How does Hive calculate the total amount of data for all tables?

1 year ago
Noah Thompson
2 minutes

One way to calculate the total amount of data in all tables is by utilizing Hive’s metadata information and aggregate functions.

  1. Firstly, query the names of all tables using the metadata information in Hive. You can obtain the table names list by running the following Hive command:
  2. List all tables.
  3. To calculate the total number of data for each table, we need to use Hive’s aggregation function COUNT(). Run the following Hive query for each table to retrieve the total amount of data:
  4. Retrieve the total number of records in the specified table.
  5. The table_name is the name of the table.
  6. Using the list of table names, you can combine the above query statement by using Hive’s looping structures like a FOR loop or a WHILE loop to iterate through each table and run the query statement.

Here is an example Hive script for calculating the total amount of data in all tables.

SET total_count = 0;

-- 获取所有表的名称
SET table_list = '';
INSERT OVERWRITE LOCAL DIRECTORY 'table_list'
ROW FORMAT DELIMITED FIELDS TERMINATED BY ' '
SELECT table_name FROM information_schema.tables WHERE table_schema = 'your_database';

-- 遍历每个表并统计数据总量
FOR table_name IN `cat table_list`
LOOP
  -- 统计数据总量
  INSERT OVERWRITE LOCAL DIRECTORY 'table_count'
  ROW FORMAT DELIMITED FIELDS TERMINATED BY ' '
  SELECT COUNT(*) FROM ${table_name};

  -- 读取数据总量并累加到总数
  SET count = `cat table_count`;
  SET total_count = total_count + count;
END LOOP;

-- 输出总数据量
SELECT total_count;

The script above writes a list of table names to a local file called “table_list”. It then uses a loop structure to iterate through each table, calculate the total amount of data, and accumulate it into the variable “total_count”. Finally, it outputs the total data amount.

Please be aware that the above example script uses local files to store the list of table names and the total amount of data for each table. You can modify it to use suitable storage methods like HDFS directories or Hive tables as needed.

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