An OVER window is a standard window used in traditional databases. Over aggregate is different from window aggregate. In streaming data that uses OVER windows, each element corresponds to an OVER window. An OVER window can be determined based on an actual row or an actual value (timestamp value) of an element. Elements of a stream are distributed across multiple windows.
In a stream that applies the OVER window, each element corresponds to an OVER window and triggers data computing once. The row determined by each element that triggers computing is the last row of the window where the element is located. In the underlying implementation of Realtime Compute, the OVER window data is centrally managed. Only one copy of the data is stored. Logically, an OVER window is created for each element. Realtime Compute for Apache Flink calculates the data for each OVER window and then deletes the data that is no longer used after the calculation is complete. For more information, see Over Aggregation.
Syntax
SELECT
agg1(col1) OVER (definition1) AS colName,
...
aggN(colN) OVER (definition1) AS colNameN
FROM Tab1;
- agg1(col1): aggregates input data based on the col1 column specified by GROUP BY.
- OVER (definition1): defines an OVER window.
- AS colName: specifies the alias of a column.
- OVER (definition1) for agg1 through aggN must be the same.
- The alias specified by AS can be queried by using an outer SQL statement.
Window types
- ROWS OVER window: Each row of elements is treated as a new computed row. A new window is generated for each row.
- RANGE OVER window: All rows of elements with the same timestamp value are treated as one computed row and are assigned to the same window.
Attributes
Orthogonal attribute | Description | proctime | eventtime |
---|---|---|---|
ROWS OVER Window | A window is determined based on the actual row of an element. | Supported | Supported |
RANGE OVER Window | A window is determined based on the timestamp value of an element. | Supported | Supported |
ROWS OVER window
- Description
For a ROWS OVER window, a window is generated for each element.
- Syntax
SELECT agg1(col1) OVER( [PARTITION BY (value_expression1,..., value_expressionN)] ORDER BY timeCol ROWS BETWEEN (UNBOUNDED | rowCount) PRECEDING AND CURRENT ROW) AS colName, ... FROM Tab1;
- value_expression: specifies the value expression used for partitioning.
- timeCol: specifies the time field used to sort elements.
- rowCount: specifies the number of rows that precede the current row.
- Example
This example describes bounded ROWS OVER windows. In this example, an on-sale product table contains item IDs, item types, launch time, and prices. Calculate the highest price among the three products similar to the current product before the current product is on sale.
- Test data
Item ID Item type On-sale time Price ITEM001 Electronic 2017-11-11 10:01:00
20 ITEM002 Electronic 2017-11-11 10:02:00
50 ITEM003 Electronic 2017-11-11 10:03:00
30 ITEM004 Electronic 2017-11-11 10:03:00
60 ITEM005 Electronic 2017-11-11 10:05:00
40 ITEM006 Electronic 2017-11-11 10:06:00
20 ITEM007 Electronic 2017-11-11 10:07:00
70 ITEM008 Clothes 2017-11-11 10:08:00
20 - Test statements
CREATE TEMPORARY TABLE tmall_item( itemID VARCHAR, itemType VARCHAR, eventtime varchar, onSellTime AS TO_TIMESTAMP(eventtime), price DOUBLE, WATERMARK FOR onSellTime AS onSellTime - INTERVAL '0' SECOND -- Define a watermark for the rowtime. ) WITH ( 'connector' = 'sls', ... ); SELECT itemID, itemType, onSellTime, price, MAX(price) OVER ( PARTITION BY itemType ORDER BY onSellTime ROWS BETWEEN 2 preceding AND CURRENT ROW) AS maxPrice FROM tmall_item;
- Test results
itemID itemType onSellTime price maxPrice ITEM001 Electronic 2017-11-11 10:01:00
20 20 ITEM002 Electronic 2017-11-11 10:02:00
50 50 ITEM003 Electronic 2017-11-11 10:03:00
30 50 ITEM004 Electronic 2017-11-11 10:03:00
60 60 ITEM005 Electronic 2017-11-11 10:05:00
40 60 ITEM006 Electronic 2017-11-11 10:06:00
20 60 ITEM007 Electronic 2017-11-11 10:07:00
70 70 ITEM008 Clothes 2017-11-11 10:08:00
20 20
- Test data
RANGE OVER window
- Description
For a RANGE OVER window, all elements with the same timestamp value are assigned to the same window.
- Syntax
SELECT agg1(col1) OVER( [PARTITION BY (value_expression1,..., value_expressionN)] ORDER BY timeCol RANGE BETWEEN (UNBOUNDED | timeInterval) PRECEDING AND CURRENT ROW) AS colName, ... FROM Tab1;
- value_expression: specifies the value expression used for partitioning.
- timeCol: specifies the time field used to sort elements.
- timeInterval: specifies the time interval between the time of the current row and that of the element row to which it can be traced back.
- Example
This example describes bounded RANGE OVER windows. In this example, an on-sale product table contains item IDs, item types, launch time, and prices. Calculate the highest price among similar products that are on sale two minutes earlier than the current product.
- Test data
Item ID Item type On-sale time Price ITEM001 Electronic 2017-11-11 10:01:00
20 ITEM002 Electronic 2017-11-11 10:02:00
50 ITEM003 Electronic 2017-11-11 10:03:00
30 ITEM004 Electronic 2017-11-11 10:03:00
60 ITEM005 Electronic 2017-11-11 10:05:00
40 ITEM006 Electronic 2017-11-11 10:06:00
20 ITEM007 Electronic 2017-11-11 10:07:00
70 ITEM008 Clothes 2017-11-11 10:08:00
20 - Test statements
CREATE TEMPORARY TABLE tmall_item( itemID VARCHAR, itemType VARCHAR, eventtime varchar, onSellTime AS TO_TIMESTAMP(eventtime), price DOUBLE, WATERMARK FOR onSellTime AS onSellTime - INTERVAL '0' SECOND -- Define a watermark for the rowtime. ) WITH ( 'connector' = 'sls', ... ); SELECT itemID, itemType, onSellTime, price, MAX(price) OVER ( PARTITION BY itemType ORDER BY onSellTime RANGE BETWEEN INTERVAL '2' MINUTE preceding AND CURRENT ROW) AS maxPrice FROM tmall_item;
- Test results
itemID itemType onSellTime price maxPrice ITEM001 Electronic 2017-11-11 10:01:00
20 20 ITEM002 Electronic 2017-11-11 10:02:00
50 50 ITEM003 Electronic 2017-11-11 10:03:00
30 50 ITEM004 Electronic 2017-11-11 10:03:00
60 60 ITEM005 Electronic 2017-11-11 10:05:00
40 60 ITEM006 Electronic 2017-11-11 10:06:00
20 40 ITEM007 Electronic 2017-11-11 10:07:00
70 70 ITEM008 Clothes 2017-11-11 10:08:00
20 20
- Test data