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Hadoop hive sqoop zookeeper hb
更新：HHH 时间：2023-1-7

6、问题及解决方案

1. 问题描述：

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

#wget http://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

# wget http://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

# wget https://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

#wget http://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）

<name>dfs.block.size</name>

<description>The default block size for newfiles</description>

</property>

<name>dfs.datanode.max.xcievers</name>

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

</description>

</property>

<name>dfs.datanode.du.reserved</name>

<description>Reserved space in bytes per volume. Always leave thismuch space free for non dfs use.</description>

</property>

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

<name>mapreduce.map.memory.mb</name>

</property>

<name>mapreduce.map.java.opts</name>

</property>

<name>mapreduce.reduce.memory.mb</name>

</property>

<name>mapreduce.reduce.java.opts</name>

</property>

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：

wget http://dist.codehaus.org/jetty/jetty-6.1.26/jetty-6.1.26.zip

wget http://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

<equalsarg1="${hadoopversion}" arg2="200" />

<then>

<propertyname="hadoop.version" value="2.5.0" />

<propertyname="hbase94.version" value="0.94.2" />

<propertyname="zookeeper.version" value="3.4.6" />

<propertyname="hadoop.version.full" value="2.5.0" />

<propertyname="hcatalog.version" value="0.13.0" />

<propertyname="hbasecompatprofile" value="2" />

<propertyname="avrohadoopprofile" value="2" />

</then>

</elseif>

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：

vim mapred-site.xml

<name>mapred.task.timeout</name>

</property>

方法2：

Configuration conf=new Configuration();

long milliSeconds = 1000*60*60;<default is 600000, likewise can give any value)

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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