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flink checkpoint 在 window 操作下 全局配置失效的问题。

已解决

背景

  • flink 版本号 1.6.2
  • flink 集群模式 flink on yarn
  • 使用flink 读取kafka 数据 简单处理之后使用自定义richWindowFunction 处理数据的时候出现异常报错:
AsynchronousException{java.lang.Exception: Could not materialize checkpoint 20 for operator Window(TumblingProcessingTimeWindows(5), ProcessingTimeTrigger, MyRichRedisWindowFuntion) (1/8).}
    at org.apache.flink.streaming.runtime.tasks.StreamTask$AsyncCheckpointExceptionHandler.tryHandleCheckpointException(StreamTask.java:1153)
    at org.apache.flink.streaming.runtime.tasks.StreamTask$AsyncCheckpointRunnable.handleExecutionException(StreamTask.java:947)
    at org.apache.flink.streaming.runtime.tasks.StreamTask$AsyncCheckpointRunnable.run(StreamTask.java:884)
    at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
    at java.util.concurrent.FutureTask.run(FutureTask.java:266)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.Exception: Could not materialize checkpoint 20 for operator Window(TumblingProcessingTimeWindows(5), ProcessingTimeTrigger, MyRichRedisWindowFuntion) (1/8).
    at org.apache.flink.streaming.runtime.tasks.StreamTask$AsyncCheckpointRunnable.handleExecutionException(StreamTask.java:942)
    ... 6 more
Caused by: java.util.concurrent.ExecutionException: java.io.IOException: Size of the state is larger than the maximum permitted memory-backed state. Size=5249873 , maxSize=5242880 . Consider using a different state backend, like the File System State backend.
    at java.util.concurrent.FutureTask.report(FutureTask.java:122)
    at java.util.concurrent.FutureTask.get(FutureTask.java:192)
    at org.apache.flink.util.FutureUtil.runIfNotDoneAndGet(FutureUtil.java:53)
    at org.apache.flink.streaming.api.operators.OperatorSnapshotFinalizer.<init>(OperatorSnapshotFinalizer.java:47)
    at org.apache.flink.streaming.runtime.tasks.StreamTask$AsyncCheckpointRunnable.run(StreamTask.java:853)
    ... 5 more
Caused by: java.io.IOException: Size of the state is larger than the maximum permitted memory-backed state. Size=5249873 , maxSize=5242880 . Consider using a different state backend, like the File System State backend.
    at org.apache.flink.runtime.state.memory.MemCheckpointStreamFactory.checkSize(MemCheckpointStreamFactory.java:64)
    at org.apache.flink.runtime.state.memory.MemCheckpointStreamFactory$MemoryCheckpointOutputStream.closeAndGetBytes(MemCheckpointStreamFactory.java:145)
    at org.apache.flink.runtime.state.memory.MemCheckpointStreamFactory$MemoryCheckpointOutputStream.closeAndGetHandle(MemCheckpointStreamFactory.java:126)
    at org.apache.flink.runtime.state.CheckpointStreamWithResultProvider$PrimaryStreamOnly.closeAndFinalizeCheckpointStreamResult(CheckpointStreamWithResultProvider.java:77)
    at org.apache.flink.runtime.state.heap.HeapKeyedStateBackend$HeapSnapshotStrategy$1.performOperation(HeapKeyedStateBackend.java:826)
    at org.apache.flink.runtime.state.heap.HeapKeyedStateBackend$HeapSnapshotStrategy$1.performOperation(HeapKeyedStateBackend.java:759)
    at org.apache.flink.runtime.io.async.AbstractAsyncCallableWithResources.call(AbstractAsyncCallableWithResources.java:75)
    at java.util.concurrent.FutureTask.run(FutureTask.java:266)
    at org.apache.flink.util.FutureUtil.runIfNotDoneAndGet(FutureUtil.java:50)
    ... 7 more
  • flink 关于 checkpoint 配置 :
fs.default-scheme: hdfs://@hadoop:9000/
fs.hdfs.hadoopconf: hdfs:///flink/data/
state.checkpoints.dir: hdfs:///flink/checkpoints/
state.checkpoints.num-retained: 20
state.savepoints.dir: hdfs:///flink/flink-savepoints/
state.backend.fs.checkpoint.dir: hdfs:///flink/state/checkpoints/

疑惑点:

全局设置 checkpoint 保存地址 ,那么window 操作的保存地址 应该也是该位置 .
但是为什么还是会将checkpoint 使用memory 方式?

尝试解决办法:

在代码层设置 checkpoint保存模式:

env.setStateBackend(new
FsStateBackend("hdfs:///flink/checkpoints/workFlowCheckpoint"));

解决前后对比 :

解决后hdfs 目录:

image

再次疑虑:

但是在1.6.2 版本 该类没设置为Deprecated ,求问 :
我这个解决办法是有什么不准确的方式么? 还是说 全局设置checkpoint 对于window 自身并没有生效?

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千狼 2018-11-09 12:13:37 8381 0
3 条回答
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  • https://github.com/Myasuka
    采纳回答

    你好,其实你是误解了 checkpoint 配置与checkpointStorage之间的关系。
    FsStateBackend <--> 使用 FsCheckpointStorage,每个FsStateBackend的state数据会写到DFS里面,返回的handle不包含实际数据,只是一个路径地址(除非实际数据的size小于state.backend.fs.memory-threshold,会存储在返回的hanlde里面)。
    MemoryStateBackend <--> 使用 MemoryBackendCheckpointStorage,每个MemoryStateBackend的state数据都会直接存储在返回的handle里面。
    如果你只配置了checkpoint path相关的配置,由于没有声明是FsStateBackend,导致会使用默认的MemoryStateBackend,从而所有的state都会直接返回给JM,当state数据量大的时候,就会因为超过阈值而报错(默认值5MB)

    2019-07-17 23:13:13
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  • 如果没有在 flink-conf.yaml 里配置 state.backend 的值,或者通过api 显示设置 statebackend,默认为 heap 模式

    2019-07-17 23:13:13
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  • 不是很了解

    2019-07-17 23:13:12
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