Combining SELECT * columns with GROUP BY query

In this article, we’ll explore how to combine the results of two separate queries into one. The first query retrieves data from a sets table and joins it with another table called themes. We’ll also use a GROUP BY clause in the second query to group the data by year.

The problem statement presents two queries that seem unrelated at first glance. However, upon closer inspection, we can see that they both perform similar operations: filtering data based on certain conditions and retrieving aggregated data.

Understanding SELECT * columns

SELECT * is a syntax for selecting all columns from a table. This is often used when you don’t know what specific columns you need to retrieve from the database. However, it’s generally considered bad practice in SQL development because it can lead to slower performance and increased memory usage.

In modern databases like PostgreSQL, using SELECT * can be avoided by specifying only the necessary columns instead of relying on asterisks.

Window Functions

Window functions are used to perform calculations across a set of rows that are related to the current row. In our case, we’re trying to use a window function called COUNT(*) OVER (PARTITION BY s.year). However, this approach won’t work because it’s not designed for this specific problem.

Common Table Expressions (CTEs)

One way to solve this problem is by using a Common Table Expression (CTE). A CTE allows us to create a temporary result set that we can reference within a single SQL statement. We’ll use a CTE to combine the data from both queries and then return it as JSON.

Combining Data with CTE

Let’s break down the solution step by step:

1. Create a Common Table Expression (CTE) called cte that joins the sets table with the themes table.
2. Filter the data in the CTE based on certain conditions, such as matching sets and themes.
3. Use the LIMIT and OFFSET clauses to paginate the results.

Combining Data with GROUP BY

To retrieve aggregated data by year, we’ll use a subquery within our CTE. This query will group the data by year and count the number of rows for each year using the GROUP BY clause.

Returning JSON Data

Finally, we’ll return the combined data as JSON using the JSON_OBJECT function in PostgreSQL.

Example Code

Here’s an example code snippet that demonstrates how to solve this problem:

WITH cte AS (
    SELECT
      s.set_num,
      s.name AS set_name,
      s.year,
      s.theme_id,
      s.num_parts,
      t.name AS theme_name
    FROM
        sets s
    INNER JOIN 
        themes t ON s.theme_id = t.id 
    WHERE
        s.year = :set_year
        AND s.name LIKE '%' || :set_name || '%'
        AND t.name LIKE '%' || :theme_name || '%'
    ORDER BY
        set_num 
    LIMIT :limit OFFSET :offset
)
SELECT
  json_object(
    'sets', (
        SELECT json_group_array(json_object(
          'set_num', set_num,
          'set_name', set_name,
          'year', year,
          'theme_id', theme_id, 
          'num_parts', num_parts,
          'theme_name', theme_name 
        ))
        FROM cte
    ),
    'setCountByYear', (
        SELECT json_group_array(json(eachYear))
        FROM (
            SELECT
              json_object(
                'key', year,
                'count', COUNT(*)
              ) AS eachYear
            FROM cte
            GROUP BY
                year
        ) j1
    )
  ) AS json;

Conclusion

In conclusion, combining SELECT * columns with GROUP BY requires a creative approach using Common Table Expressions (CTEs), subqueries, and JSON functions. By breaking down the problem into smaller components and using the right SQL syntax, we can solve this problem efficiently.

Note: This solution assumes that you’re using PostgreSQL as your database management system. If you’re using a different DBMS, some parts of the code may need to be adjusted accordingly.

Last modified on 2024-03-11