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.

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.
Note
  • 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

In Flink SQL, OVER windows are defined in compliance with the standard SQL syntax. The traditional OVER windows are not classified into fine-grained window types. OVER windows are classified into the following two types based on the ways of determining computed rows:
  • 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 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. ROWS OVER windows have two types: unbounded and bounded.

    The following figure shows unbounded ROWS OVER windows.
    Note This figure shows that elements in windows w7 and w8 of user 1 arrive at the same time, and elements in windows w3 and w4 of user 2 arrive at the same time. However, the elements are assigned to different windows. ROWS OVER windows are different from RANGE OVER windows in this aspect.
    The following figure shows bounded ROWS OVER windows, in which each window has three elements (two elements preceding the current element).
    Note As shown in the preceding figure, elements in windows w5 and w6 of user 1 arrive at the same time, and elements in windows w1 and w2 of user 2 arrive at the same time. However, the elements are assigned to different windows. ROWS OVER windows are different from RANGE OVER windows in this aspect.
  • 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. Assume that 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 Launch 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 TABLE tmall_item(
   itemID VARCHAR,
   itemType VARCHAR,
   onSellTime TIMESTAMP,
   price DOUBLE,
   WATERMARK onSellTime FOR onSellTime as withOffset(onSellTime, 0)
) 
WITH (
  type = '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

RANGE OVER window

  • Description

    For a RANGE OVER window, all elements with the same timestamp are assigned to the same window. RANGE OVER windows have two types: unbounded and bounded.

    The following figure shows unbounded RANGE OVER windows.
    Note This figure shows that elements in the two w7 windows of user 1 arrive at the same time, and elements in the two w3 windows of user 2 arrive at the same time. Elements with the same timestamp are assigned to the same window. RANGE OVER windows are different from ROWS OVER windows in this aspect.
    The following figure shows bounded RANGE OVER windows, in which a 3-second window has an interval of 2 seconds (INTERVAL '2' SECOND).
    Note As shown in the preceding figure, elements in the two w6 windows of user 1 arrive at the same time, and elements in the two w3 windows of user 2 arrive at the same time. Elements with the same timestamps are assigned to the same window. RANGE OVER windows are different from ROWS OVER windows in this aspect.
  • 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. Assume that 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 Launch 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 TABLE tmall_item(
   itemID VARCHAR,
   itemType VARCHAR,
   onSellTime TIMESTAMP,
   price DOUBLE,
   WATERMARK onSellTime FOR onSellTime as withOffset(onSellTime, 0)
) 
WITH (
  type = '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