Advanced SQL Window Functions & Ranking

This lesson provides an in-depth exploration of advanced SQL window functions and ranking functions. You will learn how to leverage these powerful tools to perform complex analytical tasks, uncover trends, and derive valuable insights for growth analysis within your datasets.

Learning Objectives

  • Understand and apply various ranking functions (RANK, DENSE_RANK, ROW_NUMBER, NTILE) to identify top performers and segment data.
  • Master window functions for calculating running totals, cumulative distributions, and percent ranks.
  • Utilize LAG and LEAD functions for comparing data across rows and identifying temporal trends.
  • Apply window functions to solve real-world growth analysis problems, such as cohort analysis and customer lifetime value calculations.

Text-to-Speech

Listen to the lesson content

Lesson Content

Introduction to Window Functions: Beyond GROUP BY

Standard aggregate functions (like SUM, AVG, COUNT) used with GROUP BY summarize data. Window functions, on the other hand, allow you to perform calculations across a set of table rows that are related to the current row, without collapsing the rows. This enables more granular analysis, retaining row-level details while adding contextual information. The basic syntax is: function_name(expression) OVER (PARTITION BY partition_expression ORDER BY order_expression) where PARTITION BY divides the data into partitions, and ORDER BY defines the order within each partition for the calculations.

Ranking Functions: Finding the Top Performers

Ranking functions assign a rank to each row within a partition based on the ORDER BY clause.

  • RANK(): Assigns a rank to each row. If there are ties, it assigns the same rank to all tied rows and skips the subsequent ranks.
  • DENSE_RANK(): Similar to RANK(), but it doesn't skip ranks for ties.
  • ROW_NUMBER(): Assigns a unique sequential integer to each row. Useful for identifying distinct rows, but doesn't handle ties.
  • NTILE(n): Divides the rows within a partition into a specified number (n) of groups (tiles) and assigns a tile number to each row. Useful for quartiles, deciles, etc.

Example (RANK): Imagine a table sales with columns customer_id, sales_date, and revenue. 

SELECT
    customer_id,
    sales_date,
    revenue,
    RANK() OVER (ORDER BY revenue DESC) AS rank_by_revenue
FROM
    sales;

This query ranks customers by their revenue, showing which customers are the top earners.

Window Functions for Cumulative Calculations

Window functions excel at calculating running totals and cumulative distributions. These calculations can reveal trends over time.

  • Running Totals: Calculated using SUM() OVER (PARTITION BY ... ORDER BY ...).
  • Cumulative Distribution (CUME_DIST): Calculates the cumulative distribution of a value within a partition. It returns the relative position of a value within a group of values.

Example (Running Total):

SELECT
    sales_date,
    revenue,
    SUM(revenue) OVER (ORDER BY sales_date) AS running_total
FROM
    sales
ORDER BY
    sales_date;

This calculates the running total revenue over time, providing a clear view of revenue growth.

LAG and LEAD: Analyzing Trends Over Time

LAG() and LEAD() functions access data from previous or subsequent rows within a partition.

  • LAG(column, offset, default): Accesses a column from a previous row. offset specifies how many rows back (default is 1). default is used if the row is outside the range.
  • LEAD(column, offset, default): Accesses a column from a subsequent row. offset specifies how many rows forward (default is 1). default is used if the row is outside the range.

Example (LAG):

SELECT
    sales_date,
    revenue,
    LAG(revenue, 1, 0) OVER (ORDER BY sales_date) AS previous_day_revenue
FROM
    sales
ORDER BY
    sales_date;

This query shows the revenue from the previous day for comparison. The 0 is the default value to return if there is no previous day.

PERCENT_RANK & Other Advanced Concepts

This powerful window function computes the percentile rank of a row within a result set, relative to other rows. It's extremely useful for understanding the relative standing of individual values within a group. It's calculated as (RANK() - 1) / (total rows in partition - 1).

Beyond these core functions, understanding the OVER() clause with ROWS and RANGE frame specifications will enable you to define dynamic windows relative to the current row, and these are essential for more granular trend analysis.

Progress
0%