Chapter 25: PostgreSQL SUM Function

Part 1: What is the SUM Function?

The SUM function calculates the total sum of a numeric column or expression. It’s an aggregate function that ignores NULL values (treats them as 0 for the sum).

The Basic Syntax

Part 2: Setting Up Our Example Data

Let’s create a comprehensive sales and inventory database to explore all the ways SUM can be used:

Part 3: Basic SUM Usage

1. Summing a Single Column

Result:

2. Sum with WHERE Clause

Result:

3. Sum with NULL Handling

Result:

Part 4: SUM with GROUP BY

This is where SUM becomes a powerful analytical tool – totaling values within groups.

1. Sum by Category

Result:

2. Sum with Multiple Grouping Columns

Result (partial):

3. Sum with ROLLUP for Subtotals

Part 5: SUM with HAVING

Filter groups based on their total:

1. Find High-Performing Categories

2. Find Top Customers

Result:

Part 6: SUM with DISTINCT

Use SUM(DISTINCT column) to sum only unique values:

Result:

This shows that quantities 1, 2, and 3 appear, and their sum (1+2+3=6) is much less than the total items sold (38) because multiple transactions use the same quantities.

Part 7: SUM with Window Functions

Window functions let you calculate running totals and other cumulative sums.

1. Running Total of Sales

Result:

2. Percentage of Total

3. Moving Average

Part 8: SUM with CASE for Conditional Totals

1. Sum with Multiple Conditions

Result:

2. Sum with FILTER (PostgreSQL-specific)

3. Complex Conditional Sums

Part 9: Real-World Business Examples

Example 1: Executive Financial Summary

Example 2: Monthly Sales Dashboard

Example 3: Inventory Valuation and Turnover

Example 4: Customer Lifetime Value Analysis

Example 5: Profitability by Product

Part 10: Performance Considerations

1. Indexes for SUM

2. Understanding SUM Performance

3. Dealing with Large Sums

For very large tables, consider:

• Materialized views for frequently-used sums

• Summary tables updated via triggers

• Approximate techniques when exact precision isn’t needed

Part 11: Common Mistakes and How to Avoid Them

Mistake 1: Not Handling NULLs in Expressions

Mistake 2: Forgetting to Join Correctly

Mistake 3: Integer Division in Averages

Mistake 4: Not Using HAVING for Group Filters

Mistake 5: Overflow with Large Sums

Summary: The SUM Philosophy

The SUM function is your fundamental tool for totaling numeric data. Master these patterns:

1. Basic totals – SUM(column) for simple additions

2. Grouped totals – SUM with GROUP BY for category analysis

3. Conditional totals – SUM with CASE or FILTER

4. Cumulative totals – SUM with window functions

5. Weighted totals – SUM(quantity * price) for complex calculations

Remember:

• SUM ignores NULL values

• Use SUM(DISTINCT) cautiously – it’s rarely what you want

• Window functions provide running totals without grouping

• Indexes on columns in WHERE and GROUP BY speed up SUM queries

• Consider data types for large sums to avoid overflow

Key applications:

• Financial reporting (“What’s our total revenue?”)

• Inventory management (“What’s the total value of stock?”)

• Customer analytics (“How much has each customer spent?”)

• Performance metrics (“What are the total sales by region?”)

• Profitability analysis (“What’s the total profit by product?”)

The SUM function transforms detailed transaction data into high-level business intelligence. It answers the question “What’s the total?” in countless variations, making it essential for anyone working with numerical data.

Would you like me to elaborate on any specific aspect of SUM, such as more complex business scenarios, performance optimization for large datasets, or integration with other aggregate functions?