Spark-Shell启动报错：Yarn应用已终止及日志路径咨询

我们安装了Hadoop 2.7.4和Spark 2.2.1，执行spark-shell命令时出现以下错误：

ERROR SparkContext: Error initializing SparkContext.
org.apache.spark.SparkException: Yarn application has already ended! It might have been killed or unable to launch application master.
    at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.waitForApplication(YarnClientSchedulerBackend.scala:85)
    at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:62)
    at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:173)
    at org.apache.spark.SparkContext.<init>(SparkContext.scala:509)
    at org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:58)
    at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
    at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
    at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
    at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:236)
    at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
    at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
    at py4j.GatewayConnection.run(GatewayConnection.java:214)
    at java.lang.Thread.run(Thread.java:750)
23/05/01 08:22:26 WARN YarnSchedulerBackend$YarnSchedulerEndpoint: Attempted to request executors before the AM has registered!
23/05/01 08:22:26 WARN MetricsSystem: Stopping a MetricsSystem that is not running
Traceback (most recent call last):
  ", line 400, in <module>
    .config('spark.debug.maxToStringFields', 1000)
  File "/opt/spark/python/lib/pyspark.zip/pyspark/sql/session.py", line 173, in getOrCreate
  File "/opt/spark/python/lib/pyspark.zip/pyspark/context.py", line 334, in getOrCreate
  File "/opt/spark/python/lib/pyspark.zip/pyspark/context.py", line 118, in __init__
  File "/opt/spark/python/lib/pyspark.zip/pyspark/context.py", line 180, in _do_init
  File "/opt/spark/python/lib/pyspark.zip/pyspark/context.py", line 273, in _initialize_context
  File "/opt/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1401, in __call__
  File "/opt/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling None.org.apache.spark.api.java.JavaSparkContext.
: org.apache.spark.SparkException: Yarn application has already ended! It might have been killed or unable to launch application master.
    at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.waitForApplication(YarnClientSchedulerBackend.scala:85)
    at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:62)
    at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:173)
    at org.apache.spark.SparkContext.<init>(SparkContext.scala:509)
    at org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:58)
    at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
    at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
    at the sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
    at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:236)
    at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
    at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
    at py4j.GatewayConnection.run(GatewayConnection.java:214)
    at java.lang.Thread.run(Thread.java:750)
Build step 'Execute shell' marked build as failure
Finished: FAILURE

请问该错误的可能原因是什么？另外无法找到服务器上的准确日志，能否告知日志的具体路径？

• YARN资源不足：NodeManager没有足够的内存或CPU分配给Application Master，导致进程启动失败被系统杀死
• 版本兼容细节冲突：虽然Hadoop 2.7.4与Spark 2.2.1官方标注兼容，但可能存在配置文件或依赖库的细微冲突
• 环境变量配置错误：HADOOP_CONF_DIR未正确设置，导致Spark无法读取YARN的核心配置（如ResourceManager地址、队列设置）
• Application Master启动异常：可能是Spark运行用户无YARN队列权限、依赖库缺失，或是NodeManager与Application Master之间存在网络端口不通的问题
• Spark资源参数不合理：提交命令中设置的spark.driver.memory、spark.executor.memory超出YARN配置的单个容器资源上限，导致应用被拒绝

YARN应用相关日志 #

• 通过命令快速查看：先通过yarn application -list -appStates FAILED获取失败应用的ID，再执行yarn logs -applicationId <应用ID>即可查看对应Application Master和Executor的日志
• 本地存储路径：默认在YARN配置yarn.nodemanager.log-dirs指定的目录下，通常是/var/log/hadoop-yarn/containers，每个应用的日志存放在以容器ID命名的子目录中

Spark Driver日志 #

• 本地提交模式下，日志直接输出在spark-shell的控制台中
• YARN集群模式下，Driver日志会合并到Application Master的日志中，可通过上述YARN日志命令查看

Hadoop YARN服务日志 #

• ResourceManager日志：默认路径为$HADOOP_HOME/logs/yarn-<用户名>-resourcemanager-<主机名>.log
• NodeManager日志：默认路径为$HADOOP_HOME/logs/yarn-<用户名>-nodemanager-<主机名>.log

内容的提问来源于stack exchange，提问作者Pravin