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Flink Window Trigger (Trigger) (I)
Flink Window Trigger (Trigger) (Part 2)

Apache Flink is an open source stream processing framework for processing unbounded and bounded data streams. In Flink's time window operation, trigger is a very important concept, which determines when the window should be calculated and the results should be output. The trigger defines the conditions when the window is ready to be processed (that is, when the calculation is triggered).
Window Type

• Time window(Time Windows): Windows based on time division, such as Tumbling Time Windows and Sliding Time Windows.
• Event time windowing(Event Time Windows): A window based on event timestamps, which can handle out-of-order data.

Trigger

Triggers are used to define the conditions for when a window is ready for computation. Flink provides some built-in triggers and allows users to customize triggers as needed.

Built-in triggers

• ProcessingTimeTrigger: A processing time-based trigger that fires whenever a specified time interval is reached.
• EventTimeTrigger: An event-time based trigger that is triggered when the end time of the window arrives. This is suitable for processing ordered or out-of-order event time data streams.
• CountTrigger: A trigger based on the number of elements, which is triggered when the number of elements in the window reaches the specified threshold.

Custom triggers

Users can create custom triggers by implementing the Trigger interface. Custom triggers can decide when to trigger window calculations based on complex logic. Custom triggers usually need to implement the following methods:

• onElement(element, timestamp, window, ctx): called when an element is added to the window.
• onEventTime(time, window, ctx, out): called when the event time of the window arrives.
• onProcessingTime(time, window, ctx, out): called when the processing time of the window arrives.
• onMerge(other): Called when two windows are merged (for example, used in a session window).
• canMerge(): Indicates whether the trigger supports window merging.

effect

• The main function of the trigger is to control the calculation timing of the window, making Flink's window operation more flexible and powerful. By selecting the appropriate trigger, you can optimize the performance and resource utilization of the stream processing application.

Example

Here is a simple example using Flink windows and triggers (assuming the Java API):

DataStream
                                   

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