在Hadoop2.7.1集群中使用Spark1.6.2 for Hadoop2.6.0时出现的问题

c9qzyr3d  于 2021-05-29  发布在  Hadoop
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我可以访问hadoop集群，版本2.7.1，它是使用hdp2.4安装的。这样的集群安装了spark，具体来说：

$ cat /usr/hdp/2.4.3.0-227/spark/RELEASE 
Spark 1.6.2.2.4.3.0-227 built for Hadoop 2.7.1.2.4.3.0-227

我正在尝试设置一个“客户端”机器，它能够远程连接到集群并部署spark作业。因此，我需要为上面相同的版本安装一个spark分布。
首先，我访问了官方的spark下载页面，但是1.6.2只适用于hadoop2.6。
然后，我决定下载spark源代码并按照本指南进行构建。有趣的是，hadoop“2.6.x和更高版本的2.x”需要构建概要文件 hadoop-2-6 . i、 如果我建立自己的Spark，我会获得一个发行版作为一个在正式的Spark下载页。
因此，我选择了针对hadoop2.6.0的spark1.6.2的官方预构建发行版。
它似乎不能正常工作。我已经提交了一个python脚本-一个非常简单的脚本，只创建了一个spark上下文-但是有一些问题（只显示日志的相关部分）：

$ ./bin/spark-submit --master yarn --deploy-mode cluster basic.py
...
17/08/28 13:08:29 INFO Client: Requesting a new application from cluster with 8 NodeManagers
17/08/28 13:08:29 INFO Client: Verifying our application has not requested more than the maximum memory capability of the cluster (24576 MB per container)
17/08/28 13:08:29 INFO Client: Will allocate AM container, with 1408 MB memory including 384 MB overhead
17/08/28 13:08:29 INFO Client: Setting up container launch context for our AM
17/08/28 13:08:29 INFO Client: Setting up the launch environment for our AM container
17/08/28 13:08:29 INFO Client: Preparing resources for our AM container
17/08/28 13:08:36 INFO Client: Uploading resource file:/Users/frb/Applications/spark-1.6.2-bin-hadoop2.6/lib/spark-assembly-1.6.2-hadoop2.6.0.jar -> hdfs://<host>:8020/user/frb/.sparkStaging/application_1495097788339_0066/spark-assembly-1.6.2-hadoop2.6.0.jar
17/08/28 13:14:40 INFO Client: Uploading resource file:basic.py -> hdfs://<host>:8020/user/frb/.sparkStaging/application_1495097788339_0066/basic.py
17/08/28 13:14:40 INFO Client: Uploading resource file:/Users/frb/Applications/spark-1.6.2-bin-hadoop2.6/python/lib/pyspark.zip -> hdfs://<host>:8020/user/frb/.sparkStaging/application_1495097788339_0066/pyspark.zip
17/08/28 13:14:41 INFO Client: Uploading resource file:/Users/frb/Applications/spark-1.6.2-bin-hadoop2.6/python/lib/py4j-0.9-src.zip -> hdfs://<host>:8020/user/frb/.sparkStaging/application_1495097788339_0066/py4j-0.9-src.zip
17/08/28 13:14:42 INFO Client: Uploading resource file:/private/var/folders/cc/p9gx2wnn3dz8g6yf_r4308fm0000gn/T/spark-0d86f1f4-d310-423a-9d2f-90e2ff46f84e/__spark_conf__3704082754178078870.zip -> hdfs://<host>:8020/user/frb/.sparkStaging/application_1495097788339_0066/__spark_conf__3704082754178078870.zip
17/08/28 13:14:42 INFO SecurityManager: Changing view acls to: frb
17/08/28 13:14:42 INFO SecurityManager: Changing modify acls to: frb
17/08/28 13:14:42 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(frb); users with modify permissions: Set(frb)
17/08/28 13:14:42 INFO Client: Submitting application 66 to ResourceManager
17/08/28 13:14:42 INFO YarnClientImpl: Submitted application application_1495097788339_0066
17/08/28 13:14:48 INFO Client: Application report for application_1495097788339_0066 (state: ACCEPTED)
17/08/28 13:14:48 INFO Client: 
     client token: N/A
     diagnostics: N/A
     ApplicationMaster host: N/A
     ApplicationMaster RPC port: -1
     queue: default
     start time: 1503918882943
     final status: UNDEFINED
     tracking URL: <host>:8088/proxy/application_1495097788339_0066/
     user: frb
17/08/28 13:14:49 INFO Client: Application report for application_1495097788339_0066 (state: ACCEPTED)
...
17/08/28 13:14:52 INFO Client: Application report for application_1495097788339_0066 (state: RUNNING)
17/08/28 13:14:52 INFO Client: 
     client token: N/A
     diagnostics: N/A
     ApplicationMaster host: 10.95.120.6
     ApplicationMaster RPC port: 0
     queue: default
     start time: 1503918882943
     final status: UNDEFINED
     tracking URL: <host>:8088/proxy/application_1495097788339_0066/
     user: frb
17/08/28 13:14:53 INFO Client: Application report for application_1495097788339_0066 (state: RUNNING)
...
17/08/28 13:14:59 INFO Client: Application report for application_1495097788339_0066 (state: ACCEPTED)
17/08/28 13:14:59 INFO Client: 
     client token: N/A
     diagnostics: N/A
     ApplicationMaster host: N/A
     ApplicationMaster RPC port: -1
     queue: default
     start time: 1503918882943
     final status: UNDEFINED
     tracking URL: <host>:8088/proxy/application_1495097788339_0066/
     user: frb
17/08/28 13:15:00 INFO Client: Application report for application_1495097788339_0066 (state: ACCEPTED)
17/08/28 13:15:01 INFO Client: Application report for application_1495097788339_0066 (state: RUNNING)
17/08/28 13:15:01 INFO Client: 
     client token: N/A
     diagnostics: N/A
     ApplicationMaster host: 10.95.58.21
     ApplicationMaster RPC port: 0
     queue: default
     start time: 1503918882943
     final status: UNDEFINED
     tracking URL: <host>:8088/proxy/application_1495097788339_0066/
     user: frb
17/08/28 13:15:02 INFO Client: Application report for application_1495097788339_0066 (state: RUNNING)
...
17/08/28 13:15:09 INFO Client: Application report for application_1495097788339_0066 (state: FINISHED)
17/08/28 13:15:09 INFO Client: 
     client token: N/A
     diagnostics: Max number of executor failures (4) reached
     ApplicationMaster host: 10.95.58.21
     ApplicationMaster RPC port: 0
     queue: default
     start time: 1503918882943
     final status: FAILED
     tracking URL: <host>:8088/proxy/application_1495097788339_0066/
     user: frb
Exception in thread "main" org.apache.spark.SparkException: Application application_1495097788339_0066 finished with failed status
    at org.apache.spark.deploy.yarn.Client.run(Client.scala:1034)
    at org.apache.spark.deploy.yarn.Client$.main(Client.scala:1081)
    at org.apache.spark.deploy.yarn.Client.main(Client.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
    at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
    at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
17/08/28 13:15:09 INFO ShutdownHookManager: Shutdown hook called
17/08/28 13:15:09 INFO ShutdownHookManager: Deleting directory /private/var/folders/cc/p9gx2wnn3dz8g6yf_r4308fm0000gn/T/spark-0d86f1f4-d310-423a-9d2f-90e2ff46f84e

如果我查看此作业的日志，我会看到：

ERROR:py4j.java_gateway:An error occurred while trying to connect to the Java server
Traceback (most recent call last):
  File "/disk0/hadoop/yarn/local/usercache/frb/appcache/application_1495097788339_0066/container_e03_1495097788339_0066_02_000001/py4j-0.9-src.zip/py4j/java_gateway.py", line 690, in start
    self.socket.connect((self.address, self.port))
  File "/usr/lib64/python2.7/socket.py", line 224, in meth
    return getattr(self._sock,name)(*args)
error: [Errno 111] Connection refused
Traceback (most recent call last):
  File "basic.py", line 36, in <module>
    sc = SparkContext(conf=conf)
  File "/disk0/hadoop/yarn/local/usercache/frb/appcache/application_1495097788339_0066/container_e03_1495097788339_0066_02_000001/pyspark.zip/pyspark/context.py", line 115, in __init__
  File "/disk0/hadoop/yarn/local/usercache/frb/appcache/application_1495097788339_0066/container_e03_1495097788339_0066_02_000001/pyspark.zip/pyspark/context.py", line 172, in _do_init
  File "/disk0/hadoop/yarn/local/usercache/frb/appcache/application_1495097788339_0066/container_e03_1495097788339_0066_02_000001/pyspark.zip/pyspark/context.py", line 235, in _initialize_context
  File "/disk0/hadoop/yarn/local/usercache/frb/appcache/application_1495097788339_0066/container_e03_1495097788339_0066_02_000001/py4j-0.9-src.zip/py4j/java_gateway.py", line 1062, in __call__
  File "/disk0/hadoop/yarn/local/usercache/frb/appcache/application_1495097788339_0066/container_e03_1495097788339_0066_02_000001/py4j-0.9-src.zip/py4j/java_gateway.py", line 631, in send_command
  File "/disk0/hadoop/yarn/local/usercache/frb/appcache/application_1495097788339_0066/container_e03_1495097788339_0066_02_000001/py4j-0.9-src.zip/py4j/java_gateway.py", line 624, in send_command
  File "/disk0/hadoop/yarn/local/usercache/frb/appcache/application_1495097788339_0066/container_e03_1495097788339_0066_02_000001/py4j-0.9-src.zip/py4j/java_gateway.py", line 579, in _get_connection
  File "/disk0/hadoop/yarn/local/usercache/frb/appcache/application_1495097788339_0066/container_e03_1495097788339_0066_02_000001/py4j-0.9-src.zip/py4j/java_gateway.py", line 585, in _create_connection
  File "/disk0/hadoop/yarn/local/usercache/frb/appcache/application_1495097788339_0066/container_e03_1495097788339_0066_02_000001/py4j-0.9-src.zip/py4j/java_gateway.py", line 697, in start
py4j.protocol.Py4JNetworkError: An error occurred while trying to connect to the Java server
ERROR:py4j.java_gateway:An error occurred while trying to connect to the Java server
Traceback (most recent call last):
  File "/disk0/hadoop/yarn/local/usercache/frb/appcache/application_1495097788339_0066/container_e03_1495097788339_0066_02_000001/py4j-0.9-src.zip/py4j/java_gateway.py", line 690, in start
    self.socket.connect((self.address, self.port))
  File "/usr/lib64/python2.7/socket.py", line 224, in meth
    return getattr(self._sock,name)(*args)
error: [Errno 111] Connection refused

i、 e.没有创建spark上下文，运行java网关的jvm和运行spark上下文的python驱动程序之间的连接失败。
这肯定与我在客户机上安装的spark distribution有关，因为：
我的客户机的spark分布被上传到clsuter，因此它是被使用的；提交时请记住以下日志：
17/08/28 13:08:36信息客户端：上载资源文件：/users/frb/applications/spark-1.6.2-bin-hadoop2.6/lib/spark-assembly-1.6.2-hadoop2.6.0.jar->hdfs://:8020/user/frb/.sparkstaging/application\u 1495097788339\u 0066/spark-assembly-1.6.2-hadoop2.6.0.jar
当在集群中提交时，也就是说，当使用hdp安装的spark的“spark 1.6.2.2.4.3.0-227为hadoop 2.7.1.2.4.3.0-227构建”版本时，上述命令同样有效。
你知道怎么解决这个问题吗？谢谢！

hadoopapache-spark

1条答案

按热度按时间
6jjcrrmo

6jjcrrmo1#

我最终解决了这个问题：
我加入了 spark-submit 命令选项 --conf spark.yarn.jars ，值为远程spark群集中spark组件jar的位置。这避免了上载我安装的客户端spark程序集jar（这是一个缓慢的过程，实际上与远程版本并不完全匹配）。
我添加到的客户端 yarn-site.xml 财产 hdp.version ，值为远程hadoop spark集群的hdp版本。这避免了在某些路径中出现替换错误，最终被揭示为我在问题中描述的连接错误。

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