Window Functions

Use window functions to compute averages, sort, rank, or count items, calculate sums, and find minimum or maximum values across windows.

Important: To query using Windows functions, you must enable Pinot's multi-stage query engine (v2). See how to enable and use the multi-stage query engine (v2).

Window Functions overview

This is an overview of the window functions feature.

Window function syntax

Pinot's window function (windowedCall) has the following syntax definition:

windowedCall:
      windowFunction
      OVER 
      window

windowFunction:
      function_name '(' value [, value ]* ')'
   |
      function_name '(' '*' ')'

window:
      '('
      [ PARTITION BY expression [, expression ]* ]
      [ ORDER BY orderItem [, orderItem ]* ]
      [
          RANGE BETWEEN frame_start AND frame_end
        |   
          ROWS BETWEEN frame_start AND frame_end
        |
          RANGE frame_start
        |
          ROWS frame_start    
      ]
      ')'
      
frame_start:
      UNBOUNDED PRECEDING
    |
      offset PRECEDING
    |
      CURRENT ROW
    |  
      offset FOLLOWING
     
frame_end:
      offset PRECEDING
    |
      CURRENT ROW
    |
      offset FOLLOWING
    |
      UNBOUNDED FOLLOWING       

• windowedCall refers to the actual windowed operation.

• windowFunction refers to the window function used, see supported window functions.
• window is the window definition / windowing mechanism, see supported window mechanism.

You can jump to the examples section to see more concrete use cases of window functions in Pinot.

Example window function query layout

The following query shows the complete components of the window function. Note that the PARTITION BY ,ORDER BY, and the FRAME clauses are all optional.

SELECT FUNC(column1) OVER (PARTITION BY column2 ORDER BY column3 RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)
    FROM tableName
    WHERE filter_clause  

Window mechanism (OVER clause)

Partition by clause

• If a PARTITION BY clause is specified, the intermediate results will be grouped into different partitions based on the values of the columns appearing in the PARTITION BY clause.

• If the PARTITION BY clause isn’t specified, the whole result will be regarded as one big partition, i.e. there is only one partition in the result set.

Order by clause

• If an ORDER BY clause is specified, all the rows within the same partition will be sorted based on the values of the columns appearing in the window ORDER BY clause. The ORDER BY clause decides the order in which the rows within a partition are to be processed.

• If no ORDER BY clause is specified while a PARTITION BY clause is specified, the order of the rows is undefined. To order the output, use a global ORDER BY clause in the query.

Frame clause

RANGE type window frames currently cannot be used with offset PRECEDING / offset FOLLOWING

The following window frame clauses are currently supported:

• RANGE frame_start where frame_start can be UNBOUNDED PRECEDING or CURRENT ROW (frame_end will default to CURRENT ROW)

• ROWS frame_start where frame_start can be UNBOUNDED PRECEDING, offset PRECEDING, or CURRENT ROW (frame_end will default to CURRENT ROW)

• RANGE BETWEEN frame_start AND frame_end; frame_start can be either UNBOUNDED PRECEDING or CURRENT ROW and frame_end can be either CURRENT ROW or UNBOUNDED FOLLOWING

• ROWS BETWEEN frame_start AND frame_end; frame_start / frame_end can be one of:

  • UNBOUNDED PRECEDING (frame_start only)

  • offset PRECEDING where offset is an integer literal

  • CURRENT ROW

  • offset FOLLOWING where offset is an integer literal

  • UNBOUNDED FOLLOWING (frame_end only)

In RANGE mode, a frame_start of CURRENT ROW means the frame starts with the current row's first peer row (a row that the window's ORDER BY clause sorts as equivalent to the current row), while a frame_end of CURRENT ROW means the frame ends with the current row's last peer row. In ROWS mode, CURRENT ROW simply means the current row.

If no ORDER BY clause is specified, the window frame will always be RANGE BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING and cannot be modified. When an ORDER BY clause is present, the default frame is RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW if no explicit window frame is defined in the query.

If there is no FRAME, no PARTITION BY, and no ORDER BY clause specified in the OVER clause (empty OVER), the whole result set is regarded as one partition, and there's one frame in the window.

The OVER clause applies a specified supported windows function to compute values over a group of rows and return a single result for each row. The OVER clause specifies how the rows are arranged and how the aggregation is done on those rows.

Inside the over clause, there are three optional components: PARTITION BY clause, ORDER BY clause, and FRAME clause.

Window functions

Window functions are commonly used to do the following:

• Compute averages
• Rank items
• Count items
• Calculate sums
• Find minimum or maximum values

Supported window functions are listed in the following table.

Function	Description	Example	Default Value When No Record Selected
AVG	Returns the average of the values for a numeric column in the defined window.	AVG(playerScore)	Double.NEGATIVE_INFINITY
BOOL_AND	Returns false if even a single value in the window is false, null if a single value in the window is null, and true if all the values in the window are true.		null
BOOL_OR	Returns true if even a single value in the window is true , null if a single value in the window is null, and false if all the values in the window are false.		null
COUNT	Returns the number of values in the window	COUNT(*)	0
MIN	Returns the minimum value of a numeric column as Double	MIN(playerScore)	null
MAX	Returns the maximum value of a numeric column as Double	MAX(playerScore)	null
SUM	Returns the sum of the values for a numeric column as Double	SUM(playerScore)	null
LEAD	The LEAD function provides access to a subsequent row within the same result set, without the need for a self-join.	LEAD(column_name, offset, default_value)
LAG	The LAG function provides access to a previous row within the same result set, without the need for a self-join.	LAG(column_name, offset, default_value)
FIRST_VALUE	The FIRST_VALUE function returns the value from the first row in the window.	FIRST_VALUE(salary)
LAST_VALUE	The LAST_VALUE function returns the value from the last row in the window	LAST_VALUE(salary)
ROW_NUMBER	Returns the number of the current row within its partition, counting from 1.	ROW_NUMBER()
RANK	Returns the rank of the current row, with gaps - i.e., the row_number of the first row in its peer group.	RANK()
DENSE_RANK	Returns the rank of the current row, without gaps.	DENSE_RANK()

Note that no window frame clause can be specified for ROW_NUMBER, RANK, and DENSE_RANK window functions since they're applied on the entire partition by definition. Similarly, no window frame clause can be specified for LAG and LEAD since the row offset is an input to those functions themselves.

Window aggregate query examples

• Sum transactions by customer ID
• Find the minimum or maximum transaction by customer ID
• Find the average transaction amount by customer ID
• Rank year-to-date sales for a sales team
• Count the number of transactions by customer ID

Sum transactions by customer ID

Calculate the rolling sum transaction amount ordered by the payment date for each customer ID (note, the default frame here is UNBOUNDED PRECEDING and CURRENT ROW).

SELECT customer_id, payment_date, amount, SUM(amount) OVER(PARTITION BY customer_id ORDER BY payment_date) from payment;

customer_id	payment_date	amount	sum
1	2023-02-14 23:22:38.996577	5.99	5.99
1	2023-02-15 16:31:19.996577	0.99	6.98
1	2023-02-15 19:37:12.996577	9.99	16.97
1	2023-02-16 13:47:23.996577	4.99	21.96
2	2023-02-17 19:23:24.996577	2.99	2.99
2	2023-02-17 19:23:24.996577	0.99	3.98
3	2023-02-16 00:02:31.996577	8.99	8.99
3	2023-02-16 13:47:36.996577	6.99	15.98
3	2023-02-17 03:43:41.996577	6.99	22.97
4	2023-02-15 07:59:54.996577	4.99	4.99
4	2023-02-16 06:37:06.996577	0.99	5.98

Find the minimum or maximum transaction by customer ID

Calculate the least (use MIN()) or most expensive (use MAX()) transaction made by each customer comparing all transactions made by the customer (default frame here is UNBOUNDED PRECEDING and UNBOUNDED FOLLOWING). The following query shows how to find the least expensive transaction.

SELECT customer_id, payment_date, amount, MIN(amount) OVER(PARTITION BY customer_id) from payment;

customer_id	payment_date	amount	min
1	2023-02-14 23:22:38.996577	5.99	0.99
1	2023-02-15 16:31:19.996577	0.99	0.99
1	2023-02-15 19:37:12.996577	9.99	0.99
2	2023-04-30 04:34:36.996577	4.99	4.99
2	2023-04-30 12:16:09.996577	10.99	4.99
3	2023-03-23 05:38:40.996577	2.99	2.99
3	2023-04-07 08:51:51.996577	3.99	2.99
3	3 | 2023-04-08 11:15:37.996577	4.99	2.99

Find the average transaction amount by customer ID

Calculate a customer’s average transaction amount for all transactions they’ve made (default frame here is UNBOUNDED PRECEDING and UNBOUNDED FOLLOWING).

SELECT customer_id, payment_date, amount, AVG(amount) OVER(PARTITION BY customer_id) from payment;

customer_id	payment_date	amount	avg
1	2023-02-14 23:22:38.996577	5.99	5.66
1	2023-02-15 16:31:19.996577	0.99	5.66
1	2023-02-15 19:37:12.996577	9.99	5.66
2	2023-04-30 04:34:36.996577	4.99	7.99
2	2023-04-30 12:16:09.996577	10.99	7.99
3	2023-03-23 05:38:40.996577	2.99	3.99
3	2023-04-07 08:51:51.996577	3.99	3.99
3	2023-04-08 11:15:37.996577	4.99	3.99

Rank year-to-date sales for a sales team

Use ROW_NUMBER() to rank team members by their year-to-date sales (default frame here is UNBOUNDED PRECEDING and UNBOUNDED FOLLOWING).

SELECT ROW_NUMBER() OVER(ORDER BY SalesYTD DESC) AS Row,   
    FirstName, LastName AS "Total sales YTD"   
FROM Sales.vSalesPerson;  

Row	FirstName	LastName	Total sales YTD
1	Joe	Smith	2251368.34
2	Alice	Davis	2151341.64
3	James	Jones	1551363.54
4	Dane	Scott	1251358.72

Count the number of transactions by customer ID

Count the number of transactions made by each customer (default frame here is UNBOUNDED PRECEDING and UNBOUNDED FOLLOWING).

SELECT customer_id, payment_date, amount, count(amount) OVER(PARTITION BY customer_id) from payment;

customer_id	payment_date	amount	count
1	2023-02-14 23:22:38.99657	10.99	2
1	2023-02-15 16:31:19.996577	8.99	2
2	2023-04-30 04:34:36.996577	23.50	3
2	2023-04-07 08:51:51.996577	12.35	3
2	2023-04-08 11:15:37.996577	8.29	3