SQL Window Functions Cheat Sheet
SQL, also known as Structured Query Language, is a powerful tool to search through large amounts of data and return specific information for analysis. Learning SQL is crucial for anyone aspiring to be a data analyst, data engineer, or data scientist, and helpful in many other fields such as web development or marketing.
In this cheat sheet, you'll find a handy collection of information about window functions in SQL including what they are and how to construct them.
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Download PDFExample dataset
We will use a dataset on the sales of bicycles as a sample. This dataset includes:
The [product] table
The product table contains the types of bicycles sold, their model year, and list price.
The [order] table
The order table contains the order_id
and its date.
The [order_items] table
The order_items table lists the orders of a bicycle store. For each order_id
, there are several products sold (product_id
). Each product_id
has a discount value.
What are Window Functions?
A window function makes a calculation across multiple rows that are related to the current row. For example, a window function allows you to calculate:
 Running totals (i.e. sum values from all the rows before the current row)
 7day moving averages (i.e. average values from 7 rows before the current row)
 Rankings
Similar to an aggregate function (GROUP BY
), a window function performs the operation across multiple rows. Unlike an aggregate function, a window function does not group rows into one single row.
Syntax
Windows can be defined in the SELECT
section of the query.
SELECT
window_function() OVER(
PARTITION BY partition_expression
ORDER BY order_expression
window_frame_extent
) AS window_column_alias
FROM table_name
To reuse the same window with several window functions, define a named window using the WINDOW
keyword. This appears in the query after the HAVING
section and before the ORDER BY
section.
SELECT
window_function() OVER(window_name)
FROM table_name
[HAVING ...]
WINDOW window_name AS (
PARTITION BY partition_expression
ORDER BY order_expression
window_frame_extent
)
[ORDER BY ...]
Order by
ORDER BY
is a subclause within the OVER
clause. ORDER BY
changes the basis on which the function assigns numbers to rows.
It is a musthave for window functions that assign sequences to rows, including RANK
and ROW_NUMBER
. For example, if we ORDER BY
the expression `price` on an ascending order, then the lowestpriced item will have the lowest rank.
Let's compare the following two queries which differ only in the ORDER BY
clause
/* Rank price from LOW>HIGH */
SELECT
product_name,
list_price,
RANK() OVER
(ORDER BY list_price ASC) rank
FROM products
/* Rank price from HIGH>LOW */
SELECT
product_name,
list_price,
RANK() OVER
(ORDER BY list_price DESC) rank
FROM products
Partition by
We can use PARTITION BY
together with OVER
to specify the column over which the aggregation is performed.
Comparing PARTITION BY
with GROUP BY
, we find the following similarity and difference:
Just like GROUP BY
, the OVER
subclause splits the rows into as many partitions as there are unique values in a column.
However, while the result of a GROUP BY
aggregates all rows, the result of a window function using PARTITION BY
aggregates each partition independently. Without the PARTITION BY
clause, the result set is one single partition.
For example, using GROUP BY
, we can calculate the average price of bicycles per model year using the following query.
SELECT
model_year,
AVG(list_price) avg_price
FROM products
GROUP BY model_year
What if we want to compare each product’s price with the average price of that year? To do that, we use the AVG()
window function and PARTITION BY
the model year, as such.
SELECT
model_year,
product_name,
list_price,
AVG(list_price) OVER
(PARTITION BY model_year)
avg_price
FROM products
Notice how the avg_price of 2018 is exactly the same whether we use the PARTITION BY
clause or the GROUP BY
clause.
Window frame extent
A window frame is the selected set of rows in the partition over which aggregation will occur. Put simply, they are a set of rows that are somehow related to the current row.
A window frame is defined by a lower bound and an upper bound relative to the current row. The lowest possible bound is the first row, which is known as UNBOUNDED PRECEDING
. The highest possible bound is the last row, which is known as UNBOUNDED FOLLOWING
. For example, if we only want to get 5 rows before the current row, then we will specify the range using 5 PRECEDING
.
Accompanying Material
You can use this https://bit.ly/3scZtOK to run any of the queries explained in this cheat sheet.
Ranking window functions
There are several window functions for assigning rankings to rows. Each of these functions requires an ORDER BY
subclause within the OVER
clause.
The following are the ranking window functions and their description:
Function Syntax 
Function Description 
Additional notes 

Assigns a sequential integer to each row within the partition of a result set. 
Row numbers are not repeated within each partition. 

Assigns a rank number to each row in a partition. 


Assigns the rank number of each row in a partition as a percentage. 


Distributes the rows of a partition into a specified number of buckets. 


The cumulative distribution: the percentage of rows less than or equal to the current row. 

We can use these functions to rank the product according to their prices.
/* Rank all products by price */
SELECT
product_name,
list_price,
ROW_NUMBER() OVER (ORDER BY list_price) AS row_num,
DENSE_RANK() OVER (ORDER BY list_price) AS dense_rank,
RANK() OVER (ORDER BY list_price) AS rank,
PERCENT_RANK() OVER (ORDER BY list_price) AS pct_rank,
NTILE(75) OVER (ORDER BY list_price) AS ntile,
CUME_DIST() OVER (ORDER BY list_price) AS cume_dist
FROM products
Value window functions
FIRST_VALUE()
and LAST_VALUE()
retrieve the first and last value respectively from an ordered list of rows, where the order is defined by ORDER BY
.
Value window function 
Function 

Returns the first value in an ordered set of values 

Returns the last value in an ordered set of values 

Returns the nth value in an ordered set of values. 
To compare the price of a particular bicycle model with the cheapest (or most expensive) alternative, we can use the FIRST_VALUE
(or LAST_VALUE
).
/* Find the difference in price from the cheapest alternative */
SELECT
product_name,
list_price,
FIRST_VALUE(list_price) OVER (
ORDER BY list_price
ROWS BETWEEN
UNBOUNDED PRECEDING
AND
UNBOUNDED FOLLOWING
) AS cheapest_price,
FROM products
/* Find the difference in price from the priciest alternative */
SELECT
product_name,
list_price,
LAST_VALUE(list_price) OVER (
ORDER BY list_price
ROWS BETWEEN
UNBOUNDED PRECEDING
AND
UNBOUNDED FOLLOWING
) AS highest_price
FROM products
Aggregate window functions
Aggregate functions available for GROUP BY
, such as COUNT(),
MIN(), MAX(), SUM(),
and AVG()
are also available as window functions.
Function Syntax 
Function Description 

Count the number of rows that have a nonnull expression in the partition. 

Find the minimum of the expression in the partition. 

Find the maximum of the expression in the partition. 

Find the mean (average) of the expression in the partition. 
Suppose we want to find the average, maximum and minimum discount for each product, we can achieve it as such.
SELECT
order_id,
product_id,
discount,
AVG(discount) OVER (PARTITION BY product_id) AS avg_discount,
MIN(discount) OVER (PARTITION BY product_id) AS min_discount,
MAX(discount) OVER (PARTITION BY product_id) AS max_discount
FROM order_items
LEAD, LAG
The LEAD
and LAG
locate a row relative to the current row.
Function Syntax 
Function Description 

Accesses the value stored in a row after the current row. 

Accesses the value stored in a row before the current row. 
Both LEAD
and LAG
take three arguments:
Expression
: the name of the column from which the value is retrievedOffset
: the number of rows to skip. Defaults to 1.Default_value
: the value to be returned if the value retrieved is null. Defaults toNULL
.
With LAG
and LEAD
, you must specify ORDER BY
in the OVER
clause.
LEAD
and LAG
are most commonly used to find the value of a previous row or the next row. For example, they are useful for calculating the yearonyear increase of business metrics like revenue.
Here is an example of using lag to compare this year's sales to last year's.
/* Find the number of orders in a year */
WITH yearly_orders AS (
SELECT
year(order_date) AS year,
COUNT(DISTINCT order_id) AS num_orders
FROM sales.orders
GROUP BY year(order_date)
)
/* Compare this year's sales to last year's */
SELECT
*,
LAG(num_orders) OVER (ORDER BY year) last_year_order,
LAG(num_orders) OVER (ORDER BY year)  num_orders diff_from_last_year
FROM yearly_orders
Similarly, we can make a comparison of each year's order with the next year's.
/* Find the number of orders in a year */
WITH yearly_orders AS (
SELECT
year(order_date) AS year,
COUNT(DISTINCT order_id) AS num_orders
FROM sales.orders
GROUP BY year(order_date)
)
/* Compare the number of years compared to next year */
SELECT *,
LEAD(num_orders) OVER (ORDER BY year) next_year_order,
LEAD(num_orders) OVER (ORDER BY year)  num_orders diff_from_next_year
FROM yearly_orders