Apache Flink – Session and Global Windows
Apache Flink supports various types of windowing strategies to group streaming data for time-based computations. Two commonly used types are Session Windows and Global Windows. Each serves different use cases in real-time data processing.
🔸 Session Window
A Session Window groups elements that arrive close together in time, separated by a defined gap of inactivity. If no events arrive within this inactivity period, a new session is started. This makes session windows highly dynamic and adaptable to event timing.
Key Characteristics:
- Dynamically adjusted based on event arrival times
- Triggered when a gap (inactivity) exceeds the defined threshold
- Each session is processed independently
Example: Group user interactions into session windows with an inactivity gap of 60 seconds, useful for analyzing user behavior or login sessions.
🔸 Global Window
A Global Window treats all elements of the stream as part of a single window. It does not use time or gaps to segment data and requires custom triggers to know when to emit the result.
Key Characteristics:
- All stream elements fall into one window
- Time boundaries are ignored; custom triggers are required
- Used for complete aggregation or monitoring use cases
Example: Calculate the total number of fraudulent activities across the entire stream, regardless of time.
Both Session and Global windows offer flexible ways to handle and analyze data in Apache Flink. Choose the windowing strategy based on your use case — whether you need time-based segmentation or want to process the stream as a whole.
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