Alternative SQL Ways to Simplify Complex Queries: Creating Views and Normalizing Tables

Alternative SQL Ways of SUM Columns

The question presented on Stack Overflow is an excellent example of how complex and ad-hoc SQL queries can become when working with tables that have many columns but no clear indication of the relationships between them. The query provided in the question uses a series of if-then statements to sum up specific columns based on the fiscal year and month.

In this response, we will explore alternative approaches to achieving similar results, focusing on creating a more normalized and maintainable database schema.

Creating a View

The first approach suggested by the user is to create a view that transforms the table into a more normalized form. A view in SQL is essentially a virtual table based on the result of a query. By creating a view, we can simplify the complexity of our queries and improve their maintainability.

Let’s examine how this works with an example:

-- Create a table for demonstration purposes
CREATE TABLE bad_schema (
  some_id integer not null,
  month1 integer,
  month2 integer,
  month3 integer,
  month4 integer,
  month5 integer,
  month6 integer,
  month7 integer,
  month8 integer,
  month9 integer,
  month10 integer,
  month11 integer,
  month12 integer,
  year integer not null,
  primary key(some_id, year)
);

-- Insert some sample data
INSERT INTO bad_schema (some_id, year, month1, month2, month3, month4, month5, month6, month7, month8, month9, month10, month11, month12)
VALUES
    (1, 2023, 100, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL),
    (2, 2024, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL),
    (1, 2023, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL);

-- Create a view to simplify the queries
CREATE VIEW good_schema AS (
  SELECT 
    some_id,
    CAST(CONCAT(year, '-', months.value, '-', '01') AS date) AS this_month,
    CASE months.value
      WHEN 1 THEN month1
      WHEN 2 THEN month2
      WHEN 3 THEN month3
      WHEN 4 THEN month4
      WHEN 5 THEN month5
      WHEN 6 THEN month6
      WHEN 7 THEN month7
      WHEN 8 THEN month8
      WHEN 9 THEN month9
      WHEN 10 THEN month10
      WHEN 11 THEN month11
      WHEN 12 THEN month12
      ELSE 'what'
    END AS data
  FROM bad_schema
  CROSS JOIN (
    SELECT * FROM generate_series(1, 12)
  ) AS months
);

-- Query the view to get the desired results
SELECT *
FROM good_schema;

-- Sum up specific columns using the view
SELECT SUM(CASE WHEN this_month BETWEEN '2023-01-01' AND '2023-05-01' THEN data ELSE NULL END) AS total_data
FROM good_schema
WHERE some_id = 1;

Normalizing the Table

The alternative approach to creating a view is to normalize the table by altering its structure to make it more intuitive and easier to query. This can be achieved through various methods, such as:

Categorization: Divide columns into logical groups based on their purpose or category.
Standardization: Use standard naming conventions for columns and ensure consistency in formatting and organization.

Let’s demonstrate how to normalize the bad_schema table by categorizing its columns into four categories: fiscal year, monthly data, and metadata:

-- Create a new table with normalized columns
CREATE TABLE good_schema (
  fiscal_year integer not null,
  month_data numeric[] not null,
  created_at timestamp not null,
  updated_at timestamp not null
);

-- Insert sample data into the normalized table
INSERT INTO good_schema (fiscal_year, month_data, created_at, updated_at)
VALUES
    (2023, ARRAY[100], NOW(), NOW()),
    (2024, ARRAY[], NOW(), NOW());

-- Update the `month_data` array to include more values
UPDATE good_schema SET month_data = ARRAY[100, 200] WHERE fiscal_year = 2023;

-- Query the normalized table for specific results
SELECT SUM(data->>'number') AS total_value
FROM good_schema
WHERE fiscal_year = 2023 AND fiscal_year BETWEEN '2023-01-01' AND '2023-05-01';

Conclusion

In this response, we explored alternative SQL ways of summing up columns by creating a view and normalizing the table. These approaches provide more maintainable and efficient solutions compared to ad-hoc queries with if-then statements.

When working with complex tables or legacy databases, consider transforming your schema to improve query readability and performance. Views can serve as virtual representations of normalized data, simplifying complex queries while reducing the risk of data inconsistencies.

Last modified on 2025-02-12