6、问题及解决方案

1.问题描述：

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WARN util.NativeCodeLoader: Unable to load native-hadoop library foryour platform… using builtin-java classes where applicable

问题原因：默认lib为32位,不支持64位。

解决办法：重新编译64位库 - 请注意在jdk1.8上会编译出错

#yum install cmake lzo-devel zlib-devel gccgcc-c++ autoconf automake libtool ncurses-devel openssl-deve

安装maven

#wgethttp://mirror.cc.columbia.edu/pub/software/apache/maven/maven-3/3.2.3/binaries/apache-maven-3.2.3-bin.tar.gz

# tar zxfapache-maven-3.2.3-bin.tar.gz -C /usr/local

# cd /usr/local

# ln -sapache-maven-3.2.3 maven

# vim/etc/profile

exportMAVEN_HOME=/usr/local/maven

exportPATH=${MAVEN_HOME}/bin:${PATH}

# source/etc/profile

安装ant

# wgethttp://apache.dataguru.cn//ant/binaries/apache-ant-1.9.4-bin.tar.gz

# tar zxf apache-ant-1.9.4-bin.tar.gz -C/usr/local

# vim /etc/profile

 exportANT_HOME=/usr/local/apache-ant-1.9.4

 exportPATH=$PATH:$ANT_HOME/bin

# source /etc/profile

安装findbugs

#wget http://prdownloads.sourceforge.net/findbugs/findbugs-2.0.3.tar.gz?download

# tar zxf findbugs-2.0.3.tar.gz -C/usr/local

# vim /etc/profile

export FINDBUGS_HOME=/opt/findbugs-2.0.3

export PATH=$PATH:$FINDBUGS_HOME/bin

安装protobuf

# wgethttps://protobuf.googlecode.com/files/protobuf-2.5.0.tar.gz

# tar zxf protobuf-2.5.0.tar.gz

# cd protobuf-2.5.0

# ./configure && make && makeinstall

下载源码包

#wgethttp://mirrors.hust.edu.cn/apache/hadoop/common/hadoop-2.5.0/hadoop-2.5.0-src.tar.gz

# tar zxf hadoop-2.5.0-src.tar.gz

# cd hadoop-2.5.0-src

#mvn clean install -DskipTests

# mvn package -Pdist,native -DskipTests -Dtar

替换旧的lib库

# mv /data/hadoop-2.5.0/lib/native /data/hadoop-2.5.0/lib/native_old

# cp -r /data/hadoop-2.5.0-src/hadoop-dist/target/hadoop-2.5.0/lib/native\

/data/hadoop-2.5.0/lib/native

# bin/hdfs getconf -namenodes

参考:

http://www.tuicool.com/articles/zaY7Rz

http://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/NativeLibraries.html#Supported_Platforms)

 

2.问题描述：

出现WARN hdfs.DFSClient:DataStreamer Exception，然后执行

sbin/stop-dfs.sh => namenode1: no datanode tostop

或hadoop dfsadmin -report查询不到集群中文件系统的信息

问题原因：重新格式化文件系统时，namenode产生的新的namespaceID与datanode所持有的namespaceID不一致造成的。

解决方案：在我们格式化namenode前，应首先删除dfs.data.dir所配置文件中的data文件夹下的所有内容。

 

3.问题描述：

ERRORorg.apache.hadoop.hdfs.server.datanode.DataNode: java.io.IOException:Incompatible namespaceIDs in 

问题原因: 每次namenode format会重新创建一个namenodeId,而dfs.data.dir参数配置的目录中包含的是上次format创建的id,和dfs.name.dir参数配置的目录中的id不一致。namenode format清空了namenode下的数据,但是没有清空datanode下的数据,导致启动时失败,所要做的就是每次fotmat前,清空dfs.data.dir参数配置的目录. 
格式化hdfs的命令 

解决方案：bin/hadoop namenode -format

 

MapReduce学习blog：http://www.cnblogs.com/xia520pi/archive/2012/05/16/2504205.html

 

4. 问题描述：

 [root@namenode1hadoop]# hadoop fs -put README.txt /         

15/01/04 21:50:49 WARN hdfs.DFSClient:DataStreamer Exception

org.apache.hadoop.ipc.RemoteException(java.io.IOException):File /README.txt._COPYING_ could only be replicated to 0 nodes instead ofminReplication (=1).  There are 6datanode(s) running and no node(s) are excluded in this operation.

问题原因：是由于hdfs-site.xml的下列配置有误（下面的参数需要根据实际情况修改）

       dfs.block.size

       268435456

       The default block size for newfiles

  

 

  

       dfs.datanode.max.xcievers

        10240

       

           An Hadoop HDFS datanode has an upper bound on the number of files thatit will serve at any one time.

       

  

 

  

       dfs.datanode.du.reserved

       32212254720

       Reserved space in bytes per volume. Always leave thismuch space free for non dfs use.

  

 

解决办法：修改上面的配置，然后重新启动。

 

5. 问题描述：

                           

问题原因：slf4j bindings 冲突

解决办法：

# mv /var/data/hive-1.40/lib/hive-jdbc-0.14.0-standalone.jar/opt/

当hive依然不能启动时，检查一下
1.查看hive-site.xml配置，会看到配置值含有"system:java.io.tmpdir"的配置项
2.新建文件夹/var/data/hive/iotmp
3.将含有"system:java.io.tmpdir"的配置项的值修改为如上地址
启动hive，成功！

 

6．问题描述

HADOOP:Error Launching job : org.apache.hadoop.yarn.exceptions.InvalidResourceRequestException:Invalid resource request, requested memory < 0, or requested memory > maxconfigured, requestedMemory=1536, maxMemory=1024

 

问题原因：mapreduce默认需要的内存为1536M，分配的过小

            mapreduce.map.memory.mb

            512

   

   

           mapreduce.map.java.opts

           -Xmx410m

   

   

           mapreduce.reduce.memory.mb

           512

   

   

           mapreduce.reduce.java.opts

           -Xmx410m

   

the 512 is value the yarn.scheduler.maximum-allocation-mb inyarn-site.xml, and the 1536 is default value ofyarn.app.mapreduce.am.resource.mb parameter in mapred-site.xml, make sure theallocation-mb>app.mapreduce.resouce will be ok.

解决办法：

调整上面的参数为2048，并扩充内存

 

7．问题描述：

Hadoop:java.lang.IncompatibleClassChangeError:

Found interface org.apache.hadoop.mapreduce.JobContext,but class was expected

问题原因： sqoop的版本和hadoop的版本不匹配

解决办法：重新编译sqoop，方法如下：

如何编译sqoop

 

第一步:

Additionally,building the documentation requires these tools:

* asciidoc
* make
* python 2.5+
* xmlto
* tar
* gzip
yum -y install git
yum -y install asciidoc
yum -y install make
yum -y install xmlto
yum -y install tar
yum -y install gzip

 

第二步：

下载相关软件包：

wgethttp://dist.codehaus.org/jetty/jetty-6.1.26/jetty-6.1.26.zip

wgethttp://mirrors.cnnic.cn/apache/sqoop/1.4.5/sqoop-1.4.5.tar.gz

 

mv jetty-6.1.26.zip/root/.m2/repository/org/mortbay/jetty/jetty/6.1.26/

 

第三步：

解压并修改相关文件：

tar -zxvf sqoop-1.4.5.tar.gz; cd sqoop-1.4.5

 

修改：build.xml后内容如下

     

     

       

       

       

       

       

        

       

     

 

 

修改550行和568行debug="${javac.debug}">

为：debug="${javac.debug}"includeantruntime="on">

 

修改：src/test/org/apache/sqoop/TestExportUsingProcedure.java

修改

修改第244行sql.append(StringUtils.repeat("?",",  ",

为：sql.append(StringUtils.repeat("?,",

 

以上配置完成修改后，执行：ant package

如果编译成功会提示：BUILD SUCCESSFUL

 

第四步：打包我们需要的sqoop安装包

编译成功后，默认会在sqoop-1.4.5/build目录下生成sqoop-1.4.5.bin__hadoop-2.5.0

tar -zcfsqoop-1.4.5.bin__hadoop-2.5.0.tar.gz sqoop-1.4.5.bin__hadoop-2.5.0

 

完毕！ 参考：http://www.aboutyun.com/thread-8462-1-1.html

 

8.问题描述：

执行命令：

# sqoopexport --connect jdbc:MySQL://10.40.214.9:3306/emails \

--usernamehive --password hive --table izhenxin \

--export-dir/user/hive/warehouse/maillog.db/izhenxin_total

 

…

Caused by:java.lang.RuntimeException: Can't parse input data: '@QQ.com'

        atizhenxin.__loadFromFields(izhenxin.java:378)

        at izhenxin.parse(izhenxin.java:306)

        atorg.apache.sqoop.mapreduce.TextExportMapper.map(TextExportMapper.java:83)

        ... 10 more

Caused by:java.lang.NumberFormatException: For input string: "@QQ.com"

…

15/01/19 23:15:21 INFO mapreduce.ExportJobBase: Transferred 0bytes in 46.0078 seconds (0 bytes/sec)

15/01/19 23:15:21 INFO mapreduce.ExportJobBase: Exported 0records.

15/01/19 23:15:21 ERROR tool.ExportTool: Error during export: Exportjob failed!

问题原因：

由于没有指定的文件的全路径导致的

事实上全路径应该是：

# hadoop fs -ls/user/hive/warehouse/maillog.db/izhenxin_total/

Found 1 items

-rw-r--r--   2 rootsupergroup       2450 2015-01-19 23:50/user/hive/warehouse/maillog.db/izhenxin_total/000000_0

解决办法：

# sqoop export --connectjdbc:mysql://10.40.214.9:3306/emails --username hive --password hive --tableizhenxin --export-dir /user/hive/warehouse/maillog.db/izhenxin_total/000000_0--input-fields-terminated-by '\t'

 

依然报错：

mysql> create table izhenxin(id int(10)unsigned NOT NULL AUTO_INCREMENT,mail_domain varchar(32) DEFAULTNULL,sent_number int,bounced_number int, deffered_number int, PRIMARY KEY(`id`)) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='sent mail';  ##原来的表

##解决办法：先删除上面的表，然后创建下面的表以适应hive的表结构

mysql> create table izhenxin(mail_domainvarchar(32) DEFAULT NULL,sent_number int,bounced_number int, deffered_numberint) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='sent mail';

##最终输出：

15/01/20 00:05:51 INFO mapreduce.ExportJobBase: Transferred6.9736 KB in 26.4035 seconds (270.4564 bytes/sec)

15/01/20 00:05:51 INFO mapreduce.ExportJobBase: Exported 132records.

 

mysql> select count(1) from izhenxin;

+----------+

| count(1) |

+----------+

|      132 |

+----------+

1 row in set (0.00 sec)

搞定！

 

9.问题描述：

15/01/27 10:48:56 INFO mapreduce.Job: Task Id :attempt_1420738964879_0244_m_000003_0, Status : FAILED

AttemptID:attempt_1420738964879_0244_m_000003_0 Timed out after600 secs

15/01/27 10:48:57 INFO mapreduce.Job:  map 75% reduce 0%

15/01/27 10:49:08 INFO mapreduce.Job:  map 100% reduce 0%

15/01/27 10:59:26 INFO mapreduce.Job: Task Id :attempt_1420738964879_0244_m_000003_1, Status : FAILED

AttemptID:attempt_1420738964879_0244_m_000003_1 Timed out after600 secs

15/01/27 10:59:27 INFO mapreduce.Job:  map 75% reduce 0%

15/01/27 10:59:38 INFO mapreduce.Job:  map 100% reduce 0%

15/01/27 11:09:55 INFO mapreduce.Job: Task Id :attempt_1420738964879_0244_m_000003_2, Status : FAILED

AttemptID:attempt_1420738964879_0244_m_000003_2 Timed out after600 secs

 

问题原因：

执行超时

 

解决办法：

vimmapred-site.xml

 mapred.task.timeout

 1800000

 

方法2：

Configuration conf=new Configuration();

 long milliSeconds = 1000*60*60;

 conf.setLong("mapred.task.timeout",milliSeconds);

 

方法3：

setmapred.tasktracker.expiry.interval=1800000;

setmapred.task.timeout= 1800000;

15/02/01 03:03:37 ERROR manager.SqlManager: Error reading fromdatabase: java.sql.SQLException: Streaming result set com

.mysql.jdbc.RowDataDynamic@4c0f73a3 is still active. Nostatements may be issued when any streaming result sets are open

 and in use on a givenconnection. Ensure that you have called .close() on any active streaming resultsets before attem

pting more queries.

java.sql.SQLException: Streaming result setcom.mysql.jdbc.RowDataDynamic@4c0f73a3 is still active. No statements may be

 issued when any streamingresult sets are open and in use on a given connection. Ensure that you havecalled .close() o

n any active streaming result sets before attempting morequeries.

 

mysql-connector-java-5.1.18-bin.jar 更换为： mysql-connector-java-5.1.32-bin.jar

 

问题：

由于2015年4月24日，openstack虚拟机整体宕机，造成hadoop运行异常，datanode无法启动

解决办法：

重新格式化namenode

然后删除hdfs/data 并赋予可写权限

/var/data/hadoop/bin/hadoop namenode -format

rm -rf /var/hadoop/tmp/dfs/data #下面两条命令在所有节点都执行

chown -R 777 /var/hadoop/tmp/dfs/data

/var/data/hadoop/sbin/hadoop-daemons.sh start datanode

 

hdfs haadmin -transitionToActive namenode1 如果两个namenode都是standby状态，用该命令提升为active 

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