closedchannelexception导致spark作业失败(dfsoutputstream.checkclosed)

eufgjt7s  于 2021-06-02  发布在  Hadoop
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我有一个spark应用程序。我正在用 saveAsNewAPIHadoopDataset ,利用 AvroKeyOutputFormat .
对于大型RDD有时我会得到这么多 ClosedChannelException 应用程序最终中止。
我在某个地方读过 hadoopConf.set("fs.hdfs.impl.disable.cache", "false"); 有帮助。
以下是我保存rdd的方法:

hadoopConf.set("fs.hdfs.impl.disable.cache", "false");
        final Job job = Job.getInstance(hadoopConf);            
        FileOutputFormat.setOutputPath(job, outPutPath);
        AvroJob.setOutputKeySchema(job, MyClass.SCHEMA$);
        job.setOutputFormatClass(AvroKeyOutputFormat.class);

        rdd                    
                .mapToPair(new PreparePairForDatnum())
                .saveAsNewAPIHadoopDataset(job.getConfiguration());

以下是堆栈跟踪:

java.nio.channels.ClosedChannelException
    at org.apache.hadoop.hdfs.DFSOutputStream.checkClosed(DFSOutputStream.java:1765)
    at org.apache.hadoop.fs.FSOutputSummer.write(FSOutputSummer.java:108)
    at org.apache.hadoop.fs.FSDataOutputStream$PositionCache.write(FSDataOutputStream.java:58)
    at java.io.DataOutputStream.write(DataOutputStream.java:107)
    at org.apache.avro.file.DataFileWriter$BufferedFileOutputStream$PositionFilter.write(DataFileWriter.java:458)
    at java.io.BufferedOutputStream.flushBuffer(BufferedOutputStream.java:82)
    at java.io.BufferedOutputStream.write(BufferedOutputStream.java:121)
    at org.apache.avro.io.BufferedBinaryEncoder$OutputStreamSink.innerWrite(BufferedBinaryEncoder.java:216)
    at org.apache.avro.io.BufferedBinaryEncoder.writeFixed(BufferedBinaryEncoder.java:150)
    at org.apache.avro.file.DataFileStream$DataBlock.writeBlockTo(DataFileStream.java:369)
    at org.apache.avro.file.DataFileWriter.writeBlock(DataFileWriter.java:395)
    at org.apache.avro.file.DataFileWriter.writeIfBlockFull(DataFileWriter.java:340)
    at org.apache.avro.file.DataFileWriter.append(DataFileWriter.java:311)
    at org.apache.avro.mapreduce.AvroKeyRecordWriter.write(AvroKeyRecordWriter.java:77)
    at org.apache.avro.mapreduce.AvroKeyRecordWriter.write(AvroKeyRecordWriter.java:39)
    at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsNewAPIHadoopDataset$1$$anonfun$12$$anonfun$apply$4.apply$mcV$sp(PairRDDFunctions.scala:1036)
    at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsNewAPIHadoopDataset$1$$anonfun$12$$anonfun$apply$4.apply(PairRDDFunctions.scala:1034)
    at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsNewAPIHadoopDataset$1$$anonfun$12$$anonfun$apply$4.apply(PairRDDFunctions.scala:1034)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1206)
    at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsNewAPIHadoopDataset$1$$anonfun$12.apply(PairRDDFunctions.scala:1042)
    at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsNewAPIHadoopDataset$1$$anonfun$12.apply(PairRDDFunctions.scala:1014)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
    at org.apache.spark.scheduler.Task.run(Task.scala:88)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
    Suppressed: java.nio.channels.ClosedChannelException
        at org.apache.hadoop.hdfs.DFSOutputStream.checkClosed(DFSOutputStream.java:1765)
        at org.apache.hadoop.fs.FSOutputSummer.write(FSOutputSummer.java:108)
        at org.apache.hadoop.fs.FSDataOutputStream$PositionCache.write(FSDataOutputStream.java:58)
        at java.io.DataOutputStream.write(DataOutputStream.java:107)
        at org.apache.avro.file.DataFileWriter$BufferedFileOutputStream$PositionFilter.write(DataFileWriter.java:458)
        at java.io.BufferedOutputStream.flushBuffer(BufferedOutputStream.java:82)
        at java.io.BufferedOutputStream.write(BufferedOutputStream.java:121)
        at org.apache.avro.io.BufferedBinaryEncoder$OutputStreamSink.innerWrite(BufferedBinaryEncoder.java:216)
        at org.apache.avro.io.BufferedBinaryEncoder.writeFixed(BufferedBinaryEncoder.java:150)
        at org.apache.avro.file.DataFileStream$DataBlock.writeBlockTo(DataFileStream.java:369)
        at org.apache.avro.file.DataFileWriter.writeBlock(DataFileWriter.java:395)
        at org.apache.avro.file.DataFileWriter.sync(DataFileWriter.java:413)
        at org.apache.avro.file.DataFileWriter.flush(DataFileWriter.java:422)
        at org.apache.avro.file.DataFileWriter.close(DataFileWriter.java:445)
        at org.apache.avro.mapreduce.AvroKeyRecordWriter.close(AvroKeyRecordWriter.java:83)
        at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsNewAPIHadoopDataset$1$$anonfun$12$$anonfun$apply$5.apply$mcV$sp(PairRDDFunctions.scala:1043)
        at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1215)
        ... 8 more
pgvzfuti

pgvzfuti1#

当遗嘱执行人被杀的时候就会发生。看看你的日志

2016-07-20 22:00:42,976 | WARN  | org.apache.spark.scheduler.cluster.YarnSchedulerBackend$YarnSchedulerEndpoint | Container container_e10838_1468831508103_1724_01_055482 on host: hostName was preempted.
2016-07-20 22:00:42,977 | ERROR | org.apache.spark.scheduler.cluster.YarnClusterScheduler | Lost executor 6 on hostName: Container container_e10838_1468831508103_1724_01_055482 on host: hostName was preempted.

如果您发现了,那么您的任务的执行者将被应用程序主程序抢占。换言之,他被杀了,又被安排了一个长队。关于抢占和Yarn调度可以在这里和这里找到。

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