在此处输入代码需要使用flink汇总kafka流中的数据值，并提出一个新主题。
聚合应该发生在事件时间上，而不是在处理时间上，这意味着数据对象中的时间戳。
按照Flink教程中的示例使用TumblingEventTimeWindow进行，但是根本没有调用聚合getResult方法。
如果我更改为TumblingProcessingTimeWIndow，则会调用getResult并将结果推送到接收器。
由于它是传感器事件，因此我们需要考虑事件时间，并且应该在事件时间而不是处理时间中进行汇总。
import java.util.Properties
import org.apache.flink.api.common.functions.AggregateFunction
import org.apache.flink.api.common.restartstrategy.RestartStrategies
import org.apache.flink.api.java.utils.ParameterTool
import org.apache.flink.formats.json.JsonNodeDeserializationSchema
import org.apache.flink.shaded.jackson2.com.fasterxml.jackson.databind.node.{JsonNodeFactory, ObjectNode}
import org.apache.flink.streaming.api.TimeCharacteristic
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor
import org.apache.flink.streaming.api.functions.{AssignerWithPeriodicWatermarks, AssignerWithPunctuatedWatermarks}
import org.apache.flink.streaming.api.scala.{StreamExecutionEnvironment, _}
import org.apache.flink.streaming.api.scala.function.ProcessWindowFunction
import org.apache.flink.streaming.api.watermark.Watermark
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows
import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.streaming.api.windowing.triggers.ContinuousEventTimeTrigger
import org.apache.flink.streaming.api.windowing.windows.TimeWindow
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer011
import org.apache.flink.util.Collector
import org.apache.log4j.Logger
import scala.collection.JavaConverters._
object DownsamplingService {
val log=Logger.getLogger("Service")
def main(args: Array[String]): Unit = {
val parameter: Properties = ParameterTool.fromArgs(args).getProperties
val env = StreamExecutionEnvironment.getExecutionEnvironment
env.getConfig.setRestartStrategy(RestartStrategies.fixedDelayRestart(4, 10000))
// create a checkpoint every 60 seconds
env.enableCheckpointing(60000)
env.setParallelism(4) //Dependsnon core
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
//var params = new Properties
parameter.put("bootstrap.servers", "kafka-server:9092")
parameter.put("zookeeper.connect", "zookeeper:2181")
parameter.put("group.id", "downsampling-consumer")
env.getConfig.setGlobalJobParameters(ParameterTool.fromArgs(args))
val kafkaDiProducer = FlinkKafkaProducer011("engine-test2", new JsonNodeDeserializationSchema, params)
var norTopics = List.tabulate(normalDiTopicCount)(n => s"$normalDiTopicPrefix$n")
val serTopics =(norTopics ::: List.tabulate(serviceDITopicCount)(n => s"$serviceDITopicCount$n")).asJava
val kafkaConsumer = new FlinkKafkaConsumer011(serTopics, new JsonNodeDeserializationSchema(), parameter)
// val ipstream= env.addSource(kafkaConsumer).name("source")
// .assignTimestampsAndWatermarks(new TimestampExtractor).keyBy(_.get("uuid").asText()).window(Tum.of(Time.minutes(1))).process(new MyProcessWindowFunction).name("aggregate")
val ipstream= env.addSource(kafkaConsumer).name("source").assignTimestampsAndWatermarks(new TimestampExtractor)
.keyBy(_.get("uuid").asText()).window(TumblingEventTimeWindows.of(Time.seconds(60))).trigger(ContinuousEventTimeTrigger.of(Time.seconds(60))).aggregate(new AverageAggregate,new MyProcessWindowFunction).name("aggregate")
//.keyBy(_.get("uuid").asText()).window(TumblingProcessingTimeWindows.of(Time.minutes(1))).aggregate(new AverageAggregate, new MyProcessWindowFunction).name("aggregate")
ipstream.print()
ipstream.addSink(kafkaDiProducer).name("kafka-push")
env.execute("aggregate-stream")
}
class TimestampExtractor extends BoundedOutOfOrdernessTimestampExtractor[ObjectNode](Time.minutes(1)) {
override def extractTimestamp(element: ObjectNode): Long = element.get(Constants.Timestamp).asLong()
}
case class Acc[T](key:String,timeStamp:Long, min:T,max:T,count:Long,result:Double)
//case class Result[T](min:T,max:T,count:Long,value:Double)
// Function definitions
/**
* The accumulator is used to keep a running sum and a count. The [getResult] method
* computes the average.
*/
class AverageAggregate extends AggregateFunction[ObjectNode, Acc[Double], ObjectNode] {
override def createAccumulator() = Acc("",0L,0,0,0L,0)
override def add(value: ObjectNode, accumulator: Acc[Double]): Acc[Double] = {
val valueCol=value.get("value").asDouble();
val maxVal=if(accumulator.timeStamp<=0 || valueCol>accumulator.max){
valueCol
}else{
accumulator.max
}
val minVal= if(accumulator.timeStamp<=0 || valueCol<accumulator.min){
valueCol
}else{
accumulator.min
}
val timeStamp=value.get("timestamp").asLong()
val newTimeSamp=if(accumulator.timeStamp<=0 || accumulator.timeStamp>timeStamp ){
timeStamp
}else{
accumulator.timeStamp
}
log.info(s"${value.get("uuid").asText()} $timeStamp $newTimeSamp $maxVal $minVal" )
Acc(value.get("uuid").asText(),newTimeSamp,minVal,maxVal,accumulator.count+1l,valueCol+accumulator.result)
}
override def getResult(accumulator: Acc[Double]):ObjectNode = {
log.info(s"${accumulator.key} ${accumulator.timeStamp} ${accumulator.result} ${accumulator.max} ${accumulator.min}" )
val result=JsonNodeFactory.instance.objectNode()
result.put("uuid",accumulator.key)
result.put("timestamp",accumulator.timeStamp)
result.put("value_max_1_m",accumulator.max)
result.put("value_min_1_m",accumulator.min)
result.put("value_sum_1_m",accumulator.result)
result.put("value_count_1_m",accumulator.count)
result.put("value_mean_1_m",accumulator.result/accumulator.count)
result
} //Acc(accumulator.min,accumulator.max,accumulator.count, accumulator.sum / accumulator.count)
override def merge(a: Acc[Double], b: Acc[Double]): Acc[Double] = {
val maxVal=if(b.max>a.max){
b.max
}else{
a.max
}
val minVal=if(b.min<a.min){
b.min
}else{
a.min
}
val newTimeSamp=if(a.timeStamp<=0 || a.timeStamp>b.timeStamp ){
b.timeStamp
}else{
a.timeStamp
}
Acc(a.key,newTimeSamp,maxVal,minVal,a.count+b.count,a.result+b.result)
}
}
class MyProcessWindowFunction extends ProcessWindowFunction[ObjectNode, ObjectNode, String, TimeWindow] {
override def process(key: String, context: Context, elements: Iterable[ObjectNode], out: Collector[ObjectNode]): Unit = {
val averages=elements.iterator.next()
out.collect(averages)
}
}
}
聚合应该发生在事件时间上，而不是在处理时间上，这意味着数据对象中的时间戳。
答案 0 :(得分：0)
将ENV Parallelism设置为1时，上面的代码起作用。然后仅生成并提高水位标记。如果您的主题具有多个分区，则flink的kafka使用者连接器似乎存在问题。
中解释