apache-spark - 具有 32GB 或更多内存的 spark worker 遇到 fatal error

Question

我在一个独立的 Spark 集群中有三个从站。每个从站有 48GB 的​​ RAM。当我为我的执行程序分配超过 31GB(例如 32GB 或更多)的 RAM 时:

.config("spark.executor.memory", "44g")

在两个大型 Dataframe 的连接过程中，执行程序在没有太多信息的情况下被终止。 Slave 驱动程序的输出消息显示“缺少 shuffle 的输出位置”:

17/09/21 12:34:18 INFO StandaloneSchedulerBackend: Granted executor ID app-20170921123240-0000/3 on hostPort XXX.XXX.XXX.92:33705 with 6 cores, 44.0 GB RAM
17/09/21 12:34:18 WARN TaskSetManager: Lost task 14.0 in stage 7.0 (TID 124, XXX.XXX.XXX.92, executor 0): ExecutorLostFailure (executor 0 exited caused by one of the running tasks) Reason: Remote RPC client disassociated. Likely due to containers exceeding thresholds, or network issues. Check driver logs for WARN messages.
17/09/21 12:34:18 WARN TaskSetManager: Lost task 5.0 in stage 7.0 (TID 115, XXX.XXX.XXX.92, executor 0): ExecutorLostFailure (executor 0 exited caused by one of the running tasks) Reason: Remote RPC client disassociated. Likely due to containers exceeding thresholds, or network issues. Check driver logs for WARN messages.
17/09/21 12:34:18 WARN TaskSetManager: Lost task 17.0 in stage 7.0 (TID 127, XXX.XXX.XXX.92, executor 0): ExecutorLostFailure (executor 0 exited caused by one of the running tasks) Reason: Remote RPC client disassociated. Likely due to containers exceeding thresholds, or network issues. Check driver logs for WARN messages.
17/09/21 12:34:18 WARN TaskSetManager: Lost task 8.0 in stage 7.0 (TID 118, XXX.XXX.XXX.92, executor 0): ExecutorLostFailure (executor 0 exited caused by one of the running tasks) Reason: Remote RPC client disassociated. Likely due to containers exceeding thresholds, or network issues. Check driver logs for WARN messages.
17/09/21 12:34:18 WARN TaskSetManager: Lost task 2.0 in stage 7.0 (TID 112, XXX.XXX.XXX.92, executor 0): ExecutorLostFailure (executor 0 exited caused by one of the running tasks) Reason: Remote RPC client disassociated. Likely due to containers exceeding thresholds, or network issues. Check driver logs for WARN messages.
17/09/21 12:34:18 WARN TaskSetManager: Lost task 11.0 in stage 7.0 (TID 121, XXX.XXX.XXX.92, executor 0): ExecutorLostFailure (executor 0 exited caused by one of the running tasks) Reason: Remote RPC client disassociated. Likely due to containers exceeding thresholds, or network issues. Check driver logs for WARN messages.
17/09/21 12:34:18 INFO DAGScheduler: Executor lost: 0 (epoch 5)
17/09/21 12:34:18 INFO BlockManagerMaster: Removal of executor 0 requested
17/09/21 12:34:18 INFO CoarseGrainedSchedulerBackend$DriverEndpoint: Asked to remove non-existent executor 0
17/09/21 12:34:18 INFO BlockManagerMasterEndpoint: Trying to remove executor 0 from BlockManagerMaster.
17/09/21 12:34:18 WARN BlockManagerMasterEndpoint: No more replicas available for rdd_10_2 !
17/09/21 12:34:18 WARN BlockManagerMasterEndpoint: No more replicas available for rdd_10_11 !
17/09/21 12:34:18 INFO StandaloneAppClient$ClientEndpoint: Executor updated: app-20170921123240-0000/3 is now RUNNING
17/09/21 12:34:18 WARN BlockManagerMasterEndpoint: No more replicas available for rdd_10_5 !
17/09/21 12:34:18 WARN BlockManagerMasterEndpoint: No more replicas available for rdd_10_8 !
17/09/21 12:34:18 INFO BlockManagerMasterEndpoint: Removing block manager BlockManagerId(0, XXX.XXX.XXX, 34840, None)
17/09/21 12:34:18 INFO BlockManagerMasterEndpoint: Trying to remove executor 0 from BlockManagerMaster.
17/09/21 12:34:18 INFO BlockManagerMaster: Removed 0 successfully in removeExecutor

Spark Master 的日志信息显示执行器已“EXITED”，然后重新启动:

17/09/21 12:34:18 INFO Master: Removing executor app-20170921123240-0000/0 because it is EXITED
17/09/21 12:34:18 INFO Master: Launching executor app-20170921123240-0000/3 on worker worker-20170921123014-152.83.247.92-33705

Spark Worker 的日志信息显示执行器以代码 134 退出

17/09/21 12:34:18 INFO Worker: Executor app-20170921123240-0000/0 finished with state EXITED message Command exited with code 134 exitStatus 134

唯一的线索似乎是在应用程序的错误日志中，显示 JRE 检测到了一个 fatal error :

#
# A fatal error has been detected by the Java Runtime Environment:
#
#  SIGSEGV (0xb) at pc=0x00007fdec0c92a73, pid=11300, tid=0x00007fd3a6951700
#
# JRE version: Java(TM) SE Runtime Environment (8.0_131-b11) (build 1.8.0_131-b11)
# Java VM: Java HotSpot(TM) 64-Bit Server VM (25.131-b11 mixed mode linux-amd64 )
# Problematic frame:
# V  [libjvm.so+0x3ffa73]  CardTableExtension::scavenge_contents_parallel(ObjectStartArray*, MutableSpace*, HeapWord*, PSPromotionManager*, unsigned int, unsigned int)+0x5e3
#
# Failed to write core dump. Core dumps have been disabled. To enable core dumping, try "ulimit -c unlimited" before starting Java again
#
# If you would like to submit a bug report, please visit:
#   http://bugreport.java.com/bugreport/crash.jsp
#

---------------  T H R E A D  ---------------

Current thread (0x0000000001c9e800):  GCTaskThread [stack: 0x00007fd3a6851000,0x00007fd3a6952000] [id=11308]

siginfo: si_signo: 11 (SIGSEGV), si_code: 1 (SEGV_MAPERR), si_addr: 0x0000000000000008

只要我为每个执行程序分配 31GB 的 RAM(或更少)，我的程序就可以正常工作。有没有人遇到过这样的问题？

Answer 1

由于 Java 存储对象引用的方式，44 GB 实际上可能给您提供比 31 GB 更小的可用堆:对于超过 32 GB 的堆大小，JVM 必须切换到 64 位对象引用，这意味着所有对象都会占用更多空间。更多详情:http://java-performance.info/over-32g-heap-java/

我的经验法则是保持在 32 GB 以下或更高(例如 50 GB)。通常使用多个 JVM 的成本效益更高，每个 JVM 的堆小于 32 GB。使用 48 GB RAM，我会坚持使用 31 GB 堆。