Hadoop – Neohope的网络笔记

Hive环境搭建02

Posted on 2016/12/21 by neohope — No Comments ↓

1、启动Hadoop

2、启动metastore

./hive --service metastore &

3、启动hive

./hive

4、建表

show tables
create table inpatient(
PATIENT_NO String COMMENT '住院号',
NAME String COMMENT '姓名',
SEX_CODE String COMMENT '性别',
BIRTHDATE TIMESTAMP COMMENT '生日',
BALANCE_COST String COMMENT '总费用'
)
COMMENT '住院患者基本信息'
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' ESCAPED BY '\\'
STORED AS TEXTFILE

5、查看表

show tables;
describe inpatient;

6、加载数据

#从hdfs加载
./hadoop fs -put ~/Deploy/testdata/inpatient.txt /usr/hadoop/
load data inpath '/usr/hadoop/inpatient.txt' into table inpatient
#从本地加载
load data local inpath '/home/hadoop/Deploy/testdata/inpatient.txt' into table inpatient

7、那先来做一个查询，看下男女各多少人

hive> select count(*),sex_code from inpatient group by sex_code;
Query ID = hadoop_20161221104618_d530ab5e-d1c9-48c9-a5da-dff47f81786d
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
  set mapreduce.job.reduces=<number>
Starting Job = job_1482286286190_0005, Tracking URL = http://hadoop-master:8088/proxy/application_1482286286190_0005/
Kill Command = /home/hadoop/Deploy/hadoop-2.5.2/bin/hadoop job  -kill job_1482286286190_0005
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2016-12-21 10:46:39,640 Stage-1 map = 0%,  reduce = 0%
2016-12-21 10:47:08,810 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 3.39 sec
2016-12-21 10:47:33,013 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 5.5 sec
MapReduce Total cumulative CPU time: 5 seconds 500 msec
Ended Job = job_1482286286190_0005
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1  Reduce: 1   Cumulative CPU: 5.5 sec   HDFS Read: 43317170 HDFS Write: 26 SUCCESS
Total MapReduce CPU Time Spent: 5 seconds 500 msec
OK
4       "0"
60479   "F"
54928   "M"
Time taken: 77.198 seconds, Fetched: 3 row(s)

8.删除表

drop table inpatient;

9.执行dfs命令

dfs -ls /

Hadoop增删改查（Java）

Posted on 2015/10/10 by neohope — No Comments ↓

需要的jar包在hadoop里都可以找到，下面的例子中，至少需要这些jar包：

commons-cli-1.2.jar
commons-collections-3.2.1.jar
commons-configuration-1.6.jar
commons-io-2.4.jar
commons-lang-2.6.jar
commons-logging-1.1.3.jar
guava-11.0.2.jar
hadoop-auth-2.7.1.jar
hadoop-common-2.7.1.jar
hadoop-hdfs-2.7.1.jar
htrace-core-3.1.0-incubating.jar
log4j-1.2.17.jar
protobuf-java-2.5.0.jar
servlet-api.jar
slf4j-api-1.7.10.jar
slf4j-log4j12-1.7.10.jar

代码如下：

package com.neohope.hadoop.test;

import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStream;
import java.io.InputStreamReader;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;

public class HDFSTest {

	static Configuration hdfsConfig;
	static {
		hdfsConfig = new Configuration();
		hdfsConfig.addResource(new Path("etc/hadoop/core-site.xml"));
		hdfsConfig.addResource(new Path("etc/hadoop/hdfs-site.xml"));
	}

	// 创建文件夹
	public static void createDirectory(String dirPath) throws IOException {
		FileSystem fs = FileSystem.get(hdfsConfig);
		Path p = new Path(dirPath);
		try {
			fs.mkdirs(p);
		} finally {
			fs.close();
		}
	}

	// 删除文件夹
	public static void deleteDirectory(String dirPath) throws IOException {
		FileSystem fs = FileSystem.get(hdfsConfig);
		Path p = new Path(dirPath);
		try {
			fs.deleteOnExit(p);
		} finally {
			fs.close();
		}
	}

	// 重命名文件夹
	public static void renameDirectory(String oldDirPath, String newDirPath)
			throws IOException {
		renameFile(oldDirPath, newDirPath);
	}

	// 枚举文件
	public static void listFiles(String dirPath) throws IOException {
		FileSystem hdfs = FileSystem.get(hdfsConfig);
		Path listf = new Path(dirPath);
		try {
			FileStatus statuslist[] = hdfs.listStatus(listf);
			for (FileStatus status : statuslist) {
				System.out.println(status.getPath().toString());
			}
		} finally {
			hdfs.close();
		}
	}

	// 新建文件
	public static void createFile(String filePath) throws IOException {
		FileSystem fs = FileSystem.get(hdfsConfig);
		Path p = new Path(filePath);
		try {
			fs.createNewFile(p);
		} finally {
			fs.close();
		}
	}

	// 删除文件
	public static void deleteFile(String filePath) throws IOException {
		FileSystem fs = FileSystem.get(hdfsConfig);
		Path p = new Path(filePath);
		try {
			fs.deleteOnExit(p);
		} finally {
			fs.close();
		}
	}

	// 重命名文件
	public static void renameFile(String oldFilePath, String newFilePath)
			throws IOException {
		FileSystem fs = FileSystem.get(hdfsConfig);
		Path oldPath = new Path(oldFilePath);
		Path newPath = new Path(newFilePath);
		try {
			fs.rename(oldPath, newPath);
		} finally {
			fs.close();
		}
	}

	// 上传文件
	public static void putFile(String locaPath, String hdfsPath)
			throws IOException {
		FileSystem fs = FileSystem.get(hdfsConfig);
		Path src = new Path(locaPath);
		Path dst = new Path(hdfsPath);
		try {
			fs.copyFromLocalFile(src, dst);
		} finally {
			fs.close();
		}
	}

	// 取回文件
	public static void getFile(String hdfsPath, String locaPath)
			throws IOException {
		FileSystem fs = FileSystem.get(hdfsConfig);
		Path src = new Path(hdfsPath);
		Path dst = new Path(locaPath);
		try {
			fs.copyToLocalFile(false, src, dst, true);
		} finally {
			fs.close();
		}
	}

	// 读取文件
	public static void readFile(String hdfsPath) throws IOException {
		FileSystem hdfs = FileSystem.get(hdfsConfig);
		Path filePath = new Path(hdfsPath);

		InputStream in = null;
		BufferedReader buff = null;
		try {
			in = hdfs.open(filePath);
			buff = new BufferedReader(new InputStreamReader(in));
			String str = null;
			while ((str = buff.readLine()) != null) {
				System.out.println(str);
			}
		} finally {
			buff.close();
			in.close();
			hdfs.close();
		}
	}

	public static void main(String[] args) throws IOException {
		System.setProperty("HADOOP_USER_NAME", "hadoop");
		// createDirectory("hdfs://hadoop-master:9000/usr");
		// createDirectory("hdfs://hadoop-master:9000/usr/hansen");
		// createDirectory("hdfs://hadoop-master:9000/usr/hansen/test");
		// renameDirectory("hdfs://hadoop-master:9000/usr/hansen/test","hdfs://hadoop-master:9000/usr/hansen/test01");
		// createFile("hdfs://hadoop-master:9000/usr/hansen/test01/hello.txt");
		// renameFile("hdfs://hadoop-master:9000/usr/hansen/test01/hello.txt","hdfs://hadoop-master:9000/usr/hansen/test01/hello01.txt");
		// putFile("hello.txt","hdfs://hadoop-master:9000/usr/hansen/test01/hello02.txt");
		// getFile("hdfs://hadoop-master:9000/usr/hansen/test01/hello02.txt","hello02.txt");
		// readFile("hdfs://hadoop-master:9000/usr/hansen/test01/hello02.txt");
		listFiles("hdfs://hadoop-master:9000/usr/hansen/test01/");
	}

}

Hadoop Windows Native 编译说明

Posted on 2015/10/09 by neohope — No Comments ↓

1、首先，下载hadoop-2.5.2-src源码

拷贝文件夹hadoop-2.5.2-src\hadoop-common-project\hadoop-common\src\main\native
拷贝文件夹from hadoop-2.5.2-src\hadoop-common-project\hadoop-common\src\main\winutils

2、设置JAVA_HOME及PATH环境变量

3、生成javah的头文件
解压hadoop-common-2.5.1.jar，然后运行

javah org.apache.hadoop.util.NativeCrc32
javah org.apache.hadoop.io.compress.lz4.Lz4Compressor
javah org.apache.hadoop.io.compress.lz4.Lz4Decompressor
javah org.apache.hadoop.io.nativeio.NativeIO
javah org.apache.hadoop.security.JniBasedUnixGroupsMapping
javah org.apache.hadoop.security.JniBasedUnixGroupsMapping

4、打开winutils.sln，修改输出路径到../bin，编译

5、打开native.sln，修改输出路径到../bin，修改winutils.lib引用地址，编译

6、拷贝exe及dll文件到HADOOP_HOME/bin，搞定

常见问题：
1、编译的硬件平台要与java位数一致（x86，x64），否则dll无法加载
2、出问题时，先运行winutils.exe，无法运行时，要安装对应VS版本的vcredist可再发行包就好了
3、如果提示”unable to load native hadoop-library for your platform”的话，那只需要在JVM启动参数中制定native library的路径，就可以了

如果比较着急的话，可以到我的github上下载2.5.2版本的native binary：hadoop-windows-native

Hadoop环境搭建（下）

Posted on 2015/09/29 by neohope — No Comments ↓

1、新建文件夹

bin/hadoop fs -ls /
bin/hadoop fs -mkdir /usr
bin/hadoop fs -mkdir /usr/neohope
bin/hadoop fs -mkdir /usr/neohope/test

2、从本地拷贝文件到hdfs

mkdir ~/test
echo hello hadoop >> ~/test/hello.txt
bin/hadoop fs -put ~/test/hello.txt /usr/neohope/test/

3、查看远程文件

bin/hadoop fs -ls /usr/neohope/test
bin/hadoop fs -cat /usr/neohope/test/hello.txt

4、从hdfs拷贝文件到本地

bin/hadoop fs -get /usr/neohope/test/hello.txt ~/test/hello1.txt
cat ~/test/hello1.txt

5、语法说明

hadoop@hadoop-master:~/hadoop-2.7.1$ bin/hadoop fs
Usage: hadoop fs [generic options]
	[-appendToFile <localsrc> ... <dst>]
	[-cat [-ignoreCrc] <src> ...]
	[-checksum <src> ...]
	[-chgrp [-R] GROUP PATH...]
	[-chmod [-R] <MODE[,MODE]... | OCTALMODE> PATH...]
	[-chown [-R] [OWNER][:[GROUP]] PATH...]
	[-copyFromLocal [-f] [-p] [-l] <localsrc> ... <dst>]
	[-copyToLocal [-p] [-ignoreCrc] [-crc] <src> ... <localdst>]
	[-count [-q] [-h] <path> ...]
	[-cp [-f] [-p | -p[topax]] <src> ... <dst>]
	[-createSnapshot <snapshotDir> [<snapshotName>]]
	[-deleteSnapshot <snapshotDir> <snapshotName>]
	[-df [-h] [<path> ...]]
	[-du [-s] [-h] <path> ...]
	[-expunge]
	[-find <path> ... <expression> ...]
	[-get [-p] [-ignoreCrc] [-crc] <src> ... <localdst>]
	[-getfacl [-R] <path>]
	[-getfattr [-R] {-n name | -d} [-e en] <path>]
	[-getmerge [-nl] <src> <localdst>]
	[-help [cmd ...]]
	[-ls [-d] [-h] [-R] [<path> ...]]
	[-mkdir [-p] <path> ...]
	[-moveFromLocal <localsrc> ... <dst>]
	[-moveToLocal <src> <localdst>]
	[-mv <src> ... <dst>]
	[-put [-f] [-p] [-l] <localsrc> ... <dst>]
	[-renameSnapshot <snapshotDir> <oldName> <newName>]
	[-rm [-f] [-r|-R] [-skipTrash] <src> ...]
	[-rmdir [--ignore-fail-on-non-empty] <dir> ...]
	[-setfacl [-R] [{-b|-k} {-m|-x <acl_spec>} <path>]|[--set <acl_spec> <path>]]
	[-setfattr {-n name [-v value] | -x name} <path>]
	[-setrep [-R] [-w] <rep> <path> ...]
	[-stat [format] <path> ...]
	[-tail [-f] <file>]
	[-test -[defsz] <path>]
	[-text [-ignoreCrc] <src> ...]
	[-touchz <path> ...]
	[-truncate [-w] <length> <path> ...]
	[-usage [cmd ...]]

Generic options supported are
-conf <configuration file>     specify an application configuration file
-D <property=value>            use value for given property
-fs <local|namenode:port>      specify a namenode
-jt <local|resourcemanager:port>    specify a ResourceManager
-files <comma separated list of files>    specify comma separated files to be copied to the map reduce cluster
-libjars <comma separated list of jars>    specify comma separated jar files to include in the classpath.
-archives <comma separated list of archives>    specify comma separated archives to be unarchived on the compute machines.

The general command line syntax is
bin/hadoop command [genericOptions] [commandOptions]

Hadoop环境搭建（中）

Posted on 2015/09/29 by neohope — No Comments ↓

1、将hadoop解压

su hadoop
cd ~
tar -zxvf /home/neohope/Desktop/hadoop-2.7.1.tar.gz

2、修改/home/hadoop/hadoop-2.7.1/etc/hadoop/路径下配置
2.1、core-site.xml

<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<!-- Put site-specific property overrides in this file. -->

<configuration>

  <property>
    <name>fs.defaultFS</name>
    <value>hdfs://hadoop-master:9000</value>
  </property>

  <property>
    <name>fs.default.name</name>
    <value>hdfs://hadoop-master:9000</value>
  </property>

  <property>
    <name>hadoop.tmp.dir</name>
    <value>file:/home/hadoop/hadoop-2.7.1/tmp</value>
  </property>

  <property>
    <name>io.file.buffer.size</name>
    <value>131702</value>
  </property>

</configuration>

2.2、hdfs-site.xml

<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<!-- Put site-specific property overrides in this file. -->

<configuration>
  <property>
    <name>dfs.namenode.name.dir</name>
    <value>file:/home/hadoop/hadoop-2.7.1/hdfs/name</value>
  </property>


  <property>
    <name>dfs.datanode.data.dir</name>
    <value>file:/home/hadoop/hadoop-2.7.1/hdfs/data</value>
  </property>


  <property>
    <name>dfs.replication</name>
    <value>3</value>
  </property>


  <property>
    <name>dfs.namenode.secondary.http-address</name>
    <value>hadoop-master:9001</value>
  </property>


  <property>
    <name>dfs.webhdfs.enabled</name>
    <value>true</value>
  </property>

</configuration>

2.3、mapred-site.xml

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<!-- Put site-specific property overrides in this file. -->

<configuration>

  <property>
    <name>mapreduce.framework.name</name>
    <value>yarn</value>
  </property>

  <property>
    <name>mapreduce.jobhistory.address</name>
    <value>hadoop-master:10020</value>
  </property>

  <property>
    <name>mapreduce.jobhistory.webapp.address</name>
    <value>hadoop-master:19888</value>
  </property>

  <property>
    <name>mapreduce.map.memory.mb</name>
    <value>2048</value>      
  </property>

  <property>
    <name>mapreduce.reduce.memory.mb</name>
    <value>2048</value>      
  </property>

</configuration>

2.4、yarn-site.xml

<?xml version="1.0"?>

<!-- Site specific YARN configuration properties -->

<configuration>

  <property>
    <name>yarn.nodemanager.aux-services</name>
    <value>mapreduce_shuffle</value>
  </property>

  <property>
    <name>yarn.nodemanager.auxservices.mapreduce.shuffle.class</name>
    <value>org.apache.hadoop.mapred.ShuffleHandler</value>
  </property>

  <property>
    <name>yarn.resourcemanager.address</name>
    <value>hadoop-master:8032</value>
  </property>

  <property>
    <name>yarn.resourcemanager.scheduler.address</name>
    <value>hadoop-master:8030</value>
  </property>

  <property>
    <name>yarn.resourcemanager.resource-ticker.address</name>
    <value>hadoop-master:8031</value>
  </property>

  <property>
    <name>yarn.resourcemanager.admin.address</name>
    <value>hadoop-master:8033</value>
  </property>

  <property>
    <name>yarn.resourcemanager.webapp.address</name>
    <value>hadoop-master:8088</value>
  </property>

  <property>
    <name>yarn.resourcemanager.resource.memory-mb</name>
    <value>2048</value>
  </property>

  <property>
    <name>yarn.nodemanager.resource.memory-mb</name>
    <value>2048</value>
  </property>

</configuration>

2.5、slaves

#localhost
hadoop-slave01
hadoop-slave02

3、修改/home/hadoop/hadoop-2.7.1/etc/hadoop/路径下JAVA路径
3.1、hadoop-env.sh

# The java implementation to use.
#export JAVA_HOME=${JAVA_HOME}
export JAVA_HOME=/usr/java/jdk1.7.0_79

3.2、yarn-env.sh

# some Java parameters
# export JAVA_HOME=/home/y/libexec/jdk1.6.0/
if [ "$JAVA_HOME" != "" ]; then
  #echo "run java in $JAVA_HOME"
  #JAVA_HOME=$JAVA_HOME
  JAVA_HOME=/usr/java/jdk1.7.0_79
fi

4、分发hadoop文件夹到各slave

scp -r /home/hadoop/hadoop-2.7.1 hadoop@hadoop-slave01:~/
scp -r /home/hadoop/hadoop-2.7.1 hadoop@hadoop-slave02:~/

5、初始化主服务器

cd ~/hadoop-2.7.1
bin/hdfs namenode -format

6、启动hadoop

cd ~/hadoop-2.7.1
sbin/start-dfs.sh
sbin/start-yarn.sh

7、查看hadoop进程信息

/usr/java/jdk1.7.0_79/bin/jps

8、查看cluster信息

http://10.10.10.3:8088

9、查看hdfs文件系统信息

http://10.10.10.3:50070

10、Hadoop常用端口如下

端口	作用
9000	fs.defaultFS
9001	dfs.namenode.rpc-address口
50070	dfs.namenode.http-address
50470	dfs.namenode.https-address
50100	dfs.namenode.backup.address
50105	dfs.namenode.backup.http-address
50090	dfs.namenode.secondary.http-address
50091	dfs.namenode.secondary.https-address
50020	dfs.datanode.ipc.address
50075	dfs.datanode.http.address
50475	dfs.datanode.https.address
50010	dfs.datanode.address
8480	dfs.journalnode.rpc-address
8481	dfs.journalnode.https-address
8032	yarn.resourcemanager.address
8088	yarn.resourcemanager.webapp.address
8090	yarn.resourcemanager.webapp.https.address
8030	yarn.resourcemanager.scheduler.address
8031	yarn.resourcemanager.resource-tracker.address
8033	yarn.resourcemanager.admin.address
8042	yarn.nodemanager.webapp.address
8040	yarn.nodemanager.localizer.address
8188	yarn.timeline-service.webapp.address
10020	mapreduce.jobhistory.address
19888	mapreduce.jobhistory.webapp.address
2888	ZooKeeper，Leader用来监听Follower的连接
3888	ZooKeeper，用于Leader选举
2181	ZooKeeper，用来监听客户端的连接
60010	hbase.master.info.port
60000	hbase.master.port
60030	hbase.regionserver.info.port
60020	hbase.regionserver.port
8080	hbase.rest.port
10000	hive.server2.thrift.port
9083	hive.metastore.uris

Hadoop环境搭建（上）

Posted on 2015/09/29 by neohope — No Comments ↓

1、环境准备

VMWare8
Debian6
JDK7
Hadoop2.7.1（这个是第一次部署时用的版本）
*如果要看后续文章，建议使用（hadoop-2.5.2  hbase-1.1.2  hive-1.2.1  spark-2.0.0）

2、安装虚拟机，安装VMTools

su
apt-get install gcc
apt-get install linux-headers-$(uname -r)
apt-get install build-essential
./vmware-install.pl

设置共享目录，将需要的文件拷贝到虚拟机。
当然也可以设置好虚拟的的ssh后，用scp或winscp将文件拷贝到虚拟机。

3.网络配置为NAT，网卡设置为dhcp
修改配置文件/etc/network/interfaces

auto lo
iface lo inet loopback

auto eth0
iface eth0 inet dhcp

根据需要（一般用不到），修改配置文件/etc/resolv.conf

nameserver xxx.xxx.xxx.xxx

重启网卡

su
ifconfig eth0 down
ifconfig eth0 up

3、安装hadoop需要的软件

su
apt-get install openssh-server
apt-get install ssh
apt-get install rsync
mkdir /usr/java
cd /usr/java
tar -zxvf /home/neohope/Desktop/jdk-7u79-linux-x64.gz

4、新建用户hadoop

sudo adduser hadoop -home /home/hadoop

5、设置环境变量
修改文件/etc/profile，增加下面的内容

export JAVA_HOME=/usr/java/jdk1.7.0_79
export JRE_HOME=$JAVA_HOME/jre
export CLASSPATH=.:$JAVA_HOME/lib:$JRE_HOME/lib:$CLASSPATH
export PATH=$JAVA_HOME/bin:$PATH

export HADOOP_HOME=/home/hadoop/hadoop-2.7.1
export PATH=$PATH:$HADOOP_HOME/lib

测试一下

source /etc/profile
cd $JAVA_HOME
echo $HADOOP_HOME

5、切换到用户hadoop，并实现ssh免密码认证
5.1RSA方式

#Ubuntu16.04以后，默认不支持dsa
#rsa证书生成
su hadoop
ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa
cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
ssh localhost

5.2DSA方式

#Ubuntu16.04以后，默认不支持dsa，要手工开启dsa支持
#编辑文件
sudo vi /etc/ssh/sshd_config
#添加下面几行
PermitRootLogin no
PermitEmptyPasswords no
PasswordAuthentication yes
PubkeyAuthentication yes
ChallengeResponseAuthentication no
PubkeyAcceptedKeyTypes=+ssh-dss

#重启sshd
systemctl reload sshd

#dsa证书生成
su hadoop
ssh-keygen -t dsa -P '' -f ~/.ssh/id_dsa
cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys
ssh localhost

6、拷贝两份虚拟机，设置这三个虚拟机的名称及固定ip（NAT），修改后配置为

机器名	IP
hadoop-master	10.10.10.3
hadoop-slave01	10.10.10.4
hadoop-slave02	10.10.10.5
主机	10.10.10.1
网关	10.10.10.2

以hadoop-master为例，其配置为

#/etc/network/interfaes
auto lo
iface lo inet loopback

auto eth0
iface eth0 inet static
address 10.10.10.3
netmask 255.0.0.0
gateway 10.10.10.2
#dns-nameservers 114.114.114.114

#/etc/hosts
127.0.0.1	localhost
10.10.10.3	hadoop-master
10.10.10.4	hadoop-slave01
10.10.10.5	hadoop-slave02

#/etc/hostname
hadoop-master

7、三台虚拟机都开启后，就可以相互用ssh直接访问了

su hadoop
ssh hadoop-master
ssh hadoop-slave01
ssh hadoop-slave02

8、Hadoop要求的ssh免密码登录范围(IP和主机名)：
1) NameNode能免密码登录所有的DataNode
2) SecondaryNameNode能免密码登录所有的DataNode
3) NameNode能免密码登录自己
4) SecondaryNameNode能免密码登录自己
5) NameNode能免密码登录SecondaryNameNode
6) SecondaryNameNode能免密码登录NameNode
7) DataNode能免密码登录自己
8) DataNode不需要配置免密码登录NameNode、SecondaryNameNode和其它DataNode

Tag Archives: Hadoop

Hive环境搭建02

Hive环境搭建01

Hadoop增删改查（Java）

Hadoop Linux Native 编译说明

Hadoop Windows Native 编译说明

Hadoop环境搭建（下）

Hadoop环境搭建（中）

Hadoop环境搭建（上）