Flink Streaming API takes inspiration from Google Data Flow model. It supports different concepts of time for its streaming API. In general, there three places where we can capture time in a streaming environment. They are as follows
The time at which event occurred on its producing device. For example in IoT project, the time at which sensor captures a reading. Generally these event times needs to embed in the record before they enter Flink. At the time processing, these timestamps are extracted and considering for windowing. Event time processing can be used for out of order events.
Processing time is the time of machine executing the stream of data processing. Processing time windowing considers only that timestamps where event is getting processed. Processing time is simplest way of stream processing as it does not require any synchronization between processing machines and producing machines. In distributed asynchronous environment processing...