SQL Median

How do you calculate the median of a dataset in SQL?

The median is the middle value in a sorted dataset. SQL doesn't have a direct median function. We need to use other functions like `PERCENTILE_CONT` or a combination of `ORDER BY` and `ROW_NUMBER` to calculate it.

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Description

Example H2

Example H3

Calculating the median in SQL requires a bit more work than using a built-in function. Unlike some other aggregate functions like `AVG` or `SUM`, SQL doesn't directly provide a `MEDIAN` function. This means we need to find a way to determine the middle value in a sorted dataset. One common approach is to use the `PERCENTILE_CONT` function, which returns the value at a specific percentile. To find the median, we use the 50th percentile. Alternatively, we can use a combination of `ORDER BY` and `ROW_NUMBER` to rank the data and then identify the middle value. This method is more flexible, but requires more code.

Why SQL Median is important

Understanding how to calculate the median is crucial for data analysis. The median provides a robust measure of central tendency, less susceptible to outliers than the mean. This is vital for understanding the typical value in a dataset, especially when dealing with skewed distributions.

SQL Median Example Usage


-- Sample tables
CREATE TABLE Customers (
    CustomerID INT PRIMARY KEY,
    FirstName VARCHAR(50),
    LastName VARCHAR(50)
);

CREATE TABLE Orders (
    OrderID INT PRIMARY KEY,
    CustomerID INT,
    OrderDate DATE,
    FOREIGN KEY (CustomerID) REFERENCES Customers(CustomerID)
);

-- Insert sample data
INSERT INTO Customers (CustomerID, FirstName, LastName) VALUES
(1, 'John', 'Doe'),
(2, 'Jane', 'Smith'),
(3, 'Peter', 'Jones');

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

-- Perform LEFT JOIN
SELECT
    c.CustomerID,
    c.FirstName,
    c.LastName,
    o.OrderID,
    o.OrderDate
FROM
    Customers c
LEFT JOIN
    Orders o ON c.CustomerID = o.CustomerID;

SQL Median Syntax

Common Mistakes

Forgetting to sort the data using `ORDER BY` when using `PERCENTILE_CONT` or `ROW_NUMBER`.
Incorrectly calculating the row number for the median (e.g., using `COUNT(*)` instead of `COUNT(*) / 2 + 1` for odd datasets).
Assuming SQL has a built-in `MEDIAN` function.
Not handling cases where the dataset has an even number of rows.

Frequently Asked Questions (FAQs)

How do I calculate the median in SQL when there is no MEDIAN() function?

Most relational databases let you approximate a missing MEDIAN() by calling PERCENTILE_CONT(0.5) inside an analytic query: SELECT PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY value) AS median FROM table; The 0.5 percentile returns the middle value of the sorted column, giving you an accurate median even for datasets with an even row count.

Should I use PERCENTILE_CONT or a ROW_NUMBER approach to find the median?

If your database supports PERCENTILE_CONT, it is the shortest and most readable way to get the median. When that function is unavailable—or when you need custom tie-breaking—you can calculate row ranks with ROW_NUMBER() or DENSE_RANK(), then pick the middle rank(s) after an ORDER BY. This window-function technique is more verbose but works in almost every SQL dialect.

How can Galaxy’s AI copilot speed up writing median queries?

Galaxy’s context-aware AI copilot can autocomplete the full PERCENTILE_CONT or ROW_NUMBER pattern for you, explain each clause inline, and even refactor the query when your table schema changes. Instead of hunting for snippets in Slack, you can store and "Endorse" a correct median query inside a Galaxy Collection so the whole team reuses one trusted version.