hive hadoop数据怎样转换

Hive是一个基于Hadoop构建的数据仓库分析系统，可以将结构化的数据文件映射为数据库表，并提供完整的SQL查询功能

1. 使用Hive SQL进行查询和转换：

   Hive支持标准的SQL查询语言（HiveQL），你可以使用它来查询、过滤、排序、分组和聚合数据。例如，如果你有一个名为sales_data的表，包含date、region和revenue列，你可以使用以下查询来计算每个地区的总收入：

   SELECT region, SUM(revenue) as total_revenue
   FROM sales_data
   GROUP BY region;
   

2. 使用MapReduce或Tez进行自定义转换：

   对于更复杂的转换任务，你可以使用Hive的MapReduce或Tez执行器编写自定义的MapReduce作业。这些作业可以在Hive中运行，并允许你处理大量数据。例如，你可以编写一个MapReduce作业来计算每个客户的平均购买金额：

   public static class AveragePurchaseMapper extends Mapper {
       private final static IntWritable one = new IntWritable(1);
       private Text word = new Text();
   
       public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
           String line = value.toString();
           String[] fields = line.split(",");
           if (fields.length >= 3) {
               word.set(fields[0]);
               context.write(word, new IntWritable(Integer.parseInt(fields[2])));
           }
       }
   }
   
   public static class AveragePurchaseReducer extends Reducer {
       private IntWritable result = new IntWritable();
   
       public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
           int sum = 0;
           for (IntWritable val : values) {
               sum += val.get();
           }
           result.set(sum / values.size());
           context.write(key, result);
       }
   }
   

   然后，你可以在Hive中创建一个外部MapReduce作业，并使用以下命令运行它：

   CREATE EXTERNAL TABLE average_purchase (region STRING, avg_revenue INT)
   ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
   STORED AS TEXTFILE
   LOCATION '/path/to/output';
   
   SELECT region, AVG(avg_revenue) as avg_purchase_amount
   FROM (
       SELECT region, CAST(sum_revenue AS INT) / COUNT(*) as avg_revenue
       FROM (
           SELECT region, SUM(revenue) as sum_revenue
           FROM sales_data
           GROUP BY region
       ) subquery
       GROUP BY region
   ) subquery2
   GROUP BY region;
   

3. 使用Hive UDF（用户自定义函数）进行转换：

   Hive UDF允许你编写自定义函数来处理数据。你可以使用Java或其他支持的语言编写UDF，并将其编译为JAR文件。然后，你可以在Hive中加载和使用这些UDF。例如，你可以编写一个UDF来将字符串转换为大写：

   public static class ToUpperCase extends Mapper {
       private final static IntWritable one = new IntWritable(1);
       private Text word = new Text();
   
       public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
           String line = value.toString();
           String[] fields = line.split(",");
           if (fields.length >= 3) {
               word.set(fields[0].toUpperCase());
               context.write(word, new IntWritable(Integer.parseInt(fields[2])));
           }
       }
   }
   

   然后，你可以在Hive中创建一个外部MapReduce作业，并使用以下命令运行它：

   CREATE EXTERNAL TABLE to_upper (region STRING, avg_revenue INT)
   ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
   STORED AS TEXTFILE
   LOCATION '/path/to/output';
   
   SELECT region, AVG(avg_revenue) as avg_purchase_amount
   FROM sales_data
   GROUP BY region;
   

这些方法可以帮助你在Hive中转换和处理数据。你可以根据具体需求选择合适的方法。