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最新Hadoop-2.7.2+hbase-1.2.0+zookeeper-3.4.8

来源:  2016-04-21 19:03:37    评论:0点击:

来自: http://www.loveweir.com/posts/view/65


Ip

 主机名

程序

进程

192.168.128.11

h1

Jdk

Hadoop

hbase

Namenode

DFSZKFailoverController

Hamster

192.168.128.12

h2

Jdk

Hadoop

hbase

Namenode

DFSZKFailoverController

Hamster

192.168.128.13

h3

Jdk

Hadoop

resourceManager

192.168.128.14

h4

Jdk

Hadoop

 

resourceManager

192.168.128.15

h5

Jdk

Hadoop

Zookeeper

Hbase

Datanode

nodeManager

JournalNode

QuorumPeerMain

HRegionServer

192.168.128.16

h6

Jdk

Hadoop

Zookeeper

Hbase

Datanode

nodeManager

JournalNode

QuorumPeerMain

HRegionServer

192.168.128.17

h7

Jdk

Hadoop

Zookeeper

hbase

Datanode

nodeManager

JournalNode

QuorumPeerMain

HRegionServer


关于准备工作  我这里就不一一写出来了,总结一下有主机名,ip,主机名和ip的映射关系,防火墙,ssh免密码,jdk的安装及环境变量的设置。

安装zookeeper h5h6h7上面

修改 /home/zookeeper-3.4.8/conf的zoo_sample.cfg

cp zoo_sample.cfg zoo.cfg

 

 

# The number of milliseconds of each tick

tickTime=2000

# The number of ticks that the initial

# synchronization phase can take

initLimit=10

# The number of ticks that can pass between

# sending a request and getting an acknowledgement

syncLimit=5

# the directory where the snapshot is stored.

# do not use /tmp for storage, /tmp here is just

# example sakes.

dataDir=/home/zookeeper-3.4.8/data

# the port at which the clients will connect

clientPort=2181

# the maximum number of client connections.

# increase this if you need to handle more clients

#maxClientCnxns=60

#

# Be sure to read the maintenance section of the

# administrator guide before turning on autopurge.

#

# http://zookeeper.apache.org/doc/current/zookeeperAdmin.html#sc_maintenance

#

# The number of snapshots to retain in dataDir

#autopurge.snapRetainCount=3

# Purge task interval in hours

# Set to "0" to disable auto purge feature

#autopurge.purgeInterval=1

server.1=h5:2888:3888

server.2=h6:2888:3888

server.3=h7:2888:3888


 

创建 data文件夹  和在里面  创建文件myid  并写入数字1

touch data/myid

 

echo 1 > data/myid

 

拷贝整个zookeeper到另外两个节点上

 

scp -r /home/zookeeper-3.4.8  h6:/home/

scp -r /home/zookeeper-3.4.8  h7:/home/

其他两个节点的myid  修改为 2  3

安装hadoop

 

/home/hadoop-2.7.2/etc/Hadoop

 

hadoop-env.sh

export JAVA_HOME=/home/jdk

 

core-site.xml

 

 

<configuration>

<!-- 指定hdfs的nameservice为masters -->

<property>

<name>fs.defaultFS</name>

<value>hdfs://masters</value>

</property>

<!-- 指定hadoop临时目录 -->

<property>

<name>hadoop.tmp.dir</name>

<value>/home/hadoop-2.7.2/tmp</value>

</property>

<!-- 指定zookeeper地址 -->

<property>

<name>ha.zookeeper.quorum</name>

<value>h5:2181,h6:2181,h7:2181</value>

</property>

</configuration>

 

 

hdfs-site.xml

 

 

 

<configuration>

<!--指定hdfs的nameservice为masters,需要和core-site.xml中的保持一致 -->

        <property>

                <name>dfs.nameservices</name>

                <value>masters</value>

        </property>

        <!-- h1下面有两个NameNode,分别是h1,h2 -->

        <property>

                <name>dfs.ha.namenodes.masters</name>

                <value>h1,h2</value>

        </property>

        <!-- h1的RPC通信地址 -->

        <property>

                <name>dfs.namenode.rpc-address.masters.h1</name>

                <value>h1:9000</value>

        </property>

        <!-- h1的http通信地址 -->

        <property>

                <name>dfs.namenode.http-address.masters.h1</name>

                <value>h1:50070</value>

        </property>

        <!-- h2的RPC通信地址 -->

        <property>

                <name>dfs.namenode.rpc-address.masters.h2</name>

                <value>h2:9000</value>

        </property>

        <!-- h2的http通信地址 -->

        <property>

                <name>dfs.namenode.http-address.masters.h2</name>

                <value>h2:50070</value>

        </property>

        <!-- 指定NameNode的元数据在JournalNode上的存放位置 -->

        <property>

                <name>dfs.namenode.shared.edits.dir</name>

                <value>qjournal://h5:8485;h6:8485;h7:8485/masters</value>

        </property>

        <!-- 指定JournalNode在本地磁盘存放数据的位置 -->

        <property>

                <name>dfs.journalnode.edits.dir</name>

                <value>/home/hadoop-2.7.2/journal</value>

        </property>

        <!-- 开启NameNode失败自动切换 -->

        <property>

                <name>dfs.ha.automatic-failover.enabled</name>

                <value>true</value>

        </property>

        <!-- 配置失败自动切换实现方式 -->

        <property>

                <name>dfs.client.failover.proxy.provider.masters</name>

                <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value>

        </property>

        <!-- 配置隔离机制方法,多个机制用换行分割,即每个机制暂用一行-->

        <property>

                <name>dfs.ha.fencing.methods</name>

                <value>

                        sshfence

                        shell(/bin/true)

                </value>

        </property>

        <!-- 使用sshfence隔离机制时需要ssh免登陆 -->

        <property>

                <name>dfs.ha.fencing.ssh.private-key-files</name>

                <value>/root/.ssh/id_rsa</value>

        </property>

        <!-- 配置sshfence隔离机制超时时间 -->

        <property>

                <name>dfs.ha.fencing.ssh.connect-timeout</name>

                <value>30000</value>

        </property>

</configuration>

 

 

 

mapred-site.xml

 

 

<configuration>

<!-- 指定mr框架为yarn方式 -->

<property>

<name>mapreduce.framework.name</name>

<value>yarn</value>

</property>

</configuration>

 

yarn-site.xml

 

<configuration>

 

<!-- 开启RM高可靠 -->

        <property>

                <name>yarn.resourcemanager.ha.enabled</name>

                <value>true</value>

        </property>

        <!-- 指定RM的cluster id -->

        <property>

                <name>yarn.resourcemanager.cluster-id</name>

                <value>RM_HA_ID</value>

        </property>

        <!-- 指定RM的名字 -->

        <property>

                <name>yarn.resourcemanager.ha.rm-ids</name>

                <value>rm1,rm2</value>

        </property>

        <!-- 分别指定RM的地址 -->

        <property>

                <name>yarn.resourcemanager.hostname.rm1</name>

                <value>h3</value>

        </property>

        <property>

                <name>yarn.resourcemanager.hostname.rm2</name>

                <value>h4</value>

        </property>

        <property>

                <name>yarn.resourcemanager.recovery.enabled</name>

                <value>true</value>

        </property>

        

        <property>

                <name>yarn.resourcemanager.store.class</name>

                <value>org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore</value>

        </property>

        <!-- 指定zk集群地址 -->

        <property>

                <name>yarn.resourcemanager.zk-address</name>

                <value>h5:2181,h6:2181,h7:2181</value>

        </property>

        <property>

                <name>yarn.nodemanager.aux-services</name>

                <value>mapreduce_shuffle</value>

        </property>

</configuration>

 

 

Slaves

 

h5

h6

h7

 

然后 拷贝到其他节点

 

 

scp -r hadoop-2.7.2 h2:/home/    等等


这个地方说明一下  yarn 的HA  是在  h3和h4  上面

 

启动顺序

###注意:严格按照下面的步骤

 

1.       启动zookeeper集群

 

[root@h6 ~]# cd /home/zookeeper-3.4.8/bin/

[root@h6 bin]# ./zkServer.sh start

 

H5  h6  h7  都一样

[root@h6 bin]# ./zkServer.sh status

查看状态

 

2.       启动journalnode

[root@h5 bin]# cd /home/hadoop-2.7.2/sbin/

[root@h5 sbin]# ./hadoop-daemons.sh start journalnode

h5: starting journalnode, logging to /home/hadoop-2.7.2/logs/hadoop-root-journalnode-h5.out

h7: starting journalnode, logging to /home/hadoop-2.7.2/logs/hadoop-root-journalnode-h7.out

h6: starting journalnode, logging to /home/hadoop-2.7.2/logs/hadoop-root-journalnode-h6.out

[root@h5 sbin]# jps

2420 JournalNode

2309 QuorumPeerMain

2461 Jps

[root@h5 sbin]# ^C

 

 

3.       格式化HDFS

 

在h1上执行命令:

hdfs namenode -format

格式化后会在根据core-site.xml中的hadoop.tmp.dir配置生成个文件

拷贝tmp  h2

[root@h1 hadoop-2.7.2]# scp -r tmp/ h2:/home/hadoop-2.7.2/

 

4. 格式化ZK(h1上执行即可)

 

[root@h1 hadoop-2.7.2]# hdfs zkfc -formatZK

 

5. 启动HDFS(h1上执行)

 

[root@h1 hadoop-2.7.2]# sbin/start-dfs.sh

16/02/25 05:01:14 WARN hdfs.DFSUtil: Namenode for ns1 remains unresolved for ID null.  Check your hdfs-site.xml file to ensure namenodes are configured properly.

16/02/25 05:01:14 WARN hdfs.DFSUtil: Namenode for ns2 remains unresolved for ID null.  Check your hdfs-site.xml file to ensure namenodes are configured properly.

16/02/25 05:01:14 WARN hdfs.DFSUtil: Namenode for ns3 remains unresolved for ID null.  Check your hdfs-site.xml file to ensure namenodes are configured properly.

Starting namenodes on [h1 h2 masters masters masters]

masters: ssh: Could not resolve hostname masters: Name or service not known

masters: ssh: Could not resolve hostname masters: Name or service not known

masters: ssh: Could not resolve hostname masters: Name or service not known

h2: starting namenode, logging to /home/hadoop-2.7.2/logs/hadoop-root-namenode-h2.out

h1: starting namenode, logging to /home/hadoop-2.7.2/logs/hadoop-root-namenode-h1.out

h5: starting datanode, logging to /home/hadoop-2.7.2/logs/hadoop-root-datanode-h5.out

h7: starting datanode, logging to /home/hadoop-2.7.2/logs/hadoop-root-datanode-h7.out

h6: starting datanode, logging to /home/hadoop-2.7.2/logs/hadoop-root-datanode-h6.out

Starting journal nodes [h5 h6 h7]

h5: journalnode running as process 2420. Stop it first.

h6: journalnode running as process 2885. Stop it first.

h7: journalnode running as process 2896. Stop it first.

Starting ZK Failover Controllers on NN hosts [h1 h2 masters masters masters]

masters: ssh: Could not resolve hostname masters: Name or service not known

masters: ssh: Could not resolve hostname masters: Name or service not known

masters: ssh: Could not resolve hostname masters: Name or service not known

h2: starting zkfc, logging to /home/hadoop-2.7.2/logs/hadoop-root-zkfc-h2.out

h1: starting zkfc, logging to /home/hadoop-2.7.2/logs/hadoop-root-zkfc-h1.out

[root@h1 hadoop-2.7.2]#

 

6. 启动YARN(是在h3上执行start-yarn.sh,把namenoderesourcemanager分开是因为性能问题,因为他们都要占用大量资源,所以把他们分开了,他们分开了就要分别在不同的机器上启动)

 

[root@h3 sbin]# ./start-yarn.sh

 

[root@h4 sbin]# ./yarn-daemons.sh start resourcemanager

 

 

验证:

 

http://192.168.128.11:50070

 

Overview 'h1:9000' (active)

 

 

http://192.168.128.12:50070

 

 

Overview 'h2:9000' (standby)

 

上传文件

[root@h4 bin]# hadoop fs -put /etc/profile /profile

[root@h4 bin]# hadoop fs -ls

ls: `.': No such file or directory

[root@h4 bin]# hadoop fs -ls /

Found 1 items

-rw-r--r--   3 root supergroup       1814 2016-02-26 19:08 /profile

[root@h4 bin]#

 

杀死h1

[root@h1 sbin]# jps

2480 NameNode

2868 Jps

2775 DFSZKFailoverController

[root@h1 sbin]# kill -9 2480

[root@h1 sbin]# jps

2880 Jps

2775 DFSZKFailoverController

[root@h1 sbin]# hadoop fs -ls /

Found 1 items

-rw-r--r--   3 root supergroup       1814 2016-02-26 19:08 /profile

 

此时 h2  变为active

 

手动启动 h1的 namenode

 

[root@h1 sbin]# ./hadoop-daemon.sh start namenode

starting namenode, logging to /home/hadoop-2.7.2/logs/hadoop-root-namenode-h1.out

[root@h1 sbin]# hadoop jar /home/hadoop-2.7.2/s

 

观察  h1 状态为standby

 

验证yarn

 

[root@h1 sbin]# hadoop jar /home/hadoop-2.7.2/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.2.jar wordcount /profile /out

16/02/26 19:14:23 INFO input.FileInputFormat: Total input paths to process : 1

16/02/26 19:14:23 INFO mapreduce.JobSubmitter: number of splits:1

16/02/26 19:14:23 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1456484773347_0001

16/02/26 19:14:24 INFO impl.YarnClientImpl: Submitted application application_1456484773347_0001

16/02/26 19:14:24 INFO mapreduce.Job: The url to track the job: http://h3:8088/proxy/application_1456484773347_0001/

16/02/26 19:14:24 INFO mapreduce.Job: Running job: job_1456484773347_0001

16/02/26 19:14:49 INFO mapreduce.Job: Job job_1456484773347_0001 running in uber mode : false

16/02/26 19:14:49 INFO mapreduce.Job:  map 0% reduce 0%

16/02/26 19:15:05 INFO mapreduce.Job:  map 100% reduce 0%

16/02/26 19:15:22 INFO mapreduce.Job:  map 100% reduce 100%

16/02/26 19:15:23 INFO mapreduce.Job: Job job_1456484773347_0001 completed successfully

16/02/26 19:15:23 INFO mapreduce.Job: Counters: 49

        File System Counters

                FILE: Number of bytes read=2099

                FILE: Number of bytes written=243781

                FILE: Number of read operations=0

                FILE: Number of large read operations=0

                FILE: Number of write operations=0

                HDFS: Number of bytes read=1901

                HDFS: Number of bytes written=1470

                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)=13014

                Total time spent by all reduces in occupied slots (ms)=13470

                Total time spent by all map tasks (ms)=13014

                Total time spent by all reduce tasks (ms)=13470

                Total vcore-milliseconds taken by all map tasks=13014

                Total vcore-milliseconds taken by all reduce tasks=13470

                Total megabyte-milliseconds taken by all map tasks=13326336

                Total megabyte-milliseconds taken by all reduce tasks=13793280

        Map-Reduce Framework

                Map input records=80

                Map output records=256

                Map output bytes=2588

                Map output materialized bytes=2099

                Input split bytes=87

                Combine input records=256

                Combine output records=156

                Reduce input groups=156

                Reduce shuffle bytes=2099

                Reduce input records=156

                Reduce output records=156

                Spilled Records=312

                Shuffled Maps =1

                Failed Shuffles=0

                Merged Map outputs=1

                GC time elapsed (ms)=395

                CPU time spent (ms)=4100

                Physical memory (bytes) snapshot=298807296

                Virtual memory (bytes) snapshot=4201771008

                Total committed heap usage (bytes)=138964992

        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=1814

        File Output Format Counters

                Bytes Written=1470

[root@h1 sbin]# hadoop fs -ls /

Found 3 items

drwxr-xr-x   - root supergroup          0 2016-02-26 19:15 /out

-rw-r--r--   3 root supergroup       1814 2016-02-26 19:08 /profile

drwx------   - root supergroup          0 2016-02-26 19:14 /tmp

[root@h1 sbin]#

 

Hadoop ha  集群搭建完成

 

安装hbase

hbase-env.sh

 

export JAVA_HOME=/home/jdk

export HBASE_MANAGES_ZK=false

 

 

 

hbase-site.xml:

 

 

<configuration>

<property>

<name>hbase.rootdir</name>

<value>hdfs://h1:9000/hbase</value>

</property>

<property>

<name>hbase.cluster.distributed</name>

<value>true</value>

</property>

 

<property>

<name>hbase.master</name>

<value>h1:60000</value>

</property>

 <property>

 <name>hbase.master.port</name>

 <value>60000</value>

 <description>The port master should bind to.</description>

 </property>

 

 

<property>

<name>hbase.zookeeper.quorum</name>

<value>h5,h6,h7</value>

</property>

<property>

<name>dfs.replication</name>

<value>3</value>

</property>

</configuration>

 

注意:$HBASE_HOME/conf/hbase-site.xml的hbase.rootdir的主机和端口号与$HADOOP_HOME/conf/core-site.xml的fs.default.name的主机和端口号一致

 

 

Regionservers:内容为:

h5

h6

h7

 

复制到h2  h5,h6,h7上面

 

整个启动顺序

 

按照上面启动hadoop  ha  的顺序  先启动好

 

然后在h1h2上启动hbase

 

./start-hbase.sh

 

 

测试进入 hbase

 

[root@h1 bin]# hbase shell

SLF4J: Class path contains multiple SLF4J bindings.

SLF4J: Found binding in [jar:file:/home/hbase-1.2.0/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]

SLF4J: Found binding in [jar:file:/home/hadoop-2.7.2/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]

SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.

SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]

HBase Shell; enter 'help<RETURN>' for list of supported commands.

Type "exit<RETURN>" to leave the HBase Shell

Version 1.2.0, r25b281972df2f5b15c426c8963cbf77dd853a5ad, Thu Feb 18 23:01:49 CST 2016

 

hbase(main):001:0> esit

NameError: undefined local variable or method `esit' for #<Object:0x7ad1caa2>

 

hbase(main):002:0> exit

 

至此全部结束。

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