hadoop对网站每日访问次数的数据按照访问次数进行升序排序的idea代码

在Hadoop框架下，处理网站访问数据并按访问次数升序排序通常会涉及到MapReduce任务，这里我会给出一个简单的伪代码思路，实际操作时可能需要配合Hadoop Streaming、Pig或直接使用Hive等工具。

// Hadoop Mapper部分
public class AccessCountMapper 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[] fields = value.toString().split(",");
        // 假设第一项是日期，第二项是访问次数
        String date = fields[0];
        int count = Integer.parseInt(fields[1]);
        
        word.set(date);
        context.write(word, one);
    }
}

// Hadoop Reducer部分
public class AccessCountReducer 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);
    }
}

// Driver代码
public class Main {
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "Access Count Sort");
        job.setJarByClass(Main.class);
        job.setMapperClass(AccessCountMapper.class);
        job.setCombinerClass(AccessCountReducer.class); // 如果数据量小，可以在这里合并减少网络传输
        job.setReducerClass(AccessCountReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        FileInputFormat.addInputPath(job, new Path("input_path")); // 输入文件路径
        FileOutputFormat.setOutputPath(job, new Path("output_path")); // 输出结果路径
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}