Window aggregate

Window aggregate overview

This is an overview of the window aggregate feature.

Window aggregate syntax

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

You can jump to the section to see more concrete use cases of window aggregate on Pinot.

Example window aggregate query layout

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

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|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]

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 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

Window aggregate functions are commonly used to do the following:

Supported window aggregate functions are listed in the following table.

Window aggregate query examples

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).

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.

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).

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).

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).