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
...
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
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) } }
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