apache hadoop
发表于：2025-02-02 作者：千家信息网编辑
千家信息网最后更新 2025年02月02日，两年多没有搭建过apache hadoop的环境了，昨天再次搭建hadoop环境，将过程记录下来，以便以后查阅。主机角色分配：NameNode、DFSZKFailoverController 角色由
千家信息网最后更新 2025年02月02日apache hadoop
两年多没有搭建过apache hadoop的环境了，昨天再次搭建hadoop环境，将过程记录下来，以便以后查阅。
主机角色分配：
NameNode、DFSZKFailoverController 角色由 oversea-stable、bus-stable 服务器承担；需要安装软件有：JDK、Hadoop2.9.1
ResourceManager角色由 oversea-stable 服务器承担；需要安装软件有：JDK、Hadoop2.9.1
JournalNode、DataNode、NodeManager角色由open-stable、permission-stable、sp-stable服务器承担；需要安装软件有：JDK、Hadoop2.9.1
zookeeper cluster的QuorumPeerMain角色由open-stable、permission-stable、sp-stable服务器承担；需要安装软件有：JDK、zookeeper3.4.12
1、环境设置
(1) 设置主机名，并配置本地解析(主机名与解析必须配置一致，否则journalnode无法启动)
[root@oversea-stable ~]# cat /etc/hosts192.168.20.68 oversea-stable192.168.20.67 bus-stable192.168.20.66 open-stable192.168.20.65 permission-stable192.168.20.64 sp-stable[root@oversea-stable ~]# 并将该文件同步到所有机器 上。
(2) 各节点同步时间
(3) 同步jdk，并在所有节点上安装jdk
(4) 配置环境变量
在/etc/profile文件中加入如下设置：
export JAVA_HOME=/usr/java/latestexport HADOOP_HOME=/opt/hadoopexport JRE_HOME=$JAVA_HOME/jreexport CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jarexport HADOOP_OPTS="-Djava.library.path=$HADOOP_HOME/lib"PATH=$JAVA_HOME/bin:$HADOOP_HOME/bin:$PATH2、配置SSH 密钥，并复制给本机(ssh本机时也需要免密码登录)
在所有机器上如下操作：
(1) 创建hadoop用户，useradd hadoop
(2) 设置hadoop用户的密码: echo "xxxxxxxx" | passwd --stdin hadoop
在其中一台server上切换hadoop： su - hadoop
并生成 ssh 密钥: ssh-keygen -b 2048 -t rsa
同步密钥到其它server 上 : scp -r .ssh server_name:~/
每台server 切换 hadoop用户，验证是否能够免密登录其它server
3、配置zookeeper
在 open-stable 、permission-stable、sp-stable server 上配置zookeeper cluster，如下操作：
[root@open-stable ~]# chmod o+w /opt[root@open-stable ~]# su - hadoop[hadoop@open-stable ~]$ wget http://mirrors.hust.edu.cn/apache/zookeeper/zookeeper-3.4.12/zookeeper-3.4.12.tar.gz[hadoop@open-stable ~]$ tar xfz zookeeper-3.4.12.tar.gz -C /opt[hadoop@open-stable ~]$ cd /opt/[hadoop@open-stable opt]$ mv zookeeper{-3.4.12,}[hadoop@open-stable opt]$ cd zookeeper/[hadoop@open-stable zookeeper]$ cp conf/zoo_sample.cfg conf/zoo.cfg[hadoop@open-stable zookeeper]$ vim conf/zoo.cfg[hadoop@open-stable zookeeper]$ grep -Pv "^(#|$)" conf/zoo.cfgtickTime=2000initLimit=10syncLimit=5dataDir=/opt/zookeeper/zkdatadataLogDir=/opt/zookeeper/zklogsclientPort=2181server.6=open-stable:2888:3888server.5=permission-stable:2888:3888server.4=sp-stable:2888:3888[hadoop@open-stable zookeeper]$ mkdir zkdata[hadoop@open-stable zookeeper]$ mkdir zklogs[hadoop@open-stable zookeeper]$ echo 6 > zkdata/myid[hadoop@open-stable zookeeper]$ bin/zkServer.sh start其它server 配置相同[hadoop@open-stable zookeeper]$ bin/zkServer.sh statusZooKeeper JMX enabled by defaultUsing config: /opt/zookeeper/bin/../conf/zoo.cfgMode: leader[hadoop@open-stable zookeeper]$ [hadoop@permission-stable zookeeper]$ bin/zkServer.sh statusZooKeeper JMX enabled by defaultUsing config: /opt/zookeeper/bin/../conf/zoo.cfgMode: follower[hadoop@permission-stable zookeeper]$[hadoop@sp-stable zookeeper]$ bin/zkServer.sh statusZooKeeper JMX enabled by defaultUsing config: /opt/zookeeper/bin/../conf/zoo.cfgMode: follower[hadoop@sp-stable zookeeper]$ 4、配置hadoop
(1) 在其中一台上配置hadoop ,如下操作：
[hadoop@oversea-stable ~]$ wget http://mirror.bit.edu.cn/apache/hadoop/common/hadoop-2.9.1/hadoop-2.9.1.tar.gz[hadoop@oversea-stable ~]$ tar xfz hadoop-2.9.1.tar.gz -C /opt/[hadoop@oversea-stable ~]$ cd /opt/[hadoop@oversea-stable opt]$ ln -s hadoop-2.9.1 hadoop[hadoop@oversea-stable opt]$ cd hadoop/etc/hadoop[hadoop@oversea-stable hadoop]$ grep JAVA_HOME hadoop-env.sh export JAVA_HOME=/usr/java/latest[hadoop@oversea-stable hadoop]$[hadoop@oversea-stable hadoop]$ tail -14 core-site.xml fs.defaultFS hdfs://inspiryhdfs hadoop.tmp.dir /opt/hadoop/tmp ha.zookeeper.quorum open-stable:2181,permission-stable:2181,sp-stable:2181 [hadoop@oversea-stable hadoop]$[hadoop@oversea-stable hadoop]$ tail -50 hdfs-site.xml dfs.nameservices inspiryhdfs dfs.ha.namenodes.inspiryhdfs nn1,nn2 dfs.namenode.rpc-address.inspiryhdfs.nn1 oversea-stable:9000 dfs.namenode.http-address.inspiryhdfs.nn1 oversea-stable:50070 dfs.namenode.rpc-address.inspiryhdfs.nn2 bus-stable:9000 dfs.namenode.http-address.inspiryhdfs.nn2 bus-stable:50070 dfs.namenode.shared.edits.dir qjournal://open-stable:8485;permission-stable:8485;sp-stable:8485/inspiryhdfs dfs.journalnode.edits.dir /opt/hadoop/journal dfs.ha.automatic-failover.enabled true dfs.client.failover.proxy.provider.inspiryhdfs org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider dfs.ha.fencing.methods sshfence dfs.ha.fencing.ssh.private-key-files /home/hadoop/.ssh/id_rsa 指定MapReduce运行在yarn框架之上[hadoop@oversea-stable hadoop]$ cp mapred-site.xml{.template,}[hadoop@oversea-stable hadoop]$ tail -6 mapred-site.xml mapreduce.framework.name yarn [hadoop@oversea-stable hadoop]$指定DataNode节点[hadoop@oversea-stable hadoop]$ cat slaves open-stablepermission-stablesp-stable[hadoop@oversea-stable hadoop]$[hadoop@oversea-stable hadoop]$ tail -11 yarn-site.xml yarn.resourcemanager.hostname oversea-stable yarn.nodemanager.aux-services mapreduce_shuffle [hadoop@oversea-stable hadoop]$(2) 将配置完毕的hadoop 同步到其它servers上
[hadoop@oversea-stable opt]$ rsync -avzoptgl hadoop-2.9.1 bus-stable:/opt/[hadoop@oversea-stable opt]$ rsync -avzoptgl hadoop-2.9.1 open-stable:/opt/[hadoop@oversea-stable opt]$ rsync -avzoptgl hadoop-2.9.1 permission-stable:/opt/[hadoop@oversea-stable opt]$ rsync -avzoptgl hadoop-2.9.1 sp-stable:/opt/其它各servers 创建 hadoop 的 soft link
(3) 启动journalnode
sbin/hadoop-daemons.sh start journalnode 在oversea-stable上格式化namenode，并启动主namenode
hadoop namenode -formatsbin/hadoop-daemon.sh start namenode [hadoop@oversea-stable hadoop]$ ls /opt/hadoop/tmp/dfs/name/current/ fsimage_0000000000000000000 seen_txid fsimage_0000000000000000000.md5 VERSION(4) standby_namenode同步数据
在oversea-stable 节点格式化namenode，并启动namenode之后，在bus-stable节点上同步namenode信息，避免再次对namenode格式化(同时保证bus-stable上也有/opt/hadoop/tmp目录)。在bus-stable上如下操作：
bin/hdfs namenode -bootstrapStandbysbin/hadoop-daemon.sh start namenode 5、格式化zkfs(让namenode可以将本机状态汇报给zookeeper)
hdfs zkfc -formatZK
(如果格式化失败，要检查 core-site.xml中指定的zookeeper地址是否完全正确)
6、启动hdfs
[hadoop@oversea-stable hadoop]$ sbin/start-dfs.sh Starting namenodes on [oversea-stable bus-stable]bus-stable: starting namenode, logging to /opt/hadoop-2.9.1/logs/hadoop-hadoop-namenode-bus-stable.outoversea-stable: starting namenode, logging to /opt/hadoop-2.9.1/logs/hadoop-hadoop-namenode-oversea-stable.outsp-stable: starting datanode, logging to /opt/hadoop-2.9.1/logs/hadoop-hadoop-datanode-sp-stable.outpermission-stable: starting datanode, logging to /opt/hadoop-2.9.1/logs/hadoop-hadoop-datanode-permission-stable.outopen-stable: starting datanode, logging to /opt/hadoop-2.9.1/logs/hadoop-hadoop-datanode-open-stable.outStarting journal nodes [open-stable permission-stable sp-stable]sp-stable: starting journalnode, logging to /opt/hadoop-2.9.1/logs/hadoop-hadoop-journalnode-sp-stable.outopen-stable: starting journalnode, logging to /opt/hadoop-2.9.1/logs/hadoop-hadoop-journalnode-open-stable.outpermission-stable: starting journalnode, logging to /opt/hadoop-2.9.1/logs/hadoop-hadoop-journalnode-permission-stable.outStarting ZK Failover Controllers on NN hosts [oversea-stable bus-stable]oversea-stable: starting zkfc, logging to /opt/hadoop-2.9.1/logs/hadoop-hadoop-zkfc-oversea-stable.outbus-stable: starting zkfc, logging to /opt/hadoop-2.9.1/logs/hadoop-hadoop-zkfc-bus-stable.out[hadoop@oversea-stable hadoop]$ 7、启动yarn(Namenode和ResourceManger如果不是同一台机器，不能在NameNode上启动 yarn，必须在ResouceManager机器上启动yarn)
[hadoop@oversea-stable hadoop]$ sbin/start-yarn.sh starting yarn daemonsstarting resourcemanager, logging to /opt/hadoop-2.9.1/logs/yarn-hadoop-resourcemanager-oversea-stable.outsp-stable: starting nodemanager, logging to /opt/hadoop-2.9.1/logs/yarn-hadoop-nodemanager-sp-stable.outopen-stable: starting nodemanager, logging to /opt/hadoop-2.9.1/logs/yarn-hadoop-nodemanager-open-stable.outpermission-stable: starting nodemanager, logging to /opt/hadoop-2.9.1/logs/yarn-hadoop-nodemanager-permission-stable.out[hadoop@oversea-stable hadoop]$ 8、验证各节点的角色
[hadoop@oversea-stable hadoop]$ sbin/start-yarn.sh starting yarn daemonsstarting resourcemanager, logging to /opt/hadoop-2.9.1/logs/yarn-hadoop-resourcemanager-oversea-stable.outsp-stable: starting nodemanager, logging to /opt/hadoop-2.9.1/logs/yarn-hadoop-nodemanager-sp-stable.outopen-stable: starting nodemanager, logging to /opt/hadoop-2.9.1/logs/yarn-hadoop-nodemanager-open-stable.outpermission-stable: starting nodemanager, logging to /opt/hadoop-2.9.1/logs/yarn-hadoop-nodemanager-permission-stable.out[hadoop@oversea-stable hadoop]$ [hadoop@oversea-stable ~]$ jps4389 DFSZKFailoverController5077 ResourceManager25061 Jps4023 NameNode[hadoop@oversea-stable ~]$[hadoop@bus-stable ~]$ jps9073 Jps29956 NameNode30095 DFSZKFailoverController[hadoop@bus-stable ~]$[hadoop@open-stable ~]$ jps2434 DataNode421 QuorumPeerMain2559 JournalNode2847 NodeManager11903 Jps[hadoop@open-stable ~]$[hadoop@permission-stable ~]$ jps30489 QuorumPeerMain32505 JournalNode9689 Jps32380 DataNode303 NodeManager[hadoop@permission-stable ~]$[hadoop@sp-stable ~]$ jps29955 DataNode30339 NodeManager30072 JournalNode6792 Jps28060 QuorumPeerMain[hadoop@sp-stable ~]$ 在浏览器中输入：http://oversea-stable:50070/，以及http://bus-stable:50070/
上面可以看到bus-stable是处于active状态，oversea-stable是处于standby，接下来测试以下namenode的高可用，当bus-stable挂掉时oversea-stable是否能够自动切换；
在bus-stable中kill掉NameNode进程
[root@bus-stable ~]# jps1614 NameNode2500 Jps1929 DFSZKFailoverController[root@bus-stable ~]# kill -9 1614再次刷新http://bus-stable:50070/，无法访问；刷新http://oversea-stable:50070/
这时oversea-stable已经处于active状态了，这说明切换是没有问题的，现在已经完成了hadoop集群的高可用的搭建；
输入：http://oversea-stable:8088 查看hadoop cluster 状态，如下所示：
9、hadoop的应用
[hadoop@oversea-stable hadoop]$ hdfs dfs -ls / Found 2 itemsdrwxr-xr-x - hadoop supergroup 0 2018-06-15 10:32 /data[hadoop@oversea-stable ~]$ hdfs dfs -put /tmp/notepad.txt /data/notepad.txt[hadoop@oversea-stable ~]$ cd /opt/hadoop[hadoop@oversea-stable hadoop]$ ls share/hadoop/mapreduce/hadoop-mapreduce-client-app-2.9.1.jar hadoop-mapreduce-client-jobclient-2.9.1.jar libhadoop-mapreduce-client-common-2.9.1.jar hadoop-mapreduce-client-jobclient-2.9.1-tests.jar lib-exampleshadoop-mapreduce-client-core-2.9.1.jar hadoop-mapreduce-client-shuffle-2.9.1.jar sourceshadoop-mapreduce-client-hs-2.9.1.jar hadoop-mapreduce-examples-2.9.1.jarhadoop-mapreduce-client-hs-plugins-2.9.1.jar jdiff[hadoop@oversea-stable hadoop]$ [hadoop@oversea-stable hadoop]$ [hadoop@oversea-stable hadoop]$ hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.9.1.jar wordcount /data /out118/06/15 11:04:53 INFO client.RMProxy: Connecting to ResourceManager at oversea-stable/192.168.20.68:803218/06/15 11:04:54 INFO input.FileInputFormat: Total input files to process : 118/06/15 11:04:54 INFO mapreduce.JobSubmitter: number of splits:118/06/15 11:04:54 INFO Configuration.deprecation: yarn.resourcemanager.system-metrics-publisher.enabled is deprecated. Instead, use yarn.system-metrics-publisher.enabled18/06/15 11:04:54 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1528979206314_000218/06/15 11:04:55 INFO impl.YarnClientImpl: Submitted application application_1528979206314_000218/06/15 11:04:55 INFO mapreduce.Job: The url to track the job: http://oversea-stable:8088/proxy/application_1528979206314_0002/18/06/15 11:04:55 INFO mapreduce.Job: Running job: job_1528979206314_000218/06/15 11:05:02 INFO mapreduce.Job: Job job_1528979206314_0002 running in uber mode : false18/06/15 11:05:02 INFO mapreduce.Job: map 0% reduce 0/06/15 11:05:08 INFO mapreduce.Job: map 100% reduce 0/06/15 11:05:14 INFO mapreduce.Job: map 100% reduce 100/06/15 11:05:14 INFO mapreduce.Job: Job job_1528979206314_0002 completed successfully18/06/15 11:05:14 INFO mapreduce.Job: Counters: 49 File System Counters FILE: Number of bytes read=68428 FILE: Number of bytes written=535339 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=88922 HDFS: Number of bytes written=58903 HDFS: Number of read operations=6 HDFS: Number of large read operations=0 HDFS: Number of write operations=2 Job Counters Launched map tasks=1 Launched reduce tasks=1 Data-local map tasks=1 Total time spent by all maps in occupied slots (ms)=3466 Total time spent by all reduces in occupied slots (ms)=3704 Total time spent by all map tasks (ms)=3466 Total time spent by all reduce tasks (ms)=3704 Total vcore-milliseconds taken by all map tasks=3466 Total vcore-milliseconds taken by all reduce tasks=3704 Total megabyte-milliseconds taken by all map tasks=3549184 Total megabyte-milliseconds taken by all reduce tasks=3792896 Map-Reduce Framework Map input records=1770 Map output records=5961 Map output bytes=107433 Map output materialized bytes=68428 Input split bytes=100 Combine input records=5961 Combine output records=2366 Reduce input groups=2366 Reduce shuffle bytes=68428 Reduce input records=2366 Reduce output records=2366 Spilled Records=4732 Shuffled Maps =1 Failed Shuffles=0 Merged Map outputs=1 GC time elapsed (ms)=145 CPU time spent (ms)=2730 Physical memory (bytes) snapshot=505479168 Virtual memory (bytes) snapshot=4347928576 Total committed heap usage (bytes)=346554368 Shuffle Errors BAD_ID=0 CONNECTION=0 IO_ERROR=0 WRONG_LENGTH=0 WRONG_MAP=0 WRONG_REDUCE=0 File Input Format Counters Bytes Read=88822 File Output Format Counters Bytes Written=58903[hadoop@oversea-stable hadoop]$[hadoop@oversea-stable hadoop]$ hdfs dfs -ls /out1/ Found 2 items-rw-r--r-- 3 hadoop supergroup 0 2018-06-15 11:05 /out1/_SUCCESS-rw-r--r-- 3 hadoop supergroup 58903 2018-06-15 11:05 /out1/part-r-00000[hadoop@oversea-stable hadoop]$ [hadoop@oversea-stable hadoop]$ hdfs dfs -cat /out1/part-r-00000自定义map-reduce函数运行任务如下效果：
[hadoop@oversea-stable hadoop]$ hadoop jar /opt/hadoop/share/hadoop/tools/lib/hadoop-streaming-2.9.1.jar -file /opt/map.py -mapper /opt/map.py -file /opt/reduce.py -reducer /opt/reduce.py -input /data/notepad.txt -output /out218/06/15 14:30:32 WARN streaming.StreamJob: -file option is deprecated, please use generic option -files instead.packageJobJar: [/opt/map.py, /opt/reduce.py, /tmp/hadoop-unjar5706672822735184593/] [] /tmp/streamjob6067385394162603509.jar tmpDir=null18/06/15 14:30:33 INFO client.RMProxy: Connecting to ResourceManager at oversea-stable/192.168.20.68:803218/06/15 14:30:33 INFO client.RMProxy: Connecting to ResourceManager at oversea-stable/192.168.20.68:803218/06/15 14:30:34 INFO mapred.FileInputFormat: Total input files to process : 118/06/15 14:30:34 INFO mapreduce.JobSubmitter: number of splits:218/06/15 14:30:34 INFO Configuration.deprecation: yarn.resourcemanager.system-metrics-publisher.enabled is deprecated. Instead, use yarn.system-metrics-publisher.enabled18/06/15 14:30:35 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1529036356241_000418/06/15 14:30:35 INFO impl.YarnClientImpl: Submitted application application_1529036356241_000418/06/15 14:30:35 INFO mapreduce.Job: The url to track the job: http://oversea-stable:8088/proxy/application_1529036356241_0004/18/06/15 14:30:35 INFO mapreduce.Job: Running job: job_1529036356241_000418/06/15 14:30:42 INFO mapreduce.Job: Job job_1529036356241_0004 running in uber mode : false18/06/15 14:30:42 INFO mapreduce.Job: map 0% reduce 0/06/15 14:30:48 INFO mapreduce.Job: map 100% reduce 0/06/15 14:30:54 INFO mapreduce.Job: map 100% reduce 100/06/15 14:30:54 INFO mapreduce.Job: Job job_1529036356241_0004 completed successfully18/06/15 14:30:54 INFO mapreduce.Job: Counters: 49 File System Counters FILE: Number of bytes read=107514 FILE: Number of bytes written=823175 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=93092 HDFS: Number of bytes written=58903 HDFS: Number of read operations=9 HDFS: Number of large read operations=0 HDFS: Number of write operations=2 Job Counters Launched map tasks=2 Launched reduce tasks=1 Data-local map tasks=2 Total time spent by all maps in occupied slots (ms)=7194 Total time spent by all reduces in occupied slots (ms)=3739 Total time spent by all map tasks (ms)=7194 Total time spent by all reduce tasks (ms)=3739 Total vcore-milliseconds taken by all map tasks=7194 Total vcore-milliseconds taken by all reduce tasks=3739 Total megabyte-milliseconds taken by all map tasks=7366656 Total megabyte-milliseconds taken by all reduce tasks=3828736 Map-Reduce Framework Map input records=1770 Map output records=5961 Map output bytes=95511 Map output materialized bytes=107520 Input split bytes=174 Combine input records=0 Combine output records=0 Reduce input groups=2366 Reduce shuffle bytes=107520 Reduce input records=5961 Reduce output records=2366 Spilled Records=11922 Shuffled Maps =2 Failed Shuffles=0 Merged Map outputs=2 GC time elapsed (ms)=292 CPU time spent (ms)=4340 Physical memory (bytes) snapshot=821985280 Virtual memory (bytes) snapshot=6525067264 Total committed heap usage (bytes)=548929536 Shuffle Errors BAD_ID=0 CONNECTION=0 IO_ERROR=0 WRONG_LENGTH=0 WRONG_MAP=0 WRONG_REDUCE=0 File Input Format Counters Bytes Read=92918 File Output Format Counters Bytes Written=5890318/06/15 14:30:54 INFO streaming.StreamJob: Output directory: /out2[hadoop@oversea-stable hadoop]$ [hadoop@oversea-stable hadoop]$ hdfs dfs -ls /out2Found 2 items-rw-r--r-- 3 hadoop supergroup 0 2018-06-15 14:30 /out2/_SUCCESS-rw-r--r-- 3 hadoop supergroup 58903 2018-06-15 14:30 /out2/part-00000[hadoop@oversea-stable hadoop]$ [hadoop@oversea-stable hadoop]$ cat /opt/map.py #!/usr/bin/pythonimport sysfor line in sys.stdin: line = line.strip() words = line.split() for word in words: print "%s\t%s" % (word, 1)[hadoop@oversea-stable hadoop]$ [hadoop@oversea-stable hadoop]$ cat /opt/reduce.py #!/usr/bin/pythonfrom operator import itemgetterimport syscurrent_word = Nonecurrent_count = 0word = Nonefor line in sys.stdin: line = line.strip() word, count = line.split('\t',1) try: count = int(count) except ValueError: continue if current_word == word: current_count += count else: if current_word: print "%s\t%s" % (current_word, current_count) current_count = count current_word = wordif word == current_word: print "%s\t%s" % (current_word, current_count)[hadoop@oversea-stable hadoop]$
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