0 机器说明
|
IP |
Role |
|
192.168.1.106 |
NameNode、DataNode、NodeManager、ResourceManager |
|
192.168.1.107 |
SecondaryNameNode、NodeManager、DataNode |
|
192.168.1.108 |
NodeManager、DataNode |
|
192.168.1.106 |
HiveServer |
1 打通无密钥
配置HDFS，首先就得把机器之间的无密钥配置上。我们这里为了方便，把机器之间的双向无密钥都配置上。
(1)产生RSA密钥信息
ssh-keygen -t rsa
一路回车，直到产生一个图形结构，此时便产生了RSA的私钥id_rsa和公钥id_rsa.pub，位于/home/user/.ssh目录中。
(2)将所有机器节点的ssh证书公钥拷贝至/home/user/.ssh/authorized_keys文件中，三个机器都一样。
(3)切换到root用户，修改/etc/ssh/sshd_config文件，配置：
RSAAuthentication yes
PubkeyAuthentication yes
AuthorizedKeysFile .ssh/authorized_keys
(4)重启ssh服务：service sshd restart
(5)使用ssh服务，远程登录：
ssh配置成功。
2 安装Hadoop2.3
将对应的hadoop2.3的tar包解压缩到本地之后，主要就是修改配置文件，文件的路径都在etc/hadoop中，下面列出几个主要的。
(1)core-site.xml
1 <configuration> 2 <property> 3 <name>hadoop.tmp.dir</name> 4 <value>file:/home/sdc/tmp/hadoop-${user.name}</value> 5 </property> 6 <property> 7 <name>fs.default.name</name> 8 <value>hdfs://192.168.1.106:9000</value> 9 </property> 10 </configuration>
(2)hdfs-site.xml
1 <configuration> 2 <property> 3 <name>dfs.replication</name> 4 <value>3</value> 5 </property> 6 <property> 7 <name>dfs.namenode.secondary.http-address</name> 8 <value>192.168.1.107:9001</value> 9 </property> 10 <property> 11 <name>dfs.namenode.name.dir</name> 12 <value>file:/home/sdc/dfs/name</value> 13 </property> 14 <property> 15 <name>dfs.datanode.data.dir</name> 16 <value>file:/home/sdc/dfs/data</value> 17 </property> 18 <property> 19 <name>dfs.replication</name> 20 <value>3</value> 21 </property> 22 <property> 23 <name>dfs.webhdfs.enabled</name> 24 <value>true</value> 25 </property> 26 </configuration>
(3)hadoop-env.sh
主要是将其中的JAVA_HOME赋值：
export JAVA_HOME=/usr/local/jdk1.6.0_27
(4)mapred-site.xml
1 <configuration> 2 <property> 3 <!-- 使用yarn作为资源分配和任务管理框架 --> 4 <name>mapreduce.framework.name</name> 5 <value>yarn</value> 6 </property> 7 <property> 8 <!-- JobHistory Server地址 --> 9 <name>mapreduce.jobhistory.address</name> 10 <value>centos1:10020</value> 11 </property> 12 <property> 13 <!-- JobHistory WEB地址 --> 14 <name>mapreduce.jobhistory.webapp.address</name> 15 <value>centos1:19888</value> 16 </property> 17 <property> 18 <!-- 排序文件的时候一次同时最多可并行的个数 --> 19 <name>mapreduce.task.io.sort.factor</name> 20 <value>100</value> 21 </property> 22 <property> 23 <!-- reuduce shuffle阶段并行传输数据的数量 --> 24 <name>mapreduce.reduce.shuffle.parallelcopies</name> 25 <value>50</value> 26 </property> 27 <property> 28 <name>mapred.system.dir</name> 29 <value>file:/home/sdc/Data/mr/system</value> 30 </property> 31 <property> 32 <name>mapred.local.dir</name> 33 <value>file:/home/sdc/Data/mr/local</value> 34 </property> 35 <property> 36 <!-- 每个Map Task需要向RM申请的内存量 --> 37 <name>mapreduce.map.memory.mb</name> 38 <value>1536</value> 39 </property> 40 <property> 41 <!-- 每个Map阶段申请的Container的JVM参数 --> 42 <name>mapreduce.map.java.opts</name> 43 <value>-Xmx1024M</value> 44 </property> 45 <property> 46 <!-- 每个Reduce Task需要向RM申请的内存量 --> 47 <name>mapreduce.reduce.memory.mb</name> 48 <value>2048</value> 49 </property> 50 <property> 51 <!-- 每个Reduce阶段申请的Container的JVM参数 --> 52 <name>mapreduce.reduce.java.opts</name> 53 <value>-Xmx1536M</value> 54 </property> 55 <property> 56 <!-- 排序内存使用限制 --> 57 <name>mapreduce.task.io.sort.mb</name> 58 <value>512</value> 59 </property> 60 </configuration>
注意上面的几个内存大小的配置，其中Container的大小一般都要小于所能申请的最大值，否则所运行的Mapreduce任务可能无法运行。
(5)yarn-site.xml
1 <configuration> 2 <property> 3 <name>yarn.nodemanager.aux-services</name> 4 <value>mapreduce_shuffle</value> 5 </property> 6 <property> 7 <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name> 8 <value>org.apache.hadoop.mapred.ShuffleHandler</value> 9 </property> 10 <property> 11 <name>yarn.resourcemanager.address</name> 12 <value>centos1:8080</value> 13 </property> 14 <property> 15 <name>yarn.resourcemanager.scheduler.address</name> 16 <value>centos1:8081</value> 17 </property> 18 <property> 19 <name>yarn.resourcemanager.resource-tracker.address</name> 20 <value>centos1:8082</value> 21 </property> 22 <property> 23 <!-- 每个nodemanager可分配的内存总量 --> 24 <name>yarn.nodemanager.resource.memory-mb</name> 25 <value>2048</value> 26 </property> 27 <property> 28 <name>yarn.nodemanager.remote-app-log-dir</name> 29 <value>${hadoop.tmp.dir}/nodemanager/remote</value> 30 </property> 31 <property> 32 <name>yarn.nodemanager.log-dirs</name> 33 <value>${hadoop.tmp.dir}/nodemanager/logs</value> 34 </property> 35 <property> 36 <name>yarn.resourcemanager.admin.address</name> 37 <value>centos1:8033</value> 38 </property> 39 <property> 40 <name>yarn.resourcemanager.webapp.address</name> 41 <value>centos1:8088</value> 42 </property> 43 </configuration>
此外，配置好对应的HADOOP_HOME环境变量之后，将当前hadoop文件发送到所有的节点，在sbin目录中有start-all.sh脚本，启动可见：
启动完成之后，有如下两个WEB界面：
http://192.168.1.106:8088/cluster
http://192.168.1.106:50070/dfshealth.html
使用最简单的命令检查下HDFS：
3 安装Hive0.12
将Hive的tar包解压缩之后，首先配置下HIVE_HOME的环境变量。然后便是一些配置文件的修改：
(1)hive-env.sh
将其中的HADOOP_HOME变量修改为当前系统变量值。
(2)hive-site.xml
- 修改hive.server2.thrift.sasl.qop属性
修改为：
- 将hive.metastore.schema.verification对应的值改为false
强制metastore的schema一致性，开启的话会校验在metastore中存储的信息的版本和hive的jar包中的版本一致性，并且关闭自动schema迁移，用户必须手动的升级hive并且迁移schema，关闭的话只会在版本不一致时给出警告。
- 修改hive的元数据存储位置，改为mysql存储：
1 <property> 2 <name>javax.jdo.option.ConnectionURL</name> 3 <value>jdbc:mysql://localhost:3306/hive?characterEncoding=UTF-8</value> 4 <description>JDBC connect string for a JDBC metastore</description> 5 </property> 6 7 <property> 8 <name>javax.jdo.option.ConnectionDriverName</name> 9 <value>com.mysql.jdbc.Driver</value> 10 <description>Driver class name for a JDBC metastore</description> 11 </property> 12 13 <property> 14 <name>javax.jdo.PersistenceManagerFactoryClass</name> 15 <value>org.datanucleus.api.jdo.JDOPersistenceManagerFactory</value> 16 <description>class implementing the jdo persistence</description> 17 </property> 18 19 <property> 20 <name>javax.jdo.option.DetachAllOnCommit</name> 21 <value>true</value> 22 <description>detaches all objects from session so that they can be used after transaction is committed</description> 23 </property> 24 25 <property> 26 <name>javax.jdo.option.NonTransactionalRead</name> 27 <value>true</value> 28 <description>reads outside of transactions</description> 29 </property> 30 31 <property> 32 <name>javax.jdo.option.ConnectionUserName</name> 33 <value>hive</value> 34 <description>username to use against metastore database</description> 35 </property> 36 37 <property> 38 <name>javax.jdo.option.ConnectionPassword</name> 39 <value>123</value> 40 <description>password to use against metastore database</description> 41 </property>
在bin下启动hive脚本，运行几个hive语句：
4 安装Mysql5.6
见http://www.cnblogs.com/Scott007/p/3572604.html
5 Pi计算实例、Hive表的计算实例运行
在Hadoop的安装目录bin子目录下，执行hadoop自带的示例，pi的计算，命令为：
./hadoop jar ../share/hadoop/mapreduce/hadoop-mapreduce-examples-2.3.0.jar pi 10 10
运行日志为：
1 Number of Maps = 10 2 Samples per Map = 10 3 14/03/20 23:50:04 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 4 Wrote input for Map #0 5 Wrote input for Map #1 6 Wrote input for Map #2 7 Wrote input for Map #3 8 Wrote input for Map #4 9 Wrote input for Map #5 10 Wrote input for Map #6 11 Wrote input for Map #7 12 Wrote input for Map #8 13 Wrote input for Map #9 14 Starting Job 15 14/03/20 23:50:06 INFO client.RMProxy: Connecting to ResourceManager at centos1/192.168.1.106:8080 16 14/03/20 23:50:07 INFO input.FileInputFormat: Total input paths to process : 10 17 14/03/20 23:50:07 INFO mapreduce.JobSubmitter: number of splits:10 18 14/03/20 23:50:08 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1395323769116_0001 19 14/03/20 23:50:08 INFO impl.YarnClientImpl: Submitted application application_1395323769116_0001 20 14/03/20 23:50:08 INFO mapreduce.Job: The url to track the job: http://centos1:8088/proxy/application_1395323769116_0001/ 21 14/03/20 23:50:08 INFO mapreduce.Job: Running job: job_1395323769116_0001 22 14/03/20 23:50:18 INFO mapreduce.Job: Job job_1395323769116_0001 running in uber mode : false 23 14/03/20 23:50:18 INFO mapreduce.Job: map 0% reduce 0% 24 14/03/20 23:52:21 INFO mapreduce.Job: map 10% reduce 0% 25 14/03/20 23:52:27 INFO mapreduce.Job: map 20% reduce 0% 26 14/03/20 23:52:32 INFO mapreduce.Job: map 30% reduce 0% 27 14/03/20 23:52:34 INFO mapreduce.Job: map 40% reduce 0% 28 14/03/20 23:52:37 INFO mapreduce.Job: map 50% reduce 0% 29 14/03/20 23:52:41 INFO mapreduce.Job: map 60% reduce 0% 30 14/03/20 23:52:43 INFO mapreduce.Job: map 70% reduce 0% 31 14/03/20 23:52:46 INFO mapreduce.Job: map 80% reduce 0% 32 14/03/20 23:52:48 INFO mapreduce.Job: map 90% reduce 0% 33 14/03/20 23:52:51 INFO mapreduce.Job: map 100% reduce 0% 34 14/03/20 23:52:59 INFO mapreduce.Job: map 100% reduce 100% 35 14/03/20 23:53:02 INFO mapreduce.Job: Job job_1395323769116_0001 completed successfully 36 14/03/20 23:53:02 INFO mapreduce.Job: Counters: 49 37 File System Counters 38 FILE: Number of bytes read=226 39 FILE: Number of bytes written=948145 40 FILE: Number of read operations=0 41 FILE: Number of large read operations=0 42 FILE: Number of write operations=0 43 HDFS: Number of bytes read=2670 44 HDFS: Number of bytes written=215 45 HDFS: Number of read operations=43 46 HDFS: Number of large read operations=0 47 HDFS: Number of write operations=3 48 Job Counters 49 Launched map tasks=10 50 Launched reduce tasks=1 51 Data-local map tasks=10 52 Total time spent by all maps in occupied slots (ms)=573584 53 Total time spent by all reduces in occupied slots (ms)=20436 54 Total time spent by all map tasks (ms)=286792 55 Total time spent by all reduce tasks (ms)=10218 56 Total vcore-seconds taken by all map tasks=286792 57 Total vcore-seconds taken by all reduce tasks=10218 58 Total megabyte-seconds taken by all map tasks=440512512 59 Total megabyte-seconds taken by all reduce tasks=20926464 60 Map-Reduce Framework 61 Map input records=10 62 Map output records=20 63 Map output bytes=180 64 Map output materialized bytes=280 65 Input split bytes=1490 66 Combine input records=0 67 Combine output records=0 68 Reduce input groups=2 69 Reduce shuffle bytes=280 70 Reduce input records=20 71 Reduce output records=0 72 Spilled Records=40 73 Shuffled Maps =10 74 Failed Shuffles=0 75 Merged Map outputs=10 76 GC time elapsed (ms)=710 77 CPU time spent (ms)=71800 78 Physical memory (bytes) snapshot=6531928064 79 Virtual memory (bytes) snapshot=19145916416 80 Total committed heap usage (bytes)=5696757760 81 Shuffle Errors 82 BAD_ID=0 83 CONNECTION=0 84 IO_ERROR=0 85 WRONG_LENGTH=0 86 WRONG_MAP=0 87 WRONG_REDUCE=0 88 File Input Format Counters 89 Bytes Read=1180 90 File Output Format Counters 91 Bytes Written=97 92 Job Finished in 175.556 seconds 93 Estimated value of Pi is 3.20000000000000000000
如果运行不起来，那说明HDFS的配置有问题啊！
Hive中执行count等语句，可以触发mapduce任务：
如果运行的时候出现类似于如下的错误：
Error in metadata: java.lang.RuntimeException: Unable to instantiate org.apache.hadoop.hive.metastore.HiveMetaStoreClient
说明元数据存储有问题，可能是以下两方面的原因：
(1)HDFS的元数据存储有问题：
$HADOOP_HOME/bin/hadoop fs -mkdir /tmp
$HADOOP_HOME/bin/hadoop fs -mkdir /user/hive/warehouse
$HADOOP_HOME/bin/hadoop fs -chmod g+w /tmp
$HADOOP_HOME/bin/hadoop fs -chmod g+w /user/hive/warehouse
(2)Mysql的授权有问题：
在mysql中执行如下命令，其实就是给Mysql中的Hive数据库赋权
grant all on db.* to hive@'%' identified by '密码';(使用户可以远程连接Mysql)
grant all on db.* to hive@'localhost' identified by '密码';(使用户可以本地连接Mysql)
flush privileges;
具体哪方面的原因，可以查看hive的日志。
-------------------------------------------------------------------------------
如果您看了本篇博客,觉得对您有所收获，请点击右下角的 [推荐]
如果您想转载本博客，请注明出处
如果您对本文有意见或者建议，欢迎留言
感谢您的阅读，请关注我的后续博客