spark 问题
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
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