Flink数据倾斜问题

 

 

这里我们使用两阶段keyby 解决该问题

之前的问题如下图所示

 

 我们期望的是

 

 

但我们的需要根据key进行聚合统计，那么把相同的key放在不同的subtask如何统计？

我们看下图（只画了主要部分）

1.首先将key打散，我们加入将key转化为 key-随机数 ,保证数据散列

2.对打散后的数据进行聚合统计，这时我们会得到数据比如 : (key1-12,1),(key1-13,19),(key1-1,20),(key2-123,11),(key2-123,10)

3.将散列key还原成我们之前传入的key，这时我们的到数据是聚合统计后的结果，不是最初的原数据

4.二次keyby进行结果统计，输出到addSink

 

import org.apache.flink.api.common.functions.AggregateFunction
import org.apache.flink.api.common.state.{ValueState, ValueStateDescriptor}
import org.apache.flink.api.java.tuple.Tuple
import org.apache.flink.api.scala.typeutils.Types
import org.apache.flink.streaming.api.functions.KeyedProcessFunction
import org.apache.flink.streaming.api.functions.windowing.WindowFunction
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.streaming.api.windowing.windows.TimeWindow
import org.apache.flink.util.Collector

object ProcessFunctionScalaV2 {


def main(args: Array[String]): Unit = {
val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
env.enableCheckpointing(2000)
val stream: DataStream[String] = env.socketTextStream("localhost", 9999)
val typeAndData: DataStream[(String, Long)] = stream.map(x => (x.split(",")(0), x.split(",")(1).toLong))
val dataStream: DataStream[(String, Long)] = typeAndData
.map(x => (x._1 + "-" + scala.util.Random.nextInt(100), x._2))
val keyByAgg: DataStream[DataJast] = dataStream.keyBy(_._1)
.timeWindow(Time.seconds(10))
.aggregate(new CountAggregate())
keyByAgg.print("第一次keyby输出")
val result: DataStream[DataJast] = keyByAgg.map(data => {
val newKey: String = data.key.substring(0, data.key.indexOf("-"))
println(newKey)
DataJast(newKey, data.count)
}).keyBy(_.key)
.process(new MyProcessFunction())
result.print("第二次keyby输出")


env.execute()
}

case class DataJast(key :String,count:Long)

//计算keyby后，每个Window中的数据总和
class CountAggregate extends AggregateFunction[(String, Long),DataJast, DataJast] {

override def createAccumulator(): DataJast = {
println("初始化")
DataJast(null,0)
}

override def add(value: (String, Long), accumulator: DataJast): DataJast = {
if(accumulator.key==null){
printf("第一次加载,key:%s,value:%d\n",value._1,value._2)
DataJast(value._1,value._2)
}else{
printf("数据累加,key:%s,value:%d\n",value._1,accumulator.count+value._2)
DataJast(value._1,accumulator.count + value._2)
}
}

override def getResult(accumulator: DataJast): DataJast = {
println("返回结果："+accumulator)
accumulator
}

override def merge(a: DataJast, b: DataJast): DataJast = {
DataJast(a.key,a.count+b.count)
}
}


/**
* 实现：
* 根据key分类，统计每个key进来的数据量，定期统计数量
*/
class MyProcessFunction extends KeyedProcessFunction[String,DataJast,DataJast]{

val delayTime : Long = 1000L * 30

lazy val valueState:ValueState[Long] = getRuntimeContext.getState[Long](new ValueStateDescriptor[Long]("ccount",classOf[Long]))

override def processElement(value: DataJast, ctx: KeyedProcessFunction[String, DataJast, DataJast]#Context, out: Collector[DataJast]): Unit = {
if(valueState.value()==0){
valueState.update(value.count)
printf("运行task:%s,第一次初始化数量:%s\n",getRuntimeContext.getIndexOfThisSubtask,value.count)
val currentTime: Long = ctx.timerService().currentProcessingTime()
//注册定时器
ctx.timerService().registerProcessingTimeTimer(currentTime + delayTime)
}else{
valueState.update(valueState.value()+value.count)
printf("运行task:%s,更新统计结果:%s\n" ,getRuntimeContext.getIndexOfThisSubtask,valueState.value())
}
}

override def onTimer(timestamp: Long, ctx: KeyedProcessFunction[String, DataJast, DataJast]#OnTimerContext, out: Collector[DataJast]): Unit = {
//定时器执行，可加入业务操作
printf("运行task:%s,触发定时器,30秒内数据一共,key:%s,value:%s\n",getRuntimeContext.getIndexOfThisSubtask,ctx.getCurrentKey,valueState.value())

//定时统计完成，初始化统计数据
valueState.update(0)
//注册定时器
val currentTime: Long = ctx.timerService().currentProcessingTime()
ctx.timerService().registerProcessingTimeTimer(currentTime + delayTime)
}
}



}

对key进行散列 

val dataStream: DataStream[(String, Long)] = typeAndData
.map(x => (x._1 + "-" + scala.util.Random.nextInt(100), x._2))

 设置窗口滚动时间，每隔十秒统计一次每隔key下的数据总量

val keyByAgg: DataStream[DataJast] = dataStream.keyBy(_._1)
.timeWindow(Time.seconds(10))
.aggregate(new AverageAggregate())
keyByAgg.print("第一次keyby输出")

还原key，并进行二次keyby，对数据总量进行累加

 

val result: DataStream[DataJast] = keyByAgg.map(data => {
val newKey: String = data.key.substring(0, data.key.indexOf("-"))
println(newKey)
DataJast(newKey, data.count)
}).keyBy(_.key)
.process(new MyProcessFunction())