Use window aggregate to compute averages, sort, rank, or count items, calculate sums, and find minimum or maximum values across window.
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).
This is an overview of the window aggregate feature.
Pinot's window function (windowedAggCall
) includes the following syntax definition:
windowAggCall
refers to the actual windowed agg operation.
windowAggFunction
refers to the aggregation function used inside a windowed aggregate, see supported window aggregate 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 aggregate on Pinot.
The following query shows the complete components of the window function. Note, PARTITION BY
and ORDER BY
are optional.
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.
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.
Important Note: in release 1.0.0 window aggregate only supports UNBOUND PRECEDING
, UNBOUND FOLLOWING
and CURRENT ROW
. frame and row count support have not been implemented yet.
{RANGE|ROWS} frame_start OR
{RANGE|ROWS} BETWEEN frame_start AND frame_end; frame_start and frame_end can be any of:
UNBOUNDED PRECEDING: expression PRECEDING. May only be allowed in ROWS mode [depends on DB, some support some don’t]
CURRENT ROW expression FOLLOWING. May only be allowed in ROWS mode [depends on DB, some support some don’t]
UNBOUNDED FOLLOWING:
If no FRAME clause is specified, then the default frame behavior depends on whether ORDER BY is present or not.
If an ORDER BY clause is specified, the default behavior is to calculate the aggregation from the beginning of the partition to the current row or UNBOUNDED PRECEDING to CURRENT ROW.
If only a PARTITION BY clause is present, the default frame behavior is to calculate the aggregation from UNBOUNDED PRECEDING to CURRENT ROW.
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 aggregate 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 aggregate functions are commonly used to do the following:
Supported window aggregate functions are listed in the following table.
Returns the average of the values for a numeric column as aDouble over the specified number of rows or partition (if applicable).
AVG(playerScore)
Double.NEGATIVE_INFINITY
BOOL_AND
Returns true if all input values are true, otherwise false
BOOL_OR
Returns true if at least one input value is true, otherwise false
Returns the count of the records as Long
COUNT(*)
0
Returns the minimum value of a numeric column as Double
MIN(playerScore)
Double.POSITIVE_INFINITY
Returns the maximum value of a numeric column as Double
MAX(playerScore)
Double.NEGATIVE_INFINITY
Assigns a unique row number to all the rows in a specified table.
ROW_NUMBER()
0
Returns the sum of the values for a numeric column as Double
SUM(playerScore)
0
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).
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
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.
1
2023-02-14 23:22:38.996577
5.99
1
2023-02-15 16:31:19.996577
0.99
1
2023-02-15 19:37:12.996577
9.99
2
2023-04-30 04:34:36.996577
4.99
2
2023-04-30 12:16:09.996577
10.99
3
2023-03-23 05:38:40.996577
2.99
3
2023-04-07 08:51:51.996577
3.99
3
3 | 2023-04-08 11:15:37.996577
4.99
Calculate a customer’s average transaction amount for all transactions they’ve made (default frame here is UNBOUNDED PRECEDING and UNBOUNDED FOLLOWING).
1
2023-02-14 23:22:38.996577
5.99
1
2023-02-15 16:31:19.996577
0.99
1
2023-02-15 19:37:12.996577
9.99
2
2023-04-30 04:34:36.996577
4.99
2
2023-04-30 12:16:09.996577
10.99
3
2023-03-23 05:38:40.996577
2.99
3
2023-04-07 08:51:51.996577
3.99
3
2023-04-08 11:15:37.996577
4.99
Use ROW_NUMBER()
to rank team members by their year-to-date sales (default frame here is UNBOUNDED PRECEDING and UNBOUNDED FOLLOWING).
1
Joe
Smith
2
Alice
Davis
3
James
Jones
4
Dane
Scott
Count the number of transactions made by each customer (default frame here is UNBOUNDED PRECEDING and UNBOUNDED FOLLOWING).
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