为什么 Spark 作业会失败并显示"Exit code: 52"

Question

我的 Spark 作业失败

了，跟踪如下：

./containers/application_1455622885057_0016/container_1455622885057_0016_01_000001/stderr-Container id: container_1455622885057_0016_01_000008
./containers/application_1455622885057_0016/container_1455622885057_0016_01_000001/stderr-Exit code: 52
./containers/application_1455622885057_0016/container_1455622885057_0016_01_000001/stderr:Stack trace: ExitCodeException exitCode=52: 
./containers/application_1455622885057_0016/container_1455622885057_0016_01_000001/stderr-      at org.apache.hadoop.util.Shell.runCommand(Shell.java:545)
./containers/application_1455622885057_0016/container_1455622885057_0016_01_000001/stderr-      at org.apache.hadoop.util.Shell.run(Shell.java:456)
./containers/application_1455622885057_0016/container_1455622885057_0016_01_000001/stderr-      at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:722)
./containers/application_1455622885057_0016/container_1455622885057_0016_01_000001/stderr-      at org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:211)
./containers/application_1455622885057_0016/container_1455622885057_0016_01_000001/stderr-      at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:302)
./containers/application_1455622885057_0016/container_1455622885057_0016_01_000001/stderr-      at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:82)
./containers/application_1455622885057_0016/container_1455622885057_0016_01_000001/stderr-      at java.util.concurrent.FutureTask.run(FutureTask.java:262)
./containers/application_1455622885057_0016/container_1455622885057_0016_01_000001/stderr-      at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
./containers/application_1455622885057_0016/container_1455622885057_0016_01_000001/stderr-      at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
./containers/application_1455622885057_0016/container_1455622885057_0016_01_000001/stderr-      at java.lang.Thread.run(Thread.java:745)
./containers/application_1455622885057_0016/container_1455622885057_0016_01_000001/stderr-
./containers/application_1455622885057_0016/container_1455622885057_0016_01_000001/stderr-
./containers/application_1455622885057_0016/container_1455622885057_0016_01_000001/stderr-Container exited with a non-zero exit code 52

我花了一段时间才弄清楚"退出代码 52"是什么意思，所以我把它放在这里是为了其他可能正在搜索的人的利益

Answer 1

退出代码 52 来自 org.apache.spark.util.SparkExitCode，它是val OOM=52的 - 即 OutOfMemoryError。这是有道理的，因为我也在容器日志中找到了这一点：

16/02/16 17:09:59 ERROR executor.Executor: Managed memory leak detected; size = 4823704883 bytes, TID = 3226
16/02/16 17:09:59 ERROR executor.Executor: Exception in task 26.0 in stage 2.0 (TID 3226)
java.lang.OutOfMemoryError: Unable to acquire 1248 bytes of memory, got 0
        at org.apache.spark.memory.MemoryConsumer.allocatePage(MemoryConsumer.java:120)
        at org.apache.spark.shuffle.sort.ShuffleExternalSorter.acquireNewPageIfNecessary(ShuffleExternalSorter.java:354)
        at org.apache.spark.shuffle.sort.ShuffleExternalSorter.insertRecord(ShuffleExternalSorter.java:375)
        at org.apache.spark.shuffle.sort.UnsafeShuffleWriter.insertRecordIntoSorter(UnsafeShuffleWriter.java:237)
        at org.apache.spark.shuffle.sort.UnsafeShuffleWriter.write(UnsafeShuffleWriter.java:164)
        at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
        at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
        at org.apache.spark.scheduler.Task.run(Task.scala:89)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
        at java.lang.Thread.run(Thread.java:745)

(请注意，目前我不确定问题是在我的代码中还是由于钨内存泄漏，但这是一个不同的问题(