想了解Hive-ContainerkilledbyYARNforexceedingmemorylimits.9.2GBof9GBphysicalmemoryused.C...的新动态吗？本文将为您提供

想了解Hive-Container killed by YARN for exceeding memory limits. 9.2 GB of 9 GB physical memory used. C...的新动态吗？本文将为您提供详细的信息，此外，我们还将为您介绍关于64-Bit Server VM warning: INFO: os::commit_memory(0x) failed; error=''Cannot allocate memory ？？？、Attempted to read or write protected memory. This is often an indication that other memory is corrupt.、Caused by: java.lang.OutOfMemoryError: bitmap size exceeds VM budget 急急、Caused by: java.lang.OutOfMemoryError: GC overhead limit exceeded的新知识。

• Hive-Container killed by YARN for exceeding memory limits. 9.2 GB of 9 GB physical memory used. C...
• 64-Bit Server VM warning: INFO: os::commit_memory(0x) failed; error=''Cannot allocate memory ？？？
• Attempted to read or write protected memory. This is often an indication that other memory is corrupt.
• Caused by: java.lang.OutOfMemoryError: bitmap size exceeds VM budget 急急
• Caused by: java.lang.OutOfMemoryError: GC overhead limit exceeded

Hive-Container killed by YARN for exceeding memory limits. 9.2 GB of 9 GB physical memory used. C...

Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 3 in stage 0.0 failed 4 times, most recent failure: Lost task 3.3 in stage 0.0 (TID 62, hadoop7, executor 17): ExecutorLostFailure (executor 17 exited caused by one of the running tasks) Reason: Container killed by YARN for exceeding memory limits. 9.2 GB of 9 GB physical memory used. Consider boosting spark.yarn.executor.memoryOverhead.
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1524)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1512)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1511)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1511)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1739)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1694)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1683)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)

ERROR : FAILED: Execution Error, return code 3 from org.apache.hadoop.hive.ql.exec.spark.SparkTask. Spark job failed because of out of memory.
INFO : Completed executing command(queryId=hive_20190529100107_063ed2a4-e3b0-48a9-9bcc-49acd51925c1); Time taken: 1441.753 seconds
Error: Error while processing statement: FAILED: Execution Error, return code 3 from org.apache.hadoop.hive.ql.exec.spark.SparkTask. Spark job failed because of out of memory. (state=42000,code=3)
Closing: 0: jdbc:hive2://hadoop1:10000/pdw_nameonce

Hive on spark时报错

解决
a.set spark.yarn.executor.memoryOverhead=512G 调大(权宜之计)，excutor-momery + memoryOverhead不能大于集群内存
b.该问题的原因是因为OS层面虚拟内存分配导致，物理内存没有占用多少，但检查虚拟内存的时候却发现OOM，因此可以通过关闭虚拟内存检查来解决该问题，yarn.nodemanager.vmem-check-enabled=false 将虚拟内存检测设置为false

64-Bit Server VM warning: INFO: os::commit_memory(0x) failed; error=''Cannot allocate memory ？？？

服务器异常了。。。。Java HotSpot(TM) 64-Bit Server VM warning: INFO: os::commit_memory(0x00000005cc400000, 349700096, 0) failed; error=''Cannot allocate memory'' (errno=12)
#
# There is insufficient memory for the Java Runtime Environment to continue.
# Native memory allocation (mmap) failed to map 349700096 bytes for committing reserved memory.
# An error report file with more information is saved as:
# /root/hs_err_pid18533.log

Attempted to read or write protected memory. This is often an indication that other memory is corrupt.

Attempted to read or write protected memory. This is often an indication that other memory is corrupt.

Line 37:         protected void Application_Start(Object sender, EventArgs e)
Line 38:         {
Line 39:             AppStartLogger.ResetLog();
Line 40:             AppStartLogger.WriteLine("Starting AspDotNetStorefront...");
Line 41: 

Source File: e:\Just\JustWeb\App_Code\Global.asax.cs    Line: 39

Symptoms
Accessing the site results in an "Attempted To Read Or Write Protected Memory" error.

Cause
There are 2 potential causes of this issue:

    1 - A site running a version of the ASPDNSF software older than 7.0.2.5 is attempting to run in a medium-trust hosting environment.

    2 - A site running a version of the ASPDNSF software older than 7.0.2.5 is running on a host that has just applied the .NET 3.5 SP1 upgrade to the servers.

NOTE: In both of these cases, the problem lies with the environment the software is running in, as configured by the host.  These are not flaws in the software, and are beyond our control.

Procedure
The best solution (regardless of which of the 2 causes above is creating your issue) is to upgrade to version 7.0.2.5 SP1 or higher of the ASPDNSF software.  Those later versions can run natively in medium-trust (thus avoiding issue #1) and work fine with the latest .NET service pack (which didn't even exist before then for us to test against). 

If upgrading is not an option at the moment, there are other workarounds for each possible cause.  Note that these are the ONLY workarounds.  We can provide no other advice than these steps:

Medium Trust:

    1 - First, have the host or server admin apply the Microsoft-created hotfix available at
http://connect.microsoft.com/VisualStudio/Downloads/DownloadDetails.aspx?DownloadID=6003

    2 - Contact support and request a special medium-trust build of the software.  We will need a valid order number to provide that.

    3 - Move to a full-trust environment (this may mean switching hosts).

.NET 3.5 SP1 Patch:

    1 - Have the host roll back the changes on the server, or move your site to a server that has not had the patch applied.

https://support.aspdotnetstorefront.com/index.php?_m=knowledgebase&_a=viewarticle&kbarticleid=208

If you are using aspdotnetstorefront and are getting the following error: Attempted to read or write protected memory. This is often an indication that other memory is corrupt. 

then please contact aspdotnetstorefront in regards to the error as you will have to upgrade your aspdotnetstorefront storefront application to version 7.0.2.5 SP1 or version 7.1.0.0 if the .NET 3.5 service pack is installed.

If you are not using aspdotnetstorefront and are getting this error, you will need to verify that you code works with the lates dotnet service packs for all versions. This error is usually remoting related, but not always.

Caused by: java.lang.OutOfMemoryError: bitmap size exceeds VM budget 急急

        }

稍后附上错误信息。

Caused by: java.lang.OutOfMemoryError: GC overhead limit exceeded

Caused by: java.lang.OutOfMemoryError: GC overhead limit exceeded

问题描述:

本地爬虫服务抛出异常java.lang.OutOfMemoryError：GC overhead limit exceeded 导致服务起不来，查看日志发现加载了太多资源到内存，本地的性能也不好，gc时间消耗的较多。

解决方案:

增加参数，-XX:-UseGCOverheadLimit，关闭这个特性; 同时增加heap大小，-Xmx1024m。

Y分析:

OOM大家都知道，就是JVM内存溢出了，那GC overhead limit exceed呢？

GC overhead limt exceed检查是Hotspot VM 1.6定义的一个策略，通过统计GC时间来预测是否要OOM了，提前抛出异常，防止OOM发生。Sun 官方对此的定义是：“并行/并发回收器在GC回收时间过长时会抛出OutOfMemroyError。过长的定义是，超过98%的时间用来做GC并且回收了不到2%的堆内存。用来避免内存过小造成应用不能正常工作。“

GC overhead limit exceed HotSpot的实现：

bool  print_gc_overhead_limit_would_be_exceeded = false;

if  (is_full_gc) {

  if (gc_cost() > gc_cost_limit &&

    free_in_old_gen < (size_t) mem_free_old_limit &&

    free_in_eden < (size_t) mem_free_eden_limit) {

    // Collections, on average, are taking too much time, and

    //      gc_cost() > gc_cost_limit

    // we have too little space available after a full gc.

    //      total_free_limit < mem_free_limit

    // where

    //   total_free_limit is the free space available in

    //     both generations

    //   total_mem is the total space available for allocation

    //     in both generations (survivor spaces are not included

    //     just as they are not included in eden_limit).

    //   mem_free_limit is a fraction of total_mem judged to be an

    //     acceptable amount that is still unused.

    // The heap can ask for the value of this variable when deciding

    // whether to thrown an OutOfMemory error.

    // Note that the gc time limit test only works for the collections

    // of the young gen + tenured gen and not for collections of the

    // permanent gen.  That is because the calculation of the space

    // freed by the collection is the free space in the young gen +

    // tenured gen.

    // At this point the GC overhead limit is being exceeded.

    inc_gc_overhead_limit_count();

    if (UseGCOverheadLimit) {

      if (gc_overhead_limit_count() >=

          AdaptiveSizePolicyGCTimeLimitThreshold){

        // All conditions have been met for throwing an out-of-memory

        set_gc_overhead_limit_exceeded(true);

        // Avoid consecutive OOM due to the gc time limit by resetting

        // the counter.

        reset_gc_overhead_limit_count();

      } else {

        // The required consecutive collections which exceed the

        // GC time limit may or may not have been reached. We

        // are approaching that condition and so as not to

        // throw an out-of-memory before all SoftRef''s have been

        // cleared, set _should_clear_all_soft_refs in CollectorPolicy.

        // The clearing will be done on the next GC.

        bool near_limit = gc_overhead_limit_near();

        if (near_limit) {

          collector_policy->set_should_clear_all_soft_refs(true);

          if (PrintGCDetails && Verbose) {

            gclog_or_tty->print_cr("  Nearing GC overhead limit, "

              "will be clearing all SoftReference");

          }

        }

      }

    }

    // Set this even when the overhead limit will not

    // cause an out-of-memory.  Diagnostic message indicating

    // that the overhead limit is being exceeded is sometimes

    // printed.

    print_gc_overhead_limit_would_be_exceeded = true;

 

  } else {

    // Did not exceed overhead limits

    reset_gc_overhead_limit_count();

  }

}

关于Hive-Container killed by YARN for exceeding memory limits. 9.2 GB of 9 GB physical memory used. C...的介绍现已完结，谢谢您的耐心阅读，如果想了解更多关于64-Bit Server VM warning: INFO: os::commit_memory(0x) failed; error=''Cannot allocate memory ？？？、Attempted to read or write protected memory. This is often an indication that other memory is corrupt.、Caused by: java.lang.OutOfMemoryError: bitmap size exceeds VM budget 急急、Caused by: java.lang.OutOfMemoryError: GC overhead limit exceeded的相关知识，请在本站寻找。