Hi Hequn, I now realize that in Production, data will not be a problem since this will be a high volume kafka topic. So, I will go with EventTime.
Still, I would like to know if I can use both TimeCharacteristic.ProcessingTime and TimeCharacteristic.EventTime in an application. *Thanks, the link you provided saved my time.* *-shyla* On Sun, Aug 5, 2018 at 9:28 AM, anna stax <annasta...@gmail.com> wrote: > Hi Hequn, > > Thanks for link. Looks like I better use ProcessingTime instead of > EventTime especially because of the 4th reason you listed.. > "Data should cover a longer time span than the window size to advance the > event time." > I need the trigger when the data stops. > > I have 1 more question. > > Can I set the TimeCharacteristic to the stream level instead on the > application level? > Can I use both TimeCharacteristic.ProcessingTime and > TimeCharacteristic.EventTime in an application. > > Thank you > > On Sat, Aug 4, 2018 at 10:05 PM, Hequn Cheng <chenghe...@gmail.com> wrote: > >> Hi shyla, >> >> I answered a similar question on stackoverflow[1], you can take a look >> first. >> >> Best, Hequn >> >> [1] https://stackoverflow.com/questions/51691269/event-time- >> window-in-flink-does-not-trigger >> >> On Sun, Aug 5, 2018 at 11:24 AM, shyla deshpande < >> deshpandesh...@gmail.com> wrote: >> >>> Hi, >>> >>> I used PopularPlacesFromKafka from dataartisans.flinktraining.exercises as >>> the basis. I made very minor changes >>> >>> and the session window is not triggered. If I use ProcessingTime instead of >>> EventTime it works. Here is my code. >>> >>> Appreciate any help. Thanks >>> >>> object KafkaEventTimeWindow { >>> >>> private val LOCAL_ZOOKEEPER_HOST = "localhost:2181" >>> private val LOCAL_KAFKA_BROKER = "localhost:9092" >>> private val CON_GROUP = "KafkaEventTimeSessionWindow" >>> private val MAX_EVENT_DELAY = 60 // events are out of order by max 60 >>> seconds >>> >>> def main(args: Array[String]) { >>> >>> val env = StreamExecutionEnvironment.getExecutionEnvironment >>> env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime) >>> >>> val kafkaProps = new Properties >>> kafkaProps.setProperty("zookeeper.connect", LOCAL_ZOOKEEPER_HOST) >>> kafkaProps.setProperty("bootstrap.servers", LOCAL_KAFKA_BROKER) >>> kafkaProps.setProperty("group.id", CON_GROUP) >>> kafkaProps.setProperty("auto.offset.reset", "earliest") >>> >>> val consumer = new FlinkKafkaConsumer011[PositionEventProto]( >>> "positionevent", >>> new PositionEventProtoSchema, >>> kafkaProps) >>> consumer.assignTimestampsAndWatermarks(new PositionEventProtoTSAssigner) >>> >>> val posstream = env.addSource(consumer) >>> >>> def convtoepochmilli(cdt: String): Long = { >>> val odt:OffsetDateTime = OffsetDateTime.parse(cdt); >>> val i:Instant = odt.toInstant(); >>> val millis:Long = i.toEpochMilli(); >>> millis >>> } >>> >>> val outputstream = posstream >>> .mapWith{case(p) => (p.getConsumerUserId, >>> convtoepochmilli(p.getCreateDateTime.getInIso8601Format))} >>> .keyBy(0) >>> .window(EventTimeSessionWindows.withGap(Time.seconds(60))) >>> .reduce { (v1, v2) => (v1._1, Math.max(v1._2 , v2._2)) } >>> >>> outputstream.print() >>> >>> // execute the transformation pipeline >>> env.execute("Output Stream") >>> } >>> >>> } >>> >>> class PositionEventProtoTSAssigner >>> extends >>> BoundedOutOfOrdernessTimestampExtractor[PositionEventProto](Time.seconds(60)) >>> { >>> >>> override def extractTimestamp(pos: PositionEventProto): Long = { >>> val odt:OffsetDateTime = >>> OffsetDateTime.parse(pos.getCreateDateTime.getInIso8601Format); >>> val i:Instant = odt.toInstant(); >>> val millis:Long = i.toEpochMilli(); >>> millis >>> } >>> } >>> >>> >>> >> >
