问题描述1

  使用spark-shell ，sc.textFile(“hdfs://test02.com:8020/tmp/w”).count 出现如下异常：

java.lang.RuntimeException: Error in configuring object

at org.apache.hadoop.util.ReflectionUtils.setJobConf(ReflectionUtils.java:109)

at org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:75)

at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:133)

at org.apache.spark.rdd.HadoopRDD.getInputFormat(HadoopRDD.scala:186)

at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:199)

at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:219)

at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:217)

at scala.Option.getOrElse(Option.scala:120)

at org.apache.spark.rdd.RDD.partitions(RDD.scala:217)

at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:32)

at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:219)

at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:217)

at scala.Option.getOrElse(Option.scala:120)

at org.apache.spark.rdd.RDD.partitions(RDD.scala:217)

at org.apache.spark.SparkContext.runJob(SparkContext.scala:1517)

at org.apache.spark.rdd.RDD.count(RDD.scala:1006)

at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:22)

at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:27)

at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:29)

at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:31)

at $iwC$$iwC$$iwC$$iwC.<init>(<console>:33)

at $iwC$$iwC$$iwC.<init>(<console>:35)

at $iwC$$iwC.<init>(<console>:37)

at $iwC.<init>(<console>:39)

at <init>(<console>:41)

at .<init>(<console>:45)

at .<clinit>(<console>)

at .<init>(<console>:7)

at .<clinit>(<console>)

at $print(<console>)

at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)

at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)

at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)

at java.lang.reflect.Method.invoke(Method.java:606)

at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)

at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1338)

at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)

at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)

at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)

at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:856)

at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:901)

at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:813)

at org.apache.spark.repl.SparkILoop.processLine$1(SparkILoop.scala:656)

at org.apache.spark.repl.SparkILoop.innerLoop$1(SparkILoop.scala:664)

at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$loop(SparkILoop.scala:669)

at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply$mcZ$sp(SparkILoop.scala:996)

at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:944)

at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:944)

at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)

at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$process(SparkILoop.scala:944)

at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:1058)

at org.apache.spark.repl.Main$.main(Main.scala:31)

at org.apache.spark.repl.Main.main(Main.scala)

at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)

at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)

at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)

at java.lang.reflect.Method.invoke(Method.java:606)

at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:569)

at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:166)

at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:189)

at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:110)

at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

Caused by: java.lang.reflect.InvocationTargetException

at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)

at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)

at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)

at java.lang.reflect.Method.invoke(Method.java:606)

at org.apache.hadoop.util.ReflectionUtils.setJobConf(ReflectionUtils.java:106)

… 61 more

Caused by: java.lang.IllegalArgumentException: Compression codec com.hadoop.compression.lzo.LzoCodec not found.

at org.apache.hadoop.io.compress.CompressionCodecFactory.getCodecClasses(CompressionCodecFactory.java:135)

at org.apache.hadoop.io.compress.CompressionCodecFactory.<init>(CompressionCodecFactory.java:175)

at org.apache.hadoop.mapred.TextInputFormat.configure(TextInputFormat.java:45)

… 66 more

Caused by: java.lang.ClassNotFoundException: Class com.hadoop.compression.lzo.LzoCodec not found

at org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:2018)

at org.apache.hadoop.io.compress.CompressionCodecFactory.getCodecClasses(CompressionCodecFactory.java:128)

… 68 more

原因：

  这是因为在hadoop 的core-site.xml 和mapred-site.xml中开启了压缩，并且压缩式lzo的。这就导致写入/上传到hdfs的文件自动被压缩为lzo了。这个时候你使用sc.textFile读取文件时就会报告一堆lzo找不到的异常。

  最根本的原因就是spark找不到hadoop-lzo.jar 和lzo本地库，你需要确保集群中每一个机器上都安装了lzo，lzop，hadoop-lzo.jar,然后修改spark-env.sh，添加SPARK_LIBRARY_PATH和SPARK_CLASSPATH，其中SPARK_LIBRARY_PATH指向lzo本地库，SPARK_CLASSPATH指向hadoop-lzo.jar。如果你从spark-shell中进行测试，在启动spark-shell时需要配置–jars和–driver-library-path。

  对于cdh集群，hadoop-lzo已经安装了。对于apache集群，你需要自己手动安装

解决办法：

• 在集群中的每一台机器上安装hadoop-lzo包。

     一般来说需要在集群中每台机器执行如下步骤：

     安装lzo lib

     安装lzop 可执行程序

     安装twitter的hadoop-lzo.jar

• 在spark-env.sh中添加SPARK_LIBRARY_PATH和SPARK_CLASSPATH变量

  添加如下变量:

 export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/cloudera/parcels/HADOOP_LZO/lib/hadoop/lib/native/*

export SPARK_LIBRARY_PATH=$SPARK_LIBRARY_PATH:/opt/cloudera/parcels/HADOOP_LZO/lib/hadoop/lib/native

SPARK_CLASSPATH=/opt/cloudera/parcels/HADOOP_LZO/lib/hadoop/lib:$SPARK_CLASSPATH

• 然后按照如下方式启用spark-shell，需要注意的是，不管你以local模式还是master模式，都需要加上如下的配置

  ./spark-shell  –jars hadoop-lzo.jar的全路径  –driver-library-path hadoop-lzo的native目录

  这个时候，你就可以在spark-shell中使用textFile读取hdfs数据了。

譬如，你可以如下启动spark-shell

./bin/spark-shell –jars /opt/cloudera/parcels/HADOOP_LZO/lib/hadoop/lib/hadoop-lzo.jar –driver-library-path /opt/cloudera/parcels/HADOOP_LZO/lib/hadoop/lib/native^C

maven pom.xml 中的scala 版本应该和spark版本一直：

  如果pom.xml 的scala版本是2.11的

  <properties>

    <scala.version>2.11.4</scala.version>

  </properties>

 那么 spark也应该是2.11的：

     <!– https://mvnrepository.com/artifact/org.apache.spark/spark-core_2.10 –>

    <dependency>

      <groupId>org.apache.spark</groupId>

      <artifactId>spark-core_2.11</artifactId>

      <version>1.6.1</version>

    </dependency>

同样，在使用scalatest和scalactic时也是如此。

问题描述2

  maven pom.xml 中的scala 版本应该和spark版本一直：

  如果pom.xml 的scala版本是2.11的

  <properties>

    <scala.version>2.11.4</scala.version>

  </properties>

 那么 spark也应该是2.11的：

     <!– https://mvnrepository.com/artifact/org.apache.spark/spark-core_2.10 –>

    <dependency>

      <groupId>org.apache.spark</groupId>

      <artifactId>spark-core_2.11</artifactId>

      <version>1.6.1</version>

    </dependency>

同样，在使用scalatest和scalactic时也是如此。

问题3.比较器异常

Caused by: java.lang.IllegalArgumentException: Comparison method violates its general contract!

at java.util.TimSort.mergeHi(TimSort.java:899)

at java.util.TimSort.mergeAt(TimSort.java:516)

at java.util.TimSort.mergeCollapse(TimSort.java:441)

at java.util.TimSort.sort(TimSort.java:245)

at java.util.Arrays.sort(Arrays.java:1438)

at scala.collection.SeqLike$class.sorted(SeqLike.scala:618)

at scala.collection.mutable.ArrayOps$ofRef.sorted(ArrayOps.scala:186)

at scala.collection.SeqLike$class.sortWith(SeqLike.scala:575)

at scala.collection.mutable.ArrayOps$ofRef.sortWith(ArrayOps.scala:186)

at bitnei.utils.Utils$.sortBy(Utils.scala:116)

at FsmTest$$anonfun$1$$anonfun$apply$mcV$sp$4.apply(FsmTest.scala:30)

… 54 more

出现这个问题的原因，是在排序时，两个相等的值没有返回true。源代码如下：

def compareDate(dateA:String,dateB:String):Boolean={
  val dateFormat=new java.text.SimpleDateFormat(“yyyyMMddHHmmss”)
  val timeA=dateFormat.parse(dateA).getTime
  val timeB=dateFormat.parse(dateB).getTime

  timeA<=timeB
}

将上面的<=换为<即可。

问题4 spark-jobserver maven 问题

<dependency>
  <groupId>spark.jobserver</groupId>
  <artifactId>job-server-api_2.11</artifactId>
  <version>0.6.2</version>
</dependency>

如上图，再引用job-server-api_2.11时，maven找不到某些jar的依赖，原因是默认中央仓库不全，需要添加其他中央仓库，如下：

</pluginRepository>
  <pluginRepository>
    <id>dl-bintray.com/</id>
    <name>Scala-Tools Maven2 Repository</name>
    <url>https://dl.bintray.com/spark-jobserver/maven/</url>
  </pluginRepository>

<repository>
  <id>dl-bintray.com/</id>
  <name>Scala-Tools Maven2 Repository</name>
  <url>https://dl.bintray.com/spark-jobserver/maven/</url>
</repository>

然后就ok了。

问题5 Spark读取hdfs数据，nameservice 无法识别

java.lang.IllegalArgumentException: java.net.UnknownHostException: nameservice1

at org.apache.hadoop.security.SecurityUtil.buildTokenService(SecurityUtil.java:414)

at org.apache.hadoop.hdfs.NameNodeProxies.createNonHAProxy(NameNodeProxies.java:164)

at org.apache.hadoop.hdfs.NameNodeProxies.createProxy(NameNodeProxies.java:129)

at org.apache.hadoop.hdfs.DFSClient.<init>(DFSClient.java:448)

at org.apache.hadoop.hdfs.DFSClient.<init>(DFSClient.java:410)

at org.apache.hadoop.hdfs.DistributedFileSystem.initialize(DistributedFileSystem.java:128)

at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2308)

解决方法：

在spark-default.sh中添加如下内容:
spark.files=/etc/hadoop/conf/core-site.xml,/etc/hadoop/conf/hdfs-site.xml

也就是说，将core-site.xml和hdfs-site.xml添加到spark.files中

问题6

 org.apache.hadoop.ipc.RemoteException(java.io.IOException): File /tmp/vehicle/result/mid/2016/09/13/_temporary/0/_temporary/attempt_201611121515_0001_m_000005_188/part-00005 could only be replicated to 0 nodes instead of minReplication (=1).  There are 6 datanode(s) running and no node(s) are excluded in this operation.

at org.apache.hadoop.hdfs.server.blockmanagement.BlockManager.chooseTarget4NewBlock(BlockManager.java:1541)

at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getAdditionalBlock(FSNamesystem.java:3289)

at org.apache.hadoop.hdfs.server.namenode.NameNodeRpcServer.addBlock(NameNodeRpcServer.java:668)

at org.apache.hadoop.hdfs.server.namenode.AuthorizationProviderProxyClientProtocol.addBlock(AuthorizationProviderProxyClientProtocol.java:212)

at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolServerSideTranslatorPB.addBlock(ClientNamenodeProtocolServerSideTranslatorPB.java:483)

at org.apache.hadoop.hdfs.protocol.proto.ClientNamenodeProtocolProtos$ClientNamenodeProtocol$2.callBlockingMethod(ClientNamenodeProtocolProtos.java)

at org.apache.hadoop.ipc.ProtobufRpcEngine$Server$ProtoBufRpcInvoker.call(ProtobufRpcEngine.java:619)

at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:1060)

at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2044)

at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2040)

at java.security.AccessController.doPrivileged(Native Method)

at javax.security.auth.Subject.doAs(Subject.java:415)

at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1671)

at org.apache.hadoop.ipc.Server$Handler.run(Server.java:2038)

at org.apache.hadoop.ipc.Client.call(Client.java:1468)

at org.apache.hadoop.ipc.Client.call(Client.java:1399)

at org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:232)

at com.sun.proxy.$Proxy13.addBlock(Unknown Source)

at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolTranslatorPB.addBlock(ClientNamenodeProtocolTranslatorPB.java:399)

at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)

at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)

at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)

at java.lang.reflect.Method.invoke(Method.java:606)

at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:187)

at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102)

at com.sun.proxy.$Proxy14.addBlock(Unknown Source)

at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.locateFollowingBlock(DFSOutputStream.java:1532)

at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.nextBlockOutputStream(DFSOutputStream.java:1349)

at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.run(DFSOutputStream.java:588)

Hdfs磁盘空间满了

JDBC问题

1.在maven中引入ojdbc方式：

首先将ojdbc安装到本地仓库

C:\Users\franciswang>mvn install:install-file -Dfile=d:/spark/lib/ojdbc6-11.2.0.3.0.jar -DgroupId=com.oracle -DartifactId=ojdbc6 -Dversion=11.2.0 -Dpackaging=jar

接下来在porm中引用：

<dependency>
  <groupId>com.oracle</groupId>
  <artifactId>ojdbc6</artifactId>
  <version>11.2.0</version>
</dependency>

当将项目打包到linux给spark执行时，总提示Cannot find or load oracle.jdbc.driver.OracleDriver，将ojdbc.jar放到classpath，项目的lib，jdk/jre/lib下都没有用，最后将其放到

Jdk/jre/lib/ext/下才好使。参考地址：

http://stackoverflow.com/questions/17701610/cannot-find-or-load-oracle-jdbc-driver-oracledriver