Median In SQL

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

The median is the middle value in a sorted dataset. SQL doesn't have a direct median function. We can calculate it using other aggregate functions and sorting.

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Description

Example H2

Example H3

Finding the median in SQL requires a multi-step approach. Unlike some other aggregate functions like AVG or SUM, there isn't a built-in MEDIAN function. This means we need to combine sorting and ranking techniques to determine the middle value. The core idea is to sort the data and then identify the value that sits in the middle. If the dataset has an even number of rows, the median is the average of the two middle values.Consider a table named 'sales' with columns 'product_name' and 'sales_amount'. To find the median sales amount, we first need to sort the sales amounts. Then, we can use a window function to rank the sales amounts. Finally, we can filter for the middle value(s). For example, if we have sales amounts of 10, 20, 30, 40, 50, the median is 30. If we have 10, 20, 30, 40, 50, 60, the median is the average of 30 and 40, which is 35.This approach is robust and works for various datasets. It's important to handle cases with an even number of rows correctly to ensure accuracy. The use of window functions makes the query efficient and scalable for larger datasets.

Why Median In SQL is important

Calculating the median is crucial for understanding the central tendency of a dataset. It's less susceptible to outliers than the mean, providing a more representative view of the typical value. This is important in various business applications, such as analyzing sales data, customer demographics, or financial performance.

Median In SQL Example Usage


SELECT
    CASE
        WHEN COUNT(*) % 2 = 0
        THEN (value_at_rank + value_at_rank_plus_one) / 2
        ELSE value_at_rank
    END AS median
FROM
    (
        SELECT
            sales_amount,
            NTILE(100) OVER (ORDER BY sales_amount) AS sales_rank
        FROM
            sales
    ) AS ranked_sales
WHERE
    sales_rank = 50; -- Adjust for the desired percentile

Median In SQL Syntax

Common Mistakes

Forgetting to sort the data before calculating the median.
Incorrectly handling cases with an even number of rows.
Using the wrong ranking function or percentile.
Not considering potential NULL values in the data.

Frequently Asked Questions (FAQs)

Which SQL window functions are most useful for calculating the median when no built-in MEDIAN() exists?

The most common approach is to pair ROW_NUMBER() or RANK() with an ordered subquery or CTE. First you sort the target column (e.g., sales_amount) in ascending order, assign each row a sequential rank, and count the total rows. Once every row is numbered, you can filter for the middle rank when the count is odd, or for the two middle ranks when it is even. This delivers the correct median without relying on vendor-specific functions, and performs well across PostgreSQL, MySQL 8+, SQL Server, and other databases.

How do you handle the median when the dataset contains an even number of rows?

If the row count is even, there is no single middle value—there are two. After ranking the ordered data, select the two rows whose ranks equal (total_rows / 2) and (total_rows / 2) + 1. Then take the average of those two values: SELECT AVG(sales_amount) FROM middle_rows. This yields the exact midpoint (e.g., sales figures of 30 and 40 become a median of 35). Ignoring this step would skew results, so always include the average logic for even-sized datasets.

Can Galaxy’s AI copilot help me write or optimize a median calculation query?

Absolutely. Because Galaxy’s AI copilot is context-aware, you can simply describe the task—“calculate the median sales_amount for the sales table” —and Galaxy will draft a window-function query that handles both odd and even row counts. It can also refactor the query if your schema changes, suggest performance improvements, and let teammates endorse the final version in a shared Collection, eliminating copy-and-paste churn in Slack or Notion.