从spark访问Keerized远程hbase群集

chhqkbe1  于 2021-06-10  发布在  Hbase
关注(0)|答案(2)|浏览(460)

我正在尝试使用hortonworks spark on hbase连接器从spark读取kerberized hbase示例中的数据。我的集群配置基本上是这样的：我将spark作业从客户机提交到远程spark独立集群，而该作业正试图从单独的hbase集群读取数据。
如果我直接在我的客户机上运行master=local[*]的spark来绕过独立集群，我可以访问远程hbase集群，只要我首先从客户机启动它就没有问题。但是，当我将我的主服务器设置为远程集群，并且所有其他配置都相同时，我在 org.apache.hadoop.hbase.security.UserProvider.instantiate(UserProvider.java:43) （下面是完整的堆栈跟踪）
有没有人完成了类似的架构，也许可以提供一些见解？尽管日志中没有提到任何身份验证问题，但我有一种预感，在从非kerberized spark集群访问hbase时，我可能遇到了身份验证问题。
完整堆栈跟踪：

org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0: java.lang.NullPointerException
        at org.apache.hadoop.hbase.security.UserProvider.instantiate(UserProvider.java:43)
        at org.apache.hadoop.hbase.client.ConnectionFactory.createConnection(ConnectionFactory.java:214)
        at org.apache.hadoop.hbase.client.ConnectionFactory.createConnection(ConnectionFactory.java:119)
        at org.apache.spark.sql.execution.datasources.hbase.TableResource.init(HBaseResources.scala:125)
        at org.apache.spark.sql.execution.datasources.hbase.ReferencedResource$class.liftedTree1$1(HBaseResources.scala:57)
        at org.apache.spark.sql.execution.datasources.hbase.ReferencedResource$class.acquire(HBaseResources.scala:54)
        at org.apache.spark.sql.execution.datasources.hbase.TableResource.acquire(HBaseResources.scala:120)
        at org.apache.spark.sql.execution.datasources.hbase.ReferencedResource$class.releaseOnException(HBaseResources.scala:74)
        at org.apache.spark.sql.execution.datasources.hbase.TableResource.releaseOnException(HBaseResources.scala:120)
        at org.apache.spark.sql.execution.datasources.hbase.TableResource.getScanner(HBaseResources.scala:144)
        at org.apache.spark.sql.execution.datasources.hbase.HBaseTableScanRDD$$anonfun$7.apply(HBaseTableScan.scala:267)
        at org.apache.spark.sql.execution.datasources.hbase.HBaseTableScanRDD$$anonfun$7.apply(HBaseTableScan.scala:266)
        at scala.collection.parallel.mutable.ParArray$Map.leaf(ParArray.scala:658)
        at scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply$mcV$sp(Tasks.scala:54)
        at scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply(Tasks.scala:53)
        at scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply(Tasks.scala:53)
        at scala.collection.parallel.Task$class.tryLeaf(Tasks.scala:56)
        at scala.collection.parallel.mutable.ParArray$Map.tryLeaf(ParArray.scala:650)
        at scala.collection.parallel.AdaptiveWorkStealingTasks$WrappedTask$class.compute(Tasks.scala:165)
        at scala.collection.parallel.AdaptiveWorkStealingForkJoinTasks$WrappedTask.compute(Tasks.scala:514)
        at scala.concurrent.forkjoin.RecursiveAction.exec(RecursiveAction.java:160)
        at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
        at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
        at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
        at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)

Driver stacktrace:
        at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
        at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
        at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
        at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
        at scala.Option.foreach(Option.scala:236)
        at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
        at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
        at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
        at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:212)
        at org.apache.spark.sql.execution.Limit.executeCollect(basicOperators.scala:165)
        at org.apache.spark.sql.execution.SparkPlan.executeCollectPublic(SparkPlan.scala:174)
        at org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1499)
        at org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1499)
        at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56)
        at org.apache.spark.sql.DataFrame.withNewExecutionId(DataFrame.scala:2086)
        at org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$execute$1(DataFrame.scala:1498)
        at org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$collect(DataFrame.scala:1505)
        at org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1375)
        at org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1374)
        at org.apache.spark.sql.DataFrame.withCallback(DataFrame.scala:2099)
        at org.apache.spark.sql.DataFrame.head(DataFrame.scala:1374)
        at org.apache.spark.sql.DataFrame.take(DataFrame.scala:1456)
        at org.apache.spark.sql.DataFrame.showString(DataFrame.scala:170)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:498)
        at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
        at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
        at py4j.Gateway.invoke(Gateway.java:259)
        at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
        at py4j.commands.CallCommand.execute(CallCommand.java:79)
        at py4j.GatewayConnection.run(GatewayConnection.java:209)
        at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.NullPointerException
        at org.apache.hadoop.hbase.security.UserProvider.instantiate(UserProvider.java:43)
        at org.apache.hadoop.hbase.client.ConnectionFactory.createConnection(ConnectionFactory.java:214)
        at org.apache.hadoop.hbase.client.ConnectionFactory.createConnection(ConnectionFactory.java:119)
        at org.apache.spark.sql.execution.datasources.hbase.TableResource.init(HBaseResources.scala:125)
        at org.apache.spark.sql.execution.datasources.hbase.ReferencedResource$class.liftedTree1$1(HBaseResources.scala:57)
        at org.apache.spark.sql.execution.datasources.hbase.ReferencedResource$class.acquire(HBaseResources.scala:54)
        at org.apache.spark.sql.execution.datasources.hbase.TableResource.acquire(HBaseResources.scala:120)
        at org.apache.spark.sql.execution.datasources.hbase.ReferencedResource$class.releaseOnException(HBaseResources.scala:74)
        at org.apache.spark.sql.execution.datasources.hbase.TableResource.releaseOnException(HBaseResources.scala:120)
        at org.apache.spark.sql.execution.datasources.hbase.TableResource.getScanner(HBaseResources.scala:144)
        at org.apache.spark.sql.execution.datasources.hbase.HBaseTableScanRDD$$anonfun$7.apply(HBaseTableScan.scala:267)
        at org.apache.spark.sql.execution.datasources.hbase.HBaseTableScanRDD$$anonfun$7.apply(HBaseTableScan.scala:266)
        at scala.collection.parallel.mutable.ParArray$Map.leaf(ParArray.scala:658)
        at scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply$mcV$sp(Tasks.scala:54)
        at scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply(Tasks.scala:53)
        at scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply(Tasks.scala:53)
        at scala.collection.parallel.Task$class.tryLeaf(Tasks.scala:56)
        at scala.collection.parallel.mutable.ParArray$Map.tryLeaf(ParArray.scala:650)
        at scala.collection.parallel.AdaptiveWorkStealingTasks$WrappedTask$class.compute(Tasks.scala:165)
        at scala.collection.parallel.AdaptiveWorkStealingForkJoinTasks$WrappedTask.compute(Tasks.scala:514)
        at scala.concurrent.forkjoin.RecursiveAction.exec(RecursiveAction.java:160)
        at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
        at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
        at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
hbaseapache-sparkpysparkremote-accesskerberos

2条答案

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jq6vz3qz

jq6vz3qz1#

出现配置问题，hbase.client.userprovider.class配置不可用。您需要确保hbase libs和conf文件位于spark执行器的路径上。

private static final String USER_PROVIDER_CONF_KEY = "hbase.client.userprovider.class";

  /**
   * Instantiate the {@link UserProvider} specified in the configuration and set the passed
   * configuration via {@link UserProvider#setConf(Configuration)}
   * @param conf to read and set on the created {@link UserProvider}
   * @return a {@link UserProvider} ready for use.
   */
  public static UserProvider instantiate(Configuration conf) {
    Class<? extends UserProvider> clazz =
        conf.getClass(USER_PROVIDER_CONF_KEY, UserProvider.class, UserProvider.class);
    return ReflectionUtils.newInstance(clazz, conf);
  }
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zvokhttg

zvokhttg2#

我偶然发现了这个症状（但根本原因可能不一样），并找到了一个非常肮脏的解决办法，你可能不想尝试。
$$上下文$$   cloudera发行版，hbase 1.2.0-cdh5.7.0
$$发行#1$$   apache/hortonworks发行版中的一些代码清理还没有移植到cloudera发行版，例如。 java.lang.NoSuchMethodError: org.apache.hadoop.hbase.client.Scan.setCaching(I)Lorg/apache/hadoop/hbase/client/Scan; $$解决方法#1$$
从horton repo下载hbase客户机jar，特别是“client”、“common”和“protocol”，适用于版本1.1.2（这是spark hbase模块的pom中显示的依赖关系）。
添加这些jar（和目录 /etc/hbase/conf/ )至 spark.driver.extraClassPath 以及spark hbase jar。
通过命令行选项将这些jar发送给执行者 --jars 以及spark hbase jar
（别忘了目录 /etc/hbase/conf/ 在 spark.executor.extraClassPath 如果conf出现在所有的yarn节点上；否则，请找到一种方法将xml传送到其容器类路径中的目录中）
$$发行#2$$   不知何故，在yarn模式下，spark执行器不能正确生成传递给方法的hbase配置 org.apache.hadoop.hbase.security.UserProvider.instantiate(Configuration) 以及 org.apache.hadoop.hbase.client.ConnectionFactory.createConnection(Configuration, boolean, ExecutorService, User) ，因此 java.lang.NullPointerException $$解决方法#2$$
从github分支1.1下载这两个类的hbase源代码
修补代码以确保 conf 参数为null时，将用调用 org.apache.hadoop.hbase.HBaseConfiguration.create() 编译这两个类，并替换原来的 .class 在适当的jar中的可执行文件与您的补丁版本
修补spark-hbase插件当然更有意义（参见那篇文章中ray3888的评论），但是scala让我恶心，所以我坚持使用纯“旧java”。

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