Hadoop分布式集群環境搭建
阿新 • • 發佈：2018-04-02
Hadoop 搭建分布式集群環境 MapReduce YARN HDFS 分布式環境搭建之環境介紹
之前我們已經介紹了如何在單機上搭建偽分布式的Hadoop環境，而在實際情況中，肯定都是多機器多節點的分布式集群環境，所以本文將簡單介紹一下如何在多臺機器上搭建Hadoop的分布式環境。
我這裏準備了三臺機器，IP地址如下：
- 192.168.77.128
- 192.168.77.130
- 192.168.77.134
首先在這三臺機器上編輯/etc/hosts配置文件，修改主機名以及配置其他機器的主機名
[root@localhost ~]# vim /etc/hosts # 三臺機器都需要操作 192.168.77.128 hadoop000 192.168.77.130 hadoop001 192.168.77.134 hadoop002 [root@localhost ~]# reboot
三臺機器在集群中所擔任的角色：
- hadoop000作為NameNode、DataNode、ResourceManager、NodeManager
- hadoop001作為DataNode、NodeManager
- hadoop002也是作為DataNode、NodeManager
配置ssh免密碼登錄
集群之間的機器需要相互通信，所以我們得先配置免密碼登錄。在三臺機器上分別運行如下命令，生成密鑰對：
[root@hadoop000 ~]# ssh-keygen -t rsa # 三臺機器都需要執行這個命令生成密鑰對 Generating public/private rsa key pair. Enter file in which to save the key (/root/.ssh/id_rsa): Enter passphrase (empty for no passphrase): Enter same passphrase again: Your identification has been saved in /root/.ssh/id_rsa. Your public key has been saved in /root/.ssh/id_rsa.pub. The key fingerprint is: 0d:00:bd:a3:69:b7:03:d5:89:dc:a8:a2:ca:28:d6:06 root@hadoop000 The key‘s randomart image is: +--[ RSA 2048]----+ | .o. | | .. | | . *.. | | B +o | | = .S . | | E. * . | | .oo o . | |=. o o | |*.. . | +-----------------+ [root@hadoop000 ~]# ls .ssh/ authorized_keys id_rsa id_rsa.pub known_hosts [root@hadoop000 ~]#
以hadoop000為主，執行以下命令，分別把公鑰拷貝到其他機器上：
[root@hadoop000 ~]# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop000
[root@hadoop000 ~]# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop001
[root@hadoop000 ~]# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop002註：其他兩臺機器也需要執行以上這三條命令。
拷貝完成之後，測試能否正常進行免密登錄：
[root@hadoop000 ~]# ssh hadoop000 Last login: Mon Apr 2 17:20:02 2018 from localhost [root@hadoop000 ~]# ssh hadoop001 Last login: Tue Apr 3 00:49:59 2018 from 192.168.77.1 [root@hadoop001 ~]# 登出 Connection to hadoop001 closed. [root@hadoop000 ~]# ssh hadoop002 Last login: Tue Apr 3 00:50:03 2018 from 192.168.77.1 [root@hadoop002 ~]# 登出 Connection to hadoop002 closed. [root@hadoop000 ~]# 登出 Connection to hadoop000 closed. [root@hadoop000 ~]#
如上，hadoop000機器已經能夠正常免密登錄其他兩臺機器，那麽我們的配置就成功了。
安裝JDK
到Oracle官網拿到JDK的下載鏈接，我這裏用的是JDK1.8，地址如下：
http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html
使用wget命令將JDK下載到/usr/local/src/目錄下，我這裏已經下載好了：
[root@hadoop000 ~]# cd /usr/local/src/
[root@hadoop000 /usr/local/src]# ls
jdk-8u151-linux-x64.tar.gz
[root@hadoop000 /usr/local/src]# 解壓下載的壓縮包，並將解壓後的目錄移動到/usr/local/目錄下：
[root@hadoop000 /usr/local/src]# tar -zxvf jdk-8u151-linux-x64.tar.gz
[root@hadoop000 /usr/local/src]# mv ./jdk1.8.0_151 /usr/local/jdk1.8編輯/etc/profile文件配置環境變量：
[root@hadoop000 ~]# vim /etc/profile # 增加如下內容
JAVA_HOME=/usr/local/jdk1.8/
JAVA_BIN=/usr/local/jdk1.8/bin
JRE_HOME=/usr/local/jdk1.8/jre
PATH=$PATH:/usr/local/jdk1.8/bin:/usr/local/jdk1.8/jre/bin
CLASSPATH=/usr/local/jdk1.8/jre/lib:/usr/local/jdk1.8/lib:/usr/local/jdk1.8/jre/lib/charsets.jar
export PATH=$PATH:/usr/local/mysql/bin/使用source命令加載配置文件，讓其生效，生效後執行java -version命令即可看到JDK的版本：
[root@hadoop000 ~]# source /etc/profile
[root@hadoop000 ~]# java -version
java version "1.8.0_151"
Java(TM) SE Runtime Environment (build 1.8.0_151-b12)
Java HotSpot(TM) 64-Bit Server VM (build 25.151-b12, mixed mode)
[root@hadoop000 ~]# 在hadoop000上安裝完JDK後，通過rsync命令，將JDK以及配置文件都同步到其他機器上：
[root@hadoop000 ~]# rsync -av /usr/local/jdk1.8 hadoop001:/usr/local
[root@hadoop000 ~]# rsync -av /usr/local/jdk1.8 hadoop002:/usr/local
[root@hadoop000 ~]# rsync -av /etc/profile hadoop001:/etc/profile
[root@hadoop000 ~]# rsync -av /etc/profile hadoop002:/etc/profile同步完成後，分別在兩臺機器上source配置文件，讓環境變量生效，生效後再執行java -version命令測試JDK是否已安裝成功。
Hadoop配置及分發
下載Hadoop 2.6.0-cdh5.7.0的tar.gz包並解壓：
[root@hadoop000 ~]# cd /usr/local/src/
[root@hadoop000 /usr/local/src]# wget http://archive.cloudera.com/cdh5/cdh/5/hadoop-2.6.0-cdh5.7.0.tar.gz
[root@hadoop000 /usr/local/src]# tar -zxvf hadoop-2.6.0-cdh5.7.0.tar.gz -C /usr/local/註：如果在Linux上下載得很慢的話，可以在windows的迅雷上使用這個鏈接進行下載。然後再上傳到Linux中，這樣就會快一些。
解壓完後，進入到解壓後的目錄下，可以看到hadoop的目錄結構如下：
[root@hadoop000 /usr/local/src]# cd /usr/local/hadoop-2.6.0-cdh5.7.0/
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0]# ls
bin cloudera examples include libexec NOTICE.txt sbin src
bin-mapreduce1 etc examples-mapreduce1 lib LICENSE.txt README.txt share
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0]#簡單說明一下其中幾個目錄存放的東西：
- bin目錄存放可執行文件
- etc目錄存放配置文件
- sbin目錄下存放服務的啟動命令
- share目錄下存放jar包與文檔
以上就算是把hadoop給安裝好了，接下來就是編輯配置文件，把JAVA_HOME配置一下：
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0]# cd etc/
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc]# cd hadoop
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# vim hadoop-env.sh
export JAVA_HOME=/usr/local/jdk1.8/ # 根據你的環境變量進行修改
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# 然後將Hadoop的安裝目錄配置到環境變量中，方便之後使用它的命令：
[root@hadoop000 ~]# vim ~/.bash_profile # 增加以下內容
export HADOOP_HOME=/usr/local/hadoop-2.6.0-cdh5.7.0/
export PATH=$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$PATH
[root@localhost ~]# source !$
source ~/.bash_profile
[root@localhost ~]#接著分別編輯core-site.xml以及hdfs-site.xml配置文件：
[root@hadoop000 ~]# cd $HADOOP_HOME
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0]# cd etc/hadoop
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# vim core-site.xml # 增加如下內容
<configuration>
<property>
<name>fs.default.name</name>
<value>hdfs://hadoop000:8020</value> # 指定默認的訪問地址以及端口號
</property>
</configuration>
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# vim hdfs-site.xml # 增加如下內容
<configuration>
<property>
<name>dfs.namenode.name.dir</name>
<value>/data/hadoop/app/tmp/dfs/name</value> # namenode臨時文件所存放的目錄
</property>
<property>
<name>dfs.datanode.data.dir</name>
<value>/data/hadoop/app/tmp/dfs/data</value> # datanode臨時文件所存放的目錄
</property>
</configuration>
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# mkdir -p /data/hadoop/app/tmp/dfs/name
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# mkdir -p /data/hadoop/app/tmp/dfs/data接下來還需要編輯yarn-site.xml配置文件：
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# vim yarn-site.xml # 增加如下內容
<configuration>
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
<property>
<name>yarn.resourcemanager.hostname</name>
<value>hadoop000</value>
</property>
</configuration>
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# 拷貝並編輯MapReduce的配置文件：
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# cp mapred-site.xml.template mapred-site.xml
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# vim !$ # 增加如下內容
<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
</configuration>
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# 最後是配置從節點的主機名，如果沒有配置主機名的情況下就使用IP：
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# vim slaves
hadoop000
hadoop001
hadoop002
[root@hadoop000 /usr/local/hadoop-2.6.0-cdh5.7.0/etc/hadoop]# 到此為止，我們就已經在hadoop000上搭建好了我們主節點（master）的Hadoop集群環境，但是還有其他兩臺作為從節點（slave）的機器沒配置Hadoop環境，所以接下來需要把hadoop000上的Hadoop安裝目錄以及環境變量配置文件分發到其他兩臺機器上，分別執行如下命令：
[root@hadoop000 ~]# rsync -av /usr/local/hadoop-2.6.0-cdh5.7.0/ hadoop001:/usr/local/hadoop-2.6.0-cdh5.7.0/
[root@hadoop000 ~]# rsync -av /usr/local/hadoop-2.6.0-cdh5.7.0/ hadoop002:/usr/local/hadoop-2.6.0-cdh5.7.0/
[root@hadoop000 ~]# rsync -av ~/.bash_profile hadoop001:~/.bash_profile
[root@hadoop000 ~]# rsync -av ~/.bash_profile hadoop002:~/.bash_profile分發完成之後到兩臺機器上分別執行source命令以及創建臨時目錄：
[root@hadoop001 ~]# source .bash_profile
[root@hadoop001 ~]# mkdir -p /data/hadoop/app/tmp/dfs/name
[root@hadoop001 ~]# mkdir -p /data/hadoop/app/tmp/dfs/data
[root@hadoop002 ~]# source .bash_profile
[root@hadoop002 ~]# mkdir -p /data/hadoop/app/tmp/dfs/name
[root@hadoop002 ~]# mkdir -p /data/hadoop/app/tmp/dfs/dataHadoop格式化及啟停
對NameNode做格式化，只需要在hadoop000上執行即可：
[root@hadoop000 ~]# hdfs namenode -format格式化完成之後，就可以啟動Hadoop集群了：
[root@hadoop000 ~]# start-all.sh
This script is Deprecated. Instead use start-dfs.sh and start-yarn.sh
18/04/02 20:10:59 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Starting namenodes on [hadoop000]
hadoop000: starting namenode, logging to /usr/local/hadoop-2.6.0-cdh5.7.0/logs/hadoop-root-namenode-hadoop000.out
hadoop000: starting datanode, logging to /usr/local/hadoop-2.6.0-cdh5.7.0/logs/hadoop-root-datanode-hadoop000.out
hadoop001: starting datanode, logging to /usr/local/hadoop-2.6.0-cdh5.7.0/logs/hadoop-root-datanode-hadoop001.out
hadoop002: starting datanode, logging to /usr/local/hadoop-2.6.0-cdh5.7.0/logs/hadoop-root-datanode-hadoop002.out
Starting secondary namenodes [0.0.0.0]
The authenticity of host ‘0.0.0.0 (0.0.0.0)‘ can‘t be established.
ECDSA key fingerprint is 4d:5a:9d:31:65:75:30:47:a3:9c:f5:56:63:c4:0f:6a.
Are you sure you want to continue connecting (yes/no)? yes # 輸入yes即可
0.0.0.0: Warning: Permanently added ‘0.0.0.0‘ (ECDSA) to the list of known hosts.
0.0.0.0: starting secondarynamenode, logging to /usr/local/hadoop-2.6.0-cdh5.7.0/logs/hadoop-root-secondarynamenode-hadoop000.out
18/04/02 20:11:21 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
starting yarn daemons
starting resourcemanager, logging to /usr/local/hadoop-2.6.0-cdh5.7.0/logs/yarn-root-resourcemanager-hadoop000.out
hadoop001: starting nodemanager, logging to /usr/local/hadoop-2.6.0-cdh5.7.0/logs/yarn-root-nodemanager-hadoop001.out
hadoop002: starting nodemanager, logging to /usr/local/hadoop-2.6.0-cdh5.7.0/logs/yarn-root-nodemanager-hadoop002.out
hadoop000: starting nodemanager, logging to /usr/local/hadoop-2.6.0-cdh5.7.0/logs/yarn-root-nodemanager-hadoop000.out
[root@hadoop000 ~]# jps # 查看是否有以下幾個進程
6256 Jps
5538 DataNode
5843 ResourceManager
5413 NameNode
5702 SecondaryNameNode
5945 NodeManager
[root@hadoop000 ~]#到另外兩臺機器上檢查進程：
hadoop001：
[root@hadoop001 ~]# jps
3425 DataNode
3538 NodeManager
3833 Jps
[root@hadoop001 ~]# hadoop002：
[root@hadoop002 ~]# jps
3171 DataNode
3273 NodeManager
3405 Jps
[root@hadoop002 ~]#各機器的進程檢查完成，並且確定沒有問題後，在瀏覽器上訪問主節點的50070端口，例如：192.168.77.128:50070。會訪問到如下頁面：
點擊 ”Live Nodes“ 查看存活的節點：
如上，可以訪問50070端口就代表集群中的HDFS是正常的。
接下來我們還需要訪問主節點的8088端口，這是YARN的web服務端口，例如：192.168.77.128:8088。如下：
點擊 “Active Nodes” 查看存活的節點：
好了，到此為止我們的Hadoop分布式集群環境就搭建完畢了，就是這麽簡單。那麽啟動了集群之後要如何關閉集群呢？也很簡單，在主節點上執行如下命令即可：
[root@hadoop000 ~]# stop-all.sh分布式環境下HDFS及YARN的使用
實際上分布式環境下HDFS及YARN的使用和偽分布式下是一模一樣的，例如HDFS的shell命令的使用方式依舊是和偽分布式下一樣的。例如：
[root@hadoop000 ~]# hdfs dfs -ls /
[root@hadoop000 ~]# hdfs dfs -mkdir /data
[root@hadoop000 ~]# hdfs dfs -put ./test.sh /data
[root@hadoop000 ~]# hdfs dfs -ls /
Found 1 items
drwxr-xr-x - root supergroup 0 2018-04-02 20:29 /data
[root@hadoop000 ~]# hdfs dfs -ls /data
Found 1 items
-rw-r--r-- 3 root supergroup 68 2018-04-02 20:29 /data/test.sh
[root@hadoop000 ~]# 在集群中的其他節點也可以訪問HDFS，而且在集群中HDFS是共享的，所有節點訪問的數據都是一樣的。例如我在hadoop001節點中，上傳一個目錄：
[root@hadoop001 ~]# hdfs dfs -ls /
Found 1 items
drwxr-xr-x - root supergroup 0 2018-04-02 20:29 /data
[root@hadoop001 ~]# hdfs dfs -put ./logs /
[root@hadoop001 ~]# hdfs dfs -ls /
drwxr-xr-x - root supergroup 0 2018-04-02 20:29 /data
drwxr-xr-x - root supergroup 0 2018-04-02 20:31 /logs
[root@hadoop001 ~]#然後再到hadoop002上查看：
[root@hadoop002 ~]# hdfs dfs -ls /
Found 2 items
drwxr-xr-x - root supergroup 0 2018-04-02 20:29 /data
drwxr-xr-x - root supergroup 0 2018-04-02 20:31 /logs
[root@hadoop002 ~]# 可以看到，不同的節點，訪問的數據也是一樣的。由於和偽分布式下的操作是一樣的，我這裏就不再過多演示了。
簡單演示了HDFS的操作之後，我們再來運行一下Hadoop自帶的案例，看看YARN上是否能獲取到任務的執行信息。隨便在一個節點上執行如下命令：
[root@hadoop002 ~]# cd /usr/local/hadoop-2.6.0-cdh5.7.0/share/hadoop/mapreduce
[root@hadoop002 /usr/local/hadoop-2.6.0-cdh5.7.0/share/hadoop/mapreduce]# hadoop jar ./hadoop-mapreduce-examples-2.6.0-cdh5.7.0.jar pi 3 4
[root@hadoop002 ~]# 申請資源：
執行任務：
然而我這不幸的執行失敗（容我喊一句當媽的撕高達）：
能咋辦，只能排錯咯，查看到命令行終端的報錯信息如下：
Note: System times on machines may be out of sync. Check system time and time zones.
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.instantiateException(SerializedExceptionPBImpl.java:168)
at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.deSerialize(SerializedExceptionPBImpl.java:106)
at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$Container.launch(ContainerLauncherImpl.java:159)
at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$EventProcessor.run(ContainerLauncherImpl.java:379)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
18/04/03 04:32:17 INFO mapreduce.Job: Task Id : attempt_1522671083370_0001_m_000002_0, Status : FAILED
Container launch failed for container_1522671083370_0001_01_000004 : org.apache.hadoop.yarn.exceptions.YarnException: Unauthorized request to start container.
This token is expired. current time is 1522701136752 found 1522673393827
Note: System times on machines may be out of sync. Check system time and time zones.
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.instantiateException(SerializedExceptionPBImpl.java:168)
at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.deSerialize(SerializedExceptionPBImpl.java:106)
at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$Container.launch(ContainerLauncherImpl.java:159)
at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$EventProcessor.run(ContainerLauncherImpl.java:379)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
18/04/03 04:32:18 INFO mapreduce.Job: Task Id : attempt_1522671083370_0001_m_000001_1, Status : FAILED
Container launch failed for container_1522671083370_0001_01_000005 : org.apache.hadoop.yarn.exceptions.YarnException: Unauthorized request to start container.
This token is expired. current time is 1522701157769 found 1522673395895
Note: System times on machines may be out of sync. Check system time and time zones.
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.instantiateException(SerializedExceptionPBImpl.java:168)
at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.deSerialize(SerializedExceptionPBImpl.java:106)
at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$Container.launch(ContainerLauncherImpl.java:159)
at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$EventProcessor.run(ContainerLauncherImpl.java:379)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
18/04/03 04:32:20 INFO mapreduce.Job: Task Id : attempt_1522671083370_0001_m_000001_2, Status : FAILED
Container launch failed for container_1522671083370_0001_01_000007 : org.apache.hadoop.yarn.exceptions.YarnException: Unauthorized request to start container.
This token is expired. current time is 1522701159832 found 1522673397934
Note: System times on machines may be out of sync. Check system time and time zones.
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.instantiateException(SerializedExceptionPBImpl.java:168)
at org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.deSerialize(SerializedExceptionPBImpl.java:106)
at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$Container.launch(ContainerLauncherImpl.java:159)
at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$EventProcessor.run(ContainerLauncherImpl.java:379)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
18/04/03 04:32:23 INFO mapreduce.Job: map 33% reduce 100%
18/04/03 04:32:24 INFO mapreduce.Job: map 100% reduce 100%
18/04/03 04:32:24 INFO mapreduce.Job: Job job_1522671083370_0001 failed with state FAILED due to: Task failed task_1522671083370_0001_m_000001
Job failed as tasks failed. failedMaps:1 failedReduces:0
18/04/03 04:32:24 INFO mapreduce.Job: Counters: 12
Job Counters
Killed map tasks=2
Launched map tasks=2
Other local map tasks=4
Data-local map tasks=3
Total time spent by all maps in occupied slots (ms)=10890
Total time spent by all reduces in occupied slots (ms)=0
Total time spent by all map tasks (ms)=10890
Total vcore-seconds taken by all map tasks=10890
Total megabyte-seconds taken by all map tasks=11151360
Map-Reduce Framework
CPU time spent (ms)=0
Physical memory (bytes) snapshot=0
Virtual memory (bytes) snapshot=0
Job Finished in 23.112 seconds
java.io.FileNotFoundException: File does not exist: hdfs://hadoop000:8020/user/root/QuasiMonteCarlo_1522701120069_2085123424/out/reduce-out
at org.apache.hadoop.hdfs.DistributedFileSystem$19.doCall(DistributedFileSystem.java:1219)
at org.apache.hadoop.hdfs.DistributedFileSystem$19.doCall(DistributedFileSystem.java:1211)
at org.apache.hadoop.fs.FileSystemLinkResolver.resolve(FileSystemLinkResolver.java:81)
at org.apache.hadoop.hdfs.DistributedFileSystem.getFileStatus(DistributedFileSystem.java:1211)
at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1750)
at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1774)
at org.apache.hadoop.examples.QuasiMonteCarlo.estimatePi(QuasiMonteCarlo.java:314)
at org.apache.hadoop.examples.QuasiMonteCarlo.run(QuasiMonteCarlo.java:354)
at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:70)
at org.apache.hadoop.examples.QuasiMonteCarlo.main(QuasiMonteCarlo.java:363)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.hadoop.util.ProgramDriver$ProgramDescription.invoke(ProgramDriver.java:71)
at org.apache.hadoop.util.ProgramDriver.run(ProgramDriver.java:144)
at org.apache.hadoop.examples.ExampleDriver.main(ExampleDriver.java:74)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.hadoop.util.RunJar.run(RunJar.java:221)
at org.apache.hadoop.util.RunJar.main(RunJar.java:136)雖然報了一大串的錯誤信息，但是從報錯信息中，可以看到第一句是System times on machines may be out of sync. Check system time and time zones.，這是說機器上的系統時間可能不同步。讓我們檢查系統時間和時區。然後我就檢查了集群中所有機器的時間，的確是不同步的。那麽要如何同步時間呢？那就要使用到ntpdate命令了，在所有機器上安裝ntp包，並執行同步時間的命令，如下：
[root@hadoop000 ~]# yum install -y ntp
[root@hadoop000 ~]# ntpdate -u ntp.api.bz完成之後再次執行之前的命令，這次任務執行成功：
將Hadoop項目運行在Hadoop集群之上
在這之前用Hadoop寫了一個統計日誌數據的小項目，現在既然我們的集群搭建成功了，那麽當然是得拿上來跑一下看看。首先將日誌文件以及jar包上傳到服務器上：
[root@hadoop000 ~]# ls
10000_access.log hadoop-train-1.0-jar-with-dependencies.jar
[root@hadoop000 ~]# 把日誌文件put到HDFS文件系統中：
[root@hadoop000 ~]# hdfs dfs -put ./10000_access.log /
[root@hadoop000 ~]# hdfs dfs -ls /
Found 5 items
-rw-r--r-- 3 root supergroup 2769741 2018-04-02 21:13 /10000_access.log
drwxr-xr-x - root supergroup 0 2018-04-02 20:29 /data
drwxr-xr-x - root supergroup 0 2018-04-02 20:31 /logs
drwx------ - root supergroup 0 2018-04-02 20:39 /tmp
drwxr-xr-x - root supergroup 0 2018-04-02 20:39 /user
[root@hadoop000 ~]#執行以下命令，將項目運行在Hadoop集群之上：
[root@hadoop000 ~]# hadoop jar ./hadoop-train-1.0-jar-with-dependencies.jar org.zero01.hadoop.project.LogApp /10000_access.log /browserout到YARN上查看任務執行時的信息：
申請資源：
執行任務：
任務執行成功：
查看輸出文件內容：
[root@hadoop000 ~]# hdfs dfs -ls /browserout
Found 2 items
-rw-r--r-- 3 root supergroup 0 2018-04-02 21:22 /browserout/_SUCCESS
-rw-r--r-- 3 root supergroup 56 2018-04-02 21:22 /browserout/part-r-00000
[root@hadoop000 ~]# hdfs dfs -text /browserout/part-r-00000
Chrome 2775
Firefox 327
MSIE 78
Safari 115
Unknown 6705
[root@hadoop000 ~]#處理結果沒有問題，到此為止，我們的測試也完成了，接下來就可以愉快的使用Hadoop集群來幫我們處理數據了（當然代碼你還是得寫的）。
從整個Hadoop分布式集群環境的搭建到使用的過程中，可以看到除了搭建與偽分布式有些許區別外，在使用上基本是一模一樣的。所以也建議在學習的情況下使用偽分布式環境即可，畢竟集群的環境比較復雜，容易出現節點間通信障礙的問題。如果卡在這些問題上，導致學習不成還氣得不行就得不償失了233。
Hadoop分布式集群環境搭建