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flink 如何实现对watermark 的checkpoint,防止数据复写

时间:2020-10-22 22:23:47      阅读:22      评论:0      收藏:0      [点我收藏+]

标签:断点   nali   soc   ali   odi   run   character   dde   max   

fink slink 后的数据被复写了???

生产环境总会遇到各种各样的莫名其名的数据,一但考虑不周便是车毁人亡啊。


 

线上sink 流是es , es 的文档id 是自定义的 id+windowSatarTime

设window size = 10min , watermark 最大延迟时间是 10s,. 数据中的event time 是乱序到达的,数据最大延迟时间是 30min

watermark 生成函数

assignTimestampsAndWatermarks(new AssignerWithPeriodicWatermarks[Goods] {
        val maxOutOfOrderness = 2L // 最大无序数据到达的时间,用来生成水印2ms
        var currentMaxTimestamp: Long = _
        val dateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss.sss")

        override def getCurrentWatermark: Watermark = {
          println(s"${dateFormat.format(new Date().getTime)} -------watermark: ${currentMaxTimestamp - maxOutOfOrderness}")
          new Watermark(currentMaxTimestamp - maxOutOfOrderness)
        }

        override def extractTimestamp(element: Goods, previousElementTimestamp: Long): Long = {
          currentMaxTimestamp = Math.max(element.time, currentMaxTimestamp)
          element.time
        }
      })

 

如果现在是10:15 分,当前win的窗口是 [10:10,10:20),意味着[09:40,09:50,10:00] 的统计值已经生成 。

此时,程序发生异常,并有checkpoint + resart 策略,那么重启后,watermark 会继续从断点处消费?window 是否还是[10:10,10:20)?

答案是不会,watermark 会从0开始增长,window 也会从新开始。

重启后,如果不幸第一条数据的eventtime 是 09:45:02 , 那么此时 watermark 是 09:45:00 , window 是 [09:40:09:50), 一段时间后数据再次会聚合生条es 记录文档 [id+09:40], sink 时之前的es 数据会被覆盖

测试:

2020-10-21 23:57:01.001 -------watermark: -2
input:Goods(id=1,count=10,time=10)               // 输入: 1,10,10
()
2020-10-21 23:57:01.001 -------watermark: 8
.... 2020-10-21 23:57:04.004 -------watermark: 8 // 输入: 0,0,0 触发异常,重启 2020-10-21 23:57:09.009 -------watermark: -2 // watermark 重新开始
.... 2020-10-21 23:57:17.017 -------watermark: -2 input:Goods(id=1,count=10,time=10) () 2020-10-21 23:57:17.017 -------watermark: 8
...

解决:

这里的  currentMaxTimestamp 本质可以看做是 Operator State , 那么可以通过实现  CheckpointedFunction、ListCheckpointed 接口来保存这个state

修改后的water mark 函数

.assignTimestampsAndWatermarks(new AssignerWithPeriodicWatermarks[Goods] with ListCheckpointed[JavaLong] {
        val maxOutOfOrderness = 2L // 最大无序数据到达的时间,用来生成水印2ms
        var currentMaxTimestamp: Long = _

        override def getCurrentWatermark: Watermark = {
          println("watermark", currentMaxTimestamp - maxOutOfOrderness)
          new Watermark(currentMaxTimestamp - maxOutOfOrderness)
        }

        override def extractTimestamp(element: Goods, previousElementTimestamp: Long): Long = {
          currentMaxTimestamp = Math.max(element.time, currentMaxTimestamp)
          element.time
        }

        override def snapshotState(checkpointId: Long, timestamp: Long): util.List[JavaLong] = {
          Collections.singletonList(currentMaxTimestamp)
        }

        override def restoreState(state: util.List[JavaLong]): Unit = {
          val stateMin = state.asScala.min
          if (stateMin > 0) currentMaxTimestamp = stateMin
        }
      })

测试:

2020-10-22 00:39:00.000 -------watermark: -2
input:Goods(id=1,count=10,time=10)      // 输入: 1,10,10
()
2020-10-22 00:39:00.000 -------watermark: 8
...
2020-10-22 00:39:03.003 -------watermark: 8
input:Goods(id=0,count=0,time=0)        // 输入: 0,0,0 触发异常,重启
2020-10-22 00:39:08.008 -------watermark: 8  // 从 checkpoints 中获取state
...
2020-10-22 00:39:23.023 -------watermark: 8
input:Goods(id=1,count=20,time=20)   // 输入: 1,20,20
()
2020-10-22 00:39:23.023 -------watermark: 18
....

完整测试程序

技术图片
import java.util.{Collections, Date}
import java.util

import scala.collection.JavaConverters._
import java.lang.{Long => JavaLong}
import java.text.SimpleDateFormat
import java.util.concurrent.TimeUnit

import org.apache.flink.api.common.restartstrategy.RestartStrategies
import org.apache.flink.api.common.time.Time
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.api.scala._
import org.apache.flink.contrib.streaming.state.RocksDBStateBackend
import org.apache.flink.streaming.api.{CheckpointingMode, TimeCharacteristic}
import org.apache.flink.streaming.api.checkpoint.ListCheckpointed
import org.apache.flink.streaming.api.environment.CheckpointConfig.ExternalizedCheckpointCleanup
import org.apache.flink.streaming.api.functions.AssignerWithPeriodicWatermarks
import org.apache.flink.streaming.api.watermark.Watermark

/**
 * CheckpointCount
 */
object WatermarkCheckpoint {

  case class Goods(var id: Int = 0, var count: Int = 0, var time: Long = 0L) {
    override def toString: String = s"Goods(id=$id,count=$count,time=$time)"
  }

  def main(args: Array[String]): Unit = {
    val dateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss.sss")
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
    env.enableCheckpointing(1000 * 10)
    env.getCheckpointConfig.setCheckpointTimeout(1000 * 60) // checkpoint 超时时间
    env.getCheckpointConfig.setMinPauseBetweenCheckpoints(1000 * 5) // 两次 checkpoint 的最小间隔
    env.getCheckpointConfig.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE) // checkpoint 模式
    env.getCheckpointConfig.setMaxConcurrentCheckpoints(2) // checkpoint 并发数
    env.getCheckpointConfig.enableExternalizedCheckpoints(ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION) // cancel job 时持久化checkopint
    env.getCheckpointConfig.setFailOnCheckpointingErrors(false) // 当checkpoint 失败时不会导致任务失败终止
    // restart strategy
    env.setRestartStrategy(
      RestartStrategies.fixedDelayRestart(2, Time.of(5, TimeUnit.SECONDS))
    )
    // state backend
    val file_rocksdb = "file:///tmp/state/rocksdb"  // 需要提前建立路径
    env.setStateBackend(new RocksDBStateBackend(file_rocksdb, true))
    env.setParallelism(1)

    env.socketTextStream("localhost", 9999)
      .filter(_.nonEmpty)
      .map(x => {
        val arr = x.split(",")
        val g = Goods(arr(0).toInt, arr(1).toInt, arr(2).toLong) // id,count,time
        println(s"input:$g")
        g
      })

      // watermark 没有 checkpoint
      /*.assignTimestampsAndWatermarks(new AssignerWithPeriodicWatermarks[Goods] {
        val maxOutOfOrderness = 2L // 最大无序数据到达的时间,用来生成水印2ms
        var currentMaxTimestamp: Long = _

        override def getCurrentWatermark: Watermark = {
          println(s"${dateFormat.format(new Date().getTime)} -------watermark: ${currentMaxTimestamp - maxOutOfOrderness}")
          new Watermark(currentMaxTimestamp - maxOutOfOrderness)
        }

        override def extractTimestamp(element: Goods, previousElementTimestamp: Long): Long = {
          currentMaxTimestamp = Math.max(element.time, currentMaxTimestamp)
          element.time
        }
      })*/

      // watermark  checkpoint
      .assignTimestampsAndWatermarks(new AssignerWithPeriodicWatermarks[Goods] with ListCheckpointed[JavaLong] {
        val maxOutOfOrderness = 2L // 最大无序数据到达的时间,用来生成水印2ms
        var currentMaxTimestamp: Long = _

        override def getCurrentWatermark: Watermark = {
          println(s"${dateFormat.format(new Date().getTime)} -------watermark: ${currentMaxTimestamp - maxOutOfOrderness}")
          new Watermark(currentMaxTimestamp - maxOutOfOrderness)
        }

        override def extractTimestamp(element: Goods, previousElementTimestamp: Long): Long = {
          currentMaxTimestamp = Math.max(element.time, currentMaxTimestamp)
          element.time
        }

        override def snapshotState(checkpointId: Long, timestamp: Long): util.List[JavaLong] = {
          Collections.singletonList(currentMaxTimestamp)
        }

        override def restoreState(state: util.List[JavaLong]): Unit = {
          val stateMin = state.asScala.min
          if (stateMin > 0) currentMaxTimestamp = stateMin
        }
      })

      .map(x => {
        if (x.id == 0) throw new RuntimeException("id is 0")
      })
      .print()

    env.execute(this.getClass.getSimpleName)
  }
}
完整测试代码

 

flink 如何实现对watermark 的checkpoint,防止数据复写

标签:断点   nali   soc   ali   odi   run   character   dde   max   

原文地址:https://www.cnblogs.com/feiquan/p/13853105.html

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