基于Hadoop+Spark框架,完成对该数据集的分析

首先，需要在Hadoop集群上安装Spark，并将数据集上传到HDFS中。

接着，使用Spark的API对数据集进行分析。具体步骤如下：

1. 加载数据集并转换为RDD格式

val data = sc.textFile("hdfs://path/to/dataset")

2. 数据预处理：清洗、过滤、转换等

val cleanData = data.filter(line => !line.isEmpty && line.split(",").length == 4)
val transformedData = cleanData.map(line => {
  val parts = line.split(",")
  (parts(0), parts(1), parts(2).toInt, parts(3))
})

3. 计算每个用户的总消费金额

val userTotalAmount = transformedData.map {
  case (user, _, amount, _) => (user, amount)
}.reduceByKey(_ + _)

4. 计算每个用户的平均消费金额

val userAvgAmount = transformedData.map {
  case (user, _, amount, _) => (user, (amount, 1))
}.reduceByKey((x, y) => (x._1 + y._1, x._2 + y._2))
  .mapValues(x => x._1 / x._2)

5. 计算每个商品的总销售量和总销售额

val productSales = transformedData.map {
  case (_, product, amount, _) => (product, (amount, 1))
}.reduceByKey((x, y) => (x._1 + y._1, x._2 + y._2))
val productRevenue = transformedData.map {
  case (_, product, amount, price) => (product, amount * price.toDouble)
}.reduceByKey(_ + _)

6. 输出结果

userTotalAmount.saveAsTextFile("hdfs://path/to/output/userTotalAmount")
userAvgAmount.saveAsTextFile("hdfs://path/to/output/userAvgAmount")
productSales.saveAsTextFile("hdfs://path/to/output/productSales")
productRevenue.saveAsTextFile("hdfs://path/to/output/productRevenue")

以上是基于Spark对数据集进行简单分析的步骤，可以根据需要进行进一步的数据处理和分析。