Small improvements matter when the volume of events is high such as when the stream store is connected to data pipelines or IoT traffic. The analytical processing may also perform repeated iterations of the events which will also be improved if each and every event processing is improved.
Flink Streaming jobs run for a long time because the stream has no bounds. The program that invokes the env.execute() is kicked off in detached mode. The other mode of execution is blocking mode and it does not apply to StreamExecutionEnvironment but only to LocalExecutionEnvironment. The job itself will either appear with status as started on success or appear as error on failure. The logs for the job execution will only be partial because the foreground disappears after making an asynchronous call.
The logs for the background will show all the activities performed after the invocation.
There are also a few ways to gather some counts programmatically. These include:
eventsRead.addSink( new SinkFunction<String> () {
Private int count;
@Override
Public void invoke(String value) throws Exception {
             count++;
             logger.error(“count = {}, valueRead ={}”, count, value) ;
           }
}) ;

And the other is with using iterative streams