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 time does not provide determinism as it is dependent on the speed at which records flow in the system.

This is time at which a particular event enters Flink. All time based operations refer to this timestamp. Ingestion time is more expensive operation than processing but it gives predictable results. Ingestion time programs cannot handle any out of order events as it assigs timestamp only after the event is entered the Flink system.

Here is an example which shows how to set event time and watermarks. In case of ingestion time and processing time, we just need to the time characteristics and watermark generation is taken care automatically. Following is a code snippet for the same.

In Java:

final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); 
env.setStreamTimeCharacteristic(TimeCharacteristic.ProcessingTime); 
//or 
env.setStreamTimeCharacteristic(TimeCharacteristic.IngestionTime); 

In Scala:

val env = StreamExecutionEnvironment.getExecutionEnvironment 
env.setStreamTimeCharacteristic(TimeCharacteristic.ProcessingTime) 
//or  
env.setStreamTimeCharacteristic(TimeCharacteristic.IngestionTime) 

In case of event time stream programs, we need to specify the way to assign watermarks and timestamps. There are two ways of assigning watermarks and timestamps:

To work with event time streams, we need to assign the time characteristic as follows

In Java:

final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); 
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime; 

In Scala:

val env = StreamExecutionEnvironment.getExecutionEnvironment 
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime) 

It is always best to store event time while storing the record in source. Flink also supports some pre-defined timestamp extractors and watermark generators. Refer to https://ci.apache.org/projects/flink/flink-docs-release-1.2/dev/event_timestamp_extractors.html.