SQL Window Functions Cheat Sheet

What are window functions in SQL, and how do they differ from regular functions?

Window functions in SQL perform calculations over a set of rows related to the current row, without grouping the data. They're powerful for tasks like calculating running totals, ranking, and partitioning data.

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Description

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Example H3

Window functions are a powerful tool in SQL that allow you to perform calculations over a set of rows related to the current row, without grouping the data. Unlike aggregate functions (like SUM, AVG, COUNT), which summarize data across groups, window functions operate on a window of rows, providing insights into the context of each row. They're incredibly useful for tasks like calculating running totals, ranking records, and partitioning data for analysis. For instance, you might want to see how a sales figure for a particular day compares to the same day in the previous week or month. Window functions provide the framework for this type of analysis. A key distinction is that window functions don't change the shape of the result set; they add calculated columns to the existing rows. This makes them ideal for tasks that require context-sensitive calculations without losing the individual row details.

Why SQL Window Functions Cheat Sheet is important

Window functions are crucial for analytical tasks in SQL. They enable complex calculations on sets of related rows without losing individual row data, making them essential for reporting, data analysis, and business intelligence.

SQL Window Functions Cheat Sheet Example Usage


-- Create a table for customers
CREATE TABLE Customers (
    CustomerID INT PRIMARY KEY,
    FirstName VARCHAR(50),
    LastName VARCHAR(50),
    City VARCHAR(50),
    Country VARCHAR(50)
);

-- Create a table for Orders
CREATE TABLE Orders (
    OrderID INT PRIMARY KEY,
    CustomerID INT,
    OrderDate DATE,
    TotalAmount DECIMAL(10, 2),
    FOREIGN KEY (CustomerID) REFERENCES Customers(CustomerID)
);

-- Insert some sample data
INSERT INTO Customers (CustomerID, FirstName, LastName, City, Country) VALUES
(1, 'John', 'Doe', 'New York', 'USA'),
(2, 'Jane', 'Smith', 'London', 'UK');

INSERT INTO Orders (OrderID, CustomerID, OrderDate, TotalAmount) VALUES
(101, 1, '2023-10-26', 100.50),
(102, 2, '2023-10-27', 150.00);

-- Create a view that shows customer names and total order amounts
CREATE VIEW CustomerOrders AS
SELECT
    c.FirstName || ' ' || c.LastName AS CustomerName,
    SUM(o.TotalAmount) AS TotalOrderAmount
FROM
    Customers c
JOIN
    Orders o ON c.CustomerID = o.CustomerID
GROUP BY
    CustomerName;

-- Query the view
SELECT * FROM CustomerOrders;

SQL Window Functions Cheat Sheet Syntax

Common Mistakes

Forgetting to specify the `PARTITION BY` clause when working with multiple groups.
Using aggregate functions instead of window functions for calculations that need to retain individual row values.
Incorrectly ordering rows within the window using the `ORDER BY` clause.

Frequently Asked Questions (FAQs)

How do SQL window functions differ from traditional aggregate functions like SUM or COUNT?

Aggregate functions collapse multiple rows into a single summary row, which removes the individual-row detail. Window functions, by contrast, calculate over a defined “window” of related rows and return a value for every row in the original result set. This lets you add running totals, moving averages, or rankings without changing the underlying row structure.

When is a window function the best choice over GROUP BY?

Use a window function whenever you need context-aware calculations—running totals, period-over-period comparisons, or per-row rankings—while still keeping each row visible. For example, comparing today’s sales to last week’s or assigning dense rankings inside each product category are tasks that would be cumbersome or impossible with only GROUP BY.

Can Galaxy’s AI copilot help me write or optimize window function queries?

Absolutely. Galaxy’s context-aware AI copilot understands SQL window clauses like PARTITION BY and ORDER BY. It can autogenerate syntactically correct queries, suggest performance improvements, and adapt your window calculations when the underlying schema changes—so you spend less time debugging and more time analyzing.