【转载】hadoop/hbase 搭建
本文转载自http://shift-alt-ctrl.iyunv.com/blog/2073581因为工作需要，我们使用hbase + hadoop存储基于用户内容的数据(UGC)，本文将描述如何逐步搭建此平台，仅作参考。
1. 环境
操作系统：Red hat 6.3，300G硬盘，双核CPU
JAVA：JDK1.6
HBASE：hbase-0.98.1
Hadoop：hadoop-2.2.0
现在我们使用3台机器(虚拟机)，来搭建hadoop环境，如下为机器列表，首先我们需要在三台机器上hosts文件中都增加如下信息，同时需要注意，局域网内的机器都需要设定网卡为“静态IP”，以防止机器重启后IP不断变化。本例中所有机器使用root用户操作。
Java代码
[*]127.0.0.1 localhost
[*]::1 localhost
[*]192.168.0.110 node1.hadoop.virtual
[*]192.168.0.111 node2.hadoop.virtual
[*]192.168.0.112 node2.hadoop.virtual
[*]##需要注意当前的机器名
[*]## 192.168.0.110(本机IP) 机器名
当然使用“hostname”转义成IP并非必须的，我们也可以直接在hadoop、hbase的配置文件使用IP，不过本人为了便于定位node所在的机器位置，就“额外”的在hosts中增加了上述配置。
2. SSH
hadoop、hbase中使用ssh实现无密码登录，在hadoop集群启动时将会依次跳转到集群中所有的机器上启动相应的进程，所以我们首先配置ssh授权信息(请首先安装ssh-keygen客户端，关于ssh客户端配置请参见其他文档)：
Java代码
[*]ssh-keygen -t rsa
一路回车，均采用默认配置，无password设置。最终会在:/root/.ssh目录下生成id_rsa.pub(如果使用其他用户登录，则保存在~/.ssh目录下)，这个文件中所包含的的就是登陆本机所需要的rsa-pub值。我们依次在上述三台机器上执行ssh-keygen命令。
我们把三台机器生成的三个rsa-pub值都依次写入每台机器的/root/.ssh/authorized_keys文件中，很多人忘记把本机的rsa-pub值写入自己的authorized_keys，这会导致本机的hadoop实例无法启动。
3. JDK安装
现在JDK1.6+版本，然后直接安装，并在配置JAVA_HOME，本实例中JDK安装在/opt/app目录下。在/etc/profile文件的尾部增加如下配置：
Java代码
[*]## set java
[*]JAVA_HOME=/opt/app/jdk1.6.0_45
[*]PATH=$PATH:/$JAVA_HOME/bin
[*]CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar
[*]JRE_HOME=$JAVA_HOME/jre
[*]export JAVA_HOME PATH CLASSPATH JRE_HOME
[*]export LD_LIBRARY_PATH=/usr/local/lib
配置完毕之后，执行“source /etc/profile”使环境变量生效；如果你的机器上已经安装了其他的JDK(比如openJDK)，你应该首先卸载或者禁用其启动文件，然后再执行上述“source”命令。(比如，使用"whereis java"指令查找出java启动文件所在位置，然后删除它，它可能位于“/usr/bin/java”中)
建议三台机器中，JDK、hadoop、hbase的安装目录都相同，这样排查问题和文件配置都会非常的方便。
4. ulimit设置
因为hadoop和hbase在运行时需要打开很多的文件，同时在面向众多Client连接时也会消耗文件描述符，所以我们需要简单的将ulimit设置的稍微大一些(默认为1024，显然太小了)，在/etc/security/limits.conf文件的尾部增加：
Java代码
[*]* soft nofile 20480
[*]* hard nofile 20480
此外还需要检测/etc/profile文件中是否也指定了“ulimit”选项，如果有，可以考虑暂时先移除它，ulimit参数将会在root用户重登陆后生效。关于linux下如何调整机器支撑的最大文件打开个数以及线程的并发数，请参见其他文档。
5. hadoop搭建与配置
hadoop-2.2是目前相对稳定和“低调”的版本，不过hadoop的版本简直是“不尽其数”，为了配合hbase-0.98，我们需要使用hadoop-2.2版本作为支撑；不过如果条件允许，建议使用hadoop-2.4+，因为hbase-1.0.x里程碑版本将最低支持到hadoop-2.4。【hbase与hadoop版本对应表】
通过apache网站下载hadoop-2.2.tar.gz文件包，解压到指定目录，本实例中将hadoop安装在“/opt/app”目录下；我们首先在一台机器上安装hadoop并配置，稍后再把依次同步到另外两台机器即可。
我们约定，node1.hadoop.virtual作为namenode，其他两台作为datanode，其中node2.hadoop.virtual作为辅助master(即secondarynamenode)，本文中将不引入yarn(mapreduce)，所以我们暂时不关心resourceManager等进程。同时约定hadoop与hbase中关于file blocksize的设定保持一致，为128M。我们依次配置如下文件(${hadoop}/etc/hadoop)：
1) masters文件：此文件只需要放置在namenode和secondarynamenode上，datanode上不需要此文件；masters文件用来指定“secondarynamenode”的地址；集群中，通常需要一个secondarynamenode。
Java代码
[*]node2.hadoop.virtual
2) slaves文件：用来指定datanode的地址，只有datanode上需要此文件；
Java代码
[*]node1.hadoop.virtual
[*]node2.hadoop.virtual
[*]node3.hadoop.virtual
如果你不希望node1同时作为datanode和namenode两种角色，你可以在slaves文件中移除它。
3) hdfs-site.xml文件：hadoop主要有hdfs和mapreduce两种核心特性，hdfs-site.xml用来配置hdfs特性。如下配置参数，仅供参考。
Java代码
[*]<?xml version="1.0" encoding="UTF-8"?>
[*]<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
[*]<!-- 默认配置以及全量参数请参考hdfs-default.xml -->
[*]<!-- http://hadoop.apache.org/docs/r2.2.0/hadoop-project-dist/hadoop-hdfs/hdfs-default.xml -->
[*]<configuration>
[*] <!-- important -->
[*] <property>
[*] <name>dfs.replication</name>
[*] <value>3</value>
[*] <!-- default 3 -->
[*] <!-- but will be changed by "file.replication" of core-site.xml -->
[*] </property>
[*] <property>
[*] <name>dfs.blocksize</name>
[*] <value>128m</value>
[*] <!-- default 64m -->
[*] </property>
[*] <!-- optional -->
[*] <property>
[*] <name>dfs.client.block.write.retries</name>
[*] <value>3</value>
[*] <!-- default 3 -->
[*] <!-- if one of replication is failure when writting,How many times shoud be retry -->
[*] </property>
[*] <property>
[*] <name>dfs.heartbeat.interval</name>
[*] <value>3</value>
[*] <!-- heartbeat between namenode and datanode,seconds -->
[*] </property>
[*]
[*] <property>
[*] <name>dfs.namenode.replication.interval</name>
[*] <value>3</value>
[*] <!-- namenode check all for replication,seconds -->
[*] </property>
[*] <property>
[*] <name>dfs.namenode.logging.level</name>
[*] <value>info</value>
[*] <!-- default info -->
[*] </property>
[*] <property>
[*] <name>dfs.datanode.handler.count</name>
[*] <value>32</value>
[*] <!-- default 10 -->
[*] <!-- production,can be more higher than it,such as 128 -->
[*] </property>
[*] <property>
[*] <name>dfs.namenode.handler.count</name>
[*] <value>32</value>
[*] <!-- default 10 -->
[*] <!-- production,can be more higher than it,but reference the number of datanodes -->
[*] </property>
[*] <property>
[*] <name>dfs.namenode.name.dir</name>
[*] <value>/data/hadoop/dfs/name</value>
[*] <!-- namenode,name table -->
[*] <!-- local filesystem -->
[*] </property>
[*] <property>
[*] <name>dfs.namenode.name.dir.restore</name>
[*] <value>false</value>
[*] <!-- default false. -->
[*] </property>
[*] <property>
[*] <name>dfs.datanode.data.dir</name>
[*] <value>/data/hadoop/dfs/data</value>
[*] <!-- datanode,data-->
[*] <!-- should be local filesystem,if as SAN system,you should test it enough -->
[*] </property>
[*] <property>
[*] <name>dfs.namenode.checkpoint.dir</name>
[*] <value>/data/hadoop/dfs/namesecondary</value>
[*] <!-- datanode,data-->
[*] </property>
[*] <!--
[*] <property>
[*] <name>dfs.datanode.address</name>
[*] <value>0.0.0.0:50010</value>
[*] </property>
[*] -->
[*]</configuration>
4) core-site.xml：hadoop全局配置
Java代码
[*]<?xml version="1.0" encoding="UTF-8"?>
[*]<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
[*]<!--
[*]http://hadoop.apache.org/docs/r2.2.0/hadoop-project-dist/hadoop-common/core-default.xml
[*]-->
[*]<configuration>
[*] <!-- very important -->
[*] <property>
[*] <name>fs.default.name</name>
[*] <value>hdfs://node1.hadoop.virtual:8020</value>
[*] <!-- you should know the ip of domain,default port is 8020 -->
[*] </property>
[*] <property>
[*] <name>file.blocksize</name>
[*] <value>134217728</value>
[*] <!-- 128M,default 64M,local file -->
[*] </property>
[*] <property>
[*] <name>hadoop.tmp.dir</name>
[*] <value>/var/hadoop/tmp</value>
[*] </property>
[*] <property>
[*] <name>file.replication</name>
[*] <value>2</value>
[*] <!-- replication factor,default 1 -->
[*] </property>
[*]
[*] <!-- optional -->
[*] <property>
[*] <name>hadoop.security.authorization</name>
[*] <value>false</value>
[*] <!-- default false -->
[*] </property>
[*] <property>
[*] <name>io.file.buffer.size</name>
[*] <value>131072</value>
[*] <!-- default 4096,4KB -->
[*] </property>
[*] <property>
[*] <name>io.bytes.per.checksum</name>
[*] <value>512</value>
[*] <!-- default 512 -->
[*] </property>
[*] <property>
[*] <name>io.skip.checksum.errors</name>
[*] <value>false</value>
[*] <!-- default false -->
[*] </property>
[*] <property>
[*] <name>io.compression.codecs</name>
[*] <value></value>
[*] <!-- default empty,a list of codecs classes,separated by ","-->
[*] <!-- but HDFS Client can specify it,use native lib -->
[*] </property>
[*] <property>
[*] <name>io.seqfile.local.dir</name>
[*] <value>/data/hadoop/io/local</value>
[*] <!-- default ${hadoop.tmp.dir}/io/local -->
[*] <!-- io.seqfile.compress.blocksize -->
[*] <!-- io.seqfile.lazydecompress -->
[*] <!-- io.seqfile.sorter.recordlimit -->
[*] </property>
[*] <property>
[*] <name>fs.trash.interval</name>
[*] <value>10080</value>
[*] <!-- max time of trash file can be kept,(:minute),default 0,disabled -->
[*] </property>
[*]
[*] <!-- bloom filter -->
[*] <property>
[*] <name>io.map.index.skip</name>
[*] <value>0</value>
[*] <!-- Number of index entries to skip between each entry.
[*] Zero by default.
[*] Setting this to values larger than zero can facilitate opening large MapFiles using less memory.
[*] -->
[*] </property>
[*] <property>
[*] <name>io.map.index.interval</name>
[*] <value>128</value>
[*] <!-- MapFile consist of two files - data file (tuples) and index file (keys).
[*] For every io.map.index.interval records written in the data file,
[*] an entry (record-key, data-file-position) is written in the index file.
[*] This is to allow for doing binary search later within the index file to look up records
[*] by their keys and get their closest positions in the data file.
[*] -->
[*] </property>
[*] <property>
[*] <name>io.mapfile.bloom.size</name>
[*] <value>1048576</value>
[*] <!-- max time of trash file can be kept,(:minute),default 0,disabled -->
[*] </property>
[*] <property>
[*] <name>io.mapfile.bloom.error.rate</name>
[*] <value>0.005</value>
[*] <!-- max time of trash file can be kept,(:minute),default 0,disabled -->
[*] </property>
[*]
[*] <!-- ha,default:disabled -->
[*] <!--
[*] <property>
[*] <name>ha.zookeeper.quorum</name>
[*] <value></value>
[*] </property>
[*] -->
[*]
[*]</configuration>
5) hadoop-env.sh： hadoop启动脚本，这个脚本中包含了JVM参数调优以及全局性参数，为了便于参考，如下为全部信息。
Java代码
[*]# Set Hadoop-specific environment variables here.
[*]
[*]# The only required environment variable is JAVA_HOME. All others are
[*]# optional. When running a distributed configuration it is best to
[*]# set JAVA_HOME in this file, so that it is correctly defined on
[*]# remote nodes.
[*]
[*]# The java implementation to use.
[*]export JAVA_HOME=/opt/app/jdk1.6.0_45
[*]
[*]# The jsvc implementation to use. Jsvc is required to run secure datanodes.
[*]#export JSVC_HOME=${JSVC_HOME}
[*]
[*]export HADOOP_CONF_DIR=${HADOOP_CONF_DIR:-"/etc/hadoop"}
[*]
[*]# Extra Java CLASSPATH elements. Automatically insert capacity-scheduler.
[*]for f in $HADOOP_HOME/contrib/capacity-scheduler/*.jar; do
[*] if [ "$HADOOP_CLASSPATH" ]; then
[*] export HADOOP_CLASSPATH=$HADOOP_CLASSPATH:$f
[*] else
[*] export HADOOP_CLASSPATH=$f
[*] fi
[*]done
[*]
[*]# The maximum amount of heap to use, in MB. Default is 1000.
[*]## 1G,every damon processor,there are about 2 ~ 4 procesors,so this value should be suitable.
[*]export HADOOP_HEAPSIZE=1024
[*]export HADOOP_NAMENODE_INIT_HEAPSIZE="512"
[*]
[*]# Extra Java runtime options. Empty by default.
[*]## default for all ,will append to name_node/data_node OPTS
[*]export HADOOP_OPTS="$HADOOP_OPTS -Djava.net.preferIPv4Stack=true -XX:MaxPermSize=256M -XX:SurvivorRatio=6 -XX:+UseConcMarkSweepGC -XX:MaxTenuringThreshold=3 -XX:CMSInitiatingOccupancyFraction=70 -XX:+UseCMSCompactAtFullCollection"
[*]
[*]# Command specific options appended to HADOOP_OPTS when specified
[*]export HADOOP_NAMENODE_OPTS="-Dhadoop.security.logger=${HADOOP_SECURITY_LOGGER:-INFO,RFAS} -Dhdfs.audit.logger=${HDFS_AUDIT_LOGGER:-INFO,NullAppender} $HADOOP_NAMENODE_OPTS -Xmx2048M"
[*]
[*]export HADOOP_DATANODE_OPTS="-Dhadoop.security.logger=ERROR,RFAS $HADOOP_DATANODE_OPTS -Xmx2048M"
[*]
[*]export HADOOP_SECONDARYNAMENODE_OPTS="-Dhadoop.security.logger=${HADOOP_SECURITY_LOGGER:-INFO,RFAS} -Dhdfs.audit.logger=${HDFS_AUDIT_LOGGER:-INFO,NullAppender} $HADOOP_SECONDARYNAMENODE_OPTS -Xmx2048M"
[*]
[*]# The following applies to multiple commands (fs, dfs, fsck, distcp etc)
[*]export HADOOP_CLIENT_OPTS="-Xmx512m $HADOOP_CLIENT_OPTS"
[*]#HADOOP_JAVA_PLATFORM_OPTS="-XX:-UsePerfData $HADOOP_JAVA_PLATFORM_OPTS"
[*]
[*]# On secure datanodes, user to run the datanode as after dropping privileges
[*]export HADOOP_SECURE_DN_USER=${HADOOP_SECURE_DN_USER}
[*]
[*]# Where log files are stored. $HADOOP_HOME/logs by default.
[*]export HADOOP_LOG_DIR=/data/hadoop/logs/hadoop
[*]
[*]# Where log files are stored in the secure data environment.
[*]export HADOOP_SECURE_DN_LOG_DIR=${HADOOP_LOG_DIR}/${HADOOP_HDFS_USER}
[*]
[*]# The directory where pid files are stored. /tmp by default.
[*]# NOTE: this should be set to a directory that can only be written to by
[*]# the user that will run the hadoop daemons. Otherwise there is the
[*]# potential for a symlink attack.
[*]export HADOOP_PID_DIR=/var/hadoop/pids
[*]export HADOOP_SECURE_DN_PID_DIR=${HADOOP_PID_DIR}
[*]
[*]# A string representing this instance of hadoop. $USER by default.
[*]export HADOOP_IDENT_STRING=$USER
配置文件调整结束后，将此hadoop-2.2安装文件全部copy到其他两台机器上(node2，node3)上；然在node1上启动集群，需要注意，在启动集群之前需要在“namenode”和“secondarynamenode”上指定format指令：
Java代码
[*]#在bin目录下
[*]> ./hadoop namenode -format
然后依次运行${hadoop}/sbin目录下的“start-dfs.sh”。如果集群启动过程中，没有任何异常，可以依次在每台机器上执行“jps”指令查看基于JMX的java进程，你或许会看到如下字样：
Java代码
[*]@node1.hadoop.virtual ~]# jps
[*]19922 DataNode
[*]19757 NameNode
[*]21569 Jps
[*]
[*]@node2.hadoop.virtual hadoop]# jps
[*]31627 Jps
[*]31466 SecondaryNameNode
[*]31324 DataNode
[*]
[*]@node3.hadoop.virtual ~]# jps
[*]15913 DataNode
[*]23508 Jps
[*]16530 NodeManager
如果某台机器上，namenode、datanode等进程启动不符合预期，请到日志文件中查看原因(本实例中，日志位于：/data/hadoop/logs)
6. hbase搭建与配置
如果上述hadoop环境就绪后，我们就可以搭建hbase集群了，不过此处需要提醒，并不是hbase存储必须使用hadoop，它可以基于本地文件系统或者其他任何hbase支持远端文件系统。
我们将hbase-0.98安装文件解压到/opt/app目录下，和hadoop目录平级，便于管理。
1) hbase-core.xml：hbase核心配置
Java代码
[*]<?xml version="1.0"?>
[*]<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
[*]<configuration>
[*] <property>
[*] <name>hbase.regionserver.port</name>
[*] <value>60020</value>
[*] <!-- server instance: service port -->
[*] </property>
[*] <property>
[*] <name>hbase.hregion.max.filesize</name>
[*] <value>2147483648</value>
[*] <!-- 2G,default 10G,max region-file size before splited -->
[*] </property>
[*] <property>
[*] <name>hbase.hregion.memstore.flush.size</name>
[*] <value>134217728</value>
[*] <!-- 128M,default -->
[*] </property>
[*] <property>
[*] <name>hbase.regionserver.handler.count</name>
[*] <value>32</value>
[*] <!-- worker thread for client RPC -->
[*] <!-- sometimes,online instance can be more higher than it -->
[*] <!-- eg: 64,or 100 -->
[*] </property>
[*] <property>
[*] <name>hbase.regionserver.lease.period</name>
[*] <value>120000</value>
[*] <!-- 2min,mr -->
[*] </property>
[*] <property>
[*] <name>hbase.rootdir</name>
[*] <value>hdfs://node1.hadoop.virtual:8020/hbase</value>
[*] <!-- hdfs -->
[*] </property>
[*] <property>
[*] <name>hbase.cluster.distributed</name>
[*] <value>true</value>
[*] </property>
[*] <!--
[*] <property>
[*] <name>hbase.zookeeper.property.clientPort</name>
[*] <value>2181</value>
[*] </property>
[*] <property>
[*] <name>hbase.zookeeper.property.dataDir</name>
[*] <value>../zookeeper</value>
[*] </property>
[*] -->
[*] <property>
[*] <name>hbase.zookeeper.quorum</name>
[*] <value>node1.hadoop.virtual,node2.hadoop.virtual,node2.hadoop.virtual</value>
[*] </property>
[*] <!-- 开发者自定义的filter或者processor类，可以放置在hdfs上,这是一种推荐的方式 -->
[*] <!-- 不过本人不希望额外引入复杂度，暂且将自定义的类放置在每个实例的本地文件中,参见hbase-env.sh -->
[*]
[*] <property>
[*] <name>hbase.dynamic.jars.dir</name>
[*] <value>hdfs://node1.hadoop.virtual:8020/hbase-extlib</value>
[*] </property>
[*]
[*] <property>
[*] <name>zookeeper.session.timeout</name>
[*] <value>30000</value>
[*] <!-- tradeoff,you should find a proper value -->
[*] </property>
[*] <property>
[*] <name>zookeeper.znode.parent</name>
[*] <value>/online-hbase</value>
[*] </property>
[*]</configuration>
hadoop并不依赖于zookeeper来保存META信息，甚至hadoop本身不具有master选举的特性；不过hbase则不同，hbase将会把META信息保存在zookeeper中，以及使用zookeeper来感知reginserver的存活状态、HMaster选举等等，所以在搭建hbase之前，需要一个即备的zookeeper集群，具体zookeeper的搭建此处将不再赘言。
2) regionservers文件：配置集群中regionserver的列表，我们将会在如下三台机器上搭建hbase集群
Java代码
[*]node1.hadoop.virtual
[*]node2.hadoop.virtual
[*]node2.hadoop.virtual
3) hbase-env.sh：hbase启动脚本，其中包含了JVM参数选项，为了便于查阅，此处为全文。
Java代码
[*]# Set environment variables here.
[*]
[*]# This script sets variables multiple times over the course of starting an hbase process,
[*]# so try to keep things idempotent unless you want to take an even deeper look
[*]# into the startup scripts (bin/hbase, etc.)
[*]
[*]# The java implementation to use. Java 1.6 required.
[*]export JAVA_HOME=/opt/app/jdk1.6.0_45
[*]
[*]# Extra Java CLASSPATH elements. Optional.
[*]export HBASE_CLASSPATH="/opt/app/hbase-0.98.1/extlib/hadoop-ext-1.0.0-SNAPSHOT.jar"
[*]
[*]# The maximum amount of heap to use, in MB. Default is 1000.
[*]# export HBASE_HEAPSIZE=1000
[*]
[*]# Extra Java runtime options.
[*]# Below are what we set by default. May only work with SUN JVM.
[*]# For more on why as well as other possible settings,
[*]# see http://wiki.apache.org/hadoop/PerformanceTuning
[*]export HBASE_OPTS="-XX:+UseConcMarkSweepGC -XX:MaxPermSize=256M -XX:SurvivorRatio=6 -XX:+UseConcMarkSweepGC -XX:MaxTenuringThreshold=5 -XX:CMSInitiatingOccupancyFraction=70 -XX:+UseCMSCompactAtFullCollection"
[*]
[*]# Uncomment one of the below three options to enable java garbage collection logging for the server-side processes.
[*]
[*]# This enables basic gc logging to the .out file.
[*]# export SERVER_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps"
[*]
[*]# This enables basic gc logging to its own file.
[*]# If FILE-PATH is not replaced, the log file(.gc) would still be generated in the HBASE_LOG_DIR .
[*]# export SERVER_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:<FILE-PATH>"
[*]
[*]# This enables basic GC logging to its own file with automatic log rolling. Only applies to jdk 1.6.0_34+ and 1.7.0_2+.
[*]# If FILE-PATH is not replaced, the log file(.gc) would still be generated in the HBASE_LOG_DIR .
[*]export SERVER_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:/data/hadoop/logs/hbase/server-gc.log.$(date +%Y%m%d%H%M) -XX:+UseGCLogFileRotation -XX:NumberOfGCLogFiles=1 -XX:GCLogFileSize=512M"
[*]
[*]# Uncomment one of the below three options to enable java garbage collection logging for the client processes.
[*]
[*]# This enables basic gc logging to the .out file.
[*]# export CLIENT_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps"
[*]
[*]# This enables basic gc logging to its own file.
[*]# If FILE-PATH is not replaced, the log file(.gc) would still be generated in the HBASE_LOG_DIR .
[*]# export CLIENT_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:<FILE-PATH>"
[*]
[*]# This enables basic GC logging to its own file with automatic log rolling. Only applies to jdk 1.6.0_34+ and 1.7.0_2+.
[*]# If FILE-PATH is not replaced, the log file(.gc) would still be generated in the HBASE_LOG_DIR .
[*]export CLIENT_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:/data/hadoop/logs/hbase/client-gc.log.$(date +%Y%m%d%H%M) -XX:+UseGCLogFileRotation -XX:NumberOfGCLogFiles=1 -XX:GCLogFileSize=512M"
[*]
[*]# Uncomment below if you intend to use the EXPERIMENTAL off heap cache.
[*]# export HBASE_OPTS="$HBASE_OPTS -XX:MaxDirectMemorySize="
[*]# Set hbase.offheapcache.percentage in hbase-site.xml to a nonzero value.
[*]
[*]
[*]# Uncomment and adjust to enable JMX exporting
[*]# See jmxremote.password and jmxremote.access in $JRE_HOME/lib/management to configure remote password access.
[*]# More details at: http://java.sun.com/javase/6/docs/technotes/guides/management/agent.html
[*]#
[*]export HBASE_JMX_BASE="-Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.authenticate=false"
[*]export HBASE_MASTER_OPTS="$HBASE_MASTER_OPTS $HBASE_JMX_BASE -Dcom.sun.management.jmxremote.port=10101 -Xmx2048M -Xms2048M -XX:MaxNewSize=512M"
[*]export HBASE_REGIONSERVER_OPTS="$HBASE_REGIONSERVER_OPTS $HBASE_JMX_BASE -Dcom.sun.management.jmxremote.port=10102 -Xmx4096M -Xms4096M -XX:MaxNewSize=1024M"
[*]# export HBASE_THRIFT_OPTS="$HBASE_THRIFT_OPTS $HBASE_JMX_BASE -Dcom.sun.management.jmxremote.port=10103"
[*]# export HBASE_ZOOKEEPER_OPTS="$HBASE_ZOOKEEPER_OPTS $HBASE_JMX_BASE -Dcom.sun.management.jmxremote.port=10104"
[*]# export HBASE_REST_OPTS="$HBASE_REST_OPTS $HBASE_JMX_BASE -Dcom.sun.management.jmxremote.port=10105"
[*]
[*]# File naming hosts on which HRegionServers will run. $HBASE_HOME/conf/regionservers by default.
[*]# export HBASE_REGIONSERVERS=${HBASE_HOME}/conf/regionservers
[*]
[*]# Uncomment and adjust to keep all the Region Server pages mapped to be memory resident
[*]#HBASE_REGIONSERVER_MLOCK=true
[*]#HBASE_REGIONSERVER_UID="hbase"
[*]
[*]# File naming hosts on which backup HMaster will run. $HBASE_HOME/conf/backup-masters by default.
[*]# export HBASE_BACKUP_MASTERS=${HBASE_HOME}/conf/backup-masters
[*]
[*]# Extra ssh options. Empty by default.
[*]# export HBASE_SSH_OPTS="-o ConnectTimeout=1 -o SendEnv=HBASE_CONF_DIR"
[*]
[*]# Where log files are stored. $HBASE_HOME/logs by default.
[*]export HBASE_LOG_DIR=/data/hadoop/logs/hbase
[*]
[*]# Enable remote JDWP debugging of major HBase processes. Meant for Core Developers
[*]# export HBASE_MASTER_OPTS="$HBASE_MASTER_OPTS -Xdebug -Xrunjdwp:transport=dt_socket,server=y,suspend=n,address=8070"
[*]# export HBASE_REGIONSERVER_OPTS="$HBASE_REGIONSERVER_OPTS -Xdebug -Xrunjdwp:transport=dt_socket,server=y,suspend=n,address=8071"
[*]# export HBASE_THRIFT_OPTS="$HBASE_THRIFT_OPTS -Xdebug -Xrunjdwp:transport=dt_socket,server=y,suspend=n,address=8072"
[*]# export HBASE_ZOOKEEPER_OPTS="$HBASE_ZOOKEEPER_OPTS -Xdebug -Xrunjdwp:transport=dt_socket,server=y,suspend=n,address=8073"
[*]
[*]# A string representing this instance of hbase. $USER by default.
[*]# export HBASE_IDENT_STRING=$USER
[*]
[*]# The scheduling priority for daemon processes. See 'man nice'.
[*]# export HBASE_NICENESS=10
[*]
[*]# The directory where pid files are stored. /tmp by default.
[*]export HBASE_PID_DIR=/var/hadoop/pids
[*]
[*]# Seconds to sleep between slave commands. Unset by default. This
[*]# can be useful in large clusters, where, e.g., slave rsyncs can
[*]# otherwise arrive faster than the master can service them.
[*]# export HBASE_SLAVE_SLEEP=0.1
[*]
[*]# Tell HBase whether it should manage it's own instance of Zookeeper or not.
[*]export HBASE_MANAGES_ZK=false
[*]
[*]# The default log rolling policy is RFA, where the log file is rolled as per the size defined for the
[*]# RFA appender. Please refer to the log4j.properties file to see more details on this appender.
[*]# In case one needs to do log rolling on a date change, one should set the environment property
[*]# HBASE_ROOT_LOGGER to "<DESIRED_LOG LEVEL>,DRFA".
[*]# For example:
[*]# HBASE_ROOT_LOGGER=INFO,DRFA
[*]# The reason for changing default to RFA is to avoid the boundary case of filling out disk space as
[*]# DRFA doesn't put any cap on the log size. Please refer to HBase-5655 for more context.
到此为之，可以把调整配置之后的hbase安装包，在三台机器上部署，并在bin目录下，我们将node3作为HMaster，所以需要在node3上执行“start-hbase.sh”,此后hbase集群将会全部启动。
可以使用jps指令查看节点是否启动正常，在node3上将会看到“HMaster,HRegionServer”两个进程，其他2个node上只会看到“HRegionServer”进程。
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