hadoop异常记录

2015/05/16 11:42
阅读数 396
下面遇到问题,提供了一些解决办法,希望有所帮助

1:Shuffle Error: Exceeded MAX_FAILED_UNIQUE_FETCHES; bailing-out 
这是reduce预处理阶段shuffle时获取已完成的map的输出失败次数超过上限造成的,上限默认为5。引起此问题的方式可能会有很多种,比如网络连接不正常,连接超时,带宽较差以及端口阻塞等,通常框架内网络情况较好是不会出现此错误的。


2:Too many fetch-failures 
Answer:
出现这个问题主要是结点间的连通不够全面。
1) 检查 、/etc/hosts
   要求本机ip 对应 服务器名
   要求要包含所有的服务器ip + 服务器名
2) 检查 .ssh/authorized_keys
   要求包含所有服务器(包括其自身)的public key

3:处理速度特别的慢 出现map很快 但是reduce很慢 而且反复出现 reduce=0% 
Answer:
结合第二点,然后
修改 conf/hadoop-env.sh 中的export HADOOP_HEAPSIZE=4000 

4:能够启动datanode,但无法访问,也无法结束的错误 
在 重新格式化一个新的分布式文件时,需要将你NameNode上所配置的dfs.name.dir这一namenode用来存放NameNode 持久存储名字空间及事务日志的本地文件系统路径删除,同时将各DataNode上的dfs.data.dir的路径 DataNode 存放块数据的本地文件系统路径的目录也删除。如本此配置就是在NameNode上删除/home/hadoop/NameData,在DataNode上 删除/home/hadoop/DataNode1和/home/hadoop/DataNode2。这是因为Hadoop在格式化一个新的分布式文件系 统时,每个存储的名字空间都对应了建立时间的那个版本(可以查看/home/hadoop /NameData/current目录下的VERSION文件,上面记录了版本信息),在重新格式化新的分布式系统文件时,最好先删除NameData 目录。必须删除各DataNode的dfs.data.dir。这样才可以使namedode和datanode记录的信息版本对应。
注意:删除是个很危险的动作,不能确认的情况下不能删除!!做好删除的文件等通通备份!!

5:java.io.IOException: Could not obtain block: blk_194219614024901469_1100 file=/user/hive/warehouse/src_20090724_log/src_20090724_log 
出现这种情况大多是结点断了,没有连接上。

6:java.lang.OutOfMemoryError: Java heap space 
出现这种异常,明显是jvm内存不够得原因,要修改所有的datanode的jvm内存大小。
Java -Xms1024m -Xmx4096m
一般jvm的最大内存使用应该为总内存大小的一半,我们使用的8G内存,所以设置为4096m,这一值可能依旧不是最优的值。

Hadoop添加节点的方法 
自己实际添加节点过程:
1. 先在slave上配置好环境,包括ssh,jdk,相关config,lib,bin等的拷贝;
2. 将新的datanode的host加到集群namenode及其他datanode中去;
3. 将新的datanode的ip加到master的conf/slaves中;
4. 重启cluster,在cluster中看到新的datanode节点;
5. 运行bin/start-balancer.sh,这个会很耗时间
备注:
1. 如果不balance,那么cluster会把新的数据都存放在新的node上,这样会降低mr的工作效率;
2. 也可调用bin/start-balancer.sh 命令执行,也可加参数 -threshold 5
   threshold 是平衡阈值,默认是10%,值越低各节点越平衡,但消耗时间也更长。
3. balancer也可以在有mr job的cluster上运行,默认dfs.balance.bandwidthPerSec很低,为1M/s。在没有mr job时,可以提高该设置加快负载均衡时间。

其他备注:
1. 必须确保slave的firewall已关闭;
2. 确保新的slave的ip已经添加到master及其他slaves的/etc/hosts中,反之也要将master及其他slave的ip添加到新的slave的/etc/hosts中


mapper及reducer个数 
url地址:  http://wiki.apache.org/hadoop/HowManyMapsAndReduces
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HowManyMapsAndReduces
Partitioning your jobintomapsandreduces
Picking the appropriatesizeforthe tasksforyour job can radically change the performanceofHadoop. Increasing the numberoftasks increases the framework overhead, but increasesloadbalancingandlowers the costoffailures.Atone extremeisthe 1 map/1 reducecasewherenothingisdistributed. The other extremeistohave 1,000,000 maps/ 1,000,000 reduceswherethe framework runsoutofresourcesforthe overhead.
NumberofMaps
The numberofmapsisusually drivenbythe numberofDFS blocksinthe input files. Although that causes peopletoadjust their DFS blocksizetoadjust the numberofmaps. Therightlevelofparallelismformaps seemstobe around 10-100 maps/node, although we have taken it upto300orsoforvery cpu-light map tasks. Task setup takes awhile, so itisbest if the maps takeatleast aminutetoexecute.
Actually controlling the numberofmapsissubtle. The mapred.map.tasks parameterisjust a hinttothe InputFormatforthe numberofmaps. ThedefaultInputFormat behavioristosplit the total numberofbytesintotherightnumberoffragments. However,inthedefaultcasethe DFS blocksizeofthe input filesistreatedasanupperboundforinput splits. Alowerboundonthe splitsizecan besetvia mapred.min.split.size. Thus, if you expect 10TBofinput dataandhave 128MB DFS blocks, you'll end up with 82k maps, unless your mapred.map.tasks is even larger. Ultimately the [WWW] InputFormat determines the number of maps.
The number of map tasks can also be increased manually using the JobConf's conf.setNumMapTasks(intnum). This can be usedtoincrease the numberofmap tasks, but willnotsetthe number below that which Hadoop determines via splitting the input data.
NumberofReduces
Therightnumberofreduces seemstobe 0.95or1.75 * (nodes * mapred.tasktracker.tasks.maximum).At0.95allofthe reduces can launch immediatelyandstart transfering map outputsasthe maps finish.At1.75 the faster nodes will finish theirfirstroundofreducesandlaunch asecondroundofreduces doing a much better jobofloadbalancing.
Currently the numberofreducesislimitedtoroughly 1000bythe buffersizefortheoutputfiles (io.buffer.size* 2 * numReduces << heapSize). This will be fixedatsomepoint, but until itisit provides a pretty firmupperbound.
The numberofreduces also controls the numberofoutputfilesintheoutputdirectory, but usually thatisnotimportant because thenextmap/reduce step will split themintoeven smaller splitsforthe maps.
The numberofreduce tasks can also be increasedinthe same wayasthe map tasks, via JobConf's conf.setNumReduceTasks(intnum).

自己的理解:
mapper个数的设置:跟input file 有关系,也跟filesplits有关系,filesplits的上线为dfs.block.size,下线可以通过mapred.min.split.size设置,最后还是由InputFormat决定。

较好的建议:
The right number of reduces seems to be 0.95 or 1.75 multiplied by (<no. of nodes> * mapred.tasktracker.reduce.tasks.maximum).increasing the number of reduces increases the framework overhead, but increases load balancing and lowers the cost of failures. 
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<property>
  <name>mapred.tasktracker.reduce.tasks.maximum</name>
  <value>2</value>
  <description>The maximum number of reduce tasks that will be run
  simultaneously by a task tracker.
  </description>
</property>


单个node新加硬盘 
1.修改需要新加硬盘的node的dfs.data.dir,用逗号分隔新、旧文件目录
2.重启dfs

同步hadoop 代码 
hadoop-env.sh
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# host:path where hadoop code should be rsync'd from.  Unset by default.
# export HADOOP_MASTER=master:/home/$USER/src/hadoop


用命令合并HDFS小文件 
hadoop fs -getmerge <src> <dest>

重启reduce job方法 
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Introduced recovery of jobs when JobTracker restarts. This facility is off by default.
Introduced config parameters"mapred.jobtracker.restart.recover","mapred.jobtracker.job.history.block.size", and"mapred.jobtracker.job.history.buffer.size".

还未验证过。

IO写操作出现问题 
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0-1246359584298, infoPort=50075, ipcPort=50020):Got exceptionwhileserving blk_-5911099437886836280_1292 to/172.16.100.165:
java.net.SocketTimeoutException: 480000 millis timeoutwhilewaitingforchannel to be readyforwrite. ch : java.nio.channels.SocketChannel[connectedlocal=/
172.16.100.165:50010 remote=/172.16.100.165:50930]
        at org.apache.hadoop.net.SocketIOWithTimeout.waitForIO(SocketIOWithTimeout.java:185)
        at org.apache.hadoop.net.SocketOutputStream.waitForWritable(SocketOutputStream.java:159)
        at org.apache.hadoop.net.SocketOutputStream.transferToFully(SocketOutputStream.java:198)
        at org.apache.hadoop.hdfs.server.datanode.BlockSender.sendChunks(BlockSender.java:293)
        at org.apache.hadoop.hdfs.server.datanode.BlockSender.sendBlock(BlockSender.java:387)
        at org.apache.hadoop.hdfs.server.datanode.DataXceiver.readBlock(DataXceiver.java:179)
        at org.apache.hadoop.hdfs.server.datanode.DataXceiver.run(DataXceiver.java:94)
        at java.lang.Thread.run(Thread.java:619)


It seems there are many reasons that it can timeout, the example given in
HADOOP-3831 is a slow reading client.

解决办法:在hadoop-site.xml中设置dfs.datanode.socket.write.timeout=0试试;


HDFS退服节点的方法 
目前版本的dfsadmin的帮助信息是没写清楚的,已经file了一个bug了,正确的方法如下:
1. 将 dfs.hosts 置为当前的 slaves,文件名用完整路径,注意,列表中的节点主机名要用大名,即 uname -n 可以得到的那个。
2. 将 slaves 中要被退服的节点的全名列表放在另一个文件里,如 slaves.ex,使用 dfs.host.exclude 参数指向这个文件的完整路径
3. 运行命令 bin/hadoop dfsadmin -refreshNodes
4. web界面或 bin/hadoop dfsadmin -report 可以看到退服节点的状态是 Decomission in progress,直到需要复制的数据复制完成为止
5. 完成之后,从 slaves 里(指 dfs.hosts 指向的文件)去掉已经退服的节点

附带说一下 -refreshNodes 命令的另外三种用途:
2. 添加允许的节点到列表中(添加主机名到 dfs.hosts 里来)
3. 直接去掉节点,不做数据副本备份(在 dfs.hosts 里去掉主机名)
4. 退服的逆操作——停止 exclude 里面和 dfs.hosts 里面都有的,正在进行 decomission 的节点的退服,也就是把 Decomission in progress 的节点重新变为 Normal (在 web 界面叫 in service)

######################################
hadoop 学习借鉴 
解决hadoop OutOfMemoryError问题:
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<property>
   <name>mapred.child.java.opts</name>
   <value>-Xmx800M -server</value>
</property>

With the right JVM size in your hadoop-site.xml , you will have to copy this
to all mapred nodes and restart the cluster.
或者:hadoop jar jarfile [main class] -D mapred.child.java.opts=-Xmx800M 

Hadoop java.io.IOException: Job failed! at org.apache.hadoop.mapred.JobClient.runJob(JobClient.java:1232) while indexing.
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when i use nutch1.0,get this error:
Hadoop java.io.IOException: Job failed! at org.apache.hadoop.mapred.JobClient.runJob(JobClient.java:1232)whileindexing.

这个也很好解决:
可以删除conf/log4j.properties,然后可以看到详细的错误报告
我这儿出现的是out of memory
解决办法是在给运行主类org.apache.nutch.crawl.Crawl加上参数:-Xms64m -Xmx512m
你的或许不是这个问题,但是能看到详细的错误报告问题就好解决了

distribute cache使用 
类似一个全局变量,但是由于这个变量较大,所以不能设置在config文件中,转而使用distribute cache
具体使用方法:(详见《the definitive guide》,P240)
1. 在命令行调用时:调用-files,引入需要查询的文件(可以是local file, HDFS file(使用hdfs://xxx?)), 或者 -archives (JAR,ZIP, tar等)
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% hadoop jar job.jar MaxTemperatureByStationNameUsingDistributedCacheFile /
  -files input/ncdc/metadata/stations-fixed-width.txt input/ncdc/alloutput


2. 程序中调用:
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publicvoidconfigure(JobConf conf) {
   metadata =newNcdcStationMetadata();
   try{
     metadata.initialize(newFile("stations-fixed-width.txt"));
   }catch(IOException e) {
     thrownewRuntimeException(e);
   }
}

另外一种间接的使用方法:在hadoop-0.19.0中好像没有
调用addCacheFile()或者addCacheArchive()添加文件,
使用getLocalCacheFiles() 或 getLocalCacheArchives() 获得文件

hadoop的job显示web 
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There are web-based interfaces to both the JobTracker (MapReduce master) and NameNode (HDFS master)whichdisplay status pages about the state of the entire system. By default, these are located at [WWW] [url]http://job.tracker.addr:50030/[/url] and [WWW] [url]http://name.node.addr:50070/.[/url]


hadoop监控 
OnlyXP(52388483) 131702
用nagios作告警,ganglia作监控图表即可

status of 255 error 
错误类型:
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java.io.IOException: Task processexitwith nonzero status of 255.
        at org.apache.hadoop.mapred.TaskRunner.run(TaskRunner.java:424)


错误原因:
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Set mapred.jobtracker.retirejob.interval and mapred.userlog.retain.hours to higher value. By default, their values are 24 hours. These might be the reasonforfailure, though I'm not sure


split size 
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FileInputFormat input splits: (详见 《the definitive guide》P190)
mapred.min.split.size: default=1, the smallest valide sizeinbytesforafilesplit.
mapred.max.split.size: default=Long.MAX_VALUE, the largest valid size.

dfs.block.size: default = 64M, 系统中设置为128M。
如果设置 minimum split size > block size, 会增加块的数量。(猜想从其他节点拿去数据的时候,会合并block,导致block数量增多) 
如果设置maximum split size < block size, 会进一步拆分block。

split size = max(minimumSize, min(maximumSize, blockSize));
其中 minimumSize < blockSize < maximumSize.

sort by value 
hadoop 不提供直接的sort by value方法,因为这样会降低mapreduce性能。
但可以用组合的办法来实现,具体实现方法见《the definitive guide》, P250
基本思想:
1. 组合key/value作为新的key;
2. 重载partitioner,根据old key来分割;
conf.setPartitionerClass(FirstPartitioner.class);
3. 自定义keyComparator:先根据old key排序,再根据old value排序;
conf.setOutputKeyComparatorClass(KeyComparator.class);
4. 重载GroupComparator, 也根据old key 来组合;  conf.setOutputValueGroupingComparator(GroupComparator.class);

small input files的处理 
对于一系列的small files作为input file,会降低hadoop效率。
有3种方法可以将small file合并处理:
1. 将一系列的small files合并成一个sequneceFile,加快mapreduce速度。
详见WholeFileInputFormat及SmallFilesToSequenceFileConverter,《the definitive guide》, P194
2. 使用CombineFileInputFormat集成FileinputFormat,但是未实现过;
3. 使用hadoop archives(类似打包),减少小文件在namenode中的metadata内存消耗。(这个方法不一定可行,所以不建议使用)
   方法:
   将/my/files目录及其子目录归档成files.har,然后放在/my目录下
   bin/hadoop archive -archiveName files.har /my/files /my
   
   查看files in the archive:
   bin/hadoop fs -lsr har://my/files.har

skip bad records 
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JobConf conf =newJobConf(ProductMR.class);
conf.setJobName("ProductMR");
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(Product.class);
conf.setMapperClass(Map.class);
conf.setReducerClass(Reduce.class);
conf.setMapOutputCompressorClass(DefaultCodec.class);
conf.setInputFormat(SequenceFileInputFormat.class);
conf.setOutputFormat(SequenceFileOutputFormat.class);
String objpath ="abc1";
SequenceFileInputFormat.addInputPath(conf,newPath(objpath));
SkipBadRecords.setMapperMaxSkipRecords(conf, Long.MAX_VALUE);
SkipBadRecords.setAttemptsToStartSkipping(conf,0);
SkipBadRecords.setSkipOutputPath(conf,newPath("data/product/skip/"));
String output ="abc";
SequenceFileOutputFormat.setOutputPath(conf,newPath(output));
JobClient.runJob(conf);


For skipping failed tasks try : mapred.max.map.failures.percent

restart 单个datanode  
如果一个datanode 出现问题,解决之后需要重新加入cluster而不重启cluster,方法如下:
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bin/hadoop-daemon.sh start datanode
bin/hadoop-daemon.sh start jobtracker



Namenode in safe mode 
解决方法
bin/hadoop dfsadmin -safemode leave

java.net.NoRouteToHostException: No route to host 
j解决方法:
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sudo/etc/init.d/iptablesstop


更改namenode后,在hive中运行select 依旧指向之前的namenode地址 
这是因为:
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When youcreate a table, hive actually stores the location of the table (e.g.
hdfs://ip:port/user/root/...)inthe SDS and DBS tablesinthe metastore . So when I bring up a new cluster the master has a new IP, but hive's metastore is still pointing to the locations within the old
cluster. I could modify the metastore to update with the new IP everytime I bring up a cluster. But the easier and simpler solution was to just use an elastic IPforthe master

所以要将metastore中的之前出现的namenode地址全部更换为现有的namenode地址


Your DataNodes won't start, and you see something like this in logs/*datanode*: 
Incompatible namespaceIDs in /tmp/hadoop-ross/dfs/data
原因:
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Your Hadoop namespaceID became corrupted. Unfortunately the easiest thing todoreformat the HDFS.

解决方法:
You need to do something like this:
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bin/stop-all.sh
rm-Rf/tmp/hadoop-your-username/*
bin/hadoopnamenode -format

12:You can run Hadoop jobs written in Java (like the grep example), but your HadoopStreaming jobs (such as the Python example that fetches web page titles) won't work. 
原因:
You might have given only a relative path to the mapper and reducer programs. The tutorial originally just specified relative paths, but absolute paths are required if you are running in a real cluster.
解决方法:
Use absolute paths like this from the tutorial:
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bin/hadoopjar contrib/hadoop-0.15.2-streaming.jar /
  -mapper  $HOME/proj/hadoop/multifetch.py         /
  -reducer $HOME/proj/hadoop/reducer.py            /
  -input   urls/*                                  /
  -output  titles


09/08/31 18:25:45 INFO hdfs.DFSClient: Exception in createBlockOutputStream java.io.IOException:Bad connect ack with firstBadLink 192.168.1.11:50010 
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> 09/08/3118:25:45 INFO hdfs.DFSClient: Abandoning block blk_-8575812198227241296_1001
> 09/08/3118:25:51 INFO hdfs.DFSClient: ExceptionincreateBlockOutputStream java.io.IOException:
Bad connect ack with firstBadLink 192.168.1.16:50010
> 09/08/3118:25:51 INFO hdfs.DFSClient: Abandoning block blk_-2932256218448902464_1001
> 09/08/3118:25:57 INFO hdfs.DFSClient: ExceptionincreateBlockOutputStream java.io.IOException:
Bad connect ack with firstBadLink 192.168.1.11:50010
> 09/08/3118:25:57 INFO hdfs.DFSClient: Abandoning block blk_-1014449966480421244_1001
> 09/08/3118:26:03 INFO hdfs.DFSClient: ExceptionincreateBlockOutputStream java.io.IOException:
Bad connect ack with firstBadLink 192.168.1.16:50010
> 09/08/3118:26:03 INFO hdfs.DFSClient: Abandoning block blk_7193173823538206978_1001
> 09/08/3118:26:09 WARN hdfs.DFSClient: DataStreamer Exception: java.io.IOException: Unable
to create new block.
>         at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.nextBlockOutputStream(DFSClient.java:2731)
>         at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.access$2000(DFSClient.java:1996)
>         at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream$DataStreamer.run(DFSClient.java:2182)
>
> 09/08/3118:26:09 WARN hdfs.DFSClient: Error Recoveryforblock blk_7193173823538206978_1001
bad datanode[2] nodes == null
> 09/08/3118:26:09 WARN hdfs.DFSClient: Could not get block locations. Sourcefile"/user/umer/8GB_input"
- Aborting...
> put: Bad connect ack with firstBadLink 192.168.1.16:50010



解决方法:
I have resolved the issue:
What i did: 

1) '/etc/init.d/iptables stop' -->stopped firewall
2) SELINUX=disabled in '/etc/selinux/config' file.-->disabled selinux
I worked for me after these two changes

解决jline.ConsoleReader.readLine在Windows上不生效问题方法 
在 CliDriver.java的main()函数中,有一条语句reader.readLine,用来读取标准输入,但在Windows平台上该语句总是 返回null,这个reader是一个实例jline.ConsoleReader实例,给Windows Eclipse调试带来不便。
我们可以通过使用java.util.Scanner.Scanner来替代它,将原来的
while ((line=reader.readLine(curPrompt+"> ")) != null)
复制代码
替换为:
Scanner sc = new Scanner(System.in);
while ((line=sc.nextLine()) != null)

重新编译发布,即可正常从标准输入读取输入的SQL语句了。

某次正常运行mapreduce实例时,抛出错误

java.io.IOException: All datanodes xxx.xxx.xxx.xxx:xxx are bad. Aborting…
at org.apache.hadoop.dfs.DFSClient$DFSOutputStream.processDatanodeError(DFSClient.java:2158)
at org.apache.hadoop.dfs.DFSClient$DFSOutputStream.access$1400(DFSClient.java:1735)
at org.apache.hadoop.dfs.DFSClient$DFSOutputStream$DataStreamer.run(DFSClient.java:1889)
java.io.IOException: Could not get block locations. Aborting…
at org.apache.hadoop.dfs.DFSClient$DFSOutputStream.processDatanodeError(DFSClient.java:2143)
at org.apache.hadoop.dfs.DFSClient$DFSOutputStream.access$1400(DFSClient.java:1735)
at org.apache.hadoop.dfs.DFSClient$DFSOutputStream$DataStreamer.run(DFSClient.java:1889)
经查明,问题原因是linux机器打开了过多的文件导致。用命令ulimit -n可以发现linux默认的文件打开数目为1024,修改/ect/security/limit.conf,增加hadoop soft 65535

再重新运行程序(最好所有的datanode都修改),问题解决

运行一段时间后hadoop不能stop-all.sh的问题,显示报错
no tasktracker to stop ,no datanode to stop
问 题的原因是hadoop在stop的时候依据的是datanode上的mapred和dfs进程号。而默认的进程号保存在/tmp下,linux默认会每 隔一段时间(一般是一个月或者7天左右)去删除这个目录下的文件。因此删掉hadoop-hadoop-jobtracker.pid和hadoop- hadoop-namenode.pid两个文件后,namenode自然就找不到datanode上的这两个进程了。
在配置文件中的export HADOOP_PID_DIR可以解决这个问题

问题:
Incompatible namespaceIDs in /usr/local/hadoop/dfs/data: namenode namespaceID = 405233244966; datanode namespaceID = 33333244
原因:
在 每次执行hadoop namenode -format时,都会为NameNode生成namespaceID,,但是在hadoop.tmp.dir目录下的DataNode还是保留上次的 namespaceID,因为namespaceID的不一致,而导致DataNode无法启动,所以只要在每次执行hadoop namenode -format之前,先删除hadoop.tmp.dir目录就可以启动成功。请注意是删除hadoop.tmp.dir对应的本地目录,而不是HDFS 目录。


Problem: NameNode is not formatted 
solution: 是因为HDFS还没有格式化,只需要运行hadoop namenode -format一下,然后再启动即可

bin/hadoop jps后报如下异常: 
[Bash shell]   纯文本查看   复制代码
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Exceptioninthread"main"java.lang.NullPointerException
        at sun.jvmstat.perfdata.monitor.protocol.local.LocalVmManager.activeVms(LocalVmManager.java:127)
        at sun.jvmstat.perfdata.monitor.protocol.local.MonitoredHostProvider.activeVms(MonitoredHostProvider.java:133)
        at sun.tools.jps.Jps.main(Jps.java:45)

原因为:
系统根目录/tmp文件夹被删除了。重新建立/tmp文件夹即可。
bin/hive中出现 unable to  create log directory /tmp/...也可能是这个原因
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