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    Realtime Compute for Apache Flink:HOP

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    Realtime Compute for Apache Flink:HOP

    Last Updated:Jun 23, 2025

    This topic describes how to use the HOP function in Realtime Compute for Apache Flink.

    Definition

    The HOP function is used to define a hopping window, which is also known as a sliding window. Unlike tumbling windows, sliding windows can overlap with each other.

    Syntax

    You can use the HOP function in the GROUP BY clause to define a sliding window. 

    HOP(<time-attr>, <slide-interval>,<size-interval>)

    Input parameters

    Parameter

    Description

    Example

    time-attr

    The parameter must be a valid time attribute field in a stream. This parameter specifies whether the time is the processing time or the event time. For more information, see Time attributes.

    -

    slide-interval

    The interval at which the sliding window moves, which defines the time difference between consecutive windows. This parameter is in the INTERVAL 'num' timeUnit format.

    INTERVAL '10' SECOND indicates that the interval at which the sliding window moves is 10 seconds.

    size-interval

    The size or duration of the sliding window, which defines the time range covered by each window. This parameter is in the INTERVAL 'num' timeUnit format.

    INTERVAL '5' MINUTE indicates that the duration of a window is 5 minutes.

    Sliding windows can be used in the following scenarios based on the values of the slide-interval and size-interval parameters:

    • If the condition slide-interval < size-interval is met, windows overlap with each other and each element is assigned to multiple windows.

    • If the condition slide-interval = size-interval is met, windows are tumbling windows.

    • If the condition slide-interval > size-interval is met, windows are sliding windows. These windows do not overlap with each other and are separated by gaps.

    In most cases, most elements are assigned to multiple windows and the windows overlap with each other. Sliding windows are well-suited for calculating moving averages. For example, if you want to calculate the data average in the last 5 minutes every 10 seconds, set slide-interval to 10 seconds and set size-interval to 5 minutes.

    Window identifier functions

    A window identifier function specifies the start time, end time, or time attribute of a window. The time attribute is used to aggregate lower-level windows.

    Function

    Return value type

    Description

    HOP_START(<time-attr>, <slide-interval>, <size-interval>)

    TIMESTAMP

    Returns the start time, including the boundary value, of a window. For example, if the time span of a window is [00:10, 00:15], 00:10 is returned.

    HOP_END(<time-attr>, <slide-interval>, <size-interval>)

    TIMESTAMP

    Returns the end time, including the boundary value, of a window. For example, if the time span of a window is [00:00, 00:15], 00:15 is returned.

    HOP_ROWTIME(<time-attr>, <slide-interval>, <size-interval>)

    TIMESTAMP (rowtime-attr)

    Returns the end time, excluding the boundary value, of a window. For example, if the time span of a window is (00:00, 00:15), 00:14:59.999 is returned. The return value is a rowtime attribute based on which time operations can be performed. This function can be used in only the windows that are defined based on the event time, such as cascading windows. For more information, see Cascading windows.

    HOP_PROCTIME (<time-attr>, <slide-interval>, <size-interval>)

    TIMESTAMP (rowtime-attr)

    Returns the end time, excluding the boundary value, of a window. For example, if the time span of a window is (00:00, 00:15), 00:14:59.999 is returned. The return value is a processing time attribute based on which time operations can be performed. This function can be used only in the windows that are defined based on the processing time, such as cascading windows. For more information, see Cascading windows.

    Example

    In the following example, a 1-minute window slides once every 30 seconds. You can use the windows to count the number of clicks per user over the last minute every 30 seconds.

    • Test data in the user_clicks table

      username (VARCHAR)

      click_url (VARCHAR)

      eventtime (VARCHAR)

      Jark

      http://taobao.com/xxx

      2024-10-10 10:00:00.0

      Jark

      http://taobao.com/xxx

      2024-10-10 10:00:10.0

      Jark

      http://taobao.com/xxx

      2024-10-10 10:00:49.0

      Jark

      http://taobao.com/xxx

      2024-10-10 10:01:05.0

      Jark

      http://taobao.com/xxx

      2024-10-10 10:01:58.0

      Timo

      http://taobao.com/xxx

      2024-10-10 10:02:10.0

    • Test statements 

      CREATE TEMPORARY TABLE user_clicks (
  username VARCHAR,
  click_url VARCHAR,
  eventtime VARCHAR,                            
  ts AS TO_TIMESTAMP(eventtime),
  WATERMARK FOR ts AS ts - INTERVAL '2' SECOND   -- Define a watermark for the rowtime. 
) WITH (
  'connector' = 'kafka',
  'topic' = '<yourTopic>',
  'properties.bootstrap.servers' = '<brokers>',
  'scan.startup.mode' = 'earliest-offset',
  'format' = 'csv'
);

CREATE TEMPORARY TABLE hop_output (
  window_start TIMESTAMP,
  window_end TIMESTAMP,
  username VARCHAR,
  clicks BIGINT
) WITH (
  'connector'='print',
  'logger'='true'
);

INSERT INTO
    hop_output
SELECT
    HOP_START (ts, INTERVAL '30' SECOND, INTERVAL '1' MINUTE),
    HOP_END (ts, INTERVAL '30' SECOND, INTERVAL '1' MINUTE),
    username,
    COUNT (click_url)
FROM
    user_clicks
GROUP BY
    HOP (ts, INTERVAL '30' SECOND, INTERVAL '1' MINUTE),username;

    • Test results

      window_start (TIMESTAMP)

      window_end (TIMESTAMP)

      username (VARCHAR)

      clicks (BIGINT)

      2024-10-10 09:59:30.0①

      2024-10-10 10:00:30.0

      Jark

      2

      2024-10-10 10:00:00.0

      2024-10-10 10:01:00.0

      Jark

      3

      2024-10-10 10:00:30.0

      2024-10-10 10:01:30.0

      Jark

      2

      2024-10-10 10:01:00.0

      2024-10-10 10:02:00.0

      Jark

      2

      2024-10-10 10:01:30.0

      2024-10-10 10:02:30.0②

      Jark

      1

      2024-10-10 10:01:30.0

      2024-10-10 10:02:30.0②

      Timo

      1

      Note

      • ①If a sliding window cannot read the time at which data enters the window, the start time of the first window is moved forward. You can use the following formula to calculate the time interval by which the start time is moved forward: Time interval = Window duration - Sliding step.

        Window duration (seconds)

        Sliding step (seconds)

        Event time

        Start time of the first window

        End time of the first window

        120

        30

        2024-07-31 10:00:00.0

        2024-07-31 09:58:30.0

        2024-07-31 10:00:30.0

        60

        10

        2024-07-31 10:00:00.0

        2024-07-31 09:59:10.0

        2024-07-31 10:00:10.0

      • ②The row with timestamp 2024-10-10 10:02:30.0 is actually not visible in the results, because the window calculation has not been triggered. An extra record from any user is needed to trigger the window calculation.

        Window calculation triggering time ≥ window_end + Watermark (such as 10:02:32.0)