大数据—— YARN

大数据—— YARN

源码见：YARN (Yet Another Resource Negotiator) 是 hadoop 2.0 引入的集群资源管理系统。用户可以将各种服务框架部署在 YARN 上，由 YARN 进行统一地管理和资源分配。 The fundamental idea of MRv2 is to split up the two major functionalities of the JobTracker, resource management and job scheduling /monitoring, into separate daemons. The idea is to have a global ResourceManager (RM) and per-application ApplicationMaster (AM). An application is either a single job in the classical sense of Map-Reduce jobs or a DAG of jobs.

YARN架构详解

Client

向RM提交任务 杀死任务

ResourceManager

ResourceManager 通常在独立的机器上以后台进程的形式运行，它是整个 集群资源的主要协调者和管理者 。 负责给用户提交的所有应用程序分配资源 ，它根据应用程序优先级、队列容量、ACLs、数据位置等信息，做出决策，然后以共享的、安全的、多租户的方式制定分配策略，调度集群资源。

NodeManager

NodeManager 是 YARN 集群中的每个具体 节点的管理者 。 主要 负责该节点内所有容器的生命周期的管理，监视资源和跟踪节点健康 。具体如下： 启动时向 ResourceManager 注册并定时发送心跳消息，等待 ResourceManager 的指令； 维护 Container 的生命周期，监控 Container 的资源使用情况； 管理任务运行时的相关依赖，根据 ApplicationMaster 的需要，在启动 Container 之前将需要的程序及其依赖拷贝到本地。

ApplicationMaster

在用户提交一个应用程序时，YARN 会启动一个轻量级的 进程 ApplicationMaster。 ApplicationMaster 负责协调来自 ResourceManager 的资源，并通过 NodeManager 监视容器内资源的使用情况，同时还负责任务的监控与容错。具体如下： 根据应用的运行状态来决定动态计算资源需求； 向 ResourceManager 申请资源，监控申请的资源的使用情况； 跟踪任务状态和进度，报告资源的使用情况和应用的进度信息； 负责任务的容错。

Container

Container 是 YARN 中的 资源抽象 ，它封装了某个节点上的多维度资源，如内存、CPU、磁盘、网络等。 当 AM 向 RM 申请资源时，RM 为 AM 返回的资源是用 Container 表示的。 YARN 会为每个任务分配一个 Container，该任务只能使用该 Container 中描述的资源。ApplicationMaster 可在 Container 内运行任何类型的任务。例如，MapReduce ApplicationMaster 请求一个容器来启动 map 或 reduce 任务

YARN执行流程

客户端client向yarn集群提交作业 , 首先①向ResourceManager申请分配资源 Resource Manager会为作业分配一个Container(Application manager),Container里面运行这(Application Manager) Resource Manager会找一个对应的NodeManager通信②，要求NodeManager在这个container上启动应用程序Application Master ③ Application Master向Resource Manager申请资源④(采用轮询的方式通过RPC协议)，Resource scheduler将资源封装发给Application master④, Application Master将获取到的资源分配给各个Node Manager，并监控运行情况⑤ Node Manage得到任务和资源开始执行作业⑥ 再细分作业的话可以分为 先执行Map Task,结束后在执行Reduce Task 最后再将结果返回給Application Master等依次往上层递交⑦

YARN环境部署

YARN on Single Node

You can run a MapReduce job on YARN in a pseudo-distributed mode by setting a few parameters and running ResourceManager daemon and NodeManager daemon in addition.The following instructions assume that 1. ~ 4. steps of the above instructions are already executed.

Configure parameters as follows:etc/hadoop/mapred-site.xml:

mapreduce.framework.name yarn

etc/hadoop/yarn-site.xml:

yarn.nodemanager.aux-services mapreduce_shuffle

Start ResourceManager daemon and NodeManager daemon:$ sbin/start-yarn.shBrowse the web interface for the ResourceManager; by default it is available at:ResourceManager - a MapReduce job.When you're done, stop the daemons with:$ sbin/stop-yarn.sh

[hadoop@hadoop000 hadoop]$ pwd /home/hadoop/app/hadoop-2.6.0-cdh5.15.1/etc/hadoop [hadoop@hadoop000 hadoop]$ vi mapred-site.xml [hadoop@hadoop000 hadoop]$ vi yarn-site.xml [hadoop@hadoop000 sbin]$ jps 7234 NodeManager 8131 Jps 7588 NameNode 7962 SecondaryNameNode 7116 ResourceManager 7791 DataNode

[hadoop@hadoop000 hadoop]$ pwd /home/hadoop/app/hadoop-2.6.0-cdh5.15.1/share/hadoop [hadoop@hadoop000 hadoop]$ ls common mapreduce mapreduce2 yarn hdfs kms mapreduce1 tools [hadoop@hadoop000 hadoop]$ pwd /home/hadoop/app/hadoop-2.6.0-cdh5.15.1/share/hadoop [hadoop@hadoop000 hadoop]$ cd mapreduce [hadoop@hadoop000 mapreduce]$ ls hadoop-mapreduce-client-app-2.6.0-cdh5.15.1.jar hadoop-mapreduce-client-common-2.6.0-cdh5.15.1.jar hadoop-mapreduce-client-core-2.6.0-cdh5.15.1.jar hadoop-mapreduce-client-hs-2.6.0-cdh5.15.1.jar hadoop-mapreduce-client-hs-plugins-2.6.0-cdh5.15.1.jar hadoop-mapreduce-client-jobclient-2.6.0-cdh5.15.1.jar hadoop-mapreduce-client-jobclient-2.6.0-cdh5.15.1-tests.jar hadoop-mapreduce-client-nativetask-2.6.0-cdh5.15.1.jar hadoop-mapreduce-client-shuffle-2.6.0-cdh5.15.1.jar hadoop-mapreduce-examples-2.6.0-cdh5.15.1.jar lib lib-examples sources

提交example案例到YARN上运行

hadoop jar hadoop-mapreduce-examples-2.6.0-cdh5.15.1.jar pi 2 3

[hadoop@hadoop000 ~]$ hadoop dfs -cat /wc/input/1.txt DEPRECATED: Use of this script to execute hdfs command is deprecated. Instead use the hdfs command for it. hello world hello hello hello world [hadoop@hadoop000 ~]$ hadoop jar hadoop-mapreduce-examples-2.6.0-cdh5.15.1.jar wordcount /wc/input /wc/output [hadoop@hadoop000 ~]$ hadoop dfs -cat /wc/output/part-r-00000 DEPRECATED: Use of this script to execute hdfs command is deprecated. Instead use the hdfs command for it. hello 4 world 2

提交流量统计案例到YARN上运行

mvn clean package -DskipTests 注意在当前的项目环境 (base) locahost:untitled5 jacksun$ mvn clean package -DskipTests [INFO] Scanning for projects... [INFO] [INFO] -----------------------< org.example:untitled5 >------------------------ [INFO] Building untitled5 1.0-SNAPSHOT [INFO] --------------------------------[ jar ]--------------------------------- [INFO] [INFO] --- maven-clean-plugin:3.1.0:clean (default-clean) @ untitled5 --- [INFO] Deleting /Users/jacksun/IdeaProjects/untitled5/target [INFO] [INFO] --- maven-resources-plugin:3.0.2:resources (default-resources) @ untitled5 --- [INFO] Using 'UTF-8' encoding to copy filtered resources. [INFO] Copying 2 resources [INFO] [INFO] --- maven-compiler-plugin:3.8.0:compile (default-compile) @ untitled5 --- [INFO] Changes detected - recompiling the module! [INFO] Compiling 15 source files to /Users/jacksun/IdeaProjects/untitled5/target/classes [INFO] [INFO] --- maven-resources-plugin:3.0.2:testResources (default-testResources) @ untitled5 --- [INFO] Using 'UTF-8' encoding to copy filtered resources. [INFO] skip non existing resourceDirectory /Users/jacksun/IdeaProjects/untitled5/src/test/resources [INFO] [INFO] --- maven-compiler-plugin:3.8.0:testCompile (default-testCompile) @ untitled5 --- [INFO] Changes detected - recompiling the module! [INFO] Compiling 2 source files to /Users/jacksun/IdeaProjects/untitled5/target/test-classes [INFO] [INFO] --- maven-surefire-plugin:2.22.1:test (default-test) @ untitled5 --- [INFO] Tests are skipped. [INFO] [INFO] --- maven-jar-plugin:3.0.2:jar (default-jar) @ untitled5 --- [INFO] Building jar: /Users/jacksun/IdeaProjects/untitled5/target/untitled5-1.0-SNAPSHOT.jar [INFO] ------------------------------------------------------------------------ [INFO] BUILD SUCCESS [INFO] ------------------------------------------------------------------------ [INFO] Total time: 43.078 s [INFO] Finished at: 2020-09-02T10:04:51+08:00 [INFO] ------------------------------------------------------------------------ (base) locahost:untitled5 jacksun$ ls D: access output src Hadoop.iml input pom.xml target (base) locahost:untitled5 jacksun$ cd target/ (base) locahost:target jacksun$ ls classes maven-status generated-sources test-classes generated-test-sources untitled5-1.0-SNAPSHOT.jar maven-archiver (base) locahost:target jacksun$

(base) locahost:target jacksun$ scp untitled5-1.0-SNAPSHOT.jar hadoop@192.168.43.200:~/lib/hadoop@192.168.43.200's password: untitled5-1.0-SNAPSHOT.jar 100% 18KB 750.6KB/s 00:00 (base) locahost:target jacksun$

- 到编译后的`/target/`目录jar包和相关的数据上传到服务器`scp xxx hadoop@localhost:dir` - 再上传到`hdfs `用`Hadoop fs -put /dir`

hadoop jar untitled5-1.0-SNAPSHOT.jar com.bigdata.hadoop.mr.access.AccessYARNApp /access/input/access.log /access/ouput/

- 执行作业` hadoop jar xx.jar `完整的类名和包名` args参数`

20/09/02 10:13:22 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:803220/09/02 10:13:22 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.20/09/02 10:13:23 INFO input.FileInputFormat: Total input paths to process : 120/09/02 10:13:24 INFO mapreduce.JobSubmitter: number of splits:120/09/02 10:13:24 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1598998523059_000320/09/02 10:13:25 INFO impl.YarnClientImpl: Submitted application application_1598998523059_000320/09/02 10:13:25 INFO mapreduce.Job: The url to track the job: 10:13:25 INFO mapreduce.Job: Running job: job_1598998523059_000320/09/02 10:13:35 INFO mapreduce.Job: Job job_1598998523059_0003 running in uber mode : false20/09/02 10:13:35 INFO mapreduce.Job: map 0% reduce 0%20/09/02 10:13:42 INFO mapreduce.Job: map 100% reduce 0%20/09/02 10:13:51 INFO mapreduce.Job: map 100% reduce 33%20/09/02 10:13:53 INFO mapreduce.Job: map 100% reduce 67%20/09/02 10:14:01 INFO mapreduce.Job: map 100% reduce 100%20/09/02 10:14:03 INFO mapreduce.Job: Job job_1598998523059_0003 completed successfully20/09/02 10:14:03 INFO mapreduce.Job: Counters: 50File System CountersFILE: Number of bytes read=1185FILE: Number of bytes written=575593FILE: Number of read operations=0FILE: Number of large read operations=0FILE: Number of write operations=0HDFS: Number of bytes read=2444HDFS: Number of bytes written=552HDFS: Number of read operations=12HDFS: Number of large read operations=0HDFS: Number of write operations=6Job Counters Killed reduce tasks=1Launched map tasks=1Launched reduce tasks=3Data-local map tasks=1Total time spent by all maps in occupied slots (ms)=13914Total time spent by all reduces in occupied slots (ms)=71064Total time spent by all map tasks (ms)=4638Total time spent by all reduce tasks (ms)=23688Total vcore-milliseconds taken by all map tasks=4638Total vcore-milliseconds taken by all reduce tasks=23688Total megabyte-milliseconds taken by all map tasks=14247936Total megabyte-milliseconds taken by all reduce tasks=72769536Map-Reduce FrameworkMap input records=23Map output records=23Map output bytes=1121Map output materialized bytes=1185Input split bytes=110Combine input records=0Combine output records=0Reduce input groups=21Reduce shuffle bytes=1185Reduce input records=23Reduce output records=21Spilled Records=46Shuffled Maps =3Failed Shuffles=0Merged Map outputs=3GC time elapsed (ms)=696CPU time spent (ms)=8510Physical memory (bytes) snapshot=783241216Virtual memory (bytes) snapshot=16559239168Total committed heap usage (bytes)=674758656Shuffle ErrorsBAD_ID=0CONNECTION=0IO_ERROR=0WRONG_LENGTH=0WRONG_MAP=0WRONG_REDUCE=0File Input Format Counters Bytes Read=2334File Output Format Counters Bytes Written=552

[hadoop@hadoop000 lib]$ hadoop fs -ls /access/ouput/Found 4 items-rw-r--r-- 1 hadoop supergroup 0 2020-09-02 10:14 /access/ouput/_SUCCESS-rw-r--r-- 1 hadoop supergroup 393 2020-09-02 10:13 /access/ouput/part-r-00000-rw-r--r-- 1 hadoop supergroup 80 2020-09-02 10:13 /access/ouput/part-r-00001-rw-r--r-- 1 hadoop supergroup 79 2020-09-02 10:13 /access/ouput/part-r-00002

[hadoop@hadoop000 lib]$ hadoop fs -cat /access/ouput/part-r-0000013480253104,180,180,36013502468823,7335,110349,11768413560436666,1116,954,207013560439658,2034,5892,792613602846565,1938,2910,484813660577991,6960,690,765013719199419,240,0,24013726230503,2481,24681,2716213726238888,12481,44681,5716213760778710,120,120,24013826544101,264,0,26413922314466,3008,3720,672813925057413,11058,48243,5930113926251106,240,0,24013926435656,132,1512,1644[hadoop@hadoop000 lib]$

 - 到`http://192.168.43.200:8088/cluster/`观察结果 

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