用Hadoop序列化实现统计每一个商品的销售情况：商品名称、销售数量、单价（元）以及该商品销售总金额

下面是一个使用Hadoop序列化实现统计每一个商品的销售情况的示例：

假设有一个SalesRecord类，该类具有以下属性和方法：

public class SalesRecord implements Writable {
    private String productName;
    private int quantity;
    private double price;

    public SalesRecord() {}

    public SalesRecord(String productName, int quantity, double price) {
        this.productName = productName;
        this.quantity = quantity;
        this.price = price;
    }

    public void write(DataOutput out) throws IOException {
        out.writeUTF(productName);
        out.writeInt(quantity);
        out.writeDouble(price);
    }

    public void readFields(DataInput in) throws IOException {
        productName = in.readUTF();
        quantity = in.readInt();
        price = in.readDouble();
    }

    public double getTotalSales() {
        return quantity * price;
    }

    public String getProductName() {
        return productName;
    }

    public int getQuantity() {
        return quantity;
    }

    public double getPrice() {
        return price;
    }
}

在上述示例中，SalesRecord类实现了Writable接口，并覆盖了write和readFields方法。write方法将SalesRecord对象的属性写入DataOutput流中，而readFields方法从DataInput流中读取属性。此外，该类还有一些辅助方法，如getTotalSales方法，用于计算销售总金额。

现在，我们可以使用以下MapReduce程序来统计每个商品的销售情况：

public class SalesAnalysis {
    public static class SalesMapper extends Mapper<LongWritable, Text, Text, SalesRecord> {
        private Text productName = new Text();
        private SalesRecord salesRecord = new SalesRecord();

        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String[] fields = value.toString().split(",");
            productName.set(fields[0]);
            salesRecord = new SalesRecord(fields[0], Integer.parseInt(fields[1]), Double.parseDouble(fields[2]));
            context.write(productName, salesRecord);
        }
    }

    public static class SalesReducer extends Reducer<Text, SalesRecord, Text, SalesRecord> {
        private SalesRecord result = new SalesRecord();

        public void reduce(Text key, Iterable<SalesRecord> values, Context context) throws IOException, InterruptedException {
            int totalQuantity = 0;
            double totalSales = 0.0;

            for (SalesRecord salesRecord : values) {
                totalQuantity += salesRecord.getQuantity();
                totalSales += salesRecord.getTotalSales();
            }

            result = new SalesRecord(key.toString(), totalQuantity, totalSales / totalQuantity);
            context.write(key, result);
        }
    }

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

在上述程序中，我们首先定义了一个SalesMapper类，该类将输入文件中的每一行解析为一个SalesRecord对象，并将其写入到一个名为productName的Text键中。接着，我们定义了一个SalesReducer类，该类将所有具有相同productName键的SalesRecord对象收集起来，并计算它们的总销售数量和总销售额。最后，我们将计算结果写入到输出文件中。

通过以上的MapReduce程序，我们可以统计每个商品的销售情况，包括商品名称、销售数量、单价以及该商品销售总金额。