Syntax OVER Clause Parameters Examples Usage Notes Related Topics

WINDOW Clause (Sliding Windows)

The WINDOW clause for a sliding windowed query specifies the rows over which analytic functions are computed across a group of rows in relation to the current row. These aggregate functions produce an output row aggregated by the keys in one or more columns for each input row. The WINDOW clause in a query specifies records in a stream partitioned by the time range interval or the number of rows, and an additional optional set of columns specified by the PARTITION BY clause. You can define named or inline window specifications that can be used in analytic functions and streaming JOIN clauses. For more information about analytic functions, see Analytic Functions.

Aggregate functions in a sliding window query are performed over each column specified in the OVER clause. The OVER clause can reference a named window specification or can be inline as part of the SELECT statement for a pump. The following examples show how to use the OVER clause to reference a named window specification and inline in the SELECT statement.

Syntax



[WINDOW window_name AS 
(
	{PARTITION BY partition_name 
		RANGE INTERVAL 'interval' {SECOND | MINUTE | HOUR} PRECEDING | 
		ROWS number PRECEDING
, …}
)

OVER Clause

The examples following show you how to use the OVER clause to reference a named window specification.

Example 1: OVER Referencing a Named Window Specification

The following example shows an aggregate function that references the window specification with the name W1. In this example, the average price is calculated over the set of records specified by the W1 window specification. To learn more about how to use the OVER clause with a window specification, see Examples, following.



AVG(price) OVER W1 AS avg_price

Example 2: OVER Referencing an Inline Window Specification

The following example shows an aggregate function that references an inline window specification. In this example, the average price is calculated over each input row with an inline window specification. To learn more about how to use the OVER clause with a window specification, see Examples, following.



AVG(price) OVER (
    PARTITION BY ticker_symbol
    RANGE INTERVAL '1' HOUR PRECEDING) AS avg_price

For more information about aggregate functions and the OVER clause, see Aggregate Functions.

Parameters

window-name

Specifies a unique name that can be referenced from OVER clauses or subsequent window definitions. The name is used in analytic functions and streaming JOIN clauses. For more information about analytic functions, see Analytic Functions.

Defines the named window specification for the WINDOW clause.

PARTITION BY partition-name

Divides rows into groups that share the same values. After rows are partitioned, the window function computes all rows that fall into the same partition as the current row.

RANGE INTERVAL 'interval' {SECOND | MINUTE | HOUR} PRECEDING

Specifies the window boundaries from the time range interval. The window function computes all rows that fall into the same time interval as the current row.

ROWS number PRECEDING

Specifies the window boundaries from the number of rows. The window function computes all rows that fall into the same number of rows.

Examples

Example Dataset

The examples following are based on the sample stock dataset that is part of Getting Started in the Amazon Kinesis Analytics Developer Guide. To run each example, you need an Amazon Kinesis Analytics application that has the sample stock ticker input stream. To learn how to create an Analytics application and configure the sample stock ticker input stream, see Getting Started in the Amazon Kinesis Analytics Developer Guide. For additional samples, see Sliding Windows.

The sample stock dataset has the schema following.



(ticker_symbol  VARCHAR(4),
sector          VARCHAR(16),
change          REAL,
price           REAL)

Example 1: Time-Based Sliding Window That References a Named Window Specification

This example defines a named window specification with a partition boundary of one minute preceding the current row. The OVER clause of the SELECT statement for the pump references the named window specification.



WINDOW W1 AS (
    PARTITION BY ticker_symbol 
    RANGE INTERVAL '1' MINUTE PRECEDING);

To run this example, create the stock sample application and run and save the SQL code following.



CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (
    ticker_symbol VARCHAR(4), 
    min_price     DOUBLE, 
    max_price     DOUBLE, 
    avg_price     DOUBLE);
CREATE OR REPLACE PUMP "STREAM_PUMP" AS 
    INSERT INTO "DESTINATION_SQL_STREAM"
    SELECT STREAM ticker_symbol,
        MIN(price) OVER W1 AS min_price,
        MAX(price) OVER W1 AS max_price,
        AVG(price) OVER W1 AS avg_price
    FROM "SOURCE_SQL_STREAM_001"
    WINDOW W1 AS (
        PARTITION BY ticker_symbol 
        RANGE INTERVAL '1' MINUTE PRECEDING);

The preceding example outputs a stream similar to the following.

Table showing stock data with columns for rowtime, ticker symbol, and price information.

Example 2: Row-Based Sliding Window That References a Named Window Specification

This example defines a named window specification with a partition boundary of two rows preceding the current row and ten rows preceding the current row. The OVER clause of the SELECT statement for the pump references the named window specification.



 WINDOW
    last2rows AS (PARTITION BY ticker_symbol ROWS 2 PRECEDING),
    last10rows AS (PARTITION BY ticker_symbol ROWS 10 PRECEDING);

To run this example, create the stock sample application and run and save the SQL code following.



CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (
    ticker_symbol      VARCHAR(4), 
    price              DOUBLE, 
    avg_last2rows      DOUBLE, 
    avg_Last10rows     DOUBLE);
CREATE OR REPLACE PUMP "myPump" AS INSERT INTO "DESTINATION_SQL_STREAM"
SELECT STREAM ticker_symbol, 
    price, 
    AVG(price) OVER last2rows, 
    AVG(price) OVER last10rows
FROM SOURCE_SQL_STREAM_001
WINDOW
    last2rows AS (PARTITION BY ticker_symbol ROWS 2 PRECEDING),
    last10rows AS (PARTITION BY ticker_symbol ROWS 10 PRECEDING);

The preceding example outputs a stream similar to the following.

Table showing stock ticker symbols, prices, and average values for multiple rows.

Example 3: Time-Based Sliding Window with Inline Window Specification

This example defines an inline window specification with a partition boundary of one minute preceding the current row. The OVER clause of the SELECT statement for the pump uses the inline window specification.

To run this example, create the stock sample application and run and save the SQL code following.



CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (
    ticker_symbol VARCHAR(4),
    price         DOUBLE,
    avg_price     DOUBLE);
CREATE OR REPLACE PUMP "STREAM_PUMP" AS 
    INSERT INTO "DESTINATION_SQL_STREAM"
    SELECT STREAM ticker_symbol, price, 
        AVG(Price) OVER (
            PARTITION BY ticker_symbol
            RANGE INTERVAL '1' HOUR PRECEDING) AS avg_price
    FROM "SOURCE_SQL_STREAM_001"

The preceding example outputs a stream similar to the following.

Table showing stock data with columns for timestamp, ticker symbol, price, and average price.

Usage Notes

For the WINDOW clause and endpoints, Amazon Kinesis Analytics SQL follows SQL-2008 standards for windows over a range.

To include the endpoints of an hour, you can use the window syntax following.



WINDOW HOUR AS (RANGE INTERVAL '1' HOUR PRECEDING)

To not include the endpoints of the previous hour, you can use the window syntax following.



WINDOW HOUR AS (RANGE INTERVAL '59:59.999' MINUTE TO SECOND(3) PRECEDING);

For more information, see Allowed and Disallowed Window Specifications.