基于eclipse map reduce hadoop的销售统计代码

好的，以下是一个简单的基于Eclipse、MapReduce和Hadoop的销售统计代码示例，供您参考：

1. Mapper类

public class SalesMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
    private Text outputKey = new Text();
    private IntWritable outputValue = new IntWritable();

    @Override
    protected void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException {
        String line = value.toString();
        String[] fields = line.split(",");
        // 获取商品名称和销售数量
        String productName = fields[0];
        int sales = Integer.parseInt(fields[1]);
        // 将商品名称作为MapReduce的输出key，销售数量作为输出value
        outputKey.set(productName);
        outputValue.set(sales);
        context.write(outputKey, outputValue);
    }
}

2. Reducer类

public class SalesReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    private IntWritable outputValue = new IntWritable();

    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context)
            throws IOException, InterruptedException {
        int sum = 0;
        for (IntWritable value : values) {
            sum += value.get();
        }
        // 输出每个商品的总销售数量
        outputValue.set(sum);
        context.write(key, outputValue);
    }
}

3. Driver类

public class SalesDriver {
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        job.setJarByClass(SalesDriver.class);
        job.setMapperClass(SalesMapper.class);
        job.setReducerClass(SalesReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        boolean result = job.waitForCompletion(true);
        System.exit(result ? 0 : 1);
    }
}

以上是一个简单的示例代码，根据实际情况和需求进行修改和完善。在运行代码前，需要将代码打包成jar包并上传到Hadoop集群进行运行。