🗓️ 16012025 1122
📎
flink_notions_of_time
Notions of time
| Notion | Description | Use case | Example |
|---|---|---|---|
| Event time | Timestamp of when an event occurred, as recorded by the producing or storing device. | Reproducible results, historical data analysis, or time-based calculations | Stock prices during a time window |
| Ingestion time | timestamp recorded by Flink at the moment it ingests the event | Quick prototyping or debugging when event time is unavailable or irrelevant | |
| Processing time | Time when a specific operator in the pipeline processes the event (system clock of the operator) | Suitable for applications where low-latency processing is more important than exact time accuracy | Monitoring |
Lateness
Lateness is defined relative to the watermarks. A Watermark(t) asserts that the stream is complete up through time t; any event following this watermark whose timestamp is ≤ t is late.
Working with Watermarks #
In order to perform event-time-based event processing, Flink needs to know the time associated with each event, and it also needs the stream to include watermarks.
The Taxi data sources used in the hands-on exercises take care of these details for you. But in your own applications you will have to take care of this yourself, which is usually done by implementing a class that extracts the timestamps from the events, and generates watermarks on demand. The easiest way to do this is by using a WatermarkStrategy: