Hadoop3.3.0 完全分布式(Fully-Distributed Operation)集群搭建需要注意的地方

                                                                                                                         卷首：业精于勤，行成于思，荒于戏，毁于随。

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思来想去还是在这里记录一下吧，之前的所谓的顾虑是Hadoop版本在变化，没有一蹴而就的固定模式。但是在自己亲自过了一下官方的文档，阻断、前进、阻断、前进、再阻断、再前进，前进、进。最后，觉得还是有必要把过程大致记录或者说是还原一番，虽然这个事情比较浪费时间，但是如果可以帮助到一些人，也就值得了，不是吗？！

--“战略上藐视敌人，战术(行动)上重视敌人“。

在这篇博客中Hadoop使用3.3.0版本(下载地址:https://www.apache.org/dyn/closer.cgi/hadoop/common/hadoop-3.3.0/hadoop-3.3.0.tar.gz)，所以在下面操作皆以此版本为前提。

三台RedhatLinux Centos6.9服务器，大致配置如下：

/etc/hosts文件

# 注释掉网络ip
#10.177.129.54    nj-gjh-54
#10.177.129.56    nj-gjh-56
#10.177.129.57    nj-gjh-57

# 使用本地网卡
172.16.0.110    nj-gjh-54
172.16.0.108    nj-gjh-56
172.16.0.114    nj-gjh-57

啰嗦几句：这三台配置用于搭建Hadoop中的HDFS、YARN完全足够用了；之前使用过cloudera manager平台管理大数据集群环境，CM和另外一家Hortonworks专业平台算是业内的前二强，听着怪别扭啊？！什么时候国内能出个大强。（CM和HortonWorks在2018年10月3号合体了，据‘路透社’报道，Cloudera通过股票方式并购了HortonWorks）。

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雷老板说过，“are u okay？”

雷老板还说过，“生死看淡，不行就干！”

雷老板还说过，”大数据环境要时钟同步。“

- 雷老板，蛤，这我可没说。。

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un：授时服务NTP --记录各种事件发生时序

--1主节点(nj-gjh-57)，提供授时服务，修改/etc/ntp.conf

# Use public servers from the pool.ntp.org project.
# Please consider joining the pool (http://www.pool.ntp.org/join.html).
# 注释掉其它server
#server 0.centos.pool.ntp.org iburst
#server 1.centos.pool.ntp.org iburst
#server 2.centos.pool.ntp.org iburst
#server 3.centos.pool.ntp.org iburst

server 127.127.1.0
Fudge 127.127.1.0 stratum 10

--2所有节点（54-56-57）

# Hosts on local network are less restricted.
#restrict 192.168.1.0 mask 255.255.255.0 nomodify notrap
restrict 172.16.0.114 nomodify notrap nopeer noquery #172.16.0.114 -- 各自ip-addr
restrict 172.16.0.1  mask 255.255.255.0 nomodify notrap #172.16.0.1 -- 网关route 查看网关命令route -n 或 netstat -rn 或 ip route show 

--3其它从节点（54-56）

# Use public servers from the pool.ntp.org project.
# Please consider joining the pool (http://www.pool.ntp.org/join.html).
#注释掉网络上的server
#server 0.centos.pool.ntp.org iburst
#server 1.centos.pool.ntp.org iburst
#server 2.centos.pool.ntp.org iburst
#server 3.centos.pool.ntp.org iburst

#使用local网络server
server 172.16.0.114  #172.16.0.114 -- 主节点IP
Fudge 172.16.0.114 stratum 10 #172.16.0.114 -- 主节点IP

--重启ntpd（54-56-57：~ service ntpd restart）

[root@nj-gjh-54 hadoop-3.3.0]# ntpq -p
     remote           refid      st t when poll reach   delay   offset  jitter
==============================================================================
*nj-gjh-57       LOCAL(0)         6 u   49   64  377    0.220    0.358   0.216
------------------------------------------------------------------------------

[root@nj-gjh-54 hadoop-3.3.0]# ntpstat 
unsynchronised
   polling server every 64 s

# ntp服务器配置完毕后，需要等待5-10分钟才能与/etc/ntp.conf中配置的标准时间进行同步
------------------------------------------------------------------------------
见证奇迹的时候到了~！
[root@nj-gjh-54 hadoop-3.3.0]# service ntpd restart
Shutting down ntpd: [  OK  ]
Starting ntpd: [  OK  ]
[root@nj-gjh-54 hadoop-3.3.0]# ntpstat 
synchronised to NTP server (172.16.0.114) at stratum 7 
   time correct to within 7949 ms
   polling server every 64 s
[root@nj-gjh-54 hadoop-3.3.0]# ntpq -p
     remote           refid      st t when poll reach   delay   offset  jitter
==============================================================================
*nj-gjh-57       LOCAL(0)         6 u   12   64    1    0.299    0.516   0.000

-------------------------------------------------------------------------------
[root@nj-gjh-56 hadoop-3.3.0]# ntpstat 
synchronised to NTP server (172.16.0.114) at stratum 7 
   time correct to within 34 ms
   polling server every 1024 s
-------------------------------------------------------------------------------
其它：
1》 watch "ntpq -p"
2》 remote：本机和上层ntp的ip或主机名，“+”表示优先，“*”表示次优先
    refid：参考上一层ntp主机地址
    st：stratum阶层
    when：多少秒前曾经同步过时间
    poll：下次更新在多少秒后
    reach：已经向上层ntp服务器要求更新的次数
    delay：网络延迟
    offset：时间补偿
    jitter：系统时间与bios时间差

》》》时钟同步设置完毕

》》》进阶：大数据集群环境下减少重复劳动工作的方式 -- Shell Script(封装rsync)

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deux:免密登陆

--1免密登陆基础（非对称加密）

ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa #RSA来自1977年由麻省理工学院罗纳德·李维斯特（Ron Rivest）、阿迪·萨莫尔（Adi Shamir）和伦纳德·阿德曼（Leonard Adleman）一起提出的，RSA就是他们三人姓氏开头字母拼在一起组成的，算法原理可以自行了解

cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys #自己登陆自己进行免密（首次ssh localhost需要密码，之后便不再需要）

ssh-copy-id nj-gjh-5？ #将公钥给其它服务器，其它服务器通过ssh方式访问

--2握手（三台服务器全部相互设置免密+自己和自己免密）

次数：(n-1)+n 
        =>n²   #平方使用alt（换挡键）+178

》》》进阶：如果更好的办法，欢迎评论区留言

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trois：确定每台服务器上的角色

--1解压 hadoop-3.3.0.tar.gz，修改配置 重点！

core-default.xml
hdfs-default.xml
hdfs-rbf-default.xml
mapred-default.xml
yarn-default.xml
----------------------
Deprecated Properties

etc/hadoop/hadoop-env.sh
--------------------------------------------------------------------------------------
# The java implementation to use. By default, this environment
# variable is REQUIRED on ALL platforms except OS X!
export JAVA_HOME=/usr/java # JAVA_HOME
：
：
：
# Extra Java runtime options for all Hadoop commands. We don't support
# IPv6 yet/still, so by default the preference is set to IPv4.
# export HADOOP_OPTS="-Djava.net.preferIPv4Stack=true"
# For Kerberos debugging, an extended option set logs more information
# export HADOOP_OPTS="-Djava.net.preferIPv4Stack=true -Dsun.security.krb5.debug=true -Dsun.security.spnego.debug"
export HADOOP_OPTS="$HADOOP_OPTS -Duser.timezone=GMT+08"  # 时区设置
-------------------------------------------------------------------------------------

[root@nj-gjh-57 hadoop-3.3.0]# cat etc/hadoop/hadoop-env.sh |grep -v "^[#,;]"|grep -v "^$"
export JAVA_HOME=/usr/java
export HADOOP_OPTS="$HADOOP_OPTS -Duser.timezone=GMT+08"
export HADOOP_OS_TYPE=${HADOOP_OS_TYPE:-$(uname -s)}

etc/hadoop/yarn-env.sh

HADOOP_OPTS="$YARN_OPTS -Duser.timezone=GMT+08" # 时区设置，之前使用YARN_OPTS--3.0之后deprecated,换成了HADOOP_OPTS
----------------------------------------------------------------------------------------

[root@nj-gjh-57 hadoop-3.3.0]# cat etc/hadoop/yarn-env.sh |grep -v "^[#,;]"|grep -v "^$"
HADOOP_OPTS="$YARN_OPTS -Duser.timezone=GMT+08"

etc/hadoop/core-site.xml 


        
                fs.defaultFS
                hdfs://nj-gjh-57:9000/
        
        
            hadoop.tmp.dir
            /usr/local/src/hadoop-3.3.0/data/tmp
        

etc/hadoop/hdfs-site.xml 


        
            dfs.replication
            3
        
        
            dfs.namenode.secondary.http-address
            nj-gjh-54:9868
        

etc/hadoop/mapred-site.xml 




    
        mapreduce.framework.name
        yarn
    
   
        mapreduce.jobhistory.address
        nj-gjh-54:10020
    
    
        mapreduce.jobhistory.webapp.address
        nj-gjh-54:19888
    

-------------------------------------------------------------------------------------

etc/hadoop/yarn-site.xml 





        
            yarn.nodemanager.aux-services
            mapreduce_shuffle
        
        
            yarn.resourcemanager.hostname
            nj-gjh-54
        
        
            yarn.log-aggregation-enable
            true
        
        
            yarn.log-aggregation.retain-seconds
            604800
        
        
            yarn.app.mapreduce.am.env
            HADOOP_MAPRED_HOME=${HADOOP_HOME}
        
        
            mapreduce.map.env
            HADOOP_MAPRED_HOME=${HADOOP_HOME}
        
        
            mapreduce.reduce.env
            HADOOP_MAPRED_HOME=${HADOOP_HOME}
        
         
            mapreduce.application.classpath
            
            /usr/local/src/hadoop-3.3.0/etc/hadoop:/usr/local/src/hadoop-3.3.0/share/hadoop/common/lib/*:/usr/local/src/hadoop-3.3.0/share/hadoop/common/*:/usr/local/src/hadoop-3.3.0/share/hadoop/hdfs:
/usr/local/src/hadoop-3.3.0/share/hadoop/hdfs/lib/*:/usr/local/src/hadoop-3.3.0/share/hadoop/hdfs/*:/usr/local/src/hadoop-3.3.0/share/hadoop/mapreduce/*:/usr/local/src/hadoop-3.3.0/share/hadoop
/yarn:/usr/local/src/hadoop-3.3.0/share/hadoop/yarn/lib/*:/usr/local/src/hadoop-3.3.0/share/hadoop/yarn/*
            
        

---------------------------------------------------------------------------------
mapreduce.application.classpath的值为，
1.export HADOOP_CLASSPATH=$(hadoop classpath)
2.echo $HADOOP_CLASSPATH

etc/hadoop/workers 
# DataNode
nj-gjh-54
nj-gjh-56
nj-gjh-57

修改完以上配置之后，将57节点的hadoop3.3.0文件夹同步给其它服务器，使用如下命令

集群中环境变量设置
/etc/profile 

export JAVA_HOME=/usr/java
export JRE_HOME=$JAVA_HOME/jre
export CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JRE_HOME/lib/tools.jar:$CLASSPATH
export HADOOP_HOME=/usr/local/src/hadoop-3.3.0
export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_HOME/lib/native
export HADOOP_OPTS="-Djava.library.path=$HADOOP_HOME/lib"
#export HADOOP_CLASSPATH=$(hadoop classpath)
export PATH=$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$JAVA_HOME/bin:$PATH
-------------------------------------------------------------------------------------

将主节点57整个/usr/local/src/hadoop-3.3.0文件夹同步到其它节点上面

rsync -av $HADOOP_HOME/ nj-gjh-54:$HADOOP_HOME/
rsync -av $HADOOP_HOME/ nj-gjh-56:$HADOOP_HOME/
-------------------------------------------------
----CAUTION：/usr/local/src/hadoop-3.3.0 没有目录就新建
之前看到其它blog中将rsync封装了一番，也是非常好用的

 

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quatre:在57节点NameNode上初始化，并启动dfs

[root@nj-gjh-57 hadoop-3.3.0]# hdfs namenode -format
日志大概如下：
2020-08-07 19:26:15,752 INFO namenode.FSImage: Allocated new BlockPoolId: BP-756426205-172.16.0.114-1596799575742
2020-08-07 19:26:15,779 INFO common.Storage: Storage directory /usr/local/src/hadoop-3.3.0/data/tmp/dfs/name has been successfully formatted.
2020-08-07 19:26:15,818 INFO namenode.FSImageFormatProtobuf: Saving image file /usr/local/src/hadoop-3.3.0/data/tmp/dfs/name/current/fsimage.ckpt_0000000000000000000 using no compression
2020-08-07 19:26:15,970 INFO namenode.FSImageFormatProtobuf: Image file /usr/local/src/hadoop-3.3.0/data/tmp/dfs/name/current/fsimage.ckpt_0000000000000000000 of size 399 bytes saved in 0 seconds .
2020-08-07 19:26:15,998 INFO namenode.NNStorageRetentionManager: Going to retain 1 images with txid >= 0
2020-08-07 19:26:16,013 INFO namenode.FSImage: FSImageSaver clean checkpoint: txid=0 when meet shutdown.
2020-08-07 19:26:16,014 INFO namenode.NameNode: SHUTDOWN_MSG: 
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at nj-gjh-57/172.16.0.114
************************************************************/

---------------------------------------------------------------------------------------
之后在HADOOP_HOME路径下可以看到data文件夹是etc/hadoop/core-site.xml配置文件设置的目录
[root@nj-gjh-57 hadoop-3.3.0]# tree data/
data/
└── tmp
    └── dfs
        └── name
            └── current
                ├── fsimage_0000000000000000000
                ├── fsimage_0000000000000000000.md5
                ├── seen_txid
                └── VERSION

4 directories, 4 files

----------------------------------------------------------------------------------------
启动Hadoop文件系统
[root@nj-gjh-57 hadoop-3.3.0]# start-dfs.sh 
WARNING: HADOOP_SECURE_DN_USER has been replaced by HDFS_DATANODE_SECURE_USER. Using value of HADOOP_SECURE_DN_USER.
Starting namenodes on [nj-gjh-57]
Starting datanodes
Starting secondary namenodes [nj-gjh-54]
2020-08-07 19:32:24,868 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

----------------------------------------------------------------------------------------
使用jps[https://docs.oracle.com/javase/7/docs/technotes/tools/share/jps.html]查看各个服务器jvm中运行的任务情况
-----------------------------------
[root@nj-gjh-57 hadoop-3.3.0]# jps
20978 DataNode
20836 NameNode
21303 Jps
-----------------------------------
[root@nj-gjh-54 hadoop-3.3.0]# jps
4624 Jps
4579 SecondaryNameNode
4462 DataNode
-----------------------------------
[root@nj-gjh-56 hadoop-3.3.0]# jps
375 DataNode
447 Jps
-----------------------------------

访问hadoop文件系统前端页面，主节点ip:9870 -- http://nj-gjh-57:9870/dfshealth.html#tab-datanode

如：http://nj-gjh-57:9870/dfshealth.html#tab-datanode

---

cinq：启动yarn -- Yet Another Resource Negotiator (YARN) 

https://hadoop.apache.org/docs/r3.3.0/hadoop-yarn/hadoop-yarn-site/YARN.html

[root@nj-gjh-54 hadoop-3.3.0]# start-yarn.sh 
Starting resourcemanager
Starting nodemanagers
-------------------------------------------------------

[root@nj-gjh-54 hadoop-3.3.0]# jps
4579 SecondaryNameNode
5333 Jps
4939 NodeManager    # yarn nm role
4462 DataNode
4798 ResourceManager # yarn rm role
-------------------------------------------------------

[root@nj-gjh-56 hadoop-3.3.0]# jps
512 NodeManager    # yarn nm role
660 Jps
375 DataNode
-------------------------------------------------------

57节点没有yarn的rm和nm角色
查了一下，貌似在hadoop的从节点上才有nodemanager
不过在设置workers[以前的版本叫slaves]的时候将57也设置了datanode
估计这个问题得去找找架构了，还需要深入的学习

启动之后访问yarn前端页面，状态更加一目了然：

如：http://nj-gjh-54:8088/cluster/nodes

---

six:使用hadoop3.3.0/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.3.0.jar 测试一下之前的搭建情况

新建一个文本文档
[root@nj-gjh-57 hadoop-3.3.0]# more ../input 
smith
allen
smile
world hello
遇贤
遇贤
hello world
fengshij
mandy.zj
allen.sf

-----------------------------------------------------------------------------------------
上传到文件系统中 固定前缀"hadoop fs -"

[root@nj-gjh-57 hadoop-3.3.0]# hadoop fs -put ../input /

-----------------------------------------------------------------------------------------
使用命令行查看上传结果
[root@nj-gjh-57 hadoop-3.3.0]# hadoop fs -ls /
Found 2 items
-rw-r--r--   3 root supergroup         83 2020-08-07 20:02 /input
drwx------   - root supergroup          0 2020-08-07 19:57 /tmp

在主节点页面上查看上传情况：

如：http://nj-gjh-57:9870/explorer.html#/

WordCount Example：

这里在任意节点执行如下命令：

[root@nj-gjh-54 hadoop-3.3.0]# hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.3.0.jar wordcount /input /output
-----------------------------------------------------------------------------------

过程控制台输出：
[root@nj-gjh-54 hadoop-3.3.0]# hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.3.0.jar wordcount /input /output
2020-08-07 20:07:31,560 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2020-08-07 20:07:32,466 INFO client.DefaultNoHARMFailoverProxyProvider: Connecting to ResourceManager at nj-gjh-54/172.16.0.110:8032
2020-08-07 20:07:33,243 INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /tmp/hadoop-yarn/staging/root/.staging/job_1596800529553_0002
2020-08-07 20:07:33,597 INFO input.FileInputFormat: Total input files to process : 1
2020-08-07 20:07:33,738 INFO mapreduce.JobSubmitter: number of splits:1
2020-08-07 20:07:33,944 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1596800529553_0002
2020-08-07 20:07:33,944 INFO mapreduce.JobSubmitter: Executing with tokens: []
2020-08-07 20:07:34,162 INFO conf.Configuration: resource-types.xml not found
2020-08-07 20:07:34,163 INFO resource.ResourceUtils: Unable to find 'resource-types.xml'.
2020-08-07 20:07:34,718 INFO impl.YarnClientImpl: Submitted application application_1596800529553_0002
2020-08-07 20:07:34,779 INFO mapreduce.Job: The url to track the job: http://nj-gjh-54:8088/proxy/application_1596800529553_0002/
2020-08-07 20:07:34,780 INFO mapreduce.Job: Running job: job_1596800529553_0002
2020-08-07 20:07:44,054 INFO mapreduce.Job: Job job_1596800529553_0002 running in uber mode : false
2020-08-07 20:07:44,055 INFO mapreduce.Job:  map 0% reduce 0%
2020-08-07 20:07:50,152 INFO mapreduce.Job:  map 100% reduce 0%
2020-08-07 20:07:57,214 INFO mapreduce.Job:  map 100% reduce 100%
2020-08-07 20:07:58,239 INFO mapreduce.Job: Job job_1596800529553_0002 completed successfully
2020-08-07 20:07:58,375 INFO mapreduce.Job: Counters: 54
        File System Counters
                FILE: Number of bytes read=124
                FILE: Number of bytes written=529975
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=175
                HDFS: Number of bytes written=82
                HDFS: Number of read operations=8
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=2
                HDFS: Number of bytes read erasure-coded=0
        Job Counters 
                Launched map tasks=1
                Launched reduce tasks=1
                Data-local map tasks=1
                Total time spent by all maps in occupied slots (ms)=3002
                Total time spent by all reduces in occupied slots (ms)=4613
                Total time spent by all map tasks (ms)=3002
                Total time spent by all reduce tasks (ms)=4613
                Total vcore-milliseconds taken by all map tasks=3002
                Total vcore-milliseconds taken by all reduce tasks=4613
                Total megabyte-milliseconds taken by all map tasks=3074048
                Total megabyte-milliseconds taken by all reduce tasks=4723712
        Map-Reduce Framework
                Map input records=10
                Map output records=12
                Map output bytes=131
                Map output materialized bytes=124
                Input split bytes=92
                Combine input records=12
                Combine output records=9
                Reduce input groups=9
                Reduce shuffle bytes=124
                Reduce input records=9
                Reduce output records=9
                Spilled Records=18
                Shuffled Maps =1
                Failed Shuffles=0
                Merged Map outputs=1
                GC time elapsed (ms)=200
                CPU time spent (ms)=1700
                Physical memory (bytes) snapshot=597913600
                Virtual memory (bytes) snapshot=5576343552
                Total committed heap usage (bytes)=531103744
                Peak Map Physical memory (bytes)=360902656
                Peak Map Virtual memory (bytes)=2794258432
                Peak Reduce Physical memory (bytes)=237010944
                Peak Reduce Virtual memory (bytes)=2782085120
        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=83
        File Output Format Counters 
                Bytes Written=82    

-----------------------------------------------------------------------------------
会发现出现了/output目录
[root@nj-gjh-57 hadoop-3.3.0]# hadoop fs -ls /
2020-08-07 20:08:46,810 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Found 3 items
-rw-r--r--   3 root supergroup         83 2020-08-07 20:02 /input
drwxr-xr-x   - root supergroup          0 2020-08-07 20:07 /output
drwx------   - root supergroup          0 2020-08-07 20:07 /tmp

-----------------------------------------------------------------------------------
统计结果：

[root@nj-gjh-56 hadoop-3.3.0]# hadoop fs -ls /output
Found 2 items
-rw-r--r--   3 root supergroup          0 2020-08-07 20:07 /output/_SUCCESS
-rw-r--r--   3 root supergroup         82 2020-08-07 20:07 /output/part-r-00000
------------------------------------

[root@nj-gjh-56 hadoop-3.3.0]# hadoop fs -cat /output/part-r-00000
allen   1
allen.sf        1
fengshij        1
hello   2
mandy.zj        1
smile   1
smith   1
world   2
遇贤    2
-----------------------------------------------------------------------------------

最终的结果也可通过页面下载到本地

如：在HDFS中 http://nj-gjh-57:9870/explorer.html#/output

使用文本编辑器打开之后内容为：

如：在YARN中 http://nj-gjh-54:8088/cluster/nodes

之前没有启动mapreduce historyserver，下面把这个角色也启动了

启动historyserver
1.mapred --daemon status historyserver # 新方式
2.mr-jobhistory-daemon.sh status historyserver # Deprecated
-------CAUTION：启动historyserver在配置的54服务上运行启动脚本（这里不知道是否有问题，之前在主节点57上运行启动脚本报错了 ==）

-------------------------------------------------------------------------
[root@nj-gjh-54 hadoop-3.3.0]# mapred --daemon start historyserver
------CAUTION:daemon 容易敲成 deamon 

-------------------------------------------------------------------------
[root@nj-gjh-54 hadoop-3.3.0]# jps
4579 SecondaryNameNode
4939 NodeManager
4462 DataNode
4798 ResourceManager
6286 Jps
6207 JobHistoryServer  # 

通过页面访问:historyserver

如：Tracking URL: http://nj-gjh-54:19888/jobhistory/job/job_1596800529553_0002

 

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sept:小结

Hadoop-Yarn Map-Reduce应用场景：离线计算框架,适用于

Strom 实时流式计算框架

Spark 内存计算框架

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                                                                                                                                                                  Next Marche：Hive