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

DanZaragoza 8年前發布 | 20K 次閱讀分布式/云計算/大數據

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

Ip	主機名	程序	進程
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

關于準備工作我這里就不一一寫出來了，總結一下有主機名，ip，主機名和ip的映射關系，防火墻，ssh免密碼，jdk的安裝及環境變量的設置。

安裝zookeeper到 h5、h6、h7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

創建 data文件夾和在里面創建文件myid 并寫入數字1

touch data/myid

echo 1 > data/myid

拷貝整個zookeeper到另外兩個節點上

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

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

其他兩個節點的myid 修改為 2 3

安裝hadoop

/home/hadoop-2.7.2/etc/Hadoop

hadoop-env.sh：

export JAVA_HOME=/home/jdk

core-site.xml：

<name>fs.defaultFS</name>

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

</property>

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

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

</property>

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

</property>

</configuration>

hdfs-site.xml：

<name>dfs.nameservices</name>

<value>masters</value>

</property>

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

</property>

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

</property>

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

</property>

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

</property>

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

</property>

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

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

</property>

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

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

</property>

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

</property>

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

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

</property>

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

<value>

sshfence

shell(/bin/true)

</value>

</property>

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

</property>

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

</property>

</configuration>

mapred-site.xml：

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

</property>

</configuration>

yarn-site.xml：

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

</property>

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

</property>

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

</property>

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

</property>

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

</property>

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

</property>

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

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

</property>

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

</property>

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

<value>mapreduce_shuffle</value>

</property>

</configuration>

Slaves：

然后拷貝到其他節點

scp -r hadoop-2.7.2 h2:/home/ 等等

這個地方說明一下 yarn 的HA 是在 h3和h4 上面

啟動順序

###注意：嚴格按照下面的步驟

1. 啟動zookeeper集群

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

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

H5 h6 h7 都一樣

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

查看狀態

2. 啟動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上執行命令:

hdfs namenode -format

格式化后會在根據core-site.xml中的hadoop.tmp.dir配置生成個文件

拷貝tmp 到 h2

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

4. 格式化ZK(在h1上執行即可)

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

5. 啟動HDFS(在h1上執行)

[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

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

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. 啟動YARN(是在h3上執行start-yarn.sh，把namenode和resourcemanager分開是因為性能問題，因為他們都要占用大量資源，所以把他們分開了，他們分開了就要分別在不同的機器上啟動)

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

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

驗證：

http://192.168.128.11:50070

Overview 'h1:9000' (active)

http://192.168.128.12:50070

Overview 'h2:9000' (standby)

上傳文件

[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]#

殺死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

此時 h2 變為active

手動啟動 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

觀察 h1 狀態為standby

驗證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

安裝hbase

hbase-env.sh：

export JAVA_HOME=/home/jdk

export HBASE_MANAGES_ZK=false

hbase-site.xml：

<name>hbase.rootdir</name>

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

</property>

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

</property>

<name>hbase.master</name>

</property>

<name>hbase.master.port</name>

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

</property>

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

</property>

<name>dfs.replication</name>

</property>

</configuration>

注意：$HBASE_HOME/conf/hbase-site.xml的hbase.rootdir的主機和端口號與$HADOOP_HOME/conf/core-site.xml的fs.default.name的主機和端口號一致

Regionservers：內容為:

復制到h2 h5,h6,h7上面

整個啟動順序

按照上面啟動hadoop ha 的順序先啟動好

然后在h1，h2上啟動hbase

./start-hbase.sh

測試進入 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

至此全部結束。

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分布式/云計算/大數據

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

安裝zookeeper到 h5、h6、h7上面

安裝hadoop

啟動順序

安裝hbase

整個啟動順序

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