我真的需要一些帮助：
我们使用Spark3.1.2使用独立集群。自从我们开始使用s3a目录提交器以来，我们的spark作业稳定性和性能显著提高！
然而，最近几天，我们对解决s3a目录提交器问题完全感到困惑，不知道你是否知道发生了什么？
我们的spark作业失败是因为Java OOM(或者更确切地说是进程限制(错误：
An error occurred while calling None.org.apache.spark.api.java.JavaSparkContext.
: java.lang.OutOfMemoryError: unable to create native thread: possibly out of memory or process/resource limits reached
at java.base/java.lang.Thread.start0(Native Method)
at java.base/java.lang.Thread.start(Thread.java:803)
at java.base/java.util.concurrent.ThreadPoolExecutor.addWorker(ThreadPoolExecutor.java:937)
at java.base/java.util.concurrent.ThreadPoolExecutor.execute(ThreadPoolExecutor.java:1343)
at java.base/java.util.concurrent.AbstractExecutorService.submit(AbstractExecutorService.java:118)
at java.base/java.util.concurrent.Executors$DelegatedExecutorService.submit(Executors.java:714)
at org.apache.spark.rpc.netty.DedicatedMessageLoop.$anonfun$new$1(MessageLoop.scala:174)
at org.apache.spark.rpc.netty.DedicatedMessageLoop.$anonfun$new$1$adapted(MessageLoop.scala:173)
at scala.collection.immutable.Range.foreach(Range.scala:158)
at org.apache.spark.rpc.netty.DedicatedMessageLoop.<init>(MessageLoop.scala:173)
at org.apache.spark.rpc.netty.Dispatcher.liftedTree1$1(Dispatcher.scala:75)
at org.apache.spark.rpc.netty.Dispatcher.registerRpcEndpoint(Dispatcher.scala:72)
at org.apache.spark.rpc.netty.NettyRpcEnv.setupEndpoint(NettyRpcEnv.scala:136)
at org.apache.spark.storage.BlockManager.<init>(BlockManager.scala:231)
at org.apache.spark.SparkEnv$.create(SparkEnv.scala:394)
at org.apache.spark.SparkEnv$.createDriverEnv(SparkEnv.scala:189)
at org.apache.spark.SparkContext.createSparkEnv(SparkContext.scala:277)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:458)
at org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:58)
at java.base/jdk.internal.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at java.base/jdk.internal.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.base/java.lang.reflect.Constructor.newInstance(Constructor.java:490)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:238)
at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.base/java.lang.Thread.run(Thread.java:834)
Spark Thread Dump在Spark驱动程序上显示了5000多个提交程序线程！这里有一个例子：
Thread ID Thread Name Thread State Thread Locks
1047 s3-committer-pool-0 WAITING
1449 s3-committer-pool-0 WAITING
1468 s3-committer-pool-0 WAITING
1485 s3-committer-pool-0 WAITING
1505 s3-committer-pool-0 WAITING
1524 s3-committer-pool-0 WAITING
1529 s3-committer-pool-0 WAITING
1544 s3-committer-pool-0 WAITING
1549 s3-committer-pool-0 WAITING
1809 s3-committer-pool-0 WAITING
1972 s3-committer-pool-0 WAITING
1998 s3-committer-pool-0 WAITING
2022 s3-committer-pool-0 WAITING
2043 s3-committer-pool-0 WAITING
2416 s3-committer-pool-0 WAITING
2453 s3-committer-pool-0 WAITING
2470 s3-committer-pool-0 WAITING
2517 s3-committer-pool-0 WAITING
2534 s3-committer-pool-0 WAITING
2551 s3-committer-pool-0 WAITING
2580 s3-committer-pool-0 WAITING
2597 s3-committer-pool-0 WAITING
2614 s3-committer-pool-0 WAITING
2631 s3-committer-pool-0 WAITING
2726 s3-committer-pool-0 WAITING
2743 s3-committer-pool-0 WAITING
2763 s3-committer-pool-0 WAITING
2780 s3-committer-pool-0 WAITING
2819 s3-committer-pool-0 WAITING
2841 s3-committer-pool-0 WAITING
2858 s3-committer-pool-0 WAITING
2875 s3-committer-pool-0 WAITING
2925 s3-committer-pool-0 WAITING
2942 s3-committer-pool-0 WAITING
2963 s3-committer-pool-0 WAITING
2980 s3-committer-pool-0 WAITING
3020 s3-committer-pool-0 WAITING
3037 s3-committer-pool-0 WAITING
3055 s3-committer-pool-0 WAITING
3072 s3-committer-pool-0 WAITING
3127 s3-committer-pool-0 WAITING
3144 s3-committer-pool-0 WAITING
3163 s3-committer-pool-0 WAITING
3180 s3-committer-pool-0 WAITING
3222 s3-committer-pool-0 WAITING
3242 s3-committer-pool-0 WAITING
3259 s3-committer-pool-0 WAITING
3278 s3-committer-pool-0 WAITING
3418 s3-committer-pool-0 WAITING
3435 s3-committer-pool-0 WAITING
3452 s3-committer-pool-0 WAITING
3469 s3-committer-pool-0 WAITING
3486 s3-committer-pool-0 WAITING
3491 s3-committer-pool-0 WAITING
3501 s3-committer-pool-0 WAITING
3508 s3-committer-pool-0 WAITING
4029 s3-committer-pool-0 WAITING
4093 s3-committer-pool-0 WAITING
4658 s3-committer-pool-0 WAITING
4666 s3-committer-pool-0 WAITING
4907 s3-committer-pool-0 WAITING
5102 s3-committer-pool-0 WAITING
5119 s3-committer-pool-0 WAITING
5158 s3-committer-pool-0 WAITING
5175 s3-committer-pool-0 WAITING
5192 s3-committer-pool-0 WAITING
5209 s3-committer-pool-0 WAITING
5226 s3-committer-pool-0 WAITING
5395 s3-committer-pool-0 WAITING
5634 s3-committer-pool-0 WAITING
5651 s3-committer-pool-0 WAITING
5668 s3-committer-pool-0 WAITING
5685 s3-committer-pool-0 WAITING
5702 s3-committer-pool-0 WAITING
5722 s3-committer-pool-0 WAITING
5739 s3-committer-pool-0 WAITING
6144 s3-committer-pool-0 WAITING
6167 s3-committer-pool-0 WAITING
6289 s3-committer-pool-0 WAITING
6588 s3-committer-pool-0 WAITING
6628 s3-committer-pool-0 WAITING
6645 s3-committer-pool-0 WAITING
6662 s3-committer-pool-0 WAITING
6675 s3-committer-pool-0 WAITING
6692 s3-committer-pool-0 WAITING
6709 s3-committer-pool-0 WAITING
7049 s3-committer-pool-0 WAITING
这是考虑到我们的设置不允许超过100个线程…或者我们不明白什么…
以下是我们的配置和设置：
fs.s3a.threads.max 100
fs.s3a.connection.maximum 1000
fs.s3a.committer.threads 16
fs.s3a.max.total.tasks 5
fs.s3a.committer.name directory
fs.s3a.fast.upload.buffer disk
io.file.buffer.size 1048576
mapreduce.outputcommitter.factory.scheme.s3a - org.apache.hadoop.fs.s3a.commit.S3ACommitterFactory
我们尝试过不同版本的spark Hadoop云库，但问题始终相同。
https://repository.cloudera.com/content/repositories/releases/org/apache/spark/spark-hadoop-cloud_2.11/2.4.0-cdh6.3.2/spark-hadoop-cloud_2.11-2.4.0-cdh6.3.2.jar
https://repository.cloudera.com/artifactory/libs-release-local/org/apache/spark/spark-hadoop-cloud_2.11/2.4.0.7.0.3.0-79/spark-hadoop-cloud_2.11-2.4.0.7.0.3.0-79.jar
https://repo1.maven.org/maven2/org/apache/spark/spark-hadoop-cloud_2.12/3.2.0/spark-hadoop-cloud_2.12-3.2.0.jar
https://repository.cloudera.com/artifactory/libs-release-local/org/apache/spark/spark-hadoop-cloud_2.12/3.1.2.7.2.12.0-291/spark-hadoop-cloud_2.12-3.1.2.7.2.12.0-291.jar
如果你能给我们指明正确的方向，我们将不胜感激😊
感谢您抽出时间！
它将是HADOOP-16570S3A提交程序在大规模的作业/任务提交中泄漏线程/引发OOM
升级到hadoop-3.3.0二进制文件进行修复。理想情况下，到3.3.1来修复一些其他问题，特别是来自spark的重复作业ID。不确定CDH发布该修复程序有多远；如果你现在真的需要，我可以锻炼。不是CDH6.x，当然是
查看这篇关于S3A调优的文章。
具体来说，我会看看这个，尽管它被分类在数组下：
您可能需要进行仔细的调整，以降低内存耗尽的风险，尤其是当数据在内存中缓冲时。有一个可调谐的数字参数：
文件系统中可用于数据的线程总数上传或任何其他排队的文件系统操作。这是在fs.s3a.threads.max.
可以排队等待执行的操作数，等待线这是在fs.s3a.max.total.tasks.中设置的
单个输出流可以激活的块数(也就是说，由线程上载或在文件系统中排队线程队列(。这是在fs.s3a.fast.upload.active.blocks.中设置的
空闲线程可以在线程池中停留的时间长度在它退役之前。这是在fs.s3a.threads.keepalivetime.中设置的
我想您可能会发现减少线程数量可以消除内存压力。
我还建议你调整fs.s3a.fast.upload.active.blocks，它也会减轻记忆压力。我认为减少线程数应该是你的第一步，因为100有点激进。您可能会受到带宽的限制，额外的线程除了消耗内存之外不太可能做任何事情。