转自:http://www.2cto.com/os/201605/510489.html
hadoop1的核心组成是两部分,即HDFS和MapReduce。在hadoop2中变为HDFS和Yarn。新的HDFS中的NameNode不再是只有一个了,可以有多个(目前只支持2个)。每一个都有相同的职能。
两个NameNode
当集群运行时,只有active状态的NameNode是正常工作的,standby状态的NameNode是处于待命状态的,时刻同步active状态NameNode的数据。一旦active状态的NameNode不能工作,通过手工或者自动切换,standby状态的NameNode就可以转变为active状态的,就可以继续工作了。这就是高可靠。
NameNode发生故障时
2个NameNode的数据其实是实时共享的。新HDFS采用了一种共享机制,JournalNode集群或者NFS进行共享。NFS是操作系统层面的,JournalNode是hadoop层面的,我们这里使用JournalNode集群进行数据共享。
实现NameNode的自动切换
需要使用ZooKeeper集群进行选择了。HDFS集群中的两个NameNode都在ZooKeeper中注册,当active状态的NameNode出故障时,ZooKeeper能检测到这种情况,它就会自动把standby状态的NameNode切换为active状态。
HDFS Federation
NameNode是核心节点,维护着整个HDFS中的元数据信息,那么其容量是有限的,受制于服务器的内存空间。当NameNode服务器的内存装不下数据后,那么HDFS集群就装不下数据了,寿命也就到头了。因此其扩展性是受限的。HDFS联盟指的是有多个HDFS集群同时工作,那么其容量理论上就不受限了,夸张点说就是无限扩展。
节点分布
配置过程详述
配置文件一共包括6个,分别是hadoop-env.sh、core-site.xml、hdfs-site.xml、mapred-site.xml、yarn-site.xml和slaves。除了hdfs-site.xml文件在不同集群配置不同外,其余文件在四个节点的配置是完全一样的,可以复制。
hadoop-env.sh
默认的HDFS路径。当有多个HDFS集群同时工作时,用户如果不写集群名称,那么默认使用哪个哪就在这里指定!该值来自于hdfs-site.xml中的配置
默认是NameNode、DataNode、JournalNode等存放数据的公共目录
ZooKeeper集群的地址和端口。注意,数量一定是奇数
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<configuration> <property> < name >fs.defaultFS</ name > <value>hdfs://cluster1</value> </property> <property> < name >hadoop.tmp.dir</ name > <value>/opt/ha/hadoop-2.7.2/data/tmp</value> </property> <property> < name >io.file.buffer. size </ name > <value>131072</value> </property> <property> < name >ha.zookeeper.quorum</ name > <value>hadoop:2181,hadoop1:2181,hadoop2:2181;slave1:2181;slave2:2181</value> </property> </configuration> |
hdfs-site.xml
这里dfs.namenode.shared.edits.dir的只在hadoop1,hadoop2中最后路径为cluster1,在slave1,slave2中最后路径为cluster2,区分开就行,可以是别的名称,还有一个core-site.xml中的fs.defaultFS在slave1和slave2中可以更改为cluster2
yarn-site.xml
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<property> < name >yarn.nodemanager.aux-services</ name > <value>mapreduce_shuffle</value> </property> <property> < name >yarn.nodemanager.aux-services.mapreduce.shuffle.class</ name > <value>org.apache.hadoop.mapred.ShuffleHandler</value> </property> <property> < name >yarn.resourcemanager.hostname</ name > <value>hadoop</value> </property> <property> < name >yarn.log-aggregation-enable</ name > <value> true </value> </property> <property> < name >yarn.log-aggregation.retain-seconds</ name > <value>604800</value> </property> |
mapred-site.xml
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<property> < name >mapreduce.framework. name </ name > <value>yarn</value> </property> <property> < name >mapreduce.job.tracker</ name > <value>hdfs://hadoop:9001</value> <final> true </final> </property> <property> < name >mapreduce.jobhistory.address</ name > <value>hadoop:10020</value> </property> <property> < name >mapreduce.jobhistory.webapp.address</ name > <value>hadoop:19888</value> </property> |
slaves
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hadoop hadoop1 hadoop2 slave1 slave2 |
启动过程
在所有zk节点启动zookeeper
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hadoop@hadoop:hadoop-2.7.2$ zkServer.sh start |
格式化zookeeper集群
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[hadoop@hadoop1 hadoop-2.7.2]$ bin/hdfs zkfc -formatZK [hadoop@slave1 hadoop-2.7.2]$ bin/hdfs zkfc -formatZK [hadoop@slave1 hadoop-2.7.2]$ zkCli.sh [zk: localhost:2181(CONNECTED) 5] ls /hadoop-ha/cluster cluster2 cluster1 |
在所有节点启动journalnode
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hadoop@hadoop:hadoop-2.7.2$ sbin/hadoop-daemon.sh start journalnode starting journalnode, logging to /opt/ha/hadoop-2.7.2/logs/hadoop-hadoop-journalnode-hadoop. out hadoop@hadoop:hadoop-2.7.2$ |
在cluster1中的nn1格式化namenode,验证并启动
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[hadoop@hadoop1 hadoop-2.7.2]$ bin/hdfs namenode -format -clusterId hadoop1 16/05/19 15:43:01 INFO common.Storage: Storage directory /opt/ha/hadoop-2.7.2/data/dfs/ name has been successfully formatted. 16/05/19 15:43:01 INFO namenode.NNStorageRetentionManager: Going to retain 1 images with txid >= 0 16/05/19 15:43:01 INFO util.ExitUtil: Exiting with status 0 16/05/19 15:43:01 INFO namenode.NameNode: SHUTDOWN_MSG: /************************************************************ SHUTDOWN_MSG: Shutting down NameNode at hadoop1/192.168.2.10 ************************************************************/ [hadoop@hadoop1 hadoop-2.7.2]$ ls data/dfs/ name / current / fsimage_0000000000000000000 seen_txid fsimage_0000000000000000000.md5 VERSION [hadoop@hadoop1 hadoop-2.7.2]$ sbin/hadoop-daemon.sh start namenode starting namenode, logging to /opt/ha/hadoop-2.7.2/logs/hadoop-hadoop-namenode-hadoop1. out [hadoop@hadoop1 hadoop-2.7.2]$ jps 9551 NameNode 9423 JournalNode 9627 Jps 9039 QuorumPeerMain |
http://hadoop1:50070查看
cluster1中另一个节点同步数据格式化,并启动
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[hadoop@hadoop2 hadoop-2.7.2]$ bin/hdfs namenode -bootstrapStandby ...... 16/05/19 15:48:27 INFO common.Storage: Storage directory /opt/ha/hadoop-2.7.2/data/dfs/ name has been successfully formatted. 16/05/19 15:48:27 INFO namenode.TransferFsImage: Opening connection to http://hadoop1:50070/imagetransfer?getimage=1&txid=0&storageInfo=-63:1280767544:0:hadoop1 16/05/19 15:48:28 INFO namenode.TransferFsImage: Image Transfer timeout configured to 60000 milliseconds 16/05/19 15:48:28 INFO namenode.TransferFsImage: Transfer took 0.00s at 0.00 KB/s 16/05/19 15:48:28 INFO namenode.TransferFsImage: Downloaded file fsimage.ckpt_0000000000000000000 size 353 bytes. 16/05/19 15:48:28 INFO util.ExitUtil: Exiting with status 0 16/05/19 15:48:28 INFO namenode.NameNode: SHUTDOWN_MSG: /************************************************************ SHUTDOWN_MSG: Shutting down NameNode at hadoop2/192.168.2.11 ************************************************************/ [hadoop@hadoop2 hadoop-2.7.2]$ ls data/dfs/ name / current / fsimage_0000000000000000000 seen_txid fsimage_0000000000000000000.md5 VERSION [hadoop@hadoop2 hadoop-2.7.2]$ [hadoop@hadoop2 hadoop-2.7.2]$ sbin/hadoop-daemon.sh start namenode starting namenode, logging to /opt/ha/hadoop-2.7.2/logs/hadoop-hadoop-namenode-hadoop2. out [hadoop@hadoop2 hadoop-2.7.2]$ jps 7196 Jps 6980 JournalNode 7120 NameNode 6854 QuorumPeerMain |
http://hadoop2:50070查看如下
使用以上步骤同是启动cluster2的两个namenode;这里省略
然后启动所有的datanode和(必须也在hadoop节点上启动)yarn
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[hadoop@hadoop1 hadoop-2.7.2]$ sbin/hadoop-daemons.sh start datanode hadoop1: starting datanode, logging to /opt/ha/hadoop-2.7.2/logs/hadoop-hadoop-datanode-hadoop1. out slave2: starting datanode, logging to /opt/ha/hadoop-2.7.2/logs/hadoop-hadoop-datanode-slave2. out hadoop2: starting datanode, logging to /opt/ha/hadoop-2.7.2/logs/hadoop-hadoop-datanode-hadoop2. out slave1: starting datanode, logging to /opt/ha/hadoop-2.7.2/logs/hadoop-hadoop-datanode-slave1. out hadoop: starting datanode, logging to /opt/ha/hadoop-2.7.2/logs/hadoop-hadoop-datanode-hadoop. out hadoop@hadoop:hadoop-2.7.2$ sbin/start-yarn.sh starting yarn daemons starting resourcemanager, logging to /opt/ha/hadoop-2.7.2/logs/yarn-hadoop-resourcemanager-hadoop. out hadoop2: starting nodemanager, logging to /opt/ha/hadoop-2.7.2/logs/yarn-hadoop-nodemanager-hadoop2. out hadoop1: starting nodemanager, logging to /opt/ha/hadoop-2.7.2/logs/yarn-hadoop-nodemanager-hadoop1. out slave2: starting nodemanager, logging to /opt/ha/hadoop-2.7.2/logs/yarn-hadoop-nodemanager-slave2. out hadoop: starting nodemanager, logging to /opt/ha/hadoop-2.7.2/logs/yarn-hadoop-nodemanager-hadoop. out slave1: starting nodemanager, logging to /opt/ha/hadoop-2.7.2/logs/yarn-hadoop-nodemanager-slave1. out hadoop@hadoop:hadoop-2.7.2$ jps 19384 JournalNode 19013 QuorumPeerMain 20649 Jps 20241 ResourceManager 20396 NodeManager 19815 DataNode [hadoop@hadoop1 hadoop-2.7.2]$ jps 10091 NodeManager 9551 NameNode 9822 DataNode 9423 JournalNode 10232 Jps 9039 QuorumPeerMain [hadoop@hadoop2 hadoop-2.7.2]$ jps 7450 NodeManager 7295 DataNode 6980 JournalNode 7120 NameNode 6854 QuorumPeerMain 7580 Jps [hadoop@slave1 hadoop-2.7.2]$ jps 3706 DataNode 3988 Jps 3374 JournalNode 3591 NameNode 3860 NodeManager 3184 QuorumPeerMain [hadoop@slave2 hadoop-2.7.2]$ jps 3023 QuorumPeerMain 3643 NodeManager 3782 Jps 3177 JournalNode 3497 DataNode 3383 NameNod |
http://hadoop:8088/cluster/nodes/
所有namenode节点启动zkfc
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[hadoop@hadoop1 hadoop-2.7.2]$ sbin/hadoop-daemon.sh start zkfc starting zkfc, logging to /opt/ha/hadoop-2.7.2/logs/hadoop-hadoop-zkfc-hadoop1. out [hadoop@hadoop1 hadoop-2.7.2]$ jps 10665 DFSZKFailoverController 9551 NameNode 9822 DataNode 9423 JournalNode 10739 Jps 9039 QuorumPeerMain 10483 NodeManager |
上传文件测试
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[hadoop@hadoop1 hadoop-2.7.2]$ bin/hdfs dfs -mkdir /test 16/05/19 16:09:19 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable [hadoop@hadoop1 hadoop-2.7.2]$ bin/hdfs dfs -put etc/hadoop/*.xml /test 16/05/19 16:09:36 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable #在slave1中查看 [hadoop@slave1 hadoop-2.7.2]$ bin/hdfs dfs -ls -R / 16/05/19 16:11:32 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable drwxr-xr-x - hadoop supergroup 0 2016-05-19 16:09 /test -rw-r --r-- 2 hadoop supergroup 4436 2016-05-19 16:09 /test/capacity-scheduler.xml -rw-r --r-- 2 hadoop supergroup 1185 2016-05-19 16:09 /test/core-site.xml -rw-r --r-- 2 hadoop supergroup 9683 2016-05-19 16:09 /test/hadoop-policy.xml -rw-r --r-- 2 hadoop supergroup 3814 2016-05-19 16:09 /test/hdfs-site.xml -rw-r --r-- 2 hadoop supergroup 620 2016-05-19 16:09 /test/httpfs-site.xml -rw-r --r-- 2 hadoop supergroup 3518 2016-05-19 16:09 /test/kms-acls.xml -rw-r --r-- 2 hadoop supergroup 5511 2016-05-19 16:09 /test/kms-site.xml -rw-r --r-- 2 hadoop supergroup 1170 2016-05-19 16:09 /test/mapred-site.xml -rw-r --r-- 2 hadoop supergroup 1777 2016-05-19 16:09 /test/yarn-site.xml [hadoop@slave1 hadoop-2.7.2]$ |
验证yarn
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[hadoop@hadoop1 hadoop-2.7.2]$ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.2.jar wordcount /test / out 16/05/19 16:15:25 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 16/05/19 16:15:26 INFO client.RMProxy: Connecting to ResourceManager at hadoop/192.168.2.3:8032 16/05/19 16:15:27 INFO input.FileInputFormat: Total input paths to process : 9 16/05/19 16:15:27 INFO mapreduce.JobSubmitter: number of splits:9 16/05/19 16:15:27 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1463644924165_0001 16/05/19 16:15:27 INFO impl.YarnClientImpl: Submitted application application_1463644924165_0001 16/05/19 16:15:27 INFO mapreduce.Job: The url to track the job: http://hadoop:8088/proxy/application_1463644924165_0001/ 16/05/19 16:15:27 INFO mapreduce.Job: Running job: job_1463644924165_0001 16/05/19 16:15:35 INFO mapreduce.Job: Job job_1463644924165_0001 running in uber mode : false 16/05/19 16:15:35 INFO mapreduce.Job: map 0% reduce 0% 16/05/19 16:15:44 INFO mapreduce.Job: map 11% reduce 0% 16/05/19 16:15:59 INFO mapreduce.Job: map 11% reduce 4% 16/05/19 16:16:08 INFO mapreduce.Job: map 22% reduce 4% 16/05/19 16:16:10 INFO mapreduce.Job: map 22% reduce 7% 16/05/19 16:16:22 INFO mapreduce.Job: map 56% reduce 7% 16/05/19 16:16:26 INFO mapreduce.Job: map 100% reduce 67% 16/05/19 16:16:29 INFO mapreduce.Job: map 100% reduce 100% 16/05/19 16:16:29 INFO mapreduce.Job: Job job_1463644924165_0001 completed successfully 16/05/19 16:16:31 INFO mapreduce.Job: Counters: 51 File System Counters FILE: Number of bytes read =25164 FILE: Number of bytes written=1258111 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read =32620 HDFS: Number of bytes written=13523 HDFS: Number of read operations=30 HDFS: Number of large read operations=0 HDFS: Number of write operations=2 Job Counters Killed map tasks=2 Launched map tasks=10 Launched reduce tasks=1 Data- local map tasks=8 Rack- local map tasks=2 Total time spent by all maps in occupied slots (ms)=381816 Total time spent by all reduces in occupied slots (ms)=42021 Total time spent by all map tasks (ms)=381816 Total time spent by all reduce tasks (ms)=42021 Total vcore-milliseconds taken by all map tasks=381816 Total vcore-milliseconds taken by all reduce tasks=42021 Total megabyte-milliseconds taken by all map tasks=390979584 Total megabyte-milliseconds taken by all reduce tasks=43029504 Map-Reduce Framework Map input records=963 Map output records=3041 Map output bytes=41311 Map output materialized bytes=25212 Input split bytes=906 Combine input records=3041 Combine output records=1335 Reduce input groups=673 Reduce shuffle bytes=25212 Reduce input records=1335 Reduce output records=673 Spilled Records=2670 Shuffled Maps =9 Failed Shuffles=0 Merged Map outputs=9 GC time elapsed (ms)=43432 CPU time spent (ms)=30760 Physical memory (bytes) snapshot=1813704704 Virtual memory (bytes) snapshot=8836780032 Total committed heap usage (bytes)=1722810368 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 =31714 File Output Format Counters Bytes Written=13523 |
http://hadoop:8088/查看
结果
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[hadoop@slave1 hadoop-2.7.2]$ bin/hdfs dfs -lsr / out lsr: DEPRECATED: Please use 'ls -R' instead . 16/05/19 16:22:14 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable -rw-r --r-- 2 hadoop supergroup 0 2016-05-19 16:16 /out/_SUCCESS -rw-r --r-- 2 hadoop supergroup 13523 2016-05-19 16:16 /out/part-r-00000 [hadoop@slave1 hadoop-2.7.2]$ |
测试故障自动转移
当前情况在网页查看hadoop1和slave1为Active状态,
那把这两个namenode关闭,再查看
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[hadoop@hadoop1 hadoop-2.7.2]$ jps 10665 DFSZKFailoverController 9551 NameNode 12166 Jps 9822 DataNode 9423 JournalNode 9039 QuorumPeerMain 10483 NodeManager [hadoop@hadoop1 hadoop-2.7.2]$ sbin/hadoop-daemon.sh stop namenode stopping namenode [hadoop@hadoop1 hadoop-2.7.2]$ jps 10665 DFSZKFailoverController 9822 DataNode 9423 JournalNode 12221 Jps 9039 QuorumPeerMain 10483 NodeManager |
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[hadoop@slave1 hadoop-2.7.2]$ sbin/hadoop-daemon.sh stop namenode stopping namenode [hadoop@slave1 hadoop-2.7.2]$ jps 3706 DataNode 3374 JournalNode 4121 NodeManager 5460 Jps 4324 DFSZKFailoverController 3184 QuorumPeerMain |
此时Active NN已经分别转移到hadoop2和slave2上了
以上是hadoop2.2.0的HDFS集群HA配置和自动切换、HDFS federation配置、Yarn配置的基本过程,其中大家可以添加其他配置,zookeeper和journalnode也不一定所有节点都启动,只要是奇数个就ok,如果集群数量多,这些及节点均可以单独配置在一个host上