SQL Percentile

How do you calculate percentiles in SQL?

SQL percentiles allow you to find data points that represent a specific percentage of the data. They are useful for understanding the distribution of values and identifying key thresholds. This is often used in business analytics to understand customer behavior or sales trends.

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Description

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Percentile calculations in SQL determine the value below which a given percentage of observations in a dataset fall. For example, the 90th percentile represents the value such that 90% of the data points are below it. This is a powerful tool for understanding the distribution of data and identifying key thresholds. For instance, in sales data, the 95th percentile of order values might indicate a high-value customer segment. Percentile calculations are crucial for understanding the spread and distribution of data, which is vital in many analytical tasks. They are particularly useful when dealing with skewed distributions where the mean or median might not accurately represent the typical value. SQL offers various ways to calculate percentiles, often using window functions, which allow you to perform calculations across a set of rows related to a given row.

Why SQL Percentile is important

Percentile calculations are essential for understanding data distribution and identifying key thresholds. They provide valuable insights into the spread of data, which is crucial for making informed decisions in various fields, including business analytics, finance, and engineering.

SQL Percentile Example Usage


CREATE TABLE Customers (
    CustomerID INT PRIMARY KEY,
    FirstName VARCHAR(50),
    LastName VARCHAR(50),
    City VARCHAR(50),
    Country VARCHAR(50),
    Email VARCHAR(100),
    Phone VARCHAR(20)
);

INSERT INTO Customers (CustomerID, FirstName, LastName, City, Country, Email) VALUES
(1, 'John', 'Doe', 'New York', 'USA', 'john.doe@example.com');

INSERT INTO Customers (CustomerID, FirstName, LastName, City, Country, Email, Phone) VALUES
(2, 'Jane', 'Smith', 'London', 'UK', 'jane.smith@example.com', '123-456-7890');

INSERT INTO Customers (CustomerID, FirstName, LastName, City, Country, Email) VALUES
(3, 'Peter', 'Jones', 'Paris', 'France', 'peter.jones@example.com');

-- Selecting customers with a phone number
SELECT * FROM Customers WHERE Phone IS NOT NULL;

-- Selecting customers without a phone number
SELECT * FROM Customers WHERE Phone IS NULL;

-- Calculating the average phone number length (ignoring NULLs)
SELECT AVG(LENGTH(Phone)) AS AveragePhoneLength FROM Customers WHERE Phone IS NOT NULL;

SQL Percentile Syntax

Common Mistakes

Forgetting to use the `ORDER BY` clause within the `PERCENTILE_CONT` function, leading to incorrect results.
Using `PERCENTILE_DISC` when a continuous percentile is needed, resulting in a potentially inaccurate value.
Misinterpreting the percentile value as a precise measure of the data, ignoring the inherent variability in data sets.
Not considering the potential impact of outliers on the percentile calculation.

Frequently Asked Questions (FAQs)

How do SQL window functions simplify percentile calculations?

Window functions like PERCENTILE_CONT() or PERCENTILE_DISC() let you compute percentiles in a single pass over the data by defining a logical window (e.g., OVER (ORDER BY value)). This avoids complex sub-queries, delivers accurate results across large tables, and makes the query easier to read and maintain—especially valuable when you iterate quickly during analysis.

When should I prefer percentiles over the mean or median?

Percentiles are most useful with skewed data distributions where a few extreme values distort the mean or even the median. For example, the 95th percentile of order values spotlights high-value customers, while the average may understate their importance. Using percentiles gives you clearer thresholds for decision-making, outlier detection, and SLA setting.

How can Galaxy help me write and share percentile queries faster?

Galaxy’s context-aware AI copilot autocompletes and optimizes percentile window functions, suggests parameterized thresholds (e.g., 90th vs. 95th), and even rewrites queries when your schema evolves. Once the query is ready, you can add it to a Collection and let teammates "Endorse" it, eliminating the need to paste SQL into Slack or Notion and ensuring everyone uses the same, trusted percentile logic.