Cfrgtkky

I have a continuing JSONArray data produced to Kafka topic,and I wanna process records with EventTime characteristic.In order to reach this goal,I have to assign watermark to each record which contained in the JSONArray.

I didn't find a convenience way to achieve this goal.My solution is consuming data from DataStreamSource> ,then iterate List and collect Object to downstream with an anonymous ProcessFunction,finally assign watermark to the this downstream.

The major code shows below:


DataStreamSource<List<MockData>> listDataStreamSource = KafkaSource.genStream(env);
SingleOutputStreamOperator<MockData> convertToPojo = listDataStreamSource
.process(new ProcessFunction<List<MockData>, MockData>() {
@Override
public void processElement(List<MockData> value, Context ctx, Collector<MockData> out)
throws Exception {
value.forEach(mockData -> out.collect(mockData));
}
});
convertToPojo.assignTimestampsAndWatermarks(
new BoundedOutOfOrdernessTimestampExtractor<MockData>(Time.seconds(5)) {
@Override
public long extractTimestamp(MockData element) {
return element.getTimestamp();
}
});
SingleOutputStreamOperator<Tuple2<String, Long>> countStream = convertToPojo
.keyBy("country").window(
SlidingEventTimeWindows.of(Time.seconds(10), Time.seconds(10)))
.process(
new FlinkEventTimeCountFunction()).name("count elements");

The code seems all right without doubt,running without error as well.But ProcessWindowFunction never triggered.I tracked the Flink source code,find EventTimeTrigger never returns TriggerResult.FIRE,causing by TriggerContext.getCurrentWatermark returns Long.MIN_VALUE all the time.

What's the proper way to process List in eventtime?Any suggestion will be appreciated.

The problem is that you are applying the keyBy and window operations to the convertToPojo stream, rather than the stream with timestamps and watermarks (which you didn't assign to a variable).

If you write the code more or less like this, it should work:

listDataStreamSource = KafkaSource ...

convertToPojo = listDataStreamSource.process ...

pojoPlusWatermarks = convertToPojo.assignTimestampsAndWatermarks ...

countStream = pojoPlusWatermarks.keyBy ...

Calling assignTimestampsAndWatermarks on the convertToPojo stream does not modify that stream, but rather creates a new datastream object that includes timestamps and watermarks. You need to apply your windowing to that new datastream.