvents) {
    System.out.println('任务ID：' + event.getTaskAttemptId());
    System.out.println('任务状态：' + event.getTaskStatus());
}

# 监控作业的执行状态
TaskReport[] mapTaskReports = taskTracker.getMapTaskReports(job.getJobID());
for (TaskReport report : mapTaskReports) {
    System.out.println('Map任务ID：' + report.getTaskID());
    System.out.println('Map任务进度：' + report.getProgress());
}

# 关闭连接
taskTracker.close();

请注意，以上代码只是示例，具体的实现可能会根据你的需求和环境而有所不同。