详细记录Linux服务器搭建Hadoop3.X完全分布式集群环境
| Hadoop节点 | NameNode | Secondary NameNode | DataNode | Resource Manager | NodeManager |
|---|---|---|---|---|---|
| node001 | * | * | * | * | |
| node002 | * | * | * | ||
| node003 | * | * |
下载Hadoop
下载地址：https://archive.apache.org/dist/hadoop/core/
cd /usr/local/program
wget https://archive.apache.org/dist/hadoop/core/hadoop-3.1.3/hadoop-3.1.3.tar.gz
tar -zxvf hadoop-3.1.3.tar.gz
mv hadoop-3.1.3 hadoop
修改集群环境
cd hadoop/etc/hadoop/
vim hadoop-env.sh
export JAVA_HOME=/usr/local/program/jdk8
export HDFS_NAMENODE_USER=root
export HDFS_DATANODE_USER=root
export HDFS_ZKFC_USER=root
export HDFS_JOURNALNODE_USER=root
export YARN_RESOURCEMANAGER_USER=root
export YARN_NODEMANAGER_USER=root
修改配置文件
vim core-site.xml: 定义系统级别的参数
<configuration>
<!-- 声明hdfs文件系统 指定某个ip地址，在ha模式中指定hdfs集群的逻辑名称 -->
<property>
<name>fs.defaultFS</name>
<value>hdfs://node001:9000</value>
</property>
<!-- 声明hadoop工作目录 -->
<property>
<name>hadoop.tmp.dir</name>
<value>file:/usr/local/program/hadoop/datas/tmp</value>
</property>
<!-- 界面访问数据使用的用户名 默认的用户权限很小，能保证数据安全 高权限的用户能看到其它用户数据-->
<property>
<name>hhadoop.http.staticuser.user</name>
<value>root</value>
</property>
</configuration>
vim hdfs-site.xml: 对HDFS的相关设置
<configuration>
<property>
<name>dfs.namenode.secondary.http-address</name>
<value>node002:9871</value>
</property>
<property>
<name>dfs.namenode.secondary.https-address</name>
<value>node003:9872</value>
</property>
<!-- 副本数量,默认值3个 -->
<property>
<name>dfs.replication</name>
<value>2</value>
</property>
<!-- namenode数据存放路径,元数据 -->
<property>
<name>dfs.namenode.name.dir</name>
<value>file:///usr/local/program/hadoop/datas/namenode</value>
</property>
<!-- datanode数据存放路径,block块 -->
<property>
<name>dfs.datanode.data.dir</name>
<value>file:///usr/local/program/hadoop/datas/datanode</value>
</property>
</configuration>
vim workers: 指定某几个节点作为数据节点
node001
node002
node003
vim mapred-site.xml
<configuration>
<!-- 指定mapreduce框架为yarn方式 -->
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
<!-- 指定mapreduce jobhistory地址 -->
<property>
<name>mapreduce.jobhistory.address</name>
<value>node001:10020</value>
</property>
<!-- 任务历史服务器的web地址 -->
<property>
<name>mapreduce.jobhistory.webapp.address</name>
<value>node001:19888</value>
</property>
<!-- 配置运行过的日志存放在hdfs上的存放路径 -->
<property>
<name>mapreduce.jobhistory.done-dir</name>
<value>/history/done</value>
</property>
<!-- 配置正在运行中的日志在hdfs上的存放路径 -->
<property>
<name>mapreudce.jobhistory.intermediate.done-dir</name>
<value>/history/done/done_intermediate</value>
</property>
<!--以下必须配置，否则运行MapReduce会提示检查是否配置-->
<property>
<name>yarn.app.mapreduce.am.env</name>
<value>HADOOP_MAPRED_HOME=/usr/local/program/hadoop</value>
</property>
<property>
<name>mapreduce.map.env</name>
<value>HADOOP_MAPRED_HOME=/usr/local/program/hadoop</value>
</property>
<property>
<name>mapreduce.reduce.env</name>
<value>HADOOP_MAPRED_HOME=/usr/local/program/hadoop</value>
</property>
</configuration>
vim yarn-site.xml
<configuration>
<!-- 指定RM的地址 -->
<property>
<name>yarn.resourcemanager.hostname</name>
<value>node001</value>
</property>
<!-- NodeManager上运行的附属服务 -->
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
</configuration>
修改环境变量
vim /etc/profile
export HADOOP_HOME=/usr/local/program/hadoop
export PATH=$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$PATH
将环境变量配置同步到其他节点
scp /etc/profile node002:/etc/profile
scp /etc/profile node003:/etc/profile
重新加载三台服务器的环境变量配置
source /etc/profile
分发软件到其他节点
scp -r hadoop node002:`pwd`
scp -r hadoop node003:/usr/local/program
格式化NameNode
在NameNode节点(node001)进行格式化
hdfs namenode -format
2022-03-20 19:21:18,897 INFO util.GSet: 0.25% max memory 810.8 MB = 2.0 MB
2022-03-20 19:21:18,897 INFO util.GSet: capacity = 2^19 = 524288 entries
2022-03-20 19:21:18,904 INFO metrics.TopMetrics: NNTop conf: dfs.namenode.top.window.num.buckets = 10
2022-03-20 19:21:18,904 INFO metrics.TopMetrics: NNTop conf: dfs.namenode.top.num.users = 10
2022-03-20 19:21:18,904 INFO metrics.TopMetrics: NNTop conf: dfs.namenode.top.windows.minutes = 1,5,25
2022-03-20 19:21:18,907 INFO namenode.FSNamesystem: Retry cache on namenode is enabled
2022-03-20 19:21:18,907 INFO namenode.FSNamesystem: Retry cache will use 0.03 of total heap and retry cache entry expiry time is 600000 millis
2022-03-20 19:21:18,908 INFO util.GSet: Computing capacity for map NameNodeRetryCache
2022-03-20 19:21:18,908 INFO util.GSet: VM type = 32-bit
2022-03-20 19:21:18,908 INFO util.GSet: 0.029999999329447746% max memory 810.8 MB = 249.1 KB
2022-03-20 19:21:18,908 INFO util.GSet: capacity = 2^16 = 65536 entries
2022-03-20 19:21:18,926 INFO namenode.FSImage: Allocated new BlockPoolId: BP-544803462-172.29.234.1-1647775278921
2022-03-20 19:21:18,936 INFO common.Storage: Storage directory /usr/local/program/hadoop/datas/namenode has been successfully formatted.
2022-03-20 19:21:18,957 INFO namenode.FSImageFormatProtobuf: Saving image file /usr/local/program/hadoop/datas/namenode/current/fsimage.ckpt_0000000000000000000 using no compression
2022-03-20 19:21:19,033 INFO namenode.FSImageFormatProtobuf: Image file /usr/local/program/hadoop/datas/namenode/current/fsimage.ckpt_0000000000000000000 of size 391 bytes saved in 0 seconds .
2022-03-20 19:21:19,044 INFO namenode.NNStorageRetentionManager: Going to retain 1 images with txid >= 0
2022-03-20 19:21:19,048 INFO namenode.FSImage: FSImageSaver clean checkpoint: txid = 0 when meet shutdown.
2022-03-20 19:21:19,048 INFO namenode.NameNode: SHUTDOWN_MSG:
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at node001/172.29.234.1
************************************************************/
启动HDFS
# 启动集群
start-dfs.sh
# 关闭集群
stop-dfs.sh
[root@node001 ~]# start-dfs.sh
Starting namenodes on [node001]
上一次登录：日 3月 20 19:36:51 CST 2022从 39.144.138.187pts/1 上
Starting datanodes
上一次登录：日 3月 20 19:36:56 CST 2022pts/0 上
node002: bash: /usr/local/program/hadoop/bin/hdfs: No such file or directory
node003: WARNING: /usr/local/program/hadoop/logs does not exist. Creating.
Starting secondary namenodes [node002]
上一次登录：日 3月 20 19:36:58 CST 2022pts/0 上
node002: bash: /usr/local/program/hadoop/bin/hdfs: No such file or directory
[root@node001 ~]# jps
5780 DataNode
6294 Jps
5581 NameNode
[root@node002 hadoop]# jps
28467 Jps
28057 DataNode
28284 SecondaryNameNode
[root@node003 ~]# jps
26387 DataNode
30106 Jps
启动yarn
start-yarn.sh
stop-yarn.sh
[root@node001 hadoop]# start-yarn.sh
Starting resourcemanager
Last login: Sat Apr 16 18:32:17 CST 2022 on pts/0
Starting nodemanagers
Last login: Sat Apr 16 18:37:20 CST 2022 on pts/0
[root@node001 hadoop]# jps
7536 DataNode
9510 ResourceManager
7369 NameNode
9676 NodeManager
10063 Jps
[root@node002 program]# jps
13328 NodeManager
12883 SecondaryNameNode
12773 DataNode
13462 Jps
[root@node003 program]# jps
4145 Jps
4006 NodeManager
3726 DataNode
测试集群
1.HDFS界面
访问NameNode节点IP:9870
命令操作测试
[root@node001 ~]# hdfs dfs -mkdir -p /test
[root@node001 ~]# hdfs dfs -put nginx.yaml /
2.YARN界面
作业测试
[root@node001 hadoop]# hdfs dfs -mkdir /input
[root@node001 hadoop]# hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar wordcount /input /output
Java HotSpot(TM) Server VM warning: You have loaded library /usr/local/program/hadoop/lib/native/libhadoop.so.1.0.0 which might have disabled stack guard. The VM will try to fix the stack guard now.
It's highly recommended that you fix the library with 'execstack -c <libfile>', or link it with '-z noexecstack'.
2022-04-16 19:23:05,698 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2022-04-16 19:23:06,343 INFO client.RMProxy: Connecting to ResourceManager at node001/172.29.234.1:8032
2022-04-16 19:23:06,793 INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /tmp/hadoop-yarn/staging/root/.staging/job_1650105444588_0003
2022-04-16 19:23:06,937 INFO sasl.SaslDataTransferClient: SASL encryption trust check: localHostTrusted = false, remoteHostTrusted = false
2022-04-16 19:23:07,067 INFO input.FileInputFormat: Total input files to process : 0
2022-04-16 19:23:07,078 INFO sasl.SaslDataTransferClient: SASL encryption trust check: localHostTrusted = false, remoteHostTrusted = false
2022-04-16 19:23:07,147 INFO sasl.SaslDataTransferClient: SASL encryption trust check: localHostTrusted = false, remoteHostTrusted = false
2022-04-16 19:23:07,159 INFO mapreduce.JobSubmitter: number of splits:0
2022-04-16 19:23:07,270 INFO sasl.SaslDataTransferClient: SASL encryption trust check: localHostTrusted = false, remoteHostTrusted = false
2022-04-16 19:23:07,689 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1650105444588_0003
2022-04-16 19:23:07,689 INFO mapreduce.JobSubmitter: Executing with tokens: []
2022-04-16 19:23:07,848 INFO conf.Configuration: resource-types.xml not found
2022-04-16 19:23:07,849 INFO resource.ResourceUtils: Unable to find 'resource-types.xml'.
2022-04-16 19:23:08,251 INFO impl.YarnClientImpl: Submitted application application_1650105444588_0003
2022-04-16 19:23:08,296 INFO mapreduce.Job: The url to track the job: http://node001:8088/proxy/application_1650105444588_0003/
2022-04-16 19:23:08,296 INFO mapreduce.Job: Running job: job_1650105444588_0003
2022-04-16 19:23:15,396 INFO mapreduce.Job: Job job_1650105444588_0003 running in uber mode : false
2022-04-16 19:23:15,397 INFO mapreduce.Job: map 0% reduce 0%
2022-04-16 19:23:21,470 INFO mapreduce.Job: map 0% reduce 100%
2022-04-16 19:23:22,499 INFO mapreduce.Job: Job job_1650105444588_0003 completed successfully
2022-04-16 19:23:22,584 INFO mapreduce.Job: Counters: 40
File System Counters
FILE: Number of bytes read=0
FILE: Number of bytes written=217846
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=0
HDFS: Number of bytes written=0
HDFS: Number of read operations=5
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters
Launched reduce tasks=1
Total time spent by all maps in occupied slots (ms)=0
Total time spent by all reduces in occupied slots (ms)=3397
Total time spent by all reduce tasks (ms)=3397
Total vcore-milliseconds taken by all reduce tasks=3397
Total megabyte-milliseconds taken by all reduce tasks=3478528
Map-Reduce Framework
Combine input records=0
Combine output records=0
Reduce input groups=0
Reduce shuffle bytes=0
Reduce input records=0
Reduce output records=0
Spilled Records=0
Shuffled Maps =0
Failed Shuffles=0
Merged Map outputs=0
GC time elapsed (ms)=61
CPU time spent (ms)=240
Physical memory (bytes) snapshot=150511616
Virtual memory (bytes) snapshot=1358278656
Total committed heap usage (bytes)=168558592
Peak Reduce Physical memory (bytes)=150511616
Peak Reduce Virtual memory (bytes)=1358278656
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Output Format Counters
Bytes Written=0
在YARN Web界面查看
在HDSF界面查看
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