熟悉常用的 HDFS 操作，仅需要完成 shell 命令；将每一步的实验结果截屏（配以文字说明，解释代码的关键点）熟悉常用的 MapReduce 操作，将每一步的实验结果截屏，配以文字说明，解释代码的关键点

很抱歉，我是一个语言模型AI，无法进行截屏操作。不过，我可以回答你关于 HDFS 和 MapReduce 的问题，并提供相应的代码示例和解释。

首先是 HDFS 操作，以下是常用的 HDFS shell 命令：

1. 查看 HDFS 目录下的文件和文件夹

hadoop fs -ls /

2. 创建 HDFS 目录

hadoop fs -mkdir /test

3. 上传本地文件到 HDFS

hadoop fs -put localfile /hdfsdir

4. 下载 HDFS 文件到本地

hadoop fs -get /hdfsdir/localfile

5. 删除 HDFS 文件

hadoop fs -rm /hdfsdir/localfile

6. 查看 HDFS 文件内容

hadoop fs -cat /hdfsdir/localfile

7. 在 HDFS 上执行 MapReduce 任务

hadoop jar /path/to/hadoop-streaming.jar -input /hdfsdir/input -output /hdfsdir/output -mapper mapper.py -reducer reducer.py

接下来是 MapReduce 操作，以下是一些常用的 MapReduce 代码示例和解释：

1. WordCount 示例

Mapper 代码：

public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();
    
    public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String line = value.toString();
        StringTokenizer tokenizer = new StringTokenizer(line);
        while (tokenizer.hasMoreTokens()) {
            word.set(tokenizer.nextToken());
            context.write(word, one);
        }
    }
}

Reducer 代码：

public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    private IntWritable result = new IntWritable();
    
    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int sum = 0;
        for (IntWritable val : values) {
            sum += val.get();
        }
        result.set(sum);
        context.write(key, result);
    }
}

2. 求平均值示例

Mapper 代码：

public class AverageMapper extends Mapper<LongWritable, Text, Text, DoubleWritable> {
    private Text year = new Text();
    private DoubleWritable temperature = new DoubleWritable();
    
    public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String line = value.toString();
        String[] fields = line.split(",");
        year.set(fields[0]);
        temperature.set(Double.parseDouble(fields[1]));
        context.write(year, temperature);
    }
}

Reducer 代码：

public class AverageReducer extends Reducer<Text, DoubleWritable, Text, DoubleWritable> {
    private DoubleWritable average = new DoubleWritable();
    
    public void reduce(Text key, Iterable<DoubleWritable> values, Context context) throws IOException, InterruptedException {
        double sum = 0;
        int count = 0;
        for (DoubleWritable val : values) {
            sum += val.get();
            count++;
        }
        double avg = sum / count;
        average.set(avg);
        context.write(key, average);
    }
}

以上是关于 HDFS 和 MapReduce 的一些常用操作和示例代码。希望对你有所帮助！