大数据学习14之实战环境搭建

文章目录

1.JDK安装
2.Scala安装
- 1.下载
- 2.解压
- 3.配置系统环境变量
- 4.检查是否安装成功
3.Maven安装
- 1.下载
- 2.解压
- 3.配置系统环境变量
- 4.检查是否安装成功
- 5.修改其conf目录下的settings.xml配置文件
4.Hadoop安装
- 1.下载
- 2.解压
- 3.配置ssh
- 4.修改配置文件
- - 1.修改hadoop-env.sh文件中，修改其中的配置JAVA环境变量，初始值默认是${JAVA_HOME}，我们需要把它改成具体的jdk所在的目录。
  - 2.修改core-site.xml文件，如果没有/home/hadoop/app/tmp文件夹，需要先创建，第一个property配置的是HDFS的NameNode的地址（主机名：端口号），第二个property配置的内容用来指定Hadoop运行时产生的文件的存放目录
  - 3.修改hdfs-site.xml文件，该文件是Hadoop的底层存储配置文件，可以配置namenode存储hdfs名字的空间的元数据文件目录，datanode上的一个数据块的物理的存储位置文件目录，用来指定HDFS保存数据副本的数量（现在是伪分布式，所以数量是1，将来的集群副本数量默认是3）等。
  - 4.修改slaves文件，，里面写上从节点所在的主机名字
  - 5.格式化namenode
- 5.配置系统环境变量
- 6.启动
- 7.检查是否安装成功
- 8.搭建yarn
- - 1. 修改mapred-site.xml文件
  - 2.修改 yarn-site.xml文件
  - 3.启动yarn
- 9 .检查hadoop是否安装成功
- 10. 检查yarn是否安装成功
5.Zookeeper安装
6.Hbase安装
- 1.下载hbase到/home/hadoop/software目录下
- 2.解压
- 3.配置系统环境变量
- 4.修改配置文件
- - 1.修改hbase-env.sh文件
  - 2.修改hbase-site.xml文件
  - 3. 修改regionservers文件
- 5.启动
- - 6.检查是否启动成功
7.Spark安装
- 1.下载源码
- 2.编译
2.解压
- 3.配置系统环境变量
- 4.检查安装是否成功
8.IDEA+Maven+Spark Streaming
- 8.1 pom.xml中添加对应的依赖

1.JDK安装

略

2.Scala安装 1.下载

官网->Download->Or are you looking for previous releases of Scala?->scala-2.11.8.tgz->存放在/home/hadoop/software目录中

2.解压

tar -zxvf scala-2.11.8.tgz -C ~/app/

3.配置系统环境变量

 vi ~/.bash_profile

添加

export SCALA_HOME=/home/hadoop/app/scala-2.11.8
export PATH=$SCALA_HOME/bin:$PATH

使其生效

source ~/.bash_profile

4.检查是否安装成功

[hadoop@hadoop000 scala-2.11.8]$ scala
Welcome to Scala 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_144).
Type in expressions for evaluation. Or try :help.

scala> 1+1
res0: Int = 2

3.Maven安装 1.下载

maven官网->Download->Previous Releases->archives->3.3.9->binaries->
apache-maven-3.3.9-bin.tar.gz或复制apache-maven-3.3.9-bin.zip 的路径wget下载到/home/hadoop/software目录下

2.解压

tar -zxvf apache-maven-3.3.9-bin.tar.gz -C ~/app/

3.配置系统环境变量

 vi ~/.bash_profile

添加

export MAVEN_HOME=/home/hadoop/app/apache-maven-3.3.9
export PATH=$MAVEN_HOME/bin:$PATH

使其生效

source ~/.bash_profile

4.检查是否安装成功

[hadoop@hadoop000 scala-2.11.8]$ mvn -v
Apache Maven 3.3.9 (bb52d8502b132ec0a5a3f4c09453c07478323dc5; 2015-11-11T00:41:47+08:00)
Maven home: /home/hadoop/app/apache-maven-3.3.9
Java version: 1.8.0_144, vendor: Oracle Corporation
Java home: /home/hadoop/app/jdk1.8.0_144/jre
Default locale: en_US, platform encoding: UTF-8
OS name: "linux", version: "2.6.32-358.el6.x86_64", arch: "amd64", family: "unix"

5.修改其conf目录下的settings.xml配置文件

在/home/hadoop下mkdir新建maven_repos文件夹
修改settings.xml中的localRepository

  /home/hadoop/maven_repos/

4.Hadoop安装 1.下载

http://archive.apache.org/dist/
hadoop-2.6.0-cdh5.7.0.tar.gz到/home/hadoop/software目录下

2.解压

tar -zxvf hadoop-2.6.0-cdh5.7.0.tar.gz -C ~/app/

3.配置ssh

/home/hadoop目录下，

ssh-keygen  -t rsa

ll -a可以查看到.ssh文件夹，里面有id_rsa 和id_rsa.pub两个文件

cp ~/.ssh/id_rsa.pub ~/.ssh/authorized_keys

4.修改配置文件

/home/hadoop/app/hadoop-2.6.0-cdh5.7.0/etc/hadoop文件夹下，

1.修改hadoop-env.sh文件中，修改其中的配置JAVA环境变量，初始值默认是${JAVA_HOME}，我们需要把它改成具体的jdk所在的目录。

export JAVA_HOME=/home/hadoop/app/jdk1.8.0_144

2.修改core-site.xml文件，如果没有/home/hadoop/app/tmp文件夹，需要先创建，第一个property配置的是HDFS的NameNode的地址（主机名：端口号），第二个property配置的内容用来指定Hadoop运行时产生的文件的存放目录


	
		fs.defaultFS
		hdfs://hadoop000:8020
	

	
		hadoop.tmp.dir
		/home/hadoop/app/tmp

3.修改hdfs-site.xml文件，该文件是Hadoop的底层存储配置文件，可以配置namenode存储hdfs名字的空间的元数据文件目录，datanode上的一个数据块的物理的存储位置文件目录，用来指定HDFS保存数据副本的数量（现在是伪分布式，所以数量是1，将来的集群副本数量默认是3）等。


	
		dfs.replication
		1

4.修改slaves文件，，里面写上从节点所在的主机名字

hadoop000

5.格式化namenode

进入目录/home/hadoop/app/hadoop-2.6.0-cdh5.7.0/bin下，执行

 ./hdfs namenode -format

5.配置系统环境变量

 vi ~/.bash_profile

添加

export HADOOP_HOME=/home/hadoop/app/hadoop-2.6.0-cdh5.7.0
export PATH=$HADOOP_HOME/bin:$PATH

使其生效

source ~/.bash_profile

6.启动

进入/home/hadoop/app/hadoop-2.6.0-cdh5.7.0/sbin文件夹下，执行

./start-dfs.sh

启动失败

[hadoop@hadoop000 sbin]$ ./start-dfs.sh
21/06/14 21:30:06 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Starting namenodes on [hadoop000]
The authenticity of host 'hadoop000 (192.168.121.131)' can't be established.
RSA key fingerprint is de:44:de:ee:0e:02:9c:2b:73:99:94:2c:af:4a:8a:ad.
Are you sure you want to continue connecting (yes/no)? y
Please type 'yes' or 'no': yes
hadoop000: Warning: Permanently added 'hadoop000,192.168.121.131' (RSA) to the list of known hosts.
hadoop@hadoop000's password: hadoop000: Agent admitted failure to sign using the key.

hadoop000: starting namenode, logging to /home/hadoop/app/hadoop-2.6.0-cdh5.7.0/logs/hadoop-hadoop-namenode-hadoop000.out
hadoop@hadoop000's password: hadoop000: Agent admitted failure to sign using the key.

hadoop000: Connection closed by UNKNOWN
Starting secondary namenodes [0.0.0.0]
The authenticity of host '0.0.0.0 (0.0.0.0)' can't be established.
RSA key fingerprint is de:44:de:ee:0e:02:9c:2b:73:99:94:2c:af:4a:8a:ad.
Are you sure you want to continue connecting (yes/no)? yes
0.0.0.0: Warning: Permanently added '0.0.0.0' (RSA) to the list of known hosts.
hadoop@0.0.0.0's password: 0.0.0.0: Agent admitted failure to sign using the key.

hadoop@0.0.0.0's password: 0.0.0.0: Permission denied, please try again.

0.0.0.0: Permission denied (publickey,gssapi-keyex,gssapi-with-mic,password).
21/06/14 21:33:56 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
[hadoop@hadoop000 sbin]$ jps
11140 Jps
10874 NameNode
[hadoop@hadoop000 sbin]$ ssh hadoop000
Agent admitted failure to sign using the key.
hadoop@hadoop000's password:
Last login: Sun Apr 4 17:14:46 2021 from 192.168.107.2

由此可知，ssh仍需要输入密码登录，未配置成功

进入.ssh文件夹，

[hadoop@hadoop000 .ssh]$ ssh-add
Identity added: /home/hadoop/.ssh/id_rsa (/home/hadoop/.ssh/id_rsa)
[hadoop@hadoop000 .ssh]$ ssh hadoop000
Last login: Mon Jun 14 21:45:37 2021 from hadoop000

成功

[hadoop@hadoop000 sbin]$ ./start-dfs.sh
21/06/14 21:56:37 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Starting namenodes on [hadoop000]
hadoop000: starting namenode, logging to /home/hadoop/app/hadoop-2.6.0-cdh5.7.0/logs/hadoop-hadoop-namenode-hadoop000.out
hadoop000: starting datanode, logging to /home/hadoop/app/hadoop-2.6.0-cdh5.7.0/logs/hadoop-hadoop-datanode-hadoop000.out
Starting secondary namenodes [0.0.0.0]
0.0.0.0: starting secondarynamenode, logging to /home/hadoop/app/hadoop-2.6.0-cdh5.7.0/logs/hadoop-hadoop-secondarynamenode-hadoop000.out
21/06/14 21:56:53 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
[hadoop@hadoop000 sbin]$ jps
12065 NameNode
12197 DataNode
12373 SecondaryNameNode
12520 Jps

我还出现./start-dfs.sh之后，使用jps查看进程没有DataNode进程的情况。有可能是我多次hadoop namenode -format格式化namenode，namenode format清空了namenode下的数据,但是没有清空datanode下的数据，导致namenode和datanode的clusterID不一致。
因为当我们使用hadoop namenode -format格式化namenode时，会在namenode数据文件夹（这个文件夹为自己配置文件中dfs.name.dir的路径）中保存一个current/VERSION文件，记录clusterID，datanode中保存的current/VERSION文件中的clustreID的值是第一次格式化保存的clusterID。
所以我选择了修改/home/hadoop/app/tmp/dfs/name/current文件夹下的VERSION中的clusterID的值，使其与/home/hadoop/app/tmp/dfs/data/current文件夹下的VERSION中的clusterID的值一致。
参考链接

7.检查是否安装成功

[hadoop@hadoop000 sbin]$ jps
12065 NameNode
12197 DataNode
12373 SecondaryNameNode
12520 Jps

虚拟机浏览器访问，http://hadoop000:50070/

并且有一个存活的节点

8.搭建yarn

/home/hadoop/app/hadoop-2.6.0-cdh5.7.0/etc/hadoop目录下

1. 修改mapred-site.xml文件

cp mapred-site.xml.template mapred-site.xml


	
    	mapreduce.framework.name
        yarn

2.修改 yarn-site.xml文件



	
	
    	yarn.nodemanager.aux-services
        mapreduce_shuffle

3.启动yarn

/home/hadoop/app/hadoop-2.6.0-cdh5.7.0/sbin目录下

 ./start-yarn.sh

此时多了NodeManager和ResourceManager两个进程

[hadoop@hadoop000 sbin]$ ./start-yarn.sh 
starting yarn daemons
starting resourcemanager, logging to /home/hadoop/app/hadoop-2.6.0-cdh5.7.0/logs/yarn-hadoop-resourcemanager-hadoop000.out
hadoop000: starting nodemanager, logging to /home/hadoop/app/hadoop-2.6.0-cdh5.7.0/logs/yarn-hadoop-nodemanager-hadoop000.out
[hadoop@hadoop000 sbin]$ jps
4292 ResourceManager
3960 SecondaryNameNode
3757 DataNode
4398 NodeManager
3647 NameNode
4447 Jps

访问http://hadoop000:8088/cluster

点一下Active Nodes才会出现Nodes列表

9 .检查hadoop是否安装成功

hadoop fs -ls / 查看的是HDFS文件系统的根目录

[hadoop@hadoop000 sbin]$ hadoop fs -ls /
21/06/20 18:26:26 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
[hadoop@hadoop000 sbin]$ hadoop fs -mkdir /data
21/06/20 18:31:28 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
[hadoop@hadoop000 sbin]$ hadoop fs -ls /
21/06/20 18:31:51 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Found 1 items
drwxr-xr-x - hadoop supergroup 0 2021-06-20 18:31 /data
[hadoop@hadoop000 sbin]$ hadoop fs -ls /data
21/06/20 18:33:26 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
[hadoop@hadoop000 sbin]$ hadoop fs -put mr-jobhistory-daemon.sh /data
21/06/20 18:34:02 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
[hadoop@hadoop000 sbin]$ hadoop fs -ls /data
21/06/20 18:34:08 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Found 1 items
-rw-r--r-- 1 hadoop supergroup 4080 2021-06-20 18:34 /data/mr-jobhistory-daemon.sh
[hadoop@hadoop000 sbin]$ hadoop fs -text /data/mr-jobhistory-daemon.sh
21/06/20 18:34:40 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
#!/usr/bin/env bash

# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#...

10. 检查yarn是否安装成功

进入/home/hadoop/app/hadoop-2.6.0-cdh5.7.0/share/hadoop/mapreduce目录下，

hadoop jar hadoop-mapreduce-examples-2.6.0-cdh5.7.0.jar pi 2 3

运行结果为

[hadoop@hadoop000 mapreduce]$ hadoop jar hadoop-mapreduce-examples-2.6.0-cdh5.7.0.jar pi 2 3
Number of Maps  = 2
Samples per Map = 3
21/06/20 18:40:08 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Wrote input for Map #0
Wrote input for Map #1
Starting Job
21/06/20 18:40:10 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
21/06/20 18:40:12 INFO input.FileInputFormat: Total input paths to process : 2
21/06/20 18:40:12 INFO mapreduce.JobSubmitter: number of splits:2
21/06/20 18:40:13 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1624184518582_0001
21/06/20 18:40:14 INFO impl.YarnClientImpl: Submitted application application_1624184518582_0001
21/06/20 18:40:14 INFO mapreduce.Job: The url to track the job: http://hadoop000:8088/proxy/application_1624184518582_0001/
21/06/20 18:40:14 INFO mapreduce.Job: Running job: job_1624184518582_0001
21/06/20 18:40:32 INFO mapreduce.Job: Job job_1624184518582_0001 running in uber mode : false
21/06/20 18:40:32 INFO mapreduce.Job:  map 0% reduce 0%
21/06/20 18:40:44 INFO mapreduce.Job:  map 50% reduce 0%
21/06/20 18:40:45 INFO mapreduce.Job:  map 100% reduce 0%
21/06/20 18:40:54 INFO mapreduce.Job:  map 100% reduce 100%
21/06/20 18:40:56 INFO mapreduce.Job: Job job_1624184518582_0001 completed successfully
21/06/20 18:40:56 INFO mapreduce.Job: Counters: 49
	File System Counters
		FILE: Number of bytes read=50
		FILE: Number of bytes written=335406
		FILE: Number of read operations=0
		FILE: Number of large read operations=0
		FILE: Number of write operations=0
		HDFS: Number of bytes read=532
		HDFS: Number of bytes written=215
		HDFS: Number of read operations=11
		HDFS: Number of large read operations=0
		HDFS: Number of write operations=3
	Job Counters 
		Launched map tasks=2
		Launched reduce tasks=1
		Data-local map tasks=2
		Total time spent by all maps in occupied slots (ms)=21358
		Total time spent by all reduces in occupied slots (ms)=7753
		Total time spent by all map tasks (ms)=21358
		Total time spent by all reduce tasks (ms)=7753
		Total vcore-seconds taken by all map tasks=21358
		Total vcore-seconds taken by all reduce tasks=7753
		Total megabyte-seconds taken by all map tasks=21870592
		Total megabyte-seconds taken by all reduce tasks=7939072
	Map-Reduce framework
		Map input records=2
		Map output records=4
		Map output bytes=36
		Map output materialized bytes=56
		Input split bytes=296
		Combine input records=0
		Combine output records=0
		Reduce input groups=2
		Reduce shuffle bytes=56
		Reduce input records=4
		Reduce output records=0
		Spilled Records=8
		Shuffled Maps =2
		Failed Shuffles=0
		Merged Map outputs=2
		GC time elapsed (ms)=1000
		CPU time spent (ms)=8100
		Physical memory (bytes) snapshot=741052416
		Virtual memory (bytes) snapshot=8293314560
		Total committed heap usage (bytes)=740294656
	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=236
	File Output Format Counters 
		Bytes Written=97
Job Finished in 45.464 seconds
Estimated value of Pi is 4.00000000000000000000

5.Zookeeper安装

略

6.Hbase安装 1.下载hbase到/home/hadoop/software目录下

hbase-1.2.0-cdh5.7.0.tar.gz

2.解压

tar -zxvf hbase-1.2.0-cdh5.7.0.tar.gz -C ~/app/

3.配置系统环境变量

 vi ~/.bash_profile

添加

export Hbase_HOME=/home/hadoop/app/hbase-1.2.0-cdh5.7.0
export PATH=$Hbase_HOME/bin:$PATH

使其生效

source ~/.bash_profile

检查一下是否生效

[hadoop@hadoop000 software]$ echo $Hbase_HOME
/home/hadoop/app/hbase-1.2.0-cdh5.7.0

4.修改配置文件

进入 /home/hadoop/app/hbase-1.2.0-cdh5.7.0/conf目录下，

1.修改hbase-env.sh文件

export JAVA_HOME=/home/hadoop/app/jdk1.8.0_144

export Hbase_MANAGES_ZK=false

2.修改hbase-site.xml文件

hbase.rootdir
这个目录是region server的共享目录，用来持久化Hbase。默认情况下Hbase是写到/tmp的。不改这个配置，数据会在重启的时候丢失。与hadoop中的core-site.xml中的配置一致。
hbase.cluster.distributed
Hbase的运行模式。false是单机模式，true是分布式模式。若为false,Hbase和Zookeeper会运行在同一个JVM里面。
hbase.zookeeper.quorum
zookeeper集群的URL配置，多个host中间用逗号（,）分割


	
		hbase.rootdir
		hdfs://hadoop000:8020/hbase
	
	
	
		hbase.cluster.distributed
		true
	
	
	
		hbase.zookeeper.quorum
		hadoop000:2181

3. 修改regionservers文件

配置regionservers这个文件的作用与hadoop中的slaves类似，用来告诉hbase集群哪些是工作节点。

hadoop000

5.启动

先启动zookeeper

zkServer.sh start

可以看到进程QuorumPeerMain

[hadoop@hadoop000 conf]$ jps
4292 ResourceManager
7829 QuorumPeerMain
3960 SecondaryNameNode
7852 Jps
3757 DataNode
4398 NodeManager
3647 NameNode

然后启动hbase
进入/home/hadoop/app/hbase-1.2.0-cdh5.7.0/bin目录下，

./start-hbase.sh

可以看到出现了HMaster和HRegionServer进程

[hadoop@hadoop000 bin]$ ./start-hbase.sh
starting master, logging to /home/hadoop/app/hbase-1.2.0-cdh5.7.0/logs/hbase-hadoop-master-hadoop000.out
Java HotSpot(TM) 64-Bit Server VM warning: ignoring option PermSize=128m; support was removed in 8.0
Java HotSpot(TM) 64-Bit Server VM warning: ignoring option MaxPermSize=128m; support was removed in 8.0
hadoop000: starting regionserver, logging to /home/hadoop/app/hbase-1.2.0-cdh5.7.0/bin/../logs/hbase-hadoop-regionserver-hadoop000.out
hadoop000: Java HotSpot(TM) 64-Bit Server VM warning: ignoring option PermSize=128m; support was removed in 8.0
hadoop000: Java HotSpot(TM) 64-Bit Server VM warning: ignoring option MaxPermSize=128m; support was removed in 8.0
[hadoop@hadoop000 bin]$ jps
8643 Jps
4292 ResourceManager
7829 QuorumPeerMain
8263 HRegionServer
3960 SecondaryNameNode
8105 HMaster
3757 DataNode
4398 NodeManager
3647 NameNode

6.检查是否启动成功

除了上述的两个进程，还可以通过访问http://hadoop000:60010来验证

执行脚本来测试一下
/home/hadoop/app/hbase-1.2.0-cdh5.7.0/bin下执行，

 ./hbase shell

[hadoop@hadoop000 bin]$ ./hbase shell
2021-06-20 21:08:39,715 INFO  [main] Configuration.deprecation: hadoop.native.lib is deprecated. Instead, use io.native.lib.available
2021-06-20 21:08:41,324 WARN  [main] util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/hadoop/app/hbase-1.2.0-cdh5.7.0/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/hadoop/app/hadoop-2.6.0-cdh5.7.0/share/hadoop/common/lib/slf4j-log4j12-1.7.5.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' for list of supported commands.
Type "exit" to leave the Hbase Shell
Version 1.2.0-cdh5.7.0, rUnknown, Wed Mar 23 11:46:29 PDT 2016

hbase(main):001:0> version
1.2.0-cdh5.7.0, rUnknown, Wed Mar 23 11:46:29 PDT 2016

hbase(main):002:0> status
1 active master, 0 backup masters, 1 servers, 0 dead, 2.0000 average load

hbase(main):003:0> list
TABLE                                                                                                                                                                                                   
0 row(s) in 0.0400 seconds

=> []
hbase(main):001:0> create 'member','info','address'
0 row(s) in 1.7180 seconds

=> Hbase::Table - member
hbase(main):002:0> list
TABLE                                                                                                                                                                                                   
member                                                                                                                                                                                                  
1 row(s) in 0.0280 seconds

=> ["member"]
hbase(main):003:0> describe 'member'
Table member is ENABLED                                                                                                                                                                                 
member                                                                                                                                                                                                  
COLUMN FAMILIES DEscriptION                                                                                                                                                                             
{NAME => 'address', BLOOMFILTER => 'ROW', VERSIONS => '1', IN_MEMORY => 'false', KEEP_DELETED_CELLS => 'FALSE', DATA_BLOCK_ENCODING => 'NONE', TTL => 'FOREVER', COMPRESSION => 'NONE', MIN_VERSIONS => 
'0', BLOCKCACHE => 'true', BLOCKSIZE => '65536', REPLICATION_SCOPE => '0'}                                                                                                                              
{NAME => 'info', BLOOMFILTER => 'ROW', VERSIONS => '1', IN_MEMORY => 'false', KEEP_DELETED_CELLS => 'FALSE', DATA_BLOCK_ENCODING => 'NONE', TTL => 'FOREVER', COMPRESSION => 'NONE', MIN_VERSIONS => '0'
, BLOCKCACHE => 'true', BLOCKSIZE => '65536', REPLICATION_SCOPE => '0'}                                                                                                                                 
2 row(s) in 0.1090 seconds

其中遇到问题：
使用hbase shell的命令list查看的时候显示为空[]，但是在创建表的时候出现ERROR: Table already exists错误
参考：
https://blog.csdn.net/huashao0602/article/details/77050929
https://www.jianshu.com/p/e1767d57f972?utm_campaign=maleskine&utm_content=note&utm_medium=seo_notes&utm_source=recommendation

原因是以前创建过这个表，但是hbase暴力删除了这个表后，zookeeper还保留了这个表的信息。
（1）通过./hbase zkcli命令进入zookeeper client模式
遇到错误：

2021-06-20 21:31:25,649 INFO  [main-SendThread(hadoop000:2181)] zookeeper.ClientCnxn: Session establishment complete on server hadoop000/192.168.121.131:2181, sessionid = 0x17a297cb8020008, negotiated timeout = 30000
JLine support is enabled

WATCHER::

WatchedEvent state:SyncConnected type:None path:null
[ERROR] Terminal initialization failed; falling back to unsupported
java.lang.IncompatibleClassChangeError: Found class jline.Terminal, but interface was expected
	at jline.TerminalFactory.create(TerminalFactory.java:101)
	at jline.TerminalFactory.get(TerminalFactory.java:159)
	at jline.console.ConsoleReader.(ConsoleReader.java:227)
	at jline.console.ConsoleReader.(ConsoleReader.java:219)
	at jline.console.ConsoleReader.(ConsoleReader.java:207)
	at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
	at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
	at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
	at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
	at org.apache.zookeeper.ZooKeeperMain.run(ZooKeeperMain.java:311)
	at org.apache.zookeeper.ZooKeeperMain.main(ZooKeeperMain.java:282)
	at org.apache.hadoop.hbase.zookeeper.ZooKeeperMainServer.main(ZooKeeperMainServer.java:108)

JLine support is disabled

解决办法：
vim bin/hbase文件，添加信息如下代码框部分：

elif [ “$COMMAND” = “zkcli” ] ; then
CLASS=“org.apache.hadoop.hbase.zookeeper.ZooKeeperMainServer”

  CLASSPATH=`echo $CLASSPATH | sed 's/jruby-cloudera-1.0.0.jar//g'`

之后重启hbase伪分布环境，之后再次进入zk的客户端不报错了

（2）ls /hbase/table查看存在的表信息

[zk: hadoop000:2181(CONNECTED) 0] ls /hbase/table
[hbase:meta, hbase:namespace, imooc_course_search_clickcount, member, course_clickcount, imooc_course_clickcount, course_search_clickcount]

（3）rmr /hbase/table/表名删除zombie table
（4）重启Hbase，这步我没有

7.Spark安装 1.下载源码

spark官网-download-历史版本spark-2.2.0.tgz ，但是要是源码，选最后一个
因为如果下载的是安装包的话，可能跟生产上有各种冲突，下载源码，根据生产上的hadoop的版本来编译出符合生产上hadoop使用的spack的版本

如何编译可以查看documentation-latest release-导航栏more-building spark
编译这步我没做，直接用的编译好了的

2.编译

spark官网，documentation->Lastest Release->More->Building Spark
在source文件夹下，解压源码

tar -xzvf spark-2.1.0.tgz

cd spark-2.1.0

注意这块的要求，不同版本的是不一样的，有maven和java等的要求。详见官网文档。
以3.2.0举例
设置maven的内存

export MAVEN_OPTS="-Xss64m -Xmx2g -XX:ReservedCodeCacheSize=1g"

继续cd到build目录下，可以看到内置的maven
我们不使用这个，自己安个maven
回到源码根目录spark-2.1.0下
mvn编译命令，指定 Hadoop 版本并启用 YARN，使用 Hive 和 JDBC 支持（ -Phive -Phive-thriftserver ）,可以通过阅读该目录下的pom.xml进行了解

如果你想使用Hadoop 2.x构建，请启用hadoop-2.7配置文件：

./build/mvn -Phadoop-2.7 -Pyarn -Dhadoop.version=2.8.5   -Phive -Phive-thriftserver   -DskipTests clean package

这条命令可能会耗费一两个小时，因为第一次运行需要下载很多资源
我们可以对其中的参数从外部进行调整，如以上命令，这就是-D代表的意思

需要用到哪个profile就在外部命令中将选择内部启用的id，这就是-P代表的意思，需要hive的话，就需要将id：hive-thriftserver通过-P加入进来

echo $HADOOP_HOME

可以看到hadoop的版本，如2.6.0-cdh5.7.0
可能会遇到HDFS和yarn版本不一样的情况，这种情况是因为在生产环境，我们的HDFS的版本比较老，在使用spark的新特性的情况下没法支持，但是我们又不敢对整个hadoop集群进行升级，因为这个升级不可预测的因素太多。那么我们可以将HDFS稍微升一个版本。这就会导致HDFS和yarn版本不一致。默认情况下，yarn.version使用的是hadoop.version，但是我们可以将两个版本通过-D分别指定。

当支持其他版本的 Scala（如 2.13）时，就需要更改 Scala 版本，可以为该版本构建。使用以下方法更改主要的 Scala 版本（例如 2.13）：

./dev/change-scala-version.sh 2.13

启用配置文件（例如 2.13）：

# For Maven
./build/mvn -Pscala-2.13 compile

maven编译出来，并没有一个压缩包供我们部署，我们可以构建一个可运行的包
推荐使用：构建可运行的发行包的方式进行编译
这是两个编译方式，之前的mvn编译，和下面说的make-distribution编译。
若要创建类似于"Spark 下载"页面分发的 Spark 分发版，并且该分发的布局使其可运行，请在项目根目录中使用。它可以像直接Maven构建一样配置Maven配置文件设置等。例：./dev/make-distribution.sh

./dev/make-distribution.sh --name custom-spark --pip --r --tgz -Psparkr -Phive -Phive-thriftserver -Pmesos -Pyarn -Pkubernetes

这将构建Spark发行版以及Python pip和R包。有关使用情况的详细信息，请运行./dev/make-distribution.sh --help
使用这种方式编译的时候，需要一定一个名字，建议直接使用hadoop的版本名如2.6.0-cdh5.7.0，–tgz打包成一个tgz包，剩下的拼上maven构建的命令就可以了，如 -Phadoop-2.7 -Pyarn -Dhadoop.version=2.8.5 -Phive -Phive-thriftserver
参考之前的：

./build/mvn -Phadoop-2.7 -Pyarn -Dhadoop.version=2.8.5   -Phive -Phive-thriftserver   -DskipTests clean package

在使用maven单独编译的时候，export MAVEN_OPTS是一定要设置的，但是使用make-distribution进行编译的时候是不需要的，因为这个设置已经封装到了make-distribution.sh脚本里面了，-DskipTests clean package也存在了。

打包后的名字如下图：

 $NAME就是--name中设置的，$Version是spark的名字，--name如果使用的是hadoop的版本号，那么打包后的文件就可以同时看到spark和hadoop的版本号了。如spark-2.1.0-bin-2.6.0-cdh5.7.0.tgz。这个包就可以直接拷贝到要部署的机器上进行部署了。

可能会遇到的问题：

解决：更换maven仓库
pom.xml中添加黄色部分，注意在1部分的下面

注意的第二个地方，内存不够或是啥的，具体看官网文档，-X查看详细信息

如果在编译过程中，你看到的异常信息不是太明显看不太懂，可以在编译命令后 -X，就能看到更详细的编译信息。

2.解压

tar -zxvf spark-2.2.0-bin-2.6.0-cdh5.7.0.tgz -C ~/app/

3.配置系统环境变量

pwd得到spark位置
/home/hadoop/app/spark-2.2.0-bin-2.6.0-cdh5.7.0

vi ~/.bash_profile

export SPARK_HOME=/home/hadoop/app/spark-2.2.0-bin-2.6.0-cdh5.7.0
export PATH=$SPARK_HOME/bin:$PATH

source ~/.bash_profile

4.检查安装是否成功

spark运行在本地测试就是local，运行集群在yarn就使用yarn
/home/hadoop/app/spark-2.2.0-bin-2.6.0-cdh5.7.0/bin目录中

./spark-shell --master local[2]

8.IDEA+Maven+Spark Streaming 8.1 pom.xml中添加对应的依赖

使用cdh，需要手动添加仓库repository，url需要自己检验一下是否有效比如到浏览器里打开
scala，kafka，spark，hadoop，hbase

spark官网https://spark.apache.org/，documentation->Latest Release,Programming Guides->Spark Streaming

_2.12代表的是scala的版本，如果scala使用2.10的话，这个地方写2.10
kafka和spark streaming整合的时候需要添加另外的依赖

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