-大数据入门-2-Hadoop-部署yarn,跑案例

Flink on YARN 部署 在大数据计算领域,Apache Flink凭借其高性能、流批统一的特性,成为实时数据处理的首选框架。而YARN作为Hadoop生态的资源调度平台,能够为Flink提供强大的资源管理能力。将Flink部署在YARN上,既能充分利用集群资源,又能实现作业的灵活调度。本文将深入讲解Flink on YARN的部署要点,结合实际操作和常见问题解决,帮助开发者快速掌握这一关键技术。 阅读详情

1

MapReduce:计算  是jar包  企业不用  代码复杂  hive sql/...       是不需要部署的  运行在yarn
Yarn: 资源和作业的调度 是需要部署的
resourcemanager   rm    资源管理者
nodemanager         nm    节点管理者

2参照官网配置yarn

[hadoop@hadoop001 hadoop]$ pwd
/home/hadoop/app/hadoop/etc/hadoop
[hadoop@hadoop001 hadoop]$ mv  mapred-site.xml.template20190711 mapred-site.xml
[hadoop@hadoop001 hadoop]$ ll
total 144
-rw-r--r-- 1 hadoop hadoop  4436 Mar 24  2016 capacity-scheduler.xml
-rw-r--r-- 1 hadoop hadoop  1335 Mar 24  2016 configuration.xsl
-rw-r--r-- 1 hadoop hadoop   318 Mar 24  2016 container-executor.cfg
-rw-r--r-- 1 hadoop hadoop   880 Jul 11 14:01 core-site.xml
-rw-r--r-- 1 hadoop hadoop  4267 Jul 11 14:43 hadoop-env.sh
-rw-r--r-- 1 hadoop hadoop  2598 Mar 24  2016 hadoop-metrics2.properties
-rw-r--r-- 1 hadoop hadoop  2490 Mar 24  2016 hadoop-metrics.properties
-rw-r--r-- 1 hadoop hadoop  9683 Mar 24  2016 hadoop-policy.xml
-rw-r--r-- 1 hadoop hadoop  1112 Jul 11 00:16 hdfs-site.xml
-rw-r--r-- 1 hadoop hadoop  1449 Mar 24  2016 httpfs-env.sh
-rw-r--r-- 1 hadoop hadoop  1657 Mar 24  2016 httpfs-log4j.properties
-rw-r--r-- 1 hadoop hadoop    21 Mar 24  2016 httpfs-signature.secret
-rw-r--r-- 1 hadoop hadoop   620 Mar 24  2016 httpfs-site.xml
-rw-r--r-- 1 hadoop hadoop  3523 Mar 24  2016 kms-acls.xml
-rw-r--r-- 1 hadoop hadoop  1611 Mar 24  2016 kms-env.sh
-rw-r--r-- 1 hadoop hadoop  1631 Mar 24  2016 kms-log4j.properties
-rw-r--r-- 1 hadoop hadoop  5511 Mar 24  2016 kms-site.xml
-rw-r--r-- 1 hadoop hadoop 11291 Mar 24  2016 log4j.properties
-rw-r--r-- 1 hadoop hadoop  1383 Mar 24  2016 mapred-env.sh
-rw-r--r-- 1 hadoop hadoop  4113 Mar 24  2016 mapred-queues.xml.template
-rw-r--r-- 1 hadoop hadoop   758 Jul 11 19:38 mapred-site.xml
-rw-r--r-- 1 hadoop hadoop   758 Mar 24  2016 mapred-site.xml.template
-rw-r--r-- 1 hadoop hadoop    10 Jul 10 19:19 slaves
-rw-r--r-- 1 hadoop hadoop  2316 Mar 24  2016 ssl-client.xml.example
-rw-r--r-- 1 hadoop hadoop  2268 Mar 24  2016 ssl-server.xml.example
-rw-r--r-- 1 hadoop hadoop  4567 Mar 24  2016 yarn-env.sh
-rw-r--r-- 1 hadoop hadoop   690 Mar 24  2016 yarn-site.xml
[hadoop@hadoop001 hadoop]$ vi mapred-site.xml     配置文件参考官网
[hadoop@hadoop001 hadoop]$ vi yarn-site.xml          配置文件参考官网
[hadoop@hadoop001 hadoop]$ cd ../../
[hadoop@hadoop001 hadoop]$ ll
total 84
drwxr-xr-x  2 hadoop hadoop  4096 Jul  9 17:34 bin
drwxr-xr-x  2 hadoop hadoop  4096 Mar 24  2016 bin-mapreduce1
drwxr-xr-x  3 hadoop hadoop  4096 Mar 24  2016 cloudera
drwxr-xr-x  6 hadoop hadoop  4096 Mar 24  2016 etc
drwxr-xr-x  5 hadoop hadoop  4096 Mar 24  2016 examples
drwxr-xr-x  3 hadoop hadoop  4096 Mar 24  2016 examples-mapreduce1
drwxr-xr-x  2 hadoop hadoop  4096 Mar 24  2016 include
drwxr-xr-x  3 hadoop hadoop  4096 Mar 24  2016 lib
drwxr-xr-x  2 hadoop hadoop  4096 Mar 24  2016 libexec
-rw-r--r--  1 hadoop hadoop 17087 Mar 24  2016 LICENSE.txt
drwxrwxr-x  2 hadoop hadoop  4096 Jul 11 14:44 logs
-rw-r--r--  1 hadoop hadoop   101 Mar 24  2016 NOTICE.txt
drwxrwxr-x  3 hadoop hadoop  4096 Jul 11 13:57 output
-rw-r--r--  1 hadoop hadoop  1366 Mar 24  2016 README.txt
drwxr-xr-x  3 hadoop hadoop  4096 Jul  9 02:45 sbin
drwxr-xr-x  4 hadoop hadoop  4096 Mar 24  2016 share
drwxr-xr-x 17 hadoop hadoop  4096 Mar 24  2016 src
[hadoop@hadoop001 hadoop]$ sbin/start-yarn.sh
starting yarn daemons
starting resourcemanager, logging to /home/hadoop/software/hadoop-2.6.0-cdh5.7.0/logs/yarn-hadoop-resourcemanager-hadoop001.out
hadoop001: starting nodemanager, logging to /home/hadoop/software/hadoop-2.6.0-cdh5.7.0/logs/yarn-hadoop-nodemanager-hadoop001.out
[hadoop@hadoop001 hadoop]$ jps
13696 Jps
13399 NodeManager
13309 ResourceManager
hadoop@hadoop001 hadoop]$ netstat -nlp|grep 8088
(Not all processes could be identified, non-owned process info
 will not be shown, you would have to be root to see it all.)
tcp        0      0 0.0.0.0:8088                0.0.0.0:*                   LISTEN      13309/java        

3修改yarn端口号

hadoop@hadoop001 hadoop]$ pwd
/home/hadoop/app/hadoop/etc/hadoop
[hadoop@hadoop001 hadoop]$ vi yarn-site.xml
[hadoop@hadoop001 hadoop]$ cat yarn-site.xml

<?xml version="1.0"?>
<!--
  Licensed 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

    http://www.apache.org/licenses/LICENSE-2.0

  Unless required by applicable law or agreed to in writing, software
  distributed under the License is distributed on an "AS IS" BASIS,
  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  See the License for the specific language governing permissions and
  limitations under the License. See accompanying LICENSE file.
-->
<configuration>
    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
     <property>
        <name>yarn.resourcemanager.webapp.address</name>
        <value>hadoop001:8081</value>
    </property>
    <property>
	<name>yarn.resourcemanager.webapp.https.address</name>
        <value>hadoop001:8090</value>
    </property>
</configuration>
[hadoop@hadoop001 hadoop]$ 

打开外部界面

在这里插入图片描述
4跑案例

[hadoop@hadoop001 hadoop]$ find ./ -name '*example*.jar'
./share/hadoop/mapreduce1/hadoop-examples-2.6.0-mr1-cdh5.7.0.jar
./share/hadoop/mapreduce2/sources/hadoop-mapreduce-examples-2.6.0-cdh5.7.0-test-sources.jar
./share/hadoop/mapreduce2/sources/hadoop-mapreduce-examples-2.6.0-cdh5.7.0-sources.jar
./share/hadoop/mapreduce2/hadoop-mapreduce-examples-2.6.0-cdh5.7.0.jar
[hadoop@hadoop001 hadoop]$ 

[hadoop@hadoop001 hadoop]$ bin/hadoop jar ./share/hadoop/mapreduce2/hadoop-mapreduce-examples-2.6.0-cdh5.7.0.jar
An example program must be given as the first argument.
Valid program names are:
  aggregatewordcount: An Aggregate based map/reduce program that counts the words in the input files.
  aggregatewordhist: An Aggregate based map/reduce program that computes the histogram of the words in the input files.
  bbp: A map/reduce program that uses Bailey-Borwein-Plouffe to compute exact digits of Pi.
  dbcount: An example job that count the pageview counts from a database.
  distbbp: A map/reduce program that uses a BBP-type formula to compute exact bits of Pi.
  grep: A map/reduce program that counts the matches of a regex in the input.
  join: A job that effects a join over sorted, equally partitioned datasets
  multifilewc: A job that counts words from several files.
  pentomino: A map/reduce tile laying program to find solutions to pentomino problems.
  pi: A map/reduce program that estimates Pi using a quasi-Monte Carlo method.
  randomtextwriter: A map/reduce program that writes 10GB of random textual data per node.
  randomwriter: A map/reduce program that writes 10GB of random data per node.
  secondarysort: An example defining a secondary sort to the reduce.
  sort: A map/reduce program that sorts the data written by the random writer.
  sudoku: A sudoku solver.
  teragen: Generate data for the terasort
  terasort: Run the terasort
  teravalidate: Checking results of terasort
  
  wordcount: A map/reduce program that counts the words in the input files.     小案例
  
  wordmean: A map/reduce program that counts the average length of the words in the input files.
  wordmedian: A map/reduce program that counts the median length of the words in the input files.
  wordstandarddeviation: A map/reduce program that counts the standard deviation of the length of the words in the input files.
[hadoop@hadoop001 hadoop]$ ll   造组词,内容需编辑
total 92
-rw-rw-r--  1 hadoop hadoop    32 Jul 11 21:11 1.log
-rw-rw-r--  1 hadoop hadoop    32 Jul 11 21:11 2.log

[hadoop@hadoop001 hadoop]$ bin/hdfs dfs -mkdir /examples    创建文件夹
19/07/13 13:32:34 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
[hadoop@hadoop001 hadoop]$ bin/hdfs dfs -mkdir /examples/input  创建input文件夹
19/07/13 13:32:58 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
[hadoop@hadoop001 hadoop]$ bin/hdfs dfs -put *.log /examples/input   把我们造好的两个文件移到/examples/input
19/07/13 13:34:11 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
[hadoop@hadoop001 hadoop]$ bin/hdfs dfs -ls /examples/input  查看
19/07/13 13:34:50 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Found 2 items
-rw-r--r--   1 hadoop supergroup         32 2019-07-13 13:34 /examples/input/1.log
-rw-r--r--   1 hadoop supergroup         32 2019-07-13 13:34 /examples/input/2.log
[hadoop@hadoop001 hadoop]$ bin/hadoop jar ./share/hadoop/mapreduce2/hadoop-mapreduce-examples-2.6.0-cdh5.7.0.jar wordcount /examples/input /examples/output1    运行
19/07/13 13:37:11 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
19/07/13 13:37:13 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
19/07/13 13:37:15 INFO input.FileInputFormat: Total input paths to process : 2
19/07/13 13:37:15 INFO mapreduce.JobSubmitter: number of splits:2
19/07/13 13:37:16 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1562986579443_0001
19/07/13 13:37:16 INFO impl.YarnClientImpl: Submitted application application_1562986579443_0001
19/07/13 13:37:17 INFO mapreduce.Job: The url to track the job: http://hadoop001:8081/proxy/application_1562986579443_0001/
19/07/13 13:37:17 INFO mapreduce.Job: Running job: job_1562986579443_0001
19/07/13 13:37:31 INFO mapreduce.Job: Job job_1562986579443_0001 running in uber mode : false
19/07/13 13:37:31 INFO mapreduce.Job:  map 0% reduce 0%
19/07/13 13:37:40 INFO mapreduce.Job:  map 50% reduce 0%
19/07/13 13:37:41 INFO mapreduce.Job:  map 100% reduce 0%
19/07/13 13:37:50 INFO mapreduce.Job:  map 100% reduce 100%   成功
19/07/13 13:37:51 INFO mapreduce.Job: Job job_1562986579443_0001 completed successfully
19/07/13 13:37:52 INFO mapreduce.Job: Counters: 49
	File System Counters
		FILE: Number of bytes read=142
		FILE: Number of bytes written=334480
		FILE: Number of read operations=0
		FILE: Number of large read operations=0
		FILE: Number of write operations=0
		HDFS: Number of bytes read=278
		HDFS: Number of bytes written=44
		HDFS: Number of read operations=9
		HDFS: Number of large read operations=0
		HDFS: Number of write operations=2
	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)=14642
		Total time spent by all reduces in occupied slots (ms)=7272
		Total time spent by all map tasks (ms)=14642
		Total time spent by all reduce tasks (ms)=7272
		Total vcore-seconds taken by all map tasks=14642
		Total vcore-seconds taken by all reduce tasks=7272
		Total megabyte-seconds taken by all map tasks=14993408
		Total megabyte-seconds taken by all reduce tasks=7446528
	Map-Reduce Framework
		Map input records=12
		Map output records=12
		Map output bytes=112
		Map output materialized bytes=148
		Input split bytes=214
		Combine input records=12
		Combine output records=12
		Reduce input groups=6
		Reduce shuffle bytes=148
		Reduce input records=12
		Reduce output records=6
		Spilled Records=24
		Shuffled Maps =2
		Failed Shuffles=0
		Merged Map outputs=2
		GC time elapsed (ms)=493
		CPU time spent (ms)=1690
		Physical memory (bytes) snapshot=685453312
		Virtual memory (bytes) snapshot=8411357184
		Total committed heap usage (bytes)=512229376
	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=64
	File Output Format Counters 
		Bytes Written=44

[hadoop@hadoop001 hadoop]$ bin/hdfs dfs -ls /examples/output1   打开
19/07/13 14:52:03 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Found 2 items
-rw-r--r--   1 hadoop supergroup          0 2019-07-13 13:37 /examples/output1/_SUCCESS  成功的标识
-rw-r--r--   1 hadoop supergroup         44 2019-07-13 13:37 /examples/output1/part-r-00000   内容在这
[hadoop@hadoop001 hadoop]$ 
[hadoop@hadoop001 hadoop]$ bin/hdfs dfs -cat /examples/output1/part-r-00000    查看
19/07/13 14:55:35 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
11	2
22	2
33	2
44	2
5	2
www.ruozedata.com	2
[hadoop@hadoop001 hadoop]$ 
[hadoop@hadoop001 hadoop]$ bin/hadoop        注:bin/hadoop fs =bin/hdfs dfs
Usage: hadoop [--config confdir] COMMAND
       where COMMAND is one of:
  fs                   run a generic filesystem user client
  version              print the version
  jar <jar>            run a jar file
  checknative [-a|-h]  check native hadoop and compression libraries availability
  distcp <srcurl> <desturl> copy file or directories recursively
  archive -archiveName NAME -p <parent path> <src>* <dest> create a hadoop archive
  classpath            prints the class path needed to get the
  credential           interact with credential providers
                       Hadoop jar and the required libraries
  daemonlog            get/set the log level for each daemon
  trace                view and modify Hadoop tracing settings
 or
  CLASSNAME            run the class named CLASSNAME

Most commands print help when invoked w/o parameters.
[hadoop@hadoop001 hadoop]$ 

在这里插入图片描述

​Hadoop大数据平台(上) Hadoop工作原理、部署、Yarn​ ​​​​ 阅读详情

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在职AI算法工程师,擅长计算机视觉,YOLO目标检测、分割等,擅长web、pyqt界面可视化,好内容持续更新中,来这里跟大家一起学习,共同进步 2263

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(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】

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Camera2Demo

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《计算机控制技术及应用》通过总结作者的科研成果和吸收国内外的先进理论、方法和技术,以计算机控制系统的网络化、开放化、智能化和集成化发展趋势为主线索,主要介绍计算机控制系统的基本概念和计算机控制系统的典型形式,计算机控制系统的数学描述与基本分析方法,以及数字PID控制器、最少拍控制系统、达林算法、状态变量设计法等设计方法及其各种改进算法。重点突出计算机控制系统输入输出通道接口的设计方法和典型控制系统的设计案例与实现方法等。

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大数据学习之(yarn)

3.ApplicationMaster启动后首先向ResourceManager注册,这样用户可以直接通过ResourceManager查看应用程序的运行状态,然后它将为各个任务申请资源,并监控它的运行状态,直到运行结束,即重复步骤4~7;AM挂掉:若挂掉,RM负责重启,其实RM上有一个RMApplicationMaster, 是AM,上面保存已经完成的task,若重启AM,无需重新运行已经完成的task。NM挂掉:不止一个,当一个挂了,会通过心跳方式通知RM,RM将情况通知对应AM,AM作进一步处理。

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Hadoop大数据应用:Linux 部署 MapReduce 与 Yarn

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大数据技术基础实验十二,YARN集群配置启动。

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大数据Hadoop之——安装部署hadoop

vi /etc/sysconfig/network-scripts/ifcfg-ens32 ---- 根据自己网卡设置。-- 设置secondname的端口,如果是完全分布式的集群模式,则改为hadoop003 -->-- 设置 MapReduce 应用程序的执行框架为 YARN 即 MR运行的资源调度模式--->-- 设置secondname的端口,完全分布式的集群模式,改为hadoop003 -->-- 设为 true,YARN 会在容器超出其申请的物理内存时终止它 -->

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黑马程序员的大数据学习笔记~

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